Customer data analysis – part 1

Customer behavior data analysis – part 1

The key asset of any company is its customers. Therefore, it is crucial to identify their needs and preferences as well as to know the factors affecting their behavior. The collected customer data allows predicting customer behavior and creating appropriate marketing offers, sales plans, and retention programs that match customers’ needs.

Data mining tools are used to create models that predict customer behavior by using historical data. These methods can be applied to answer questions like:

  • Which product to offer to our customers?
  • Which customers will respond to a particular marketing campaign?
  • How to identify customers who will resign from our services?
  • Which customers are the most valuable, and how to keep them?

Below we present examples of challenges which can be solved by applying Data Mining methods.

Challenge Solution – analysis type Benefits

The Marketing Department is planning a “direct mailing” campaign. To minimize the cost of the campaign, it should determine which customers are most likely to respond to the offer.

Scoring – assessing the probability of the response to a particular offer

The result is the client’s score calculated on the basis of the available information about him. Scoring shows the probability, that a given event will occur (e.g. if the client will respond to a particular offer: yes / no).

  • Creating a list of customers who are most likely to respond positively to a ‘direct mailing’ campaign
  • Identification of these characteristics of customers, which significantly influence the probability of a positive response
  • Lower campaign costs due to more accurate targeting of offers and smaller size of the target group

The Sales Department, in order to increase sales, has to decide which combination of products should be recommended to a particular customer.

Cross/Up Selling

Cross/Up selling, supported by statistical data analyses and Data Mining techniques, provides the means to offer optimal product combinations to particular customers in order to match their current needs.

Basic analyses, that support Cross/Up Selling include Market Basket Analysis (identification of products or services that are most frequently purchased together) and Best Next Offer models (which product should be recommended to a customer).

  • Identification of products or services which customers frequently purchase together
  • Increasing customer loyalty and decreasing the risk of defection, by offering the customers, products which better adjust to their expectations
  • Increase in sales

The Marketing Department is to prepare or modify the offered products and services, so that it optimally matches the needs of different customers.

Automatic customer segmentation and profiling

Segmentation is based on the grouping of customers with similar profiles and behavior. This kind of analysis is based on automatic allocation of customers into segments based on the available customer data.

The division can be carried out by taking into account a variety of characteristics, such as demographic and behavioral data. On the basis of this segmentation it is possible to infer the customers’ profile from the segments they have been assigned to.

  • Defining characteristic groups of customers
  • Determining the client profile for a particular segment
  • Discovering niche customer groups, which can remain unnoticed in the case of segmentation which does not use Data Mining techniques
  • Analyzing the migration of customers between various segments
  • Better understanding of customer needs

The CRM Department is to determine the profile of clients who intend to resign from our services, and then take action to keep them.

Churn Analysis

Churn Analysis combined with Data Mining methods allow to specify, on the basis of historical data, the probability that a certain client will stop using the company’s services at a given moment.

  • Identification of customer groups characterized by the high probability of defection (churn)
  • Obtaining information which enables to undertake appropriate preventive measures
  • Predicting losses caused by customer churn

In the next part you will learn, what types of analyses are used in estimating the customer value over time, in selecting best parameters for the given product or service and in effective sales forecasting.

Pin It on Pinterest