# The 10 best metrics for marketplace businesses

## Why use metrics for marketplace businesses?

Marketplaces are unique business models that connect buyers with sellers, creating a platform where transactions occur. To ensure the health and growth of these platforms, it's pivotal to measure specific metrics that can gauge performance, user engagement, and overall business success. Key metrics offer invaluable insights into which areas are thriving and which need improvement, helping marketplace businesses streamline operations, optimize user experience, and ultimately drive revenue. By tracking the right metrics, companies can make data-driven decisions that enhance both buyer and seller satisfaction.

## The top 10 metrics for marketplace businesses

### 1. Gross merchandise volume (GMV)

GMV measures the total sales dollar value of merchandise sold through a marketplace within a specific period. It’s a critical indicator of the marketplace’s size and growth potential.

- **How GMV is calculated:** Sum of the sales prices of all items sold during the period.
- **What tools can be used to get GMV data:** Analytics platforms like Google Analytics, Mixpanel, or data from your internal transaction databases.
- **What average, good, and best in class look like for GMV:** A good benchmark depends on the industry, but steadily increasing GMV over time is always a positive sign. For instance, early-stage marketplaces might aim for a GMV increase of 15-25% month-over-month.

### 2. Active user rate

Understanding the active user rate helps a marketplace business gauge engagement and loyalty among its users, both buyers, and sellers.

- **How active user rate is calculated:** Number of active users (those who performed a meaningful action like buying, listing, or browsing) divided by total registered users.
- **What tools can be used to get active user rate data:** Tools like Google Analytics, Mixpanel, Amplitude.
- **What average, good, and best in class look like for active user rate:** Average active user rate varies by industry, but a monthly active user rate of 20-30% is considered good. Best-in-class can exceed 50%.

### 3. Customer acquisition cost (CAC)

CAC indicates how much it costs to acquire a new customer, an essential metric for managing marketing efficiency and scaling operations sustainably.

- **How CAC is calculated:** Total marketing and sales costs divided by the number of new customers acquired during the same period.
- **What tools can be used to get CAC data:** Marketing platforms like HubSpot, Marketo, and customer relationship management (CRM) systems such as Salesforce.
- **What average, good, and best in class look like for CAC:** Varies widely by industry, but a low CAC compared to the average customer lifetime value (CLV) is ideal. A ratio of 1:3 (CAC: CLV) is generally considered good.

### 4. Retention rate

Retention rate measures the percentage of users who continue to use the marketplace over a given period. High retention indicates satisfied customers and effective user engagement strategies.

- **How retention rate is calculated:** Number of users remaining at end of period divided by the number at the start, expressed as a percentage.
- **What tools can be used to get retention rate data:** Cohort analysis tools like Mixpanel, Kissmetrics, or custom-built dashboards.
- **What average, good, and best in class look like for retention rate:** A monthly retention rate of 20% is average, 35-40% is good, and 50%+ is best in class.

### 5. Take rate

The take rate is the percentage of the transaction value that the marketplace retains as revenue. It’s a key profitability metric.

- **How take rate is calculated:** (Total commission and fees revenue / GMV) × 100.
- **What tools can be used to get take rate data:** Financial planning and analysis tools like Planful or custom transaction data reports.
- **What average, good, and best in class look like for take rate:** The take rate often ranges between 5-20% depending on the industry and competitive landscape.

### 6. Listing to sold ratio

This metric helps understand the effectiveness of the marketplace in converting listed items into sales, critical for optimizing the seller experience.

- **How listing to sold ratio is calculated:** Number of items sold divided by the number of items listed, expressed as a percentage.
- **What tools can be used to get listing to sold ratio data:** Marketplace management tools or proprietary analytics systems.
- **What average, good, and best in class look like for listing to sold ratio:** Average can be around 10-30%, good is 40-50%, and best in class is 60%+.

### 7. Customer lifetime value (CLV)

CLV estimates the total revenue a business can expect from a single customer account, helping to make better budgeting and marketing decisions.

- **How CLV is calculated:** Average purchase value multiplied by purchase frequency over the customer lifetime.
- **What tools can be used to get CLV data:** CLV can be calculated in CRM tools like Salesforce, HubSpot, or specific SaaS tools like Custora.
- **What average, good, and best in class look like for CLV:** The higher the CLV, the better—typically, a CLV that is at least 3x higher than the CAC is a good benchmark.

### 8. Average transaction value (ATV)

ATV measures the average value of each transaction taking place on the marketplace, essential for understanding spend behaviour.

- **How ATV is calculated:** Total sales revenue divided by the number of transactions.
- **What tools can be used to get ATV data:** Sales analytics platforms and transaction databases.
- **What average, good, and best in class look like for ATV:** This varies by industry, but a consistently rising ATV indicates users are finding more value on the platform over time.

### 9. Churn rate

Churn rate signifies the percentage of users who stop using the marketplace during a given period. Managing a low churn rate is critical for growth.

- **How churn rate is calculated:** (Number of churned users during the period / total users at the start of the period) × 100.
- **What tools can be used to get churn rate data:** Customer analytics tools like Mixpanel, Amplitude.
- **What average, good, and best in class look like for churn rate:** Average varies by industry, but an annual churn rate under 20% is good, under 5% is exceptional.

### 10. NPS (Net Promoter Score)

NPS measures customer satisfaction and loyalty by asking users how likely they are to recommend the marketplace to others. It’s a strong indicator of user sentiment.

- **How NPS is calculated:** Customers are asked to rate from 0-10. Subtract the percentage of detractors (0-6) from the percentage of promoters (9-10).
- **What tools can be used to get NPS data:** Survey tools like SurveyMonkey, Qualtrics, and specialty NPS tools like Delighted.
- **What average, good, and best in class look like for NPS:** An NPS of 30-40 is considered good; 50+ is excellent.

## How to track metrics for marketplace businesses

Tracking these metrics effectively requires powerful and efficient tools. You can use a goal-tracking tool like [Tability](https://www.tability.io) to save time and help teams stay focused on the right metrics to improve. Tability allows for seamless alignment of metrics with company goals, ensuring that all efforts are directed towards actionable results. Additionally, integrating Tability with other analytics and CRM platforms can provide a comprehensive view of all vital marketplace metrics, enhancing clarity and decision-making.

## FAQ

### What is the most important metric for a marketplace?

The importance can vary, but GMV is often considered one of the most critical metrics as it denotes the overall market activity and health of the platform.

### How often should we review marketplace metrics?

It's recommended to review these metrics at least monthly. However, some high-priority metrics like active user rate and churn rate might require weekly reviews to catch and address issues promptly.

### How can we improve our marketplace’s active user rate?

To improve active user rate, focus on user engagement strategies such as personalised recommendations, regular communication through emails or notifications, enhancing user experience, and continually adding value to both buyers and sellers on the platform.

### What if our CAC is too high?

If your Customer Acquisition Cost (CAC) is high, consider optimizing your marketing channels, improving the efficiency of your sales funnel, or focusing on organic growth strategies such as content marketing and referrals.

### What’s a good strategy to reduce churn rate?

To reduce churn, provide excellent customer service, collect and act on user feedback, offer loyalty programs or incentives, and continually improve the overall user experience on your platform.

By carefully selecting and rigorously tracking these metrics, marketplace businesses can ensure sustainable growth and increased satisfaction among users.

The 10 best metrics for marketplace businesses

# The 10 best metrics for SaaS
## Why use metrics for SaaS?
Metrics are essential for the success of any Software as a Service (SaaS) business. They provide actionable insights into how your business is performing, identify areas for improvement, and help in making data-driven decisions. By closely monitoring these metrics, SaaS companies can optimize their marketing strategies, improve customer satisfaction, and increase their return on investment. Whether you're a startup looking to grow or an established business aiming to scale, keeping an eye on the right metrics is crucial.

## The top 10 metrics for SaaS
### 1. Monthly Recurring Revenue (MRR)
Monthly Recurring Revenue (MRR) is a crucial metric for SaaS companies because it indicates the predictable revenue expected every month from your customers. It helps in measuring growth, setting revenue targets, and forecasting future earnings.

- **How MRR is calculated**: MRR is calculated by multiplying the number of active subscribers by the average revenue per user (ARPU) for a month.
- **What tools can be used to get MRR data**: Tools like [Baremetrics](https://baremetrics.com/), [ChartMogul](https://chartmogul.com/), and [ProfitWell](https://www.profitwell.com/) can automate MRR calculations.
- **What average, good, and best in class look like for MRR**: Average MRR varies widely based on the industry and the target customer base but increasing MRR and a low churn rate indicate good financial health.

### 2. Customer Churn Rate
Customer Churn Rate is vital as it measures the percentage of customers who stop subscribing to your service over a specific period. High churn rates can severely impact growth and profitability.

- **How Customer Churn Rate is calculated**: Customer Churn Rate is calculated by dividing the number of customers lost during a period by the number of customers at the start of that period.
- **What tools can be used to get Customer Churn Rate data**: Tools like [ChurnZero](https://churnzero.net/), [Zaius](https://www.zaius.com/), and [Retain](https://beloved.io/retain/) can track churn metrics.
- **What average, good, and best in class look like for Customer Churn Rate**: A churn rate below 5% is considered good; below 2% is best in class.

### 3. Customer Lifetime Value (CLV)
Customer Lifetime Value (CLV) is a critical metric that estimates the total revenue a business can reasonably expect from a single customer account throughout their relationship.

- **How CLV is calculated**: CLV is calculated by multiplying the average purchase value by the average purchase frequency rate and the average customer lifespan.
- **What tools can be used to get CLV data**: Tools like [Klaviyo](https://www.klaviyo.com/), [HubSpot](https://www.hubspot.com/), and [Custora](https://custora.com/) provide CLV analytics.
- **What average, good, and best in class look like for CLV**: A higher CLV than your Customer Acquisition Cost (CAC) is essential; a ratio of 3:1 is typically considered good.

### 4. Customer Acquisition Cost (CAC)
Customer Acquisition Cost (CAC) is the total cost associated with acquiring a new customer, including marketing and sales expenses.

- **How CAC is calculated**: CAC is calculated by dividing total marketing and sales costs by the number of new customers acquired in a given period.
- **What tools can be used to get CAC data**: Tools like [Google Analytics](https://analytics.google.com/), [Marketo](https://www.marketo.com/), and [Salesforce](https://www.salesforce.com/) can help track CAC.
- **What average, good, and best in class look like for CAC**: A low CAC relative to CLV is ideal; startups often aim for a CAC payback period of less than 12 months.

### 5. Net Promoter Score (NPS)
Net Promoter Score (NPS) measures customer satisfaction and loyalty by asking customers how likely they are to recommend your service to others on a scale of 0-10.

- **How NPS is calculated**: NPS is calculated by subtracting the percentage of Detractors (0-6) from the percentage of Promoters (9-10).
- **What tools can be used to get NPS data**: Tools like [SurveyMonkey](https://www.surveymonkey.com/), [Delighted](https://delighted.com/), and [Qualtrics](https://www.qualtrics.com/) are popular for NPS surveys.
- **What average, good, and best in class look like for NPS**: An NPS score above 30 is generally considered good; above 50 is excellent.

