Accurate and effective decisions are those which are based on facts. All business decisions concerning

the company, industry and brand development are based on data analysis. These analyses change data into useful information which enables optimization of sales and marketing activities.

iReward24 helps to solve business problems through gaining knowledge from statistical data analyses.
The scope of the conducted analysis involves both the elaboration of sales and transactional data as well as quality and text data which originate from loyalty programs.

We base our work on the latest achievements
in the domain of data analysis to ensure the highest quality and accuracy of gained results.

Our expertise and experience based on previous projects allow us to support our partners in the following areas:

•   Purchase and sales data analysis:

-  shopping basket analysis
-  customer loyalty analysis
-  discovering customer purchasing behavioral patterns and shopping cycles
-  sales predictive models,
-  Customer Value Models (Customer Lifetime Value)
-  discovering customer profiles
-  customer potential and profitability analysis
-  multidimensional segmentation
-  describing the ideal customer

•    Marketing analyses:

- evaluation and estimation models for promotional campaigns
- segmentation: demographic, psychographic, RFM (Recency, