How to Fix Customer Churn

Getting customers is hard. Keeping them is harder. As we move into an era where realized customer value is the most important marker for growth for enterprise B2B SaaS companies, it becomes important for business teams to make sure that they’re building with customer retention as one of their top goals.

Some of the questions that we often see business teams asking are:

• Why are my customers leaving? How do I stop them?
• Who are my most valuable customers?
• What drives customer loyalty?
• How is my customer base changing?

Answering these questions often involves a complicated journey across different functions in the company, because the data points required to answer them are spread everywhere: from actually using the product to interacting with the customer success manager or filling out support tickets.

In this article, we will go through the strategy that you can adopt at your organization to:

• Find customer signals of low engagement...
• Leverage them to build a better product, and...
• Convert churn to revenue

In order to do this, we will go through the following steps. The following framework helps you execute cross-functional analyses not just for customer churn, but any theme that your growth team requires.

Clarify your focus

This is the starting point for deciding what the goals of your analysis are. These goals could look something like this:

• Reduce customer churn
• Increase the number of subscription upgrades
• Acquire high-value customers

Deciding on your focus early on helps you define a boundary of effort and impact and provides clear communication to all stakeholders about what the analysis is (and isn’t) going to achieve.

Ask questions

Ask the questions to which you will need answers in order to reach your set goal. These answers are going to be the pieces in a puzzle that come together to create the bigger picture.

For example, if your goal is to reduce customer churn, then the questions might look something like this:

• How many customers actually churned from the product within the last month?
• What were the usage patterns of these customers?
• How many current customers are showing patterns similar to the churned customers?

Keep in mind here that your analysis needs to be both quantitative and qualitative. In the above context, even though analyzing the data may give you a list of churned and at-risk customers, you will not be able to know their specific pain points with the product unless you dive deep into their engagement, how they have interacted with the customer success team, and what their users think about your product.

So, in addition to quantitative analysis, you should also plan qualitative research, which could look something like this:

• Understanding the core pain points that the customer wants to solve using your product
• Analyzing customer product usage sessions to find gaps in the product UX
• Most importantly, conducting user interviews to understand how your product is able to help them (or not)

Form your team

Once you have decided on the questions you need to answer, it’s time to put together a team who can help you reach your goal. The purpose of this team is simple:

• Get the right data
• Brainstorm on insights
• Take action

For example, in order to reduce churn in a B2B enterprise SaaS company, this team may include the following folks:

• Data analysts for quantitative analysis of customer data
• Product manager / UX researchers to conduct user interviews and give insights on product modifications
• Customer success managers who can report the health of their accounts

However, it is important to note that the impact of insights generated by this team often goes beyond just this team: they impact the entire business, starting from marketing and sales all the way to support and finance. Hence, you should consider your entire business, across verticals, as a stakeholder when you want to communicate these insights.

With your team in place, you can now move on to execution and reach the goal of your analysis.

Action!

Now is the time when you work to collect the different footprints of your customers across your product (the "answers" to the questions you jotted down above).

The first step in this process is to list down the metrics that you will need. As an example, for reducing customer churn, you might be interested in the following (at your required granularity):

• Number of support tickets raised over time
• Number of DAUs/WAUs/MAUs over time
• Number of churned customers over time

You’d want to track many more metrics based on the specific context of your product. This list is really about assembling all the pieces in the puzzle to form a coherent picture of your customers.

Since this analysis spans multiple different business functions (product, customer success, support, etc.), it is important to keep a few things in mind:

• Focus on the specific metrics that are key business levers
• Make sure that these metrics have consistent definitions across all stakeholders
• Choose the granularity of your analysis
• Depending on your context, you may choose to do this analysis at a customer or customer segment level
• Your metrics must be calculated at your chosen granularity (high-level reporting doesn’t yield useful insights for a lower granularity)

Once you have reported on the required metrics and performed the relevant qualitative analysis with customers - your team is ready to brainstorm solutions. You should collate all your research into specific actionable for each stakeholder: from product and UX to customer success and support.

However, the party doesn’t stop there. Most business problems (like customer churn) aren’t one-off solutions, they require consistent cadence and continuing resolution. If you have been able to successfully solve your problem once, make sure you put a process in place to make sure that your business can keep taking advantage of it.

The Problem

There are many practical challenges when you actually go about implementing the above framework: as a SaaS professional, you might already be aware of these. One of the biggest challenges, after navigating through your organization, is making sure the data that you’re referring to is correct and actually helps bring the team together rather than creating function-specific silos of data.

For the data to be high quality and actionable, your business needs to have consistent, accurate, and reliable reporting of metrics: starting from definition all the way to calculation and consumption on a report. Moreover, these metrics should be consistent across business functions (eg.: “number of bug tickets” shouldn’t mean different things for product and support teams)

Further, the metrics must be actionable. That doesn’t just mean that the number being reflected by the metric should be relevant to the business: it also means that the metric should be delivered to all stakeholders (across business functions) in a form that they can consume.

While many SaaS tools today help you solve specific problems local to one business function, they don’t allow different teams to come together and collaborate on goals that really move the needle on your business. As we move into 2023 in an era where retaining and maximizing value for your customers is one of the primary goals for businesses, collaboration on customer data becomes one of the top priorities for business teams.

Houseware aims to solve exactly this problem. Instead of dividing business data into different silos for each different team and function, we believe that data, analysis, and action should be centralized.

In order to do this, we connect right on top of the existing source of truth for all your company’s data: your data warehouse. This allows you to build standardized business metrics on a central platform while enriching the data relevant to your customers in a single place. But that’s not all, with all the pieces of your puzzle on a single board, you can share them more easily with all stakeholders across your company, generate insights faster and drive better decision-making. What’s more: we don’t need to move your data through complicated ETL pipelines, so you pay a fraction of what you do to typical SaaS vendors.

Our mission is simple: to drive analysis at the speed of thought. And our approach enables customers to simplify their workflows to seamlessly achieve their business goals.