### 6. Gross Margin
Gross Margin is the difference between revenue and the cost of goods sold (COGS), expressed as a percentage of revenue. It indicates the efficiency of your business processes.

- **How Gross Margin is calculated**: Gross Margin is calculated by subtracting COGS from total revenue and then dividing the result by total revenue.
- **What tools can be used to get Gross Margin data**: Accounting software like [QuickBooks](https://quickbooks.intuit.com/), [Xero](https://www.xero.com/), and [FreshBooks](https://www.freshbooks.com/) can be used to track gross margins.
- **What average, good, and best in class look like for Gross Margin**: SaaS companies often have high gross margins; 70-80% is typical, and best-in-class can exceed 80%.

### 7. Daily Active Users (DAU) / Monthly Active Users (MAU)
DAU/MAU ratio indicates the engagement level of your users. A higher ratio shows that users find value in your service and return frequently.

- **How DAU/MAU is calculated**: DAU/MAU is calculated by dividing the number of daily active users by the number of monthly active users.
- **What tools can be used to get DAU/MAU data**: Tools like [Mixpanel](https://mixpanel.com/), [Amplitude](https://amplitude.com/), and [Google Analytics](https://analytics.google.com/) help in tracking user engagement.
- **What average, good, and best in class look like for DAU/MAU**: A DAU/MAU ratio of 20% is average, 30-40% is good, and 50% or higher is exceptional.

### 8. Customer Support Tickets
The number of customer support tickets can be an indicator of your service quality and customer satisfaction. A higher number could indicate underlying issues with your product.

- **How Customer Support Tickets is calculated**: Count the number of support tickets opened over a specific period.
- **What tools can be used to get Customer Support Tickets data**: Customer support platforms like [Zendesk](https://www.zendesk.com/), [Freshdesk](https://freshdesk.com/), and [Intercom](https://www.intercom.com/) can manage and track support tickets.
- **What average, good, and best in class look like for Customer Support Tickets**: The average number of tickets should ideally decrease over time as issues are resolved; trends depend on user base size.

### 9. Conversion Rate
Conversion Rate measures the percentage of visitors who take a desired action (e.g., signing up for a trial or subscribing).

- **How Conversion Rate is calculated**: Conversion Rate is calculated by dividing the number of conversions by the total number of visitors and multiplying by 100 to get a percentage.
- **What tools can be used to get Conversion Rate data**: A/B testing and analytics tools like [Optimizely](https://www.optimizely.com/), [Google Analytics](https://analytics.google.com/), and [Crazy Egg](https://www.crazyegg.com/) can track conversion rates.
- **What average, good, and best in class look like for Conversion Rate**: Conversion rates vary; a 2-3% rate is average, 5-7% is good, and anything above 8% is excellent.

### 10. Average Revenue Per User (ARPU)
ARPU measures the average amount of revenue generated per user. It's useful for understanding revenue at a per-customer level and for segmenting customers.

- **How ARPU is calculated**: ARPU is calculated by dividing the total revenue by the number of users.
- **What tools can be used to get ARPU data**: Tools like [Baremetrics](https://baremetrics.com/), [ChartMogul](https://chartmogul.com/), and [ProfitWell](https://www.profitwell.com/) can track ARPU.
- **What average, good, and best in class look like for ARPU**: ARPU varies between industries; higher ARPU typically indicates better pricing strategy and value proposition.

## How to track metrics for SaaS
Accurately tracking metrics for your SaaS business can be time-consuming, but it's essential for sustained growth and success. Utilizing a goal-tracking tool like [Tability](https://www.tability.io) can save time and help teams stay focused on the right metrics. Tability allows you to set, track and visualise your team's key performance indicators in an organised manner, making it easier to monitor progress and make informed decisions. Additionally, the tool provides reminders and progress updates to ensure that your team stays on track.

## FAQ

**Q: Why is it important to track MRR?**
A: MRR provides predictable and consistent insight into your recurring revenue, making it easier to forecast growth, set realistic financial goals, and evaluate the effectiveness of your sales and marketing strategies.

**Q: What is a good CAC to LTV ratio?**
A: A good CAC to LTV (Customer Acquisition Cost to Lifetime Value) ratio is generally considered to be 3:1. This means that the lifetime value of a customer should be three times the cost of acquiring that customer.

**Q: How often should I track these metrics?**
A: It is beneficial to track these metrics monthly to provide a consistent view of your performance. However, some metrics like DAU/MAU and customer support tickets may require daily or weekly monitoring.

**Q: What if my churn rate is high?**
A: A high churn rate indicates that you are losing customers more quickly than you should, which can harm growth and revenue. Focus on understanding the reasons behind churn by collecting customer feedback and addressing recurring issues.

**Q: Which metric is the most important?**
A: The importance of a metric can vary based on your specific business goals. However, metrics like MRR, Churn Rate, and CLV are generally considered critical for assessing the health and growth potential of SaaS businesses.

By understanding and tracking these key metrics, SaaS companies can make informed decisions that drive growth, improve customer satisfaction, and measure their success more effectively.

The 10 best metrics for SaaS

# The 10 best metrics for startups

## Why use metrics for startups?

For any startup, the early stages are crucial. Founders and teams need to be flexible, responsive, and data-driven to ensure business growth and sustainability. Metrics allow startups to measure progress, assess strategies, and make informed decisions. Effective use of metrics accelerates growth, optimises resource allocation, and attracts investors by showcasing a data-backed understanding of the market and business performance.

## The top 10 metrics for startups

### 1. Customer acquisition cost (CAC)

Understanding how much it costs to acquire a customer is fundamental for a startup. It helps in managing budgets effectively and ensuring marketing and sales efforts are sustainable.

**How CAC is calculated:**
CAC is calculated by dividing all the costs spent on acquiring more customers (marketing expenses, sales team costs, etc.) by the number of customers acquired in a given period.

**What tools can be used to get CAC data:**
Tools like HubSpot, Salesforce, and Google Analytics can provide data needed to calculate CAC.

**What average, good, and best in class look like for CAC:**
- Average: $50
- Good: $20
- Best in class: $10

### 2. Customer lifetime value (CLTV)

CLTV estimates the total revenue a business can reasonably expect from a single customer account over the lifetime of their relationship with the business. This metric helps in understanding the long-term value of customer relationships compared to acquisition costs.

**How CLTV is calculated:**
CLTV is calculated by multiplying the average purchase value, purchase frequency, and customer lifespan.

**What tools can be used to get CLTV data:**
Tools like Kissmetrics, Mixpanel, and Stripe can help track and calculate CLTV.

**What average, good, and best in class look like for CLTV:**
- Average: $200
- Good: $500
- Best in class: $1,000+

### 3. Monthly recurring revenue (MRR)

MRR is a measure of the predictable and recurring revenue components of your business. It’s crucial for subscription-based startups to gauge financial health and growth.

**How MRR is calculated:**
MRR is calculated by multiplying the number of monthly subscribers by the average revenue per user (ARPU).

**What tools can be used to get MRR data:**
Tools like Chargebee, Baremetrics, and ProfitWell can help track MRR.

**What average, good, and best in class look like for MRR:**
- Average: $5,000
- Good: $50,000
- Best in class: $100,000+

### 4. Churn rate

Churn rate measures the percentage of customers who stop using a product during a given time period. Reducing churn is vital for sustainable growth.

**How churn rate is calculated:**
Churn rate is calculated by dividing the number of customers lost during a period by the number of customers at the start of the period.

**What tools can be used to get churn rate data:**
Tools like Recurly, ChartMogul, and CustomerGauge can help measure churn rates.

**What average, good, and best in class look like for churn rate:**
- Average: 5-7%
- Good: 3-5%
- Best in class: <2%

### 5. Burn rate

Burn rate is the rate at which a startup is losing money. It's essential for understanding how long a startup can operate before it needs additional funding.

**How burn rate is calculated:**
Burn rate is calculated by subtracting total revenue from total expenses over a month.

**What tools can be used to get burn rate data:**
Tools like QuickBooks, Xero, and Bench can help track burn rate.

**What average, good, and best in class look like for burn rate:**
- Average: $50,000
- Good: $20,000
- Best in class: $10,000

### 6. Net promoter score (NPS)

NPS measures customer experience and predicts business growth. It gauges customer loyalty by asking them how likely they are to recommend your product to others.

**How NPS is calculated:**
NPS is calculated by asking customers to rate their likelihood of recommending you on a scale from 0-10, subtracting the percentage of detractors (0-6 ratings) from the percentage of promoters (9-10 ratings).

**What tools can be used to get NPS data:**
Tools like SurveyMonkey, Delighted, and Promoter.io can gather NPS data.

**What average, good, and best in class look like for NPS:**
- Average: 20-30
- Good: 40-50
- Best in class: 70+

### 7. Gross margin

Gross margin measures the financial health of your product sales process by comparing revenue from sales to the cost of goods sold (COGS).

**How gross margin is calculated:**
Gross margin is calculated by subtracting COGS from total sales revenue and dividing by total sales revenue (expressed as a percentage).

**What tools can be used to get gross margin data:**
Tools like QuickBooks, FreshBooks, and Clear Books can track gross margin.

**What average, good, and best in class look like for gross margin:**
- Average: 40-50%
- Good: 60-70%
- Best in class: 70%+

### 8. Activation rate

Activation rate measures the percentage of new users who take a specific action that indicates they are getting value from the product, such as completing a setup wizard or using a key feature.

**How activation rate is calculated:**
Activation rate is calculated by dividing the number of users who complete the activation action by the total number of new users.

**What tools can be used to get activation rate data:**
Tools like Mixpanel, Amplitude, and Google Analytics can help track activation rates.

**What average, good, and best in class look like for activation rate:**
- Average: 30-40%
- Good: 50-60%
- Best in class: 70%+

### 9. Daily active users (DAU)/Monthly active users (MAU)

DAU/MAU is a ratio that measures user engagement by comparing daily users to monthly users. A high ratio indicates strong user retention and product value.

**How DAU/MAU is calculated:**
DAU/MAU is calculated by dividing the number of unique active daily users by the number of unique active monthly users.

**What tools can be used to get DAU/MAU data:**
Tools like Mixpanel, Firebase, and Localytics can track DAU/MAU.

**What average, good, and best in class look like for DAU/MAU:**
- Average: 10-15%
- Good: 20-25%
- Best in class: 30%+

### 10. Viral coefficient

Viral coefficient measures the rate at which your product is being spread by users. A higher viral coefficient indicates quicker and more organic growth.

**How viral coefficient is calculated:**
Viral coefficient is calculated by multiplying the number of invites sent by current users by the conversion rate of those invites.

**What tools can be used to get viral coefficient data:**
Tools like Gleam, ReferralCandy, and Influitive can help measure viral coefficients.

