Leading and Lagging Indicators for Data-Driven Startups
A not-so-good way of building a startup is choosing an ideia, spending a lot of time turning it into a product and forgetting about (prospective) customers.
A better way of doing it:
- Build: quickly turning ideas into products;
- Measure: collecting (creating) data about how customers respond to what you’ve built;
- Learn: analyzing data to decide whether to pivot or keep going;
Sounds familiar? Maybe that’s because I’ve just described the scientific approach to creating and managing startups Eric Ries presents in the book Lean Startup.
In this article, I specifically want to talk about two topics related to the ‘Measure’ phase of Ries’ methodology: lagging and leading indicators.
What is a lagging indicator?
Lagging indicators can be defined as i) direct measurements of past success, which ii) changes cannot be observed instantly, usually taking weeks, months or even longer, and iii) represent the effect in a cause-and-effect relationship.
Lagging Indicator Examples
- ROI for adversiting campaigns
- Customer Acquisition Cost (CAC)
- Sales Qualified Leads (SQLs)
- Marketing Qualified Leads (MQLs)
- Revenue (e.g., Anual Recurring Revenue)
- Customer support tickets solved over a specific timeframe
- Churn rate
Why are lagging indicators important?
Because lagging indicators are an easy way to check if a startup is achieving its main goals. Without them we’d be in the dark about the progress being made. Also, we’d be lacking a valuable starting point for highlighting points of leverage relevant to achieving said goals.
Your outcomes are a lagging measure of your habits.
Your net worth is a lagging measure of your financial habits.
Your weight is a lagging measure of your eating habits.
Your knowledge is a lagging measure of your learning habits.
Your clutter is a lagging measure of your cleaning habits.
- James Clear, Atomic Habits
What is a leading indicator?
Leading indicators can be defined as indirect measurements of future success, which ii) changes can be observed instantly and iii) represent one of many causes, with different significance levels, in a cause-and-effect relationship.
- List measurable activities that temporally precede a result represented by a lagging indicator;
- Calculate the correlation coefficient between the chosen lagging indicator and each measurable activity;
- Sort them in descending order to view the best leading indicator candidates;
Remember: correlation does not imply causation, so continuously validating your leading indicators is just as important as identifying them!
Value stream mapping
- List company-wide strategic indicators and metrics;
- Link each one of them to one or more teams along the company’s value stream (sales, software engineering, HR, etc);
- Ask three main questions to different stakeholders from each team to identify possible leading indicators:
— Why is the metric/indicator current target (not) being met?
— What is within our circle of influence and impacts the metric/indicator?
— What is beyond our circle of influence and impacts the metric/indicator?
Remember: different teams will have different comprehension and execution levels on data-informed work, so it’s totally acceptable not knowing how to better answer these questions!
The most important thing here is, at least, starting a constant push towards continuously giving better answer for them over time!
Owner-Customer Role Play
- Recruit different stakeholders from different teams along the company’s value stream to a role play;
- Have everyone pretend to be the owner of the company and write down, individually, the most important (lagging) indicator or metric they would need to properly manage it;
- Reveal the answers to every participant;
- Have everyone pretend to be a customer, supposing a similar product or service from a competitor is easily avaliable and similarly priced, and write down, individually, why and how to decide which product or service to choose;
- Organize and synthetize what was written into a possible lagging and leading indicators proposal;
Remember: biases will be inevitable due to past experiences behind each person’s view, so using data to continuosly validate and assign proper weight to each leading indicator is very important!
Besides, any proposal made needs trust and support from the participants to truly have success!
Leading Indicator Examples
- Unique website visitors
- Growth experiments run
- Daily active users
- Average revenue per user
- Number of unsolved customer support tickets per agent
- Number of customer support tickets reopened
- Key feature usage rate
Why are leading indicators important?
Because leading indicators are a reliable (even if not so easy) way to check if the work done is actually generating impact to a startup’s goals.
Without them we’d be in the dark about how meaninful people’s efforts (i.e., performance management) are. Also, we’d be lacking a valuable starting point for learning how to improve said efforts are over time.
Connecting lagging and leading indicators to deliver business impact
Working with lagging and leading indicators is equally necessary to practicing a solid management strategy and avoiding some common startup pitfalls, for example:
- Working with lagging indicators without linking them to leading indicators, usually means not being systematic on finding actionable approaches to growth (A.K.A. modelling growth).
The opposite is also an issue:
- Working with leading indicators without linking them to lagging indicators, usually means being busy, but not necessarily successful (A.K.A. working hard, but not so smart until people burn out).
As startups grow, the Build-Measure-Learn cycle is not only about the relationship between customer and product. It is also about the relationship between people: why people build, what people learn, and, most importantlu, how people measure becomes the first step for creating sustainable relationships and businesses.