How it Works
Customer retention relates to a business’s strategies and activities to keep its current customers. Customer retention analytics can facilitate such activities by providing predictive metrics of customers who are expected to churn.
Data-driven customer retention strategies can be very rewarding, often driving profit. Research demonstrates that organisations that make extensive use of customer data analytics for business decision making can see profit improvements of over 100%, versus those that don’t (source: McKinsey & Company).
You can significantly reduce churn and improve customer retention through utilising your data in the following ways:
- Formulate and implement a data strategy
To implement a successful data strategy, you should make use of the data you have, understand what the data is telling you and implement organisational change accordingly. Analytics should be viewed as a strategic driver of growth rather than using it in a silo.
Effective steps that you can take include:
- Ensuring that your organisational KPIs are automated, scalable and repeatable
- As a company, collectively define the priority problems that you strive to solve
- Group the problems by ‘data’ and ‘system’ issues, being mindful that issues don’t
tend to be with data but with how people use or manage it
- Put tasks in priority order, at the same time as evaluating if your plan is technically
- Review your progress on a three-monthly basis to keep on track
- Ensure behaviour change across your organisation
- Identify and segment those who are less likely to churn
Customers who are similar to your target customers are less likely to churn. Use the data you have about the features and characteristics of your current customers and create a list of prospective customers, applying algorithms that compare the two. Similar characteristics include:
- Job title
- Company size
- Annual spend
Segment your customers based on this information and this will enable you to identify the features of the prospective customers, or high-quality leads, that you should target.
- Utilise machine learning methods to formulate predictive models
To create a robust customer retention strategy, predictive analytics is a valuable tool that you can use to make predictions about the future. Using historical data, relationships among varied metrics can be analysed to predict what customers like and dislike.
In terms of customer retention, machine learning will quickly and accurately expose the underlying reasons why customers are churning; predictive analytics for churn. It can also identify why customers are loyal to your brand. This is achieved through the analysis of data, statistics and probability to find correlations between variables to assist in optimising crucial outcomes such as customer retention. Such models are applied to new customer data to make informed predictions.
In comparison to human analysis, machine learning algorithms can deliver insights rapidly owing to their processing capabilities. Owing to their iterative nature, the more data they consume, the better they perform. The following examples demonstrate how predictive analytics can be a hugely valuable tool in the retail, financial services and manufacturing industries:
Retail: a retailer can use predictive analytics to identify which up-sell or cross-sell products will be the most relevant based on a customer’s past purchase or browsing history. They can utilise it to establish the optimal attainable prices for each customer and identify the right pricing to increase sales. With real-time machine learning, the impact of competitive pricing on sales, and the adequate frequency of price-based promotions can be found.
Financial services: an insurance company, for example, would benefit from using customer retention analytics to secure targeted insurance plans, speed up claims processing, and offer personalised customer experiences. This all creates a competitive advantage that will attract new customers and retain existing ones.
Manufacturing: those in the manufacturing industry would reap the benefits of predictive analytics in order to analyse the history of demands, providing valuable insight into consumer buying habits, the availability of raw materials, impacts of a trade war, supplier issues, shipping barriers, and other potential disruptions.
- Utilise segmentation to increase customer retention
Use data analytics to segment your customers and prospects into different groups, identifying how each segment engages with your brand and your products and services. You will be able to draw insights from each subgroup so that you can tailor your communications and strategies accordingly in order to optimise retention of your most valued customers. Awareness of customer value will enable you to make important business decisions.
Customer data that is important to analyse includes:
- Products and services purchased by category and customer type
- Frequency of purchase
- Purchase value
This will enable you to establish what types of customers are generating the most revenue.
Effective customer segmentation will enable you to create highly targeted product and
service recommendation offers. You can segment by, for example:
- Historical value
- Lifetime value
- Value over the next 12 months
- Average customer value by segme
Utilising the correct segmentation means that you can create highly targeted product and service recommendations for your customers and prospects.
A retailer, for example could offer its customers discounts based on different channels, such as online, mobile app or in-store, with different customers receiving different offers based upon their purchase value. Monitoring the seasonality and time-sensitivity of their promotional codes will enable an understanding of how a demographic responds to their sales communications and take relevant actions accordingly to maximise potential revenue
Retaining your existing customers will enable you to see the following benefits:
- Retaining your current customers costs less
Attracting a new customer can be five times more expensive than retaining an existing customer. Loyal customers therefore are valuable assets that you should seek to keep. Finding out what makes your current customers stay and why they keep buying from you means that you can take the right steps in keeping the right customers.
- The opportunity to up or cross sell to current customers
Current customers are much easier to market and sell to. Selling a new product or service to
your existing customer base is far less costly than selling them to new customers; customer
acquisition activities can be costly.
- Increasing customer retention rates can enable sustainable growth
Increasing customer retention rates by just 5% can increase profits from anywhere between
25 – 95% (source: Harvard Business Review). Sustainable business growth is much more
likely to thrive by retaining your existing customers.
About the Partner
Since 2014, Catalyst BI has been at the forefront of revolutionising the way businesses make decisions. As trusted business intelligence consultants, we help teams and organisations in both the public and private sectors throughout the UK harness the power of data to drive smart and strategic decisions. With a proven track record of success, we’ve helped over 450 satisfied customers achieve their business goals and accelerate their transformation objectives. Whether you’re looking to make sense of complex data or gain valuable insights into your operations, Catalyst BI has the expertise and solutions you need to take your business to new heights of success.
Global Pharmaceutical Company
The company experienced rapid growth but faced a setback with a double-digit loss in annual revenue due to customer churn. This churn involved replacing 3,500 customer-product combinations to meet sales targets. Recognising the potential of AI to predict and mitigate this churn, but with no internal skillset or tools, the company sought an external solution to empower its sales team.
The company chose to collaborate with DataRobot due to their strong partnership with Catalyst and based on Gartner’s AutoML recommendations.
- Explainability: Clear understanding of the factors contributing to customer churn predictions.
- User Interface: Integration with other relevant data, like sales history, so that sales reps could access comprehensive information in one place.
- Feedback Loop: The system needed to allow sales reps to add notes, change statuses, and communicate strategies to management.
- DataRobot serves as the predictive engine which integrates with user-friendly BI interfaces. The integration was seamless, allowing for daily data updates between the systems.
- A 20% churn reduction was observed in the trial area, equivalent to a £2.3 million revenue increase.
- Multiple use cases and high-value projects are now in progress.
- The company has dedicated resources for AI and data science, maintaining a consistent development approach but remaining flexible in deployment strategies.