There has been a lot of speculation about a lasting VC winter looming ahead. Certainly, in today’s funding environment (you may call it a difficult one, a crisis, or simply a recession), data is your best friend. Historically, data analytics and business intelligence tools performed well during recessions.

Before you know it, being data-driven will not give your company a competitive edge anymore. It will be the new standard that all companies need to live up to in order to stay relevant.

B2C companies have headed product and consumer centric company-building in the past. Concepts such as PLG (product-led-growth) apply to B2B companies just as well. Simply put, it describes a Go-to-Market strategy that relies on an excellent product and user experience, rather than a sales-led strategy.

Whether you are selling a B2B software product, a consumer app, just starting out or have already established your business in different markets – if you want to develop a market-leading product, you will need to obsess about your data. Here are three ways it will help your company get out alive.

1. Focus on the right metrics and grow sustainably

This is probably the most obvious one: the higher your LTV:CAC ratio, the more efficient your investment is in growth initiatives. This means more revenue generated per euro spent on customer acquisition. Having left La La Land, investors are back to reality. They will pay more and more attention to indicators for sustainable and efficient businesses, even in the early stages.

Big visions and ambitions will not lose their importance – but do not assume that they will get you anywhere alone. The following is an example of a counterintuitive trade-off between two metrics, aiming to answer the question: Should you focus on growth or retention improvements? The graph shows the impact on LTV of two different scenarios: 1) increasing the customer base (growth) vs. 2) increasing your customer’s retention.

Increasing your customer base will have a more immediate impact on revenue. But its relationship to LTV is linear and a much smaller increase in retention is needed to outperform a relatively large increase in cohort size. Zeya Yang (2019) explains the calculation in detail. While discussing why (or if) one should optimise metrics before focusing on growth goes beyond the scope of this article, you can find many great resources online which I am also happy to share.

2. Make good decisions and have productive discussions

Even the most conscious of us are subject to unconscious biases. Not only that, but good decisions can be counter-intuitive and not easily discovered. Companies that establish a culture in which every team member, independent of their area of expertise or seniority, makes data-driven decisions, will combat biased decision-making and fallacy. While using data as a basis for decision-making does not guarantee getting everything right on the first try, it will make discussions efficient and discussable in the first place.

Take the following example: Your basis for making a certain decision are four pros for Option 1 and two pros and two cons each for Options 2 and 3. It will be hard for anyone to argue objectively against or in favour of one of those options. Does Option 1 win because it has the most pros? Is one of Option 3’s pros more meaningful than all other pros combined? Instead, select key metrics and rank all options in a matrix. Start by discussing which metrics are the right ones to use, their weights, and if they were calculated correctly.

Having data translators or engineering and analytics teams supporting the commercial roles will neither be enough, nor efficient. Every team and every individual should make it a habit to base arguments and decisions on data. When I joined Stocard as an intern in 2019, I found myself lucky to experience a culture where data-driven decisions truly were the norm with a vast majority of employees using the product analytics tool every day. Every decision made was supported by data. The great part was how the company let every employee do just that.

The following three things are necessary to support a truly data-driven culture:

• Provide easy access to the data through the right tools (e.g. Mixpanel)
• Educate employees, ideally, as early as the onboarding process: The goal should not only be to get familiar with the tools but also help everyone understand the impact a data-driven decision can have.
• Lead by example and be consistent: Small habits can help spread the approach company-wide. For instance, when communicating a decision or making a suggestion in your company chat, always attach a screenshot of the query you ran in your analytics tool to help you make that decision.

To be clear: This goes far beyond product teams. It includes but is not limited to decisions such as which bug to fix first, which country to expand to, which customer segment to focus on, which design choice to go with – you name it.

3. Grow faster, systematically

Do not assume that being data-driven is only relevant when you already have a lot of customers (a lot of data to analyse). Especially in the early stages, most likely you will need to grow your startup with limited resources. While this would partly fall under point 2 “Make good decisions”, I wanted to highlight it: A/B test! A/B testing is arguably one of the cheapest ways to improve any metric. For example, Stocard ran more than 200 A/B tests to onboard 60 million users before being acquired by Klarna. Duolingo ran more than 2,000. While most A/B tests will focus on increasing conversion rates and retention, there are no limits to what you can test and it is not limited to Marketing or Product teams. I think that A/B testing can help B2B software businesses just as much as B2C companies. While you might not be selling to the end-user, they are the ones interacting with your product and making the decision to use it - or not.

Reference: Math of Growth vs Retention by Zeya Zang