Customer Analytics

Solutions

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.

Solution

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.

Solution

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 %.

Benefits for the company

  • Increases the success rate (conversion) of cross-sell and up-sell campaigns;
  • Increases the profitability of the organisation and marketing activities;
  • Improves the quality of customer experience by optimising offers for customers in a way that reflects their current needs;
  • Allows more efficient allocation and capacity use of communication channels.

Approach

Predictive models created using statistical methods can identify customers with a higher willingness to buy (Propensity to Buy). Based on the use of these models in cross-selling and up-selling campaigns, priority lists can be created. The company can use these lists 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.

For this method, it is necessary to have data about past customer behaviour and interactions. The data-driven approach doesn’t take into account significant new changes in the services model (e.g. new service able to do everything online). It reflects the current state of the service level and offerings.

It’s better to combine this method with additional identification of sales-related events to help to build a better sales story for the customers.

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.

Solution

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.

Benefits

  • Using CLV to target campaigns generates higher revenues.
  • CLV has the potential to decrease the customer churn rate and allows to retain customers with a high future value.
  • CLV improves the overall profitability of the organisation and return on investment of marketing campaigns.
  • CLV can function as the primary indicator of business performance.

Method

In the beginning, we define the purpose of the CLV model and the target, based on the given business-use case.
In the second stage, we start working with data that needs to be profiled, cleaned and accepted. We face the task of creating customer micro-segmentation that will explain the present value. The quality of the micro-segmentation determines the quality of the CLV model as a whole.

In the third stage, we work with the matrix to transition between microsegments. The distribution of customers by microsegment is projected for the future, and their future value is discounted in the present. The output is the allocation of discounted future values via the microsegment at the customer level.

In the fourth stage, CLV is visualised and interpreted using dashboards, and data provisions for other systems are generated. At this point, CLV can be utilized for business decisions and in the client's customer serving processes.
 

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Veronika Bauer

Veronika Bauer

Senior Manager

Veronika is a Senior Manager in the Advanced Analytics department. She specialises mainly in analytical end-to-end solutions for clients from the finance, energy and retail industries. She focuses on ... More

Tervel Šopov

Tervel Šopov

Senior Manager

Tervel is a Senior Manager in the Analytics & Information Management team of the Consulting function of Deloitte Czech Republic. He has more than 6 years of experience with the data analytics area. He... More