Customer Analytics


Customer Analytics

Having infinite options regardless of what product or service customers are looking for, it is crucial to understand their buying habits and behavior.

Customer analytics enables businesses to deliver relevant offers that attract vast majority of their customers. It consists of three main pillars – Customer Retention, Targeting & Lifetime Value.

Customer Retention

Customer retention can assist you in understanding your disengaged customers, help identify which have the highest probability of leaving and assist you in creating and managing your customer retention.


Customer retention is an end-to-end solution. It helps reduce customer churn and determine the cost of retention. It can also include preparing a retention dashboard or setting up a reactive or predictive retention using the PtC, PtS or CLV models.

The customer churn rate primarily is a problem in highly competitive environments in saturated markets (finance, retail, energy). Many companies nevertheless underestimate the seriousness of this problem or incorrectly focus on the retention of unprofitable customers. There is, however, a verified and analytics-based road to success.

Thanks to proactive retention using the Propensity to Churn model we decreased the churn rate of one of the largest Czech banks by 41%.

Customer Targeting

Many companies suffer from ineffective cross-selling campaigns. Customers are bothered by the offers, and the campaigns are not performing well.

The Customer Targeting approach enables company to get the best possible scenario with a fixed marketing budget. It includes decisions about which product the customer is most likely to buy, as well as the preferred communication channels.


Predictive models created using statistical methods are able to identify customers with higher willingness to buy (Propensity to Buy). Based on the use of these models in cross-sell and up-sell campaigns, priority lists can be created that the company can use to concentrate its resources on the most valuable customers, for example. Using the Net Lift approach (incremental response), we ensure that client targeting has a visible impact and that marketing costs are spent efficiently.

By deploying a Propensity to Buy model on a pension fund product we increased the conversion of a campaign of a medium-sized bank by 48 %.

Customer Lifetime Value

Not all customers are worth the same. The Customer Lifetime Value (CLV) is a crucial input for your retention offers, changes in your services model or changes in your product portfolio. CLV focuses on your customers’ potential for the future, not on their value and profit from the past.

Using CLV metrics improves the overall profitability of the organization and the return on investment of marketing campaigns.


Customer Lifetime Value (CLV) represents the net present value of a future profit or revenue from a specific customer. Instead of measuring the customer’s past value, CLV focuses on their future potential that has a significant impact on business decision-making, for example in the following areas:

  • Targeting marketing and retention campaigns;
  • Determining the level of customer services;
  • Design of new potential products/business plans;
  • Planning and forecasts of the entire business; and
  • Portfolio valuation.

Thanks to a change in the retention process using CLV and Propensity to Churn models we improved client retention of a large Czech bank 4.9 times.

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Tervel Šopov

Tervel Šopov


Tervel is a consulting director responsible for AI&Data Strategy, Data Science and Machine learning market offerings in Deloitte Central Europe. Tervel has worked in the space of analytics, data and A... More