The Startup Founder's Guide to Analytics

The essential business metrics for each department

Rob Boyle
February 2, 2022
Understanding business data is essential for startups looking to expand profitably. Here are a few metrics each department should consider.

What is analytics (and why should founders care)?

Analytics is the general term for measurement or data analysis to understand past events and predict future outcomes. It is also the term for the techniques and methods used in business, science, engineering, and other areas to explore and interpret data.

A good understanding of analytics helps founders make informed decisions about their business. These decisions can be based on facts rather than emotions.

A startup founder should be well-versed in the basics of their business. But, that doesn't mean they need to be an expert. Analytics can help them understand the ins and outs of their company at speed.

Every department benefits from analytics

Financial analytics

Financial analytics is the process of collecting, analysing and interpreting financial data to make better business decisions.

Analytics can help you make better strategic decisions about operations, investments, revenue streams and other opportunities for increasing growth.

Metrics to measure

  • Gross profit margin. The percentage of a companies revenue that is greater than the cost of goods sold (COGS)
  • Fixed vs variable costs. Fixed costs remain the same regardless of how much you produce. Variable costs vary depending on production. Example variable expenses include the cost of materials or software licences. A fixed cost could consist of rent payments or property taxes.
  • Runway. How many months do you have left before your startup runs out of cash? Your runway will get shorter if your expenses exceed your revenue every month.
  • Burn rate. The amount of money you lose each month if your expenses exceed revenue.
  • Daily sales outstanding. The average number of days it takes you to collect payment from customers after completing a deal.
  • Monthly Recurring Revenue (MRR). For subscription businesses - the total monthly amount of subscription income from customers. See also Annual Recurring Revenue (ARR).

People analytics

People analytics is a field of study that uses analytical methods to identify and evaluate employee data. It considers individual, team or group-related metrics and identifies ways to optimise employee performance.

HR analytics can drive decisions about hiring, performance reviews, promotions and other human resource issues.

Organisations with the capability to act on employee data can:

  • increase their competitiveness 
  • improve employees' experiences at work
  • reduce turnover and retain knowledge
  • attract new talent

Metrics to measure

  • Labour turnover rate. How many employees leave over a 12 month period? And how many of those are 'regretted' leavers that you would have liked to have kept? 
  • Time to hire. How long does your recruitment process take?
  • Absence rate. Are there patterns or trends to levels of sickness absence? 
  • Employee engagement. Use a regular staff survey and pulse checks to gauge sentiment and job satisfaction. A decline in engagement could lead to an increased turnover or absence rate.
  • Diversity, equity and inclusion. What is the gender and ethnic balance of your company? Are there pay gaps?
  • KPIs and performance goals. Are you setting challenging goals for employees to achieve? Are they realistic?

Marketing analytics

Marketing analytics is the process of gathering data to measure marketing efforts and calculate the return on investment (ROI). 

It helps marketers improve campaign spending and understand ways to improve channel performance, channel value and scalability. 

Metrics to measure

  • Lead to customer conversion rate. How many leads generated through marketing are converting to paying customers?
  • Customer acquisition cost (CAC). How much are you spending on sales and marketing to acquire each customer?
  • Customer lifetime value (CLV). Over the duration of their time as customers, how much do you expect them to generate in profit?
  • Average order value (AOV). The amount a customer spends each time they make a purchase.
  • Marketing qualified leads (MQL). Leads generated through marketing that meet set criteria or lead score.
  • Sales qualified leads (SQL). The number of leads passed from marketing that sales have qualified as a potential fit.
  • Return on marketing investment (ROI). Sales growth minus marketing spend, divided by marketing spend.
  • Customer retention/churn. The percentage of customers/subscribers that cancel over a set period.

Product metrics

Product analytics provide insight into a product-market fit, how users interact with your product, and how to improve the user experience. 

Data can be used to measure the quality of individual customer experience, user retention, and the success or failure of new product launches. 

As a result, it is an essential part of the development cycle. It provides insight into the effectiveness of your business model or marketing strategy.

Metrics to measure

  • Daily active users (DAU). How often are users logging in? Is this trending upwards? How you define an active user is key to understanding retention. Simply logging in may not be enough. Ideally, you want them to undertake an action like making a purchase or posting a comment.
  • New vs returning users. Are you engaging users enough to keep them coming back?
  • Time to value. How long does it take for users to get value from your product? A social media tool could consider a user active after adding ten contacts to their network. For a social posting tool, it could be the duration it takes them to schedule their first five posts.
  • Net Promoter Score (NPS). Measure the overall satisfaction users have with your product. Survey users to understand the Promoters, Passives and Detractors.

How do you get started with your analytics stack?

You don't need complex and expensive data tools to get started. However, they will help as you get more advanced. Google analytics and a spreadsheet tool (Excel/Google Sheets) are enough. These can give you insight into your customer journey and help calculate essential metrics.

As you expand, you should consider investing in a CRM like HubSpot and data visualisation tools like Tableau/Power BI. These will enable the automation and digestible presentation of data so you can focus on growth.

If you understand Python, there are an array of libraries you can import for analysis and visualisation

  • Pandas for data analysis and manipulation
  • Numpy for numerical computation
  • Matplotlib for plotting
  • Seaborn for statistical data visualisation

Mistakes to avoid

Focusing on vanity metrics

Often, a business can get lost in vanity metrics trending upwards but not contributing to the company. Common examples include likes and followers on social media or unqualified leads that do not lead to sales. It is better to focus on a business-driven metric like lead-customer conversion rate.

Tracking too many KPIs

Suppose you become too obsessed with measuring many metrics. In that case, you cannot optimise every target without overstretching and introducing conflict between teams. 

For instance, if you aim to increase lead generation and reduce churn, you could overwhelm your customer success team. They cannot manage onboarding a batch of new customers without limiting their available time to maintain existing clients.

Not cleaning data before running analyses

Suppose you are pulling data from various platforms or buying from a third party. In that case, you need to ensure the data is clean and accurate before drawing any conclusions. False positives/negatives can lead you towards failure if you rely exclusively on it.

Common issues with 'dirty' data include incomplete fields, misspelt records and duplicate entries.

Relying solely on the data without understanding context

If you are measuring data without fully analysing what the data means you may misinterpret its potential impact. For example, revenues trending upwards may seem positive, even if your costs reduce. But, have you benchmarked your growth against the general market? If competitors are growing faster, there is a risk that you lose market share. 

Why is a data strategy important for startups?

Understanding business data is essential for startups looking to expand into new markets without burning through cash.

If you are growing fast but want to benchmark and plan strategically, we can provide a strategy audit to help you unlock the data in your business

About the author

Rob Boyle is the founder of Jigsaw Metric and oversees content strategy and research projects. 

As a child of small business owners, Rob understands the challenges of growing without resources. He set up Jigsaw Metric as a side project to help more small businesses grow from 10 to 1,000 customers. 

For Rob, digging into the data and seeing KPI charts trend upwards is the most rewarding part of the role.

When not devouring business plans and books, Rob enjoys playing guitar and spending quality time with his infant daughter and toddler son.

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