**What average, good, and best in class look like for viral coefficient:**
- Average: 0.5-0.9
- Good: 1.0-1.2
- Best in class: 1.5+

## How to track metrics for startups

Tracking metrics for startups doesn't have to be a complex or time-consuming task. There are numerous goal-tracking and analytics tools available that can simplify this process. One such tool is [Tability](https://www.tability.io). Tability can help you save time and keep your team focused on the right metrics to improve. By regularly updating and reviewing these metrics, you'll be able to stay aligned with your business goals and react promptly to any emerging trends or challenges.

## FAQ

**Q: Why is CAC important for startups?**
A: CAC is crucial because it helps startups understand the cost effectiveness of their customer acquisition strategies, enabling them to allocate resources better and optimise marketing efforts.

**Q: How often should startups track these metrics?**
A: Startups should monitor key metrics like MRR, churn rate, and burn rate on a monthly basis. Other metrics can be tracked quarterly, depending on the business model and growth stage.

**Q: Can different industries have different benchmarks for these metrics?**
A: Yes, benchmarks can vary significantly depending on the industry, target market, and business model. It’s important to compare your metrics against industry-specific standards.

**Q: What’s the most critical metric for early-stage startups?**
A: While all metrics are important, early-stage startups often focus most on CAC, CLTV, and burn rate as they establish product-market fit and scale their customer base.

By tracking and optimising these 10 key metrics, startups can steer their growth and build a more sustainable, data-driven approach to success.

The 10 best metrics for startups

# The 10 best metrics for operations managers

## Why use metrics for operations managers?

Metrics are essential for operations managers because they provide quantifiable data that can be used to assess the effectiveness and efficiency of business processes. By using metrics, operations managers can identify areas for improvement, track progress over time, and make informed decisions based on objective data. Metrics also help in aligning the operations with the overall strategic goals of the organisation. Whether it's improving production efficiency, reducing costs, or enhancing customer satisfaction, having the right metrics in place allows operations managers to focus their efforts on the most impactful areas.

## The top 10 metrics for operations managers

### 1. Overall Equipment Effectiveness (OEE)

OEE is a comprehensive metric that evaluates how effectively a manufacturing process is utilised. It takes into account the availability, performance, and quality of equipment, helping operations managers to pinpoint inefficiencies.

- How OEE is calculated: OEE is calculated by multiplying Availability (percentage of scheduled time equipment is operating), Performance (speed at which equipment operates as a percentage of its designed speed), and Quality (percentage of good parts produced).
- What tools can be used to get OEE data: Tools such as [SensrTrx](https://www.sensrtrx.com), [UpKeep](https://www.onupkeep.com), and [MachineMetrics](https://www.machinemetrics.com) can be used.
- What average, good, and best in class look like for OEE: Average OEE is around 60-70%, good OEE is 80-85%, and best in class is 90% and above.

### 2. First Pass Yield (FPY)

FPY measures the percentage of products that are manufactured correctly and to specifications the first time through the production process, without needing any rework.

- How FPY is calculated: FPY is calculated by dividing the number of good units produced by the total number of units going through the production process.
- What tools can be used to get FPY data: Tools such as [Tableau](https://www.tableau.com), [SAP Quality Management](https://www.sap.com/products/quality-management.html), and [Qlik](https://www.qlik.com) can be used.
- What average, good, and best in class look like for FPY: Average FPY is around 85%, good FPY is 90-95%, and best in class is above 95%.

### 3. Cycle Time

Cycle time refers to the total time from the beginning to the end of a process, which is important for understanding the throughput and efficiency of your operations.

- How cycle time is calculated: Cycle time is calculated by measuring the start and end time of a process and taking the difference between the two.
- What tools can be used to get cycle time data: Tools such as [LeanKit](https://leankit.com), [Asana](https://asana.com), and [JIRA](https://www.atlassian.com/software/jira) can be used.
- What average, good, and best in class look like for cycle time: Benchmarking for cycle time varies widely depending on industry and product, but reducing it by even a small percentage can be significant.

### 4. On-Time Delivery (OTD)

OTD measures the percentage of units delivered on or before the promised date, and is crucial for customer satisfaction and operational efficiency.

- How OTD is calculated: OTD is calculated by dividing the number of units delivered on or before the promised date by the total number of units delivered.
- What tools can be used to get OTD data: Tools such as [Oracle SCM](https://www.oracle.com/scm/), [SAP SCM](https://www.sap.com/products/scm.html), and [Epicor](https://www.epicor.com) can be used.
- What average, good, and best in class look like for OTD: Average OTD is around 85-90%, good OTD is 95%, and best in class is 98% and above.

### 5. Inventory Turnover

Inventory turnover measures how often inventory is sold and replaced over a specific period. This helps in efficiency and cash flow management.

- How inventory turnover is calculated: Inventory turnover is calculated by dividing the cost of goods sold by the average inventory.
- What tools can be used to get inventory turnover data: Tools such as [NetSuite](https://www.netsuite.com), [QuickBooks](https://quickbooks.intuit.com), and [Fishbowl Inventory](https://www.fishbowlinventory.com) can be used.
- What average, good, and best in class look like for inventory turnover: Average inventory turnover is 5-6 times per year, good is 8-9, and best in class is 10 and above.

### 6. Cost Per Unit

Cost per unit measures the cost incurred by a company to produce one unit of product. It helps identify cost-saving opportunities and efficiency improvements.

- How cost per unit is calculated: Cost per unit is calculated by dividing the total cost of production (including fixed and variable costs) by the number of units produced.
- What tools can be used to get cost per unit data: Tools such as [Excel](https://www.microsoft.com/en-us/microsoft-365/excel), [SAP Business ByDesign](https://www.sap.com/products/business-bydesign.html), and [Sage](https://www.sage.com).
- What average, good, and best in class look like for cost per unit: This can vary widely based on industry and product but reducing your cost per unit by any amount is beneficial.

### 7. Scrap Rate

Scrap rate measures the percentage of materials that are discarded during the production process. Lowering the scrap rate can lead to significant cost savings and efficiency improvements.

- How scrap rate is calculated: Scrap rate is calculated by dividing the weight or number of rejected materials by the total weight or number of materials produced, and then multiplying by 100.
- What tools can be used to get scrap rate data: Tools such as [Manufacturing ERP software](https://www.epicor.com), [Minitab](https://www.minitab.com), and [SPC for Excel](https://www.spcforexcel.com).
- What average, good, and best in class look like for scrap rate: Average scrap rate is around 3-5%, good is 2-3%, and best in class is below 1%.

### 8. Capacity Utilisation

Capacity utilisation measures how much of your total available capacity is actually being used. This is important for understanding how well resources are being utilised.

- How capacity utilisation is calculated: Capacity utilisation is calculated by dividing actual output by potential output and then multiplying by 100.
- What tools can be used to get capacity utilisation data: Tools such as [Capacity](https://www.capacity.com), [Smartsheet](https://www.smartsheet.com), and [Odoo](https://www.odoo.com) can be used.
- What average, good, and best in class look like for capacity utilisation: Average capacity utilisation is around 70-75%, good is 80-85%, and best in class is 90% and above.

### 9. Downtime

Downtime measures the amount of time equipment is not in operation. Tracking downtime is crucial for identifying causes of stoppages and areas for improvement.

- How downtime is calculated: Downtime is calculated by measuring the total time equipment is not operational within a specific period.
- What tools can be used to get downtime data: Tools such as [MaintainX](https://www.getmaintainx.com), [Fiix](https://www.fiixsoftware.com), and [PEMAC](https://www.pemac.com) can be used.
- What average, good, and best in class look like for downtime: Average downtime can vary significantly by industry, but reducing downtime by even a small percentage can result in significant productivity gains.

### 10. Order Fulfilment Cycle Time (OFCT)

OFCT measures the average time taken to fulfil customer orders, from the time the order is received till it is shipped. This helps improve customer satisfaction and operational efficiency.

- How OFCT is calculated: OFCT is calculated by measuring the time interval from order receipt to shipment for each order, then averaging this time.
- What tools can be used to get OFCT data: Tools such as [ShipStation](https://www.shipstation.com), [TradeGecko](https://www.tradegecko.com), and [Brightpearl](https://www.brightpearl.com) can be used.
- What average, good, and best in class look like for OFCT: This can vary widely, but for e-commerce, an average OFCT is 2-3 days, good is 1 day, and best in class is same-day shipping.

## How to track metrics for operations managers

To effectively track metrics for operations management, consider using a goal-tracking tool like [Tability](https://www.tability.io). Tability can save you time and help your team stay focused on the right metrics to improve. Tability allows you to set KPIs, track progress, and gain insights through visual data representations, thereby enabling more informed decision-making.

## FAQ

**Q: What are KPIs and why are they important?**
A: KPIs (Key Performance Indicators) are measurable values that demonstrate how effectively an organisation is achieving key business objectives. They are important because they provide focus for strategic and operational improvement, and help in decision-making by providing valuable data.

**Q: How often should I review my metrics?**
A: The frequency of review depends on the specific metric and its relevance to your operations. However, a general rule of thumb is to review operational metrics at least monthly, and more frequently for metrics that have significant impact on day-to-day operations.

**Q: What is the first step in implementing these metrics?**
A: The first step is to clearly define your business objectives and align your metrics with these objectives. Ensure you have the necessary tools and processes in place to accurately collect and analyse data for each metric.

**Q: Can Tability integrate with other software?**
A: Yes, Tability can integrate with a variety of other software tools to sync data and provide a more streamlined tracking and reporting process. Check Tability's integrations page for more details.

**Q: What should I do if my metrics are below average?**
A: If your metrics are below average, the first step is to identify the root causes of the underperformance. Then, develop a plan to address these issues. Implement continuous monitoring and adjust your approach as needed to improve over time.

By focusing on these top 10 metrics and consistently tracking them, operations managers can drive significant improvements in efficiency, performance, and overall business outcomes.

The 10 best metrics for operations managers

# The 10 best metrics for talent acquisition

## Why use metrics for talent acquisition?

In an era where talent is a key driver of organisational success, understanding and optimising your talent acquisition processes can provide a significant competitive edge. Metrics for talent acquisition help organisations measure the efficiency, effectiveness, and overall impact of their recruitment processes. By monitoring these metrics, you can identify areas for improvement, streamline processes, and ensure you're attracting and retaining the best talent. 

## The top 10 metrics for talent acquisition

### 1. Time to hire

Time to hire measures the duration between when a job requisition is opened and when an offer is accepted. It is essential because it indicates the efficiency of your hiring process.

- **How time to hire is calculated:** Calculate by subtracting the date the job requisition was opened from the date the offer was accepted.
- **What tools can be used to get time to hire data:** Applicant Tracking Systems (ATS) like [Greenhouse](https://www.greenhouse.io/), [Lever](https://www.lever.co/), or [Jobvite](https://www.jobvite.com/).
- **What average, good, and best in class look like for time to hire:** 
  - Average: 30-45 days 
  - Good: 20-30 days 
  - Best in class: 10-20 days

### 2. Cost per hire

Cost per hire evaluates the total expense involved in hiring a new employee, which includes advertising costs, recruiter salaries, referral bonuses, and other related expenses. It ensures financial efficiency in the hiring process.

