Customer data analysis – part 2

In the previous post we presented a few methods of data analysis, which are used to identify customer needs and preferences and allow us to predict their behavior. Such knowledge results in building better marketing and sales offers which meet specific customer expectations. In today’s article we present further examples of Data Mining methods that can be applied in daily business operations.

 

Challenge Solution – analysis type Benefits

The Marketing Department has to determine which marketing strategy to apply to selected customers depending on their potential value.

Lifetime Value Analysis

Lifetime Value (LTV) analysis is used for assessing the current value of future customer profits – it estimates the expected profit a customer will generate during the whole period of using the company’s services.

  • Estimating the potential value of a customer for the company
  • Determining the factors affecting customer value
  • Segmenting the customer population with respect to the magnitude of future profits
  • Choosing target groups for marketing campaigns

The Sales Department has to determine the volume of sales in the next quarter.

Sales forecasting

This solution applies methods of stochastic processes analysis to predict profit or sales volumes on the basis of data like profit and sales volume in the previous periods, changes in macroeconomic conditions, the results of competitors’ promotional campaigns or other random factors.

  • Forecasting revenue from sales of products and services
  • Estimating the budget for subsequent periods taking into account the actual market trends
  • Optimal production management or submission of delivery orders

The CRM Department has to determine when to intensify efforts in order to maintain the selected clients.

Survival Analysis

The main purposes of Survival analysis are to estimate the time a customer will subscribe to a service or estimate the probability of customer’s defection in subsequent life cycles. This information allows the company to determine the predicted period of retaining the customer and introduce an appropriate loyalty policy.

  • Estimating the survival time distribution for particular customers or whole customer groups
  • The ability to monitor the changes in survival time distribution as they are affected by various factors (such as marketing campaigns)
  • Better understanding of customer behaviour by analyzing the factors which determine customer lifetime value

The Marketing Department aims to determine which set of features of a given product is preferred by customers and how this product shows out against the competition.

Conjoint Analysis

Conjoint analysis allows to compare different variants of a given offer on the basis of their usability to customers. The result of Conjoint analysis is the selection of the best combination of features for the analysed offer.

  • Obtaining information about the combination of product features that is most desired by the customer
  • Better matching product features to customers’ expectations
  • Forecasting the market share of products

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