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October 29, 2020

Customer Retention Analysis and Churn Prediction for B2B Company


Artur Haponik

CEO & Co-Founder

Reading time:

7 minutes

Customer retention analysis and churn prediction are terms that many people associate primarily with the e-commerce world. But other companies can also measure these two factors, even B2B ones. In fact, measuring customer retention and customer churn can significantly improve your business results and future predictions. With appropriate data analytics services and data monitoring, your B2B company can obtain valuable insight that will shape its development.

Customer retention analysis and churn prediction are factors that are tightly associated with each other and, in essence, they refer to the same problem. If you cannot retain your customers, the customer churn index grows. Naturally, every company tries to curb the customer churn index and maximize their efforts that result in customer retention.

Customer churn is a percentage index, typically calculated on a monthly basis by dividing the number of customers who departed your company in a given month by their initial number.

Customer retention is the process of engaging existing customers to continue buying products or services from your business.

The whole point of measuring customer retention analysis and customer churn is based on predicting how your company will grow and at what pace. The more clients you acquire each month, the more your clients purchase from you, the more stable development you celebrate.

Crafting a Data-Driven Strategy with Customer Retention Analysis

Nowadays, the sales and marketing departments in B2B companies operate almost exclusively on data. The internet, along with modern data analytics techniques, allows marketing professionals to measure virtually every element of their offer, strategy, efforts, and results. Indeed, today, companies know much more about their clients than they did just ten years ago.

If you want to maximize customer retention, you need a data-driven strategy. With appropriate AI-fueled algorithms and applications, you can easily indicate clients that are most likely to churn. With this knowledge, you can look for patterns and undertake necessary actions that will curb churn.

A data-driven customer retention strategy

Let us go further. Customer retention analysis is another data-driven tool that helps you understand how your business decisions and adjustments influence your clients. This way, for instance, you can spot and understand trends in products or services that result in clients’ satisfaction or dissatisfaction.

We frequently begin building a customer retention strategy with analysis based on cohorts (they help analyze how many clients leave the company and over what period) and demographic indicators. Such analysis enables companies to obtain insights on what influences specific customer decisions. Some of the most commonly analyzed indicators are:

  • Price changes
  • New products or services
  • Product upgrades
  • Changes in offer or communication, etc.

In order to make this knowledge useful, it has to be presented in an understandable way. This is where data visualization comes into play. Data analytics and AI consulting companies use various dashboards that make data visualization accessible.

data visualization accessible, charts

In fact, dashboards provide a convenient interface enabling organizations to visualize and analyze data according to their Key Performance Indicators (KPIs).

Customer Retention Analysis: Benefits of data visualization

Thanks to visualization tools, you can quickly:

  • Split customers into various cohorts or customized lists to find out who really is driving your business growth and answer complex questions about your next investments.
  • Find correlations between different subscriptions and your company’s business activities.
  • Obtain detailed information on the reasons for customer churn.
  • Analyze historical customer activity.

Customer Retention Analysis: Behavior analysis

Another immensely important aspect of customer retention analysis and customer churn prediction is based on behavior analysis. In essence, it shows the information on why an individual client decides to leave. Several possible reasons cause customer churn.

Customer Retention Analysis: BEHAVIOR ANALYSIS, shop

In this article, we want to show these that are most common in B2B relations:

  • Poor customer service: According to Gladly’s Customer Expectations Report 2020, over 50% of consumers depart companies after just one or two poor customer experiences. Your takeaway: Take good care of UX!
  • Too high prices: Each B2B company has to balance attractive prices and their profits, but if prices are too high, you can expect that your clients will eventually look for better deals elsewhere.
  • Lack of marketing: Even B2B companies have to promote their services if they want to grow. If you neglect this area and focus only on the already acquired customers, you put yourself at risk.

Customer Retention Analysis: How B2B Companies Combat Churn

Naturally, undertaken actions depend on the reasons that cause customer churn.

customer retention analysis, blue

We can indicate the three most popular activities that help companies retain their customers and curb churn:

  • Improvement of customer service: Organizations that want to stop customer churn pay a lot of attention to improving customer services and adapt to current market trends.
  • Additional activities: Many companies try to engage their customers by offering them various activities and incentives such as loyalty and affiliate programs or discounts for regular clients.
  • Content strategy: This element is particularly important in B2B relations. Clients want to cooperate with companies they trust. Publishing useful/engaging content can help you gain this trust. Show your clients that you are an experienced partner and share your knowledge on a blog or via newsletters.

Now, let us see what devising a customer retention strategy looks like.

Customer Retention Analysis & Churn Prediction implementation

Typically, designing and implementing a decent customer retention strategy comprises four crucial steps:

Step 1: Processes analysis

Designing a strategy always starts with analyzing the client’s company. We analyze business processes in your company, perform data analysis, run statistical analysis of all available attributes, analyze existing data structure, as well as activities of the company’s crucial departments (sales, marketing, customer service, etc.)

Step 2: Data preparation

Every AI or machine learning algorithm or application that analyzes customer churn operates on your company’s data. Initial data preparation is indispensable to make this process efficient and accurate. At this point, we aggregate data and create all possible variables. Importantly, we do not limit ourselves to simple aggregation, but instead, we extract all possible insights from each feature.

your data

For example, let’s say we have the “income” variable. Based on it, we can prepare more variables, such as:

  • Last month income/revenue amount
  • Average income/revenue in the previous X months
  • Nominal growth/decrease in income/revenue in the last X months

Gaining as thorough insight as possible is always our core goal.

Step 3: Modeling

At this stage, we build and train machine learning models that will analyze your data. We always train several models and optimize hyper-parameters to make this step as efficient as possible.

Step 4: Deployment and integration

This is the last stage, where we finish working on our model and integrate it with your data infrastructure. Every data analytics solution has to be fully connected to the client’s data warehouse. Frequently, we integrate our solution with the client’s CRM software as well.

What results can you expect?

Obviously, there’s no place for art for art’s sake here. Everything we do has to be measurable and purposeful.

new results, black board, chalk

Companies that utilize data analytics in order to build a fully-fledged customer retention strategy can expect to:

  • Retain more customers and curb the customer churn index.
  • Obtain an ML-based model which helps to prevent churn with an accuracy of up to 90%.
  • Have an intuitive and convenient dashboard that presents all the relevant indicators in a transparent and legible way.
  • Understand their clients more thoroughly.
  • Save a lot of time previously spent on analyzing data.
  • Improve the efficiency and performance of the marketing and customer service departments.

As you can see, it’s definitely worth measuring customer churn and building a customer retention strategy. If you are interested in improving your customer retention, we recommend you read more about Customer Retention Analysis.


Data Analytics