- **How cost per hire is calculated:** Sum all the hiring-related expenses and divide by the number of hires.
- **What tools can be used to get cost per hire data:** HR software like [Workday](https://www.workday.com/), [BambooHR](https://www.bamboohr.com/), or [Zoho Recruit](https://www.zoho.com/recruit/).
- **What average, good, and best in class look like for cost per hire:** 
  - Average: £3,000-£4,000 
  - Good: £2,000-£3,000 
  - Best in class: Less than £2,000

### 3. Quality of hire

Quality of hire measures the value that new hires bring to the organisation, typically assessed through performance ratings, retention rates, and feedback from managers.

- **How quality of hire is calculated:** Commonly measured through performance evaluations, retention rates, and feedback during the first year.
- **What tools can be used to get quality of hire data:** Performance management tools like [Lattice](https://www.lattice.com/), [15Five](https://www.15five.com/), or [Reflektive](https://www.reflektive.com/).
- **What average, good, and best in class look like for quality of hire:** 
  - Depends on your scoring system, but generally, improved performance ratings within the first year indicate a good quality of hire.

### 4. Offer acceptance rate

Offer acceptance rate gauges the percentage of job offers that are accepted by candidates. A high rate suggests competitive offers and effective candidate matching.

- **How offer acceptance rate is calculated:** Divide the number of accepted offers by the total number of offers made, then multiply by 100.
- **What tools can be used to get offer acceptance rate data:** ATS platforms like [Greenhouse](https://www.greenhouse.io/), [Lever](https://www.lever.co/), or [Jobvite](https://www.jobvite.com/).
- **What average, good, and best in class look like for offer acceptance rate:** 
  - Average: 75-85%
  - Good: 85-90%
  - Best in class: 90-95%

### 5. Candidate experience

Candidate experience assesses the overall experience of candidates throughout the recruitment process, from application to onboarding.

- **How candidate experience is calculated:** Typically measured through surveys sent to candidates after the hiring process concludes.
- **What tools can be used to get candidate experience data:** Survey tools like [SurveyMonkey](https://www.surveymonkey.com/), [Typeform](https://www.typeform.com/), or hiring platforms with built-in feedback systems.
- **What average, good, and best in class look like for candidate experience:** 
  - Average: Mixed feedback 
  - Good: Generally positive feedback 
  - Best in class: Highly positive feedback with actionable insights for improvement

### 6. Source of hire

Source of hire identifies which channels (e.g., job boards, social media, referrals) are the most effective in attracting candidates. This metric helps optimise recruitment strategies by focusing on the most productive sources.

- **How source of hire is calculated:** Track the number of hires originating from each source and calculate the percentage share each source contributes.
- **What tools can be used to get source of hire data:** ATS platforms like [Greenhouse](https://www.greenhouse.io/), [Lever](https://www.lever.co/), or analytics tools like [Google Analytics](https://analytics.google.com/).
- **What average, good, and best in class look like for source of hire:** 
  - Varies widely by industry but focusing on sources which consistently yield quality candidates.

### 7. Recruitment funnel effectiveness

Recruitment funnel effectiveness measures the conversion rates at each stage of the recruitment process, helping to identify bottlenecks and improve the flow from application to hire.

- **How recruitment funnel effectiveness is calculated:** Track the number of candidates that move from one stage to the next, calculate conversion rates for each stage.
- **What tools can be used to get recruitment funnel effectiveness data:** ATS platforms like [Greenhouse](https://www.greenhouse.io/), [Lever](https://www.lever.co/).
- **What average, good, and best in class look like for recruitment funnel effectiveness:** 
  - Average: Conversion rates are lower at the initial stages, higher at the final stages.
  - Good: 20-30% conversion between stages.
  - Best in class: 30-50% conversion between stages.

### 8. Candidate diversity

Candidate diversity tracks the diversity of candidates throughout the hiring process, ensuring fair representation of different demographics.

- **How candidate diversity is calculated:** Collect demographic data voluntarily provided by candidates, assess the composition of your applicant pool, shortlists, and hires.
- **What tools can be used to get candidate diversity data:** HR software like [Workday](https://www.workday.com/), [BambooHR](https://www.bamboohr.com/), and diversity-focused platforms like [Diversio](https://www.diversio.com/).
- **What average, good, and best in class look like for candidate diversity:** 
  - Goals vary by organisation; the key is to consistently work towards improving representation.

### 9. Hiring manager satisfaction

Hiring manager satisfaction measures the satisfaction of hiring managers with the recruitment process and the suitability of candidates presented to them.

- **How hiring manager satisfaction is calculated:** Conduct surveys or interviews with hiring managers post-hire.
- **What tools can be used to get hiring manager satisfaction data:** Survey platforms like [SurveyMonkey](https://www.surveymonkey.com/), [Typeform](https://www.typeform.com/), or performance management tools.
- **What average, good, and best in class look like for hiring manager satisfaction:** 
  - Average: Mixed feedback.
  - Good: Generally positive feedback with minor improvement areas.
  - Best in class: Highly positive feedback with clear indications of a streamlined process.

### 10. Employee retention rate

Employee retention rate tracks how long new hires stay with the organisation, providing insights into the success of the hiring and onboarding process.

- **How employee retention rate is calculated:** Divide the number of employees who remain after a certain period by the total number of hires in that period, then multiply by 100.
- **What tools can be used to get employee retention rate data:** HR software like [Workday](https://www.workday.com/), [BambooHR](https://www.bamboohr.com/), or [ADP](https://www.adp.co.uk/).
- **What average, good, and best in class look like for employee retention rate:** 
  - Average: 70-80% (one-year retention)
  - Good: 80-90% (one-year retention)
  - Best in class: Over 90% (one-year retention)

## How to track metrics for talent acquisition

Tracking metrics for talent acquisition can be time-consuming, but it is crucial for improving your recruitment processes. Using a goal-tracking tool like [Tability](https://www.tability.io) can save you time and help your team stay focused on the right metrics to improve. Tability allows you to set clear goals, monitor progress, and make data-driven decisions based on real-time insights.

Whether you use Tability or other specialised ATS platforms and HR software, ensure that your tools are integrated, accessible, and user-friendly. Regularly review and analyse your metrics to identify trends, pinpoint areas needing improvement, and celebrate successes. 

## FAQ

**Q: What is the most important talent acquisition metric?**
A: The most important metric can vary based on your organisational goals, but commonly, time to hire, quality of hire, and cost per hire are considered highly crucial as they directly impact the efficiency, effectiveness, and financial health of the recruitment process.

**Q: How often should we review talent acquisition metrics?**
A: Ideally, talent acquisition metrics should be reviewed monthly to ensure ongoing optimisation. However, some organisations may choose to review them quarterly or biannually based on their specific needs and resource availability.

**Q: Can tracking too many metrics be counterproductive?**
A: Yes, tracking too many metrics can lead to data overload and distract from focusing on critical areas. It’s important to prioritise and focus on the most impactful metrics that align with your recruitment goals.

**Q: How can we improve our candidate experience metric?**
A: Improving candidate experience involves clear communication, providing timely updates, ensuring a seamless interview process, and offering constructive feedback regardless of the hiring outcome. Regularly collecting and acting on candidate feedback is also key.

**Q: Why is candidate diversity an important metric?**
A: Candidate diversity ensures that you are attracting a wide range of talent and promoting a inclusive work environment, which can enhance creativity, innovation, and overall organisational performance.


The 10 best metrics for talent acquisition

# The 10 best metrics for software quality

## Why use metrics for software quality?

In the realm of software development, maintaining high software quality is paramount for ensuring user satisfaction, reducing maintenance costs, and securing long-term success. This is where software quality metrics play a crucial role. By measuring different aspects of software quality, development teams can pinpoint areas of improvement, benchmark their performance, and make informed decisions to optimise their development processes.

## The top 10 metrics for software quality

### 1. Defect density

Defect density measures the number of defects or bugs relative to the size of the software. It is a crucial metric for understanding the overall quality of the software and ensuring it meets quality standards before release.

- **How defect density is calculated**: Divide the number of defects by the size of the software (usually measured in lines of code or function points).
- **What tools can be used to get defect density data**: Tools like Jira (https://www.atlassian.com/software/jira) and Bugzilla (https://www.bugzilla.org) are useful for tracking defects.
- **What average, good, and best in class look like for defect density**:
  - Average: 5-10 defects per KLOC (thousand lines of code)
  - Good: 1-5 defects per KLOC
  - Best in class: <1 defect per KLOC

### 2. Code coverage

Code coverage measures the percentage of code that is executed by automated tests. Higher code coverage typically indicates a more thoroughly tested and reliable codebase.

- **How code coverage is calculated**: Divide the number of lines of code executed by the total number of lines of code, then multiply by 100 to get a percentage.
- **What tools can be used to get code coverage data**: Tools like JaCoCo (https://www.jacoco.org) and Cobertura (http://cobertura.github.io/cobertura/) are commonly used for code coverage analysis.
- **What average, good, and best in class look like for code coverage**:
  - Average: 60-70%
  - Good: 70-85%
  - Best in class: >85%

### 3. Mean time to resolution (MTTR)

MTTR measures the average time taken to resolve bugs or issues. A lower MTTR indicates quicker resolution and less impact on users, contributing to higher software quality.

- **How MTTR is calculated**: Add the total time taken to resolve all issues and divide by the number of issues.
- **What tools can be used to get MTTR data**: Tools like Jira (https://www.atlassian.com/software/jira) and ServiceNow (https://www.servicenow.com) can help track issue resolution times.
- **What average, good, and best in class look like for MTTR**:
  - Average: Several days
  - Good: 1-2 days
  - Best in class: <24 hours

### 4. Defect resolution rate

This metric measures the percentage of identified defects that are resolved over a specific time period. A high defect resolution rate indicates an effective quality assurance process.

- **How defect resolution rate is calculated**: Divide the number of resolved defects by the total number of reported defects, then multiply by 100 to get a percentage.
- **What tools can be used to get defect resolution rate data**: Tools like Bugzilla (https://www.bugzilla.org) and GitHub Issues (https://github.com/features/issues) are useful.
- **What average, good, and best in class look like for defect resolution rate**:
  - Average: 70-80%
  - Good: 80-90%
  - Best in class: >90%

### 5. Customer-reported defects

This metric counts the number of defects reported by customers after the product has been released. Fewer customer-reported defects indicate higher software quality and better user satisfaction.

- **How customer-reported defects is calculated**: Simply count the number of defects reported by customers.
- **What tools can be used to get customer-reported defects data**: Helpdesk tools like Zendesk (https://www.zendesk.com) and Freshdesk (https://freshdesk.com) can track customer-reported issues.
- **What average, good, and best in class look like for customer-reported defects**:
  - Average: Varies widely based on application size and user base
  - Good: Lower than industry benchmarks
  - Best in class: Minimal to zero

### 6. Change failure rate

Change failure rate measures the percentage of changes made to the software that result in failures, requiring rollback or subsequent fixes. Lower rates indicate higher quality code changes.

- **How change failure rate is calculated**: Divide the number of failed changes by the total number of changes, then multiply by 100 to get a percentage.
- **What tools can be used to get change failure rate data**: CI/CD tools like Jenkins (https://www.jenkins.io) and CircleCI (https://circleci.com) track change success and failure rates.
- **What average, good, and best in class look like for change failure rate**:
  - Average: 10-20%
  - Good: 5-10%
  - Best in class: <5%

### 7. Release frequency

Release frequency measures how often new versions of the software are released. Higher release frequency, coupled with low defect rates, can indicate an agile and efficient development process with consistent quality.

- **How release frequency is calculated**: Count the number of releases within a specific time period.
- **What tools can be used to get release frequency data**: Continuous integration and deployment tools like GitLab (https://about.gitlab.com) and Bitbucket (https://bitbucket.org) offer insights into release frequency.
- **What average, good, and best in class look like for release frequency**:
  - Average: Monthly or bi-monthly
  - Good: Bi-weekly
  - Best in class: Weekly or daily

### 8. Escaped defects

Escaped defects are issues that were not detected during the testing phase and are discovered post-release. Lower escaped defects suggest a robust testing process and higher overall software quality.

- **How escaped defects is calculated**: Count the number of defects found post-release.
- **What tools can be used to get escaped defects data**: Tracking tools like Jira (https://www.atlassian.com/software/jira) and Azure DevOps (https://azure.microsoft.com/en-us/services/devops/) monitor defects across stages.
- **What average, good, and best in class look like for escaped defects**:
  - Average: Varies based on project complexity
  - Good: Significantly lower than industry averages
  - Best in class: Near zero

### 9. Technical debt

Technical debt refers to the additional work required to improve the code quality. Managing technical debt is essential to maintaining software quality over time.

- **How technical debt is calculated**: Several methods exist, commonly through measuring the effort needed to fix bad code against the ideal solution.
- **What tools can be used to get technical debt data**: Tools like SonarQube (https://www.sonarqube.org) and CodeClimate (https://codeclimate.com) provide insights into technical debt.
- **What average, good, and best in class look like for technical debt**:
  - Average: Subject to project size and history
  - Good: Constantly monitored and reduced
  - Best in class: Minimal to zero debt

### 10. Test automation coverage

Test automation coverage measures the proportion of testing chores that are automated. Higher automation coverage often correlates with faster, more reliable testing cycles.

- **How test automation coverage is calculated**: Divide the number of automated tests by the total number of tests, then multiply by 100 to get a percentage.
- **What tools can be used to get test automation coverage data**: Frameworks like Selenium (https://www.selenium.dev) and TestComplete (https://smartbear.com/product/testcomplete/overview/) are widely used.
- **What average, good, and best in class look like for test automation coverage**:
  - Average: 30-50%
  - Good: 50-70%
  - Best in class: >70%

## How to track metrics for software quality

Tracking metrics effectively can be time-consuming and challenging without the right tools. To streamline the process, consider using a goal-tracking tool like [Tability](https://www.tability.io). Tability can help your team stay focused on the right metrics, providing a collaborative platform to track progress and make data-driven decisions to improve software quality. By having a clear view of your metrics, you can identify areas that need attention and act promptly to ensure consistent improvement.

## FAQ

**Q: How often should we review our software quality metrics?**

A: It's generally a good practice to review software quality metrics on a regular basis, such as weekly or bi-weekly. More frequent reviews allow for quicker identification and resolution of any issues, ensuring the team stays on track with quality goals.

**Q: Can these metrics be used for any type of software development project?**

A: Yes, these metrics can be applied across various types of software development projects, including web applications, mobile apps, and enterprise software. Although the specific targets for each metric may vary based on the nature and complexity of the project, the general principles of measuring and improving quality remain consistent.

**Q: What should we do if one of our metrics is below the desired target?**

A: If a metric is below the desired target, the team should investigate the underlying causes. This might involve conducting root cause analysis, examining current processes, or seeking feedback from team members. Once the issues are identified, implement corrective actions and monitor the results to ensure improvement.

**Q: Can we prioritise certain metrics over others?**

A: Absolutely. Depending on your project's specific goals and current challenges, you may choose to prioritise certain metrics over others. It's essential to focus on the metrics that align most closely with your project objectives and have the most significant impact on the overall software quality.

The 10 best metrics for software quality

# The 10 best metrics for project management

## Why use metrics for project management?

Project management is a multifaceted discipline that involves planning, executing, and overseeing projects to achieve specific goals within defined parameters such as scope, time, and budget. Using metrics is essential to ensure that projects stay on track, resources are utilised efficiently, and stakeholders are satisfied. Metrics provide objective data that helps project managers make informed decisions, identify bottlenecks, and validate project success.

## The top 10 metrics for project management

### 1. Project Schedule Variance (SV)

Project Schedule Variance measures the difference between the planned progress and the actual progress of a project. This metric helps project managers identify delays early and take corrective actions.

- **How SV is calculated**: Subtract the planned progress from the actual progress (usually measured in work hours or days).

- **Tools**: Microsoft Project, Smartsheet, Trello.

- **Benchmarks**: 
  - Average: ±10% variance
  - Good: ±5% variance
  - Best in class: ±2% variance

### 2. Cost Performance Index (CPI)

The Cost Performance Index is a measure of how efficiently project costs are being managed. A CPI greater than 1 indicates that the project is under budget, while a CPI less than 1 indicates that the project is over budget.

- **How CPI is calculated**: Divide the earned value (EV) by the actual cost (AC).

- **Tools**: Primavera P6, Oracle EPPM, MS Project.

- **Benchmarks**:
  - Average: 0.95 to 1.05
  - Good: 1.05 to 1.10
  - Best in class: >1.10

### 3. Resource Utilisation

Resource Utilisation measures how effectively the project's resources (human, material, and financial) are used. High resource utilisation rates indicate efficient use of resources, while low rates could suggest underutilisation.

- **How resource utilisation is calculated**: Divide the actual hours worked by the available hours and multiply by 100 to get a percentage.

- **Tools**: Hub Planner, Resource Guru, Float.

- **Benchmarks**:
  - Average: 70-80%
  - Good: 80-90%
  - Best in class: 90-100%

### 4. Scope Creep

Scope Creep measures the extent to which the project scope expands over time without corresponding adjustments to time, cost, or resources. It is an important metric for maintaining project discipline.

- **How scope creep is calculated**: Compare the initial scope to the current scope and measure the differences in deliverables or requirements.

- **Tools**: Wrike, Asana, Monday.com.

- **Benchmarks**:
  - Average: 10-15%
  - Good: 5-10%
  - Best in class: <5%

### 5. On-time Completion Rate

The On-time Completion Rate measures the percentage of project tasks completed by their due dates. This metric is crucial for assessing the project schedule adherence.

- **How on-time completion rate is calculated**: Divide the number of tasks completed on time by the total number of tasks and multiply by 100 to get a percentage.

- **Tools**: Jira, MS Project, Trello.

- **Benchmarks**:
  - Average: 70-80%
  - Good: 80-90%
  - Best in class: 90-100%

### 6. Budget Variance

Budget Variance measures the difference between the budgeted cost of a project and the actual cost. Keeping this variance low is critical to staying within financial constraints.

- **How budget variance is calculated**: Subtract the budgeted cost from the actual cost.

- **Tools**: QuickBooks, Zoho Projects, FreshBooks.

- **Benchmarks**:
  - Average: ±10%
  - Good: ±5%
  - Best in class: <±2%

### 7. Customer Satisfaction

Customer Satisfaction measures how happy the project's stakeholders are with the outcome. This metric is key for assessing the project's success from the client's perspective.

- **How customer satisfaction is calculated**: Typically gathered through surveys or feedback forms and analysed using a satisfaction scale (e.g., 1 to 10).

- **Tools**: SurveyMonkey, Qualtrics, Typeform.

- **Benchmarks**:
  - Average: Satisfaction score of 7-8
  - Good: Satisfaction score of 8-9
  - Best in class: Satisfaction score of 9-10

### 8. Risk Management

Risk Management measures the effectiveness of a project's risk management strategies. It helps in assessing how well potential issues are identified, mitigated, and managed.

- **How risk management is calculated**: Evaluate the number of identified risks, mitigated risks, and the impact of realised risks against the project goals.

- **Tools**: RiskWatch, LogicManager, ARM.

- **Benchmarks**:
  - Average: 70-80% of risks effectively managed
  - Good: 80-90%
  - Best in class: 90-100%

### 9. Earned Value Management (EVM)

Earned Value Management is a comprehensive metric that integrates scope, schedule, and cost to assess project performance and progress. EVM helps predict future performance trends.

- **How EVM is calculated**: Combine the earned value, planned value, and actual cost to monitor cost and schedule variances.

- **Tools**: MS Project, Oracle Primavera, Deltek Cobra.

- **Benchmarks**:
  - Average: CPI and SPI between 0.9 and 1.1
  - Good: CPI and SPI between 1.0 and 1.1
  - Best in class: CPI and SPI > 1.1

### 10. Team Productivity

Team Productivity measures the output of the project team and is a good indicator of team efficiency and morale. High productivity often correlates with successful project outcomes.

- **How team productivity is calculated**: Measure the output in terms of tasks completed or milestones achieved per unit of time (e.g., per week or month).

- **Tools**: Jira, Slack, Microsoft Teams.

- **Benchmarks**:
  - Average: Meeting 80-90% of weekly goals
  - Good: Meeting 90-100% of weekly goals
  - Best in class: Exceeding 100% of weekly goals

## How to track metrics for project management

Tracking metrics can be cumbersome if done manually, but there are many tools available to simplify the process. Using a goal-tracking tool like [Tability](https://www.tability.io) can save time and help teams stay focused on the right metrics to improve. These tools usually offer features like progress tracking, reporting, and collaborative dashboards.

By automating data collection and reporting, project managers can focus more on analysis and strategy rather than administrative tasks. These tools also facilitate real-time monitoring, making it easier to identify issues early and make timely adjustments.

## FAQ

**1. What are project management metrics?**

Project management metrics are quantitative measures used to assess various aspects of project performance, such as schedule adherence, cost efficiency, resource utilisation, and stakeholder satisfaction.

**2. Why are project management metrics important?**

Metrics provide objective data that allows for informed decision-making, performance tracking, and process improvement. They help ensure that projects are completed on time, within budget, and to the satisfaction of stakeholders.

**3. How often should I track project management metrics?**

The frequency of tracking depends on the project's scope and complexity. For most projects, weekly or bi-weekly tracking is sufficient. High-risk or fast-paced projects may require daily monitoring.

**4. Can I track project management metrics manually?**

While it is possible to track metrics manually, using specialised tools like [Tability](https://www.tability.io), MS Project, or Trello can greatly simplify the process and increase accuracy.

**5. What if my project's metrics are not meeting benchmarks?**

If metrics are not meeting benchmarks, it's essential to identify the root causes. This can involve re-evaluating resource allocation, revising timelines, or adjusting project scope. Continuous monitoring and adjustments are key to success.

By leveraging the right metrics, tools, and tracking practices, project managers can significantly enhance their project's chances of success.

The 10 best metrics for project management

# The 10 best metrics for customer engagement

## Why use metrics for customer engagement?

Customer engagement is a critical aspect of any business strategy. It allows companies to understand how actively customers are interacting with their brand, products, or services. By measuring customer engagement, businesses can glean insights into customer satisfaction, loyalty, and overall experience, helping to improve strategies and foster long-term relationships. Effective customer engagement metrics ensure that businesses are responsive to customer needs, fostering a customer-centric culture that drives growth and innovation.

## The top 10 metrics for customer engagement

### 1. Customer Satisfaction Score (CSAT)

Measuring customer satisfaction is fundamental for determining how happy customers are with a company's products, services, or experiences. High CSAT scores indicate positive customer experiences, while low scores suggest areas requiring improvement.

#### How CSAT is calculated

Customers are typically asked to rate their satisfaction on a scale of 1-5. The CSAT score is then calculated by dividing the number of satisfied customers (ratings of 4 or 5) by the total number of respondents and multiplying by 100 to get a percentage.

#### What tools can be used to get CSAT data

- **SurveyMonkey**: [SurveyMonkey](https://www.surveymonkey.com)
- **Qualtrics**: [Qualtrics](https://www.qualtrics.com)
- **Google Forms**: [Google Forms](https://forms.google.com)

#### What average, good, and best in class look like for CSAT

- **Average**: 70-79%
- **Good**: 80-89%
- **Best in class**: 90-100%

### 2. Net Promoter Score (NPS)

NPS measures customer willingness to recommend a company's products or services to others, thereby indicating overall customer loyalty and satisfaction. 

#### How NPS is calculated

Customers are asked to rate their likelihood of recommending the product/service on a scale of 0-10. Scores of 9-10 are promoters, 7-8 are passives, and 0-6 are detractors. NPS is calculated by subtracting the percentage of detractors from the percentage of promoters.

#### What tools can be used to get NPS data

- **Delighted**: [Delighted](https://delighted.com)
- **Promoter.io**: [Promoter.io](https://www.promoter.io)
- **Medallia**: [Medallia](https://www.medallia.com)

#### What average, good, and best in class look like for NPS

- **Average**: 0-30
- **Good**: 30-50
- **Best in class**: 50-70+

### 3. Customer Effort Score (CES)

CES measures how easy it is for customers to interact with a company, whether through purchasing, seeking support, or other transactions. Lower effort scores correlate with higher loyalty.

#### How CES is calculated

Customers are asked to rate their ease of interaction on a scale, usually from 1 (very difficult) to 7 (very easy). The score averages out the ratings to give a single customer effort score.

#### What tools can be used to get CES data

- **Zendesk**: [Zendesk](https://www.zendesk.com)
- **HubSpot Service Hub**: [HubSpot Service Hub](https://www.hubspot.com/products/service)
- **Typeform**: [Typeform](https://www.typeform.com)

#### What average, good, and best in class look like for CES

- **Average**: 3-4
- **Good**: 5-6
- **Best in class**: 6-7

### 4. Churn Rate

Churn rate measures the percentage of customers who stop using a company's products or services within a specific period. Lower churn rates indicate higher customer retention and satisfaction.

#### How churn rate is calculated

The churn rate is calculated by dividing the number of customers lost during a period by the number of customers at the start of that period, and then multiplying by 100 to get a percentage.

#### What tools can be used to get churn rate data

- **Baremetrics**: [Baremetrics](https://www.baremetrics.com)
- **ChartMogul**: [ChartMogul](https://www.chartmogul.com)
- **ProfitWell**: [ProfitWell](https://www.profitwell.com)

#### What average, good, and best in class look like for churn rate

- **Average**: 5-10%
- **Good**: 3-5%
- **Best in class**: 1-2%

### 5. Customer Lifetime Value (CLV)

CLV estimates the total revenue a business can generate from a single customer account over the entire duration of their relationship. A higher CLV indicates a stronger, more profitable customer relationship.

#### How CLV is calculated

CLV is calculated by multiplying the average purchase value, purchase frequency, and customer lifespan. (Average purchase value x Purchase frequency) x Customer lifespan = CLV.

#### What tools can be used to get CLV data

- **KISSmetrics**: [KISSmetrics](https://www.kissmetrics.io)
- **Google Analytics**: [Google Analytics](https://analytics.google.com)
- **TrackMaven**: [TrackMaven](https://www.trackmaven.com)

#### What average, good, and best in class look like for CLV

- **Average**: Varies by industry; generally calculated against profit margins
- **Good**: Increasing month-over-month
- **Best in class**: High-value annual contracts/memberships

### 6. Engagement Rate

Engagement rate measures the level of interaction that customers have with a brand's content, such as likes, shares, comments, and other forms of engagement on social media or web platforms.

#### How engagement rate is calculated

Engagement rate is calculated by dividing the total engagement (e.g., likes, shares, comments) by the total number of followers or views, then multiplying by 100 to get a percentage.

#### What tools can be used to get engagement rate data

- **Hootsuite**: [Hootsuite](https://www.hootsuite.com)
- **Sprout Social**: [Sprout Social](https://sproutsocial.com)
- **Buffer**: [Buffer](https://buffer.com)

#### What average, good, and best in class look like for engagement rate

- **Average**: 1-2%
- **Good**: 2-4%
- **Best in class**: 5%+

### 7. Active Users

Active users refer to the number of unique users engaging with a product or service within a specific time frame. Higher numbers of active users indicate strong customer interest and usage.

#### How active users are calculated

The metric is typically categorized as daily active users (DAU) or monthly active users (MAU) based on the number of unique users in those periods.

#### What tools can be used to get active users data

- **Mixpanel**: [Mixpanel](https://mixpanel.com)
- **Google Analytics**: [Google Analytics](https://analytics.google.com)
- **Amplitude**: [Amplitude](https://amplitude.com)

#### What average, good, and best in class look like for active users

- **Average**: Varies by business type and industry
- **Good**: Consistently growing user base
- **Best in class**: DAU/MAU ratio of 20%+

### 8. Time on Site

Time on site measures the average amount of time users spend on a website or digital platform. Higher time on site indicates better content engagement and likely interest in products/services.

#### How time on site is calculated

This metric is usually recorded through analytics tools that track user sessions from entry to exit on the site, giving an average duration based on total session time divided by the number of sessions.

#### What tools can be used to get time on site data

- **Google Analytics**: [Google Analytics](https://analytics.google.com)
- **Crazy Egg**: [Crazy Egg](https://www.crazyegg.com)
- **Hotjar**: [Hotjar](https://www.hotjar.com)

#### What average, good, and best in class look like for time on site

- **Average**: 2-3 minutes
- **Good**: 3-5 minutes
- **Best in class**: 5+ minutes

### 9. Page Views per Session

Page views per session indicates the average number of pages a user views during a single session on a website. Higher page views per session suggest better engagement with content.

#### How page views per session is calculated

Divide the total number of page views by the total number of sessions within a specific period.

#### What tools can be used to get page views per session data

- **Google Analytics**: [Google Analytics](https://analytics.google.com)
- **Adobe Analytics**: [Adobe Analytics](https://www.adobe.com/analytics/adobe-analytics.html)
- **Matomo**: [Matomo](https://matomo.org)

#### What average, good, and best in class look like for page views per session

- **Average**: 1-2 pages
- **Good**: 3-4 pages
- **Best in class**: 5+ pages

### 10. Email Click-Through Rate (CTR)

Email CTR measures the percentage of email recipients who clicked on one or more links in an email. Higher CTRs indicate more engaging and relevant email content.

#### How email CTR is calculated

The email CTR is calculated by dividing the number of unique clicks by the number of delivered emails, then multiplying by 100 to get a percentage.

#### What tools can be used to get email CTR data

- **Mailchimp**: [Mailchimp](https://mailchimp.com)
- **Constant Contact**: [Constant Contact](https://www.constantcontact.com)
- **Campaign Monitor**: [Campaign Monitor](https://www.campaignmonitor.com)

#### What average, good, and best in class look like for email CTR

- **Average**: 2-3%
- **Good**: 3-5%
- **Best in class**: 5%+

## How to track metrics for customer engagement

Tracking customer engagement metrics can be time-consuming but is essential for making data-driven decisions. Using a goal-tracking tool like [Tability](https://www.tability.io) can save time and help teams stay focused on the right metrics to improve. Tability supports tracking multiple KPIs, visualising progress, and ensuring that teams stay aligned towards their goals.

## FAQ

### Why are customer engagement metrics important?

Customer engagement metrics give businesses insight into customer behaviour, satisfaction, and loyalty, helping to identify areas for improvement and growth.

### How often should customer engagement metrics be reviewed?

It depends on the metric. Some can be reviewed daily or weekly (e.g., active users, time on site), while others might be better suited for monthly or quarterly evaluations (e.g., NPS, CLV).

### Can these metrics be applied to all business types?

Yes, although the specific benchmarks and importance may vary, customer engagement metrics can be universally applied across different industries and business models.

### What should be done if a metric shows poor performance?

Identify the root causes, formulate a strategy to tackle the issues, and regularly monitor the impact of implemented changes to improve performance.

By consistently monitoring and optimising these 10 customer engagement metrics, businesses can better understand their customers and create more effective strategies to enhance the customer experience.

The 10 best metrics for customer engagement

# The 10 best metrics for link building

## Why use metrics for link building?

Link building is a crucial element of any successful SEO strategy. It involves acquiring hyperlinks from other websites to your own. Monitoring the effectiveness of your link-building efforts is necessary to understand the impact of these links on your site's authority and search engine rankings. Trackable metrics provide invaluable insights into the quality and effectiveness of your link-building campaigns, guiding you toward better decision-making and refined strategies.

## The top 10 metrics for link building

### 1. Domain Authority (DA)

Domain Authority (DA) is a score developed by Moz that predicts how well a website will rank on search engine result pages (SERPs). It's a critical metric for evaluating the potential impact of links from different sites.

#### How DA is calculated
DA is calculated using various factors, including linking root domains, the number of total links, and other SEO metrics. It scores from 1 to 100, with higher numbers indicating a greater ability to rank.

#### What tools can be used to get DA data
- [Moz](https://moz.com)
- [Ahrefs](https://ahrefs.com)
- [SEMrush](https://www.semrush.com)

#### What average, good, and best in class look like for DA
- Average: 20-30
- Good: 40-50
- Best in Class: 60-100

### 2. Page Authority (PA)

Page Authority (PA) is similar to Domain Authority but focuses on the ranking strength of individual pages rather than entire domains.

#### How PA is calculated
PA is calculated using an algorithm similar to DA, factoring in link metrics like total links and linking root domains to a specific page.

#### What tools can be used to get PA data
- [Moz](https://moz.com)
- [Ahrefs](https://ahrefs.com)

#### What average, good, and best in class look like for PA
- Average: 20-30
- Good: 40-50
- Best in Class: 60-100

### 3. Number of Referring Domains

The number of referring domains indicates how many unique domains link to your website. More referring domains generally mean a broader and potentially more influential backlink profile.

#### How the number of referring domains is calculated
It is calculated by counting the number of unique domain names that link to your website over a given period.

#### What tools can be used to get referring domains data
- [Ahrefs](https://ahrefs.com)
- [SEMrush](https://www.semrush.com)
- [Majestic](https://majestic.com)

#### What average, good, and best in class look like for referring domains
- Average: 50-100
- Good: 500-1,000
- Best in Class: 5,000+

### 4. Backlink Growth Rate

The backlink growth rate tracks the speed at which your site acquires new backlinks. A consistent, positive growth rate can indicate successful link-building efforts.

#### How backlink growth rate is calculated
It is calculated by comparing the number of new backlinks acquired over different time periods, usually month-over-month.

#### What tools can be used to get backlink growth rate data
- [Ahrefs](https://ahrefs.com)
- [SEMrush](https://www.semrush.com)

#### What average, good, and best in class look like for backlink growth rate
- Average: 3-5 new backlinks per month
- Good: 10-20 new backlinks per month
- Best in Class: 50+ new backlinks per month

### 5. Anchor Text Diversity

Anchor text diversity refers to the variety of anchor texts used in the backlinks pointing to your site. Overuse of exact-match anchor text can be seen as manipulative by search engines, making diversity critical.

#### How anchor text diversity is calculated
It is calculated by analysing the variety of phrases used as anchor texts linking to your site.

#### What tools can be used to get anchor text diversity data
- [Ahrefs](https://ahrefs.com)
- [SEMrush](https://www.semrush.com)

#### What average, good, and best in class look like for anchor text diversity
- Average: Limited variety, overuse of one or two anchor texts
- Good: Balanced use of various relevant anchor texts
- Best in Class: High diversity with organic-looking anchor texts

### 6. Toxic Links

Toxic links are spammy or low-quality links that can harm your site's SERP rankings and overall SEO health. Identifying and disavowing these links is vital.

#### How toxic links are identified
Tools evaluate the spam score, the relevance of the linking site, and other metrics to identify potentially toxic links.

#### What tools can be used to get toxic links data
- [SEMrush](https://www.semrush.com)
- [Ahrefs](https://ahrefs.com)
- [Moz](https://moz.com)

#### What average, good, and best in class look like for toxic links
- Average: 5-10 toxics links in total
- Good: 1-5 toxic links in total
- Best in Class: 0 toxic links

### 7. Trust Flow

Trust Flow is a metric developed by Majestic that evaluates the quality of your site's backlinks. Higher Trust Flow indicates a larger number of high-quality links.

#### How Trust Flow is calculated
It is calculated based on the quality of the sites linking to your site, factoring in the Trust Flow of those linking sites.

#### What tools can be used to get Trust Flow data
- [Majestic](https://majestic.com)

#### What average, good, and best in class look like for Trust Flow
- Average: 0-15
- Good: 15-30
- Best in Class: 30-100

### 8. Citation Flow

Citation Flow measures the influence of a website based on the number of sites linking to it, without considering the quality of those links.

#### How Citation Flow is calculated
It is calculated as the number of backlinks and their combined influence.

#### What tools can be used to get Citation Flow data
- [Majestic](https://majestic.com)

#### What average, good, and best in class look like for Citation Flow
- Average: 0-20
- Good: 20-40
- Best in Class: 50+

### 9. Organic Traffic

Organic traffic measures how many visitors come to your site from search engines, which can indicate the effectiveness of your link-building efforts.

#### How organic traffic is calculated
It is calculated by analysing the number of visitors arriving via unpaid search results.

#### What tools can be used to get organic traffic data
- [Google Analytics](https://analytics.google.com)
- [SEMrush](https://www.semrush.com)
- [Ahrefs](https://ahrefs.com)

#### What average, good, and best in class look like for organic traffic
- Average: Varies by industry and site size
- Good: Steady month-over-month growth
- Best in Class: Significant month-over-month growth with high visitor numbers

### 10. Keyword Rankings

Keyword rankings track where your site appears in the SERPs for specific targeted keywords. Successful link-building efforts should result in improved rankings for relevant keywords.

#### How keyword rankings are calculated
It involves tracking the positions of target keywords in search engine result pages over time.

#### What tools can be used to get keyword rankings data
- [Google Search Console](https://search.google.com/search-console)
- [Ahrefs](https://ahrefs.com)
- [SEMrush](https://www.semrush.com)

#### What average, good, and best in class look like for keyword rankings
- Average: Position 50-100
- Good: Position 11-50
- Best in Class: Position 1-10

## How to track metrics for link building

Tracking metrics for link building can be a complex and time-consuming process. By using reliable SEO and analytics tools, you can simplify this task. Tools like [Ahrefs](https://ahrefs.com), [SEMrush](https://www.semrush.com), and [Moz](https://moz.com) provide comprehensive data to help you monitor these metrics effectively. Additionally, a goal-tracking tool like [Tability](https://www.tability.io) can save time and help teams stay focused on the right metrics to improve. Tability allows you to set goals, measure results, and stay aligned with your link-building strategy.

## FAQ

### Why is Domain Authority crucial for link building?
Domain Authority helps assess the potential impact of a backlink from a specific site, guiding you to more valuable link-building opportunities.

### What is the difference between Domain Authority and Page Authority?
While Domain Authority evaluates the ranking potential of an entire domain, Page Authority assesses the ranking ability of individual pages.

### How often should I track these link-building metrics?
It's generally recommended to monitor these metrics monthly to identify trends and make timely adjustments to your strategy.

### Can multiple low-quality links hurt my site's SEO?
Yes, multiple low-quality or spammy links can negatively impact your SEO by associating your site with disreputable sources, making it crucial to manage and disavow toxic links.

### What is a healthy backlink growth rate?
A steady backlink growth rate varies by industry but aiming for at least 10-20 new high-quality backlinks per month is generally considered good.

By focusing on these top 10 link-building metrics, you can create a more effective and data-driven link-building strategy, leading to better SEO performance and higher rankings.

The 10 best metrics for link building

# The 10 Best Metrics for Local SEO

## Why use metrics for local SEO?

In the bustling world of digital marketing, local SEO (Search Engine Optimization) has become pivotal for businesses aiming to attract foot traffic and enhance their visibility in specific geographic areas. Unlike traditional SEO, local SEO targets potential customers in a specific location, making it crucial for businesses like restaurants, retail stores, and local service providers. Tracking local SEO metrics provides valuable insights into how well your efforts are working, which helps in refining strategies to attract more customers and improve your online presence.

## The top 10 metrics for local SEO

### 1. Local search rankings
How well your business ranks in local search results directly impacts its visibility. Higher rankings generally lead to increased traffic and customer engagement.

- **How local search rankings are calculated:** Search engines like Google use algorithms that consider various factors like relevance, distance, and prominence to rank local businesses.
- **What tools can be used to get local search ranking data:** Tools like [Moz Local](https://moz.com/products/local) and [BrightLocal](https://www.brightlocal.com/) can help track your local search rankings.
- **What average, good, and best in class look like for local search rankings:**
  - *Average:* Ranking on the second or third page of search results.
  - *Good:* Ranking within the top 10 results.
  - *Best in class:* Ranking within the top 3 results or appearing in the "local pack."

### 2. Google My Business (GMB) interactions
Google My Business (GMB) is a crucial tool for any local business. Monitoring interactions on your GMB profile helps you understand customer engagement and areas for improvement.

- **How GMB interactions are calculated:** GMB interactions include views, clicks, calls, bookings, and direction requests.
- **What tools can be used to get GMB interaction data:** Google My Business Insights and [Whitespark](https://whitespark.ca/) can provide detailed interaction reports.
- **What average, good, and best in class look like for GMB interactions:**
  - *Average:* A few interactions per week.
  - *Good:* Consistent daily interactions.
  - *Best in class:* High volume of interactions, multiple times per day.

### 3. Online reviews and ratings
Online reviews significantly impact local SEO as they influence consumer trust and search engine rankings.

- **How online reviews and ratings are calculated:** The number of reviews, average rating, and the recency of reviews.
- **What tools can be used to get online review data:** Platforms like [Yelp](https://www.yelp.com/), [Google Reviews](https://support.google.com/business/answer/7035772?hl=en), and [Trustpilot](https://www.trustpilot.com/) are commonly used.
- **What average, good, and best in class look like for online reviews and ratings:**
  - *Average:* A rating of around 3 stars with less frequent reviews.
  - *Good:* A rating between 4-4.5 stars with regular reviews.
  - *Best in class:* A rating above 4.5 stars with daily reviews.

### 4. Local citations
Local citations refer to your business's online mentions including its name, address, and phone number. Consistent and accurate citations boost local SEO.

- **How local citations are calculated:** The volume, accuracy, and quality of citations across the web.
- **What tools can be used to get local citation data:** Use tools like [Yext](https://www.yext.com/), [Moz Local](https://moz.com/products/local), and [Synup](https://synup.com/).
- **What average, good, and best in class look like for local citations:**
  - *Average:* Inconsistent or few citations.
  - *Good:* Consistent citations on major directories.
  - *Best in class:* High volume of accurate citations across multiple trusted local directories.

### 5. Click-through rate (CTR) from local search
CTR measures how often people who see your local listing click on it. A higher CTR usually indicates more effective listings.

- **How CTR from local search is calculated:** Divide the number of clicks by the number of impressions, then multiply by 100 to get a percentage.
- **What tools can be used to get CTR data:** Use [Google Search Console](https://search.google.com/search-console/about) and [Ahrefs](https://ahrefs.com/) for CTR insights.
- **What average, good, and best in class look like for CTR:**
  - *Average:* Around 1-2%.
  - *Good:* Between 3-5%.
  - *Best in class:* Above 6%.

### 6. Local backlinks
Local backlinks are links from local websites, blogs, and forums to your website. They are a strong signal to search engines about your business’s relevance and authority in the local area.

- **How local backlinks are calculated:** The quantity, quality, and relevance of backlinks from local sites to your own.
- **What tools can be used to get local backlink data:** Track using tools like [Ahrefs](https://ahrefs.com/), [SEMrush](https://www.semrush.com/), and [Majestic](https://majestic.com/).
- **What average, good, and best in class look like for local backlinks:**
  - *Average:* A few low-quality or irrelevant backlinks.
  - *Good:* Several high-quality, relevant backlinks.
  - *Best in class:* A high number of high-quality, relevant backlinks from authoritative local sources.

### 7. Social media engagement
Engagement on social media platforms helps in brand visibility and can significantly impact local SEO through increased traffic and social signals.

- **How social media engagement is calculated:** Likes, shares, comments, and overall interactions on social platforms.
- **What tools can be used to get social media engagement data:** Utilize [Hootsuite](https://hootsuite.com/), [Buffer](https://buffer.com/), and [Sprout Social](https://sproutsocial.com/).
- **What average, good, and best in class look like for social media engagement:**
  - *Average:* Minimal interaction with sporadic engagement.
  - *Good:* Regular interaction with consistent engagement.
  - *Best in class:* High levels of consistent engagement and interaction.

### 8. Website traffic from local searches
Monitoring website traffic specifically from local searches is crucial. Higher local traffic indicates that your local SEO efforts are driving relevant visitors to your site.

- **How website traffic from local searches is calculated:** Track the number of visitors coming to your site from local search queries.
- **What tools can be used to get website traffic data:** Tools such as [Google Analytics](https://analytics.google.com/analytics/web/provision/#/provision) and [Statcounter](https://statcounter.com/) are useful.
- **What average, good, and best in class look like for website traffic from local searches:**
  - *Average:* Low levels of local traffic.
  - *Good:* Moderate increases in local traffic over time.
  - *Best in class:* High and consistent levels of local traffic.

### 9. Mobile traffic
Since a significant number of local searches are performed on mobile devices, monitoring mobile traffic helps in understanding user behaviour and optimizing mobile user experience.

- **How mobile traffic is calculated:** The number of users visiting your site via mobile devices.
- **What tools can be used to get mobile traffic data:** Utilize [Google Analytics](https://analytics.google.com/analytics/web/provision/#/provision) and [Mixpanel](https://mixpanel.com/).
- **What average, good, and best in class look like for mobile traffic:**
  - *Average:* Low percentage of mobile visits.
  - *Good:* A substantial percentage of visits from mobile devices.
  - *Best in class:* High and steadily increasing percentage of mobile visits.

### 10. Local search impressions
Impressions indicate how often your business appears in local search results, even if users don’t click on your listing. This metric is crucial for understanding your visibility.

- **How local search impressions are calculated:** The number of times your business is shown in local search results.
- **What tools can be used to get local search impressions data:** [Google Search Console](https://search.google.com/search-console/about) and [SEMrush](https://www.semrush.com/).
- **What average, good, and best in class look like for local search impressions:**
  - *Average:* Low number of impressions.
  - *Good:* Moderate and increasing number of impressions.
  - *Best in class:* High and consistent number of impressions.

## How to track metrics for local SEO

Tracking local SEO metrics can be time-consuming but is crucial for the success of your local SEO strategies. Fortunately, there are goal-tracking tools like [Tability](https://www.tability.io) that can save you time and help your team stay focused on the right metrics to improve. By utilizing such tools, you can automate the data collection process, make more informed decisions, and continuously refine your local SEO efforts.

## FAQ

**Why is tracking local SEO metrics important?**
Tracking local SEO metrics allows you to measure the effectiveness of your local SEO strategies, identify areas of improvement, and ensure you are attracting and engaging local customers.

**How often should I track my local SEO metrics?**
It is generally recommended to track your local SEO metrics on a weekly or monthly basis to identify trends and make timely adjustments to your strategy.

**What is a good starting point for a local SEO strategy?**
A good starting point is to claim and optimize your Google My Business profile, ensure your website is mobile-friendly, and build accurate local citations.

**Which tool is best for tracking local SEO metrics?**
There is no one-size-fits-all, but tools like Google Analytics, Google Search Console, and specialized SEO tools like Moz Local and BrightLocal are highly recommended for tracking various aspects of local SEO.

**How quickly can I see results from local SEO efforts?**
Local SEO is a long-term strategy, and it may take several months to start seeing significant results in terms of rankings and traffic. However, optimizing your Google My Business profile can have quicker impacts.

By focusing on these top 10 metrics and using the right tools, you can significantly enhance the effectiveness of your local SEO efforts and make informed decisions to grow your local business presence.

The 10 Best Metrics for Local SEO

# The 10 best metrics for DevOps

## Why use metrics for DevOps?

Metrics are essential for any DevOps team to ensure the development and operational aspects of software are going smoothly. Properly implementing and tracking metrics in DevOps allows teams to identify bottlenecks, ensure high availability, maintain performance standards, and improve overall efficiency. Metrics provide objective data that can help teams make informed decisions, pinpoint areas for improvement, and demonstrate the value of DevOps practices to stakeholders. 

## The top 10 metrics for DevOps

### 1. Deployment frequency

Deployment frequency measures how often new code is deployed to production. High deployment frequency indicates a team's ability to deliver new features and updates rapidly.

#### How deployment frequency is calculated
Count the number of deployments within a specific period (e.g., daily, weekly, monthly).

#### What tools can be used to get deployment frequency data
- [Jenkins](https://www.jenkins.io/)
- [GitLab CI/CD](https://about.gitlab.com/features/ci-cd/)
- [CircleCI](https://circleci.com/)

#### What average, good, and best in class look like for deployment frequency
- **Average**: 1-2 deployments per week
- **Good**: 3-4 deployments per week
- **Best in class**: Daily or multiple deployments per day

### 2. Lead time for changes

Lead time for changes reflects the time it takes to go from code committed to code successfully running in production. Lower lead times indicate a more efficient development pipeline.

#### How lead time for changes is calculated
Measure the elapsed time from the commit to deployment in production.

#### What tools can be used to get lead time data
- [Jenkins](https://www.jenkins.io/)
- [GitLab CI/CD](https://about.gitlab.com/features/ci-cd/)
- [CircleCI](https://circleci.com/)

#### What average, good, and best in class look like for lead time for changes
- **Average**: 1-2 weeks
- **Good**: A few days
- **Best in class**: Under 1 day

### 3. Change failure rate

Change failure rate measures the percentage of deployments causing a failure in production. Lower rates suggest reliable and stable deployments.

#### How change failure rate is calculated
(Number of failed deployments / Total number of deployments) x 100

#### What tools can be used to get change failure rate data
- [Datadog](https://www.datadoghq.com/)
- [New Relic](https://newrelic.com/)
- [PagerDuty](https://www.pagerduty.com/)

#### What average, good, and best in class look like for change failure rate
- **Average**: 15-20%
- **Good**: 5-10%
- **Best in class**: Below 5%

### 4. Mean time to recovery (MTTR)

MTTR measures the average time it takes to restore service after an incident or failure. Lower MTTR indicates a team's ability to quickly address and resolve issues.

#### How mean time to recovery is calculated
Total time to resolve incidents / Number of incidents

#### What tools can be used to get MTTR data
- [PagerDuty](https://www.pagerduty.com/)
- [Datadog](https://www.datadoghq.com/)
- [Splunk](https://www.splunk.com/)

#### What average, good, and best in class look like for mean time to recovery
- **Average**: A few hours
- **Good**: Under 1 hour
- **Best in class**: Under 30 minutes

### 5. Availability/Uptime

Availability measures the proportion of time that a system is operational and accessible. Higher availability means better reliability and user satisfaction.

#### How availability is calculated
(Uptime / (Uptime + Downtime)) x 100

#### What tools can be used to get availability data
- [UptimeRobot](https://uptimerobot.com/)
- [Pingdom](https://www.pingdom.com/)
- [Datadog](https://www.datadoghq.com/)

#### What average, good, and best in class look like for availability
- **Average**: 99.0%
- **Good**: 99.9%
- **Best in class**: 99.99% or higher

### 6. Incident frequency

Incident frequency tracks how often incidents occur within a certain period, providing insights into system stability and areas that need improvement.

#### How incident frequency is calculated
Count the number of incidents within a specified period (e.g., weekly, monthly).

#### What tools can be used to get incident frequency data
- [JIRA](https://www.atlassian.com/software/jira)
- [ServiceNow](https://www.servicenow.com/)
- [PagerDuty](https://www.pagerduty.com/)

#### What average, good, and best in class look like for incident frequency
- **Average**: Depends on the system's complexity
- **Good**: 1-5 incidents per month
- **Best in class**: Less than 1 incident per month

### 7. Error rates

Error rates measure the percentage of requests that result in errors, showing how frequently the system is failing to perform correctly.

#### How error rates are calculated
(Number of failed requests / Total number of requests) x 100

#### What tools can be used to get error rates data
- [Sentry](https://sentry.io/)
- [New Relic](https://newrelic.com/)
- [Datadog](https://www.datadoghq.com/)

#### What average, good, and best in class look like for error rates
- **Average**: 1-2%
- **Good**: Below 1%
- **Best in class**: Less than 0.1%

### 8. Infrastructure as code (IaC) deployment success rate

Tracking the success rate of IaC deployments helps ensure that the infrastructure is being updated and maintained correctly.

#### How IaC deployment success rate is calculated
(Number of successful IaC deployments / Total IaC deployments) x 100

#### What tools can be used to get IaC deployment success rate data
- [Terraform](https://www.terraform.io/)
- [Ansible](https://www.ansible.com/)
- [Chef](https://www.chef.io/)

#### What average, good, and best in class look like for IaC deployment success rate
- **Average**: 90-95%
- **Good**: 95-98%
- **Best in class**: Above 98%

### 9. Test coverage

Test coverage indicates the extent to which your codebase is covered by automated tests, ensuring code quality and stability.

#### How test coverage is calculated
(Number of lines of code tested by automated tests / Total lines of code) x 100

#### What tools can be used to get test coverage data
- [SonarQube](https://www.sonarqube.org/)
- [Coveralls](https://coveralls.io/)
- [Codecov](https://codecov.io/)

#### What average, good, and best in class look like for test coverage
- **Average**: 50-60%
- **Good**: 70-80%
- **Best in class**: Above 90%

### 10. Customer ticket volume

Customer ticket volume measures the number of tickets raised by customers, providing insights into user-reported issues and service quality.

#### How customer ticket volume is calculated
Count the number of customer tickets within a specific period (e.g., weekly, monthly).

#### What tools can be used to get customer ticket volume data
- [Zendesk](https://www.zendesk.com/)
- [Freshdesk](https://freshdesk.com/)
- [JIRA Service Desk](https://www.atlassian.com/software/jira/service-desk)

#### What average, good, and best in class look like for customer ticket volume
- **Average**: Depends on the user base
- **Good**: Fewer than 20 tickets per month
- **Best in class**: Fewer than 10 tickets per month

## How to track metrics for DevOps

Tracking DevOps metrics effectively requires the right tools and practices. A goal-tracking tool like [Tability](https://www.tability.io) can save time and help teams stay focused on important metrics. DevOps teams need to regularly review and adjust their metrics to adapt to changes and ensure continuous improvement. Automating the collection and analysis of these metrics allows more timely and accurate insights, enabling decisions that drive performance and reliability.

## FAQ

**Q: Why are deployment frequency and lead time important?**  
A: High deployment frequency and low lead time for changes indicate that a team can quickly deliver features and updates, which helps in maintaining a competitive edge and meeting user demands promptly.

**Q: What is the significance of change failure rate and MTTR?**  
A: A low change failure rate means your deployments rarely introduce errors, and a low MTTR means issues are resolved quickly, both of which are critical for maintaining system reliability and customer satisfaction.

**Q: How can high availability benefit an organisation?**  
A: High availability means your system is more reliable and accessible, leading to increased user trust and satisfaction as well as potentially higher revenue.

**Q: What tools are best for tracking error rates and test coverage?**  
A: Tools like [Sentry](https://sentry.io/) and [New Relic](https://newrelic.com/) are great for tracking error rates, while [SonarQube](https://www.sonarqube.org/) is excellent for monitoring test coverage.

**Q: How does customer ticket volume relate to system performance?**  
A: If a high number of tickets are being raised by customers, it often indicates underlying issues with system performance, usability, or reliability. Reducing customer ticket volume is a key indicator of improving service quality.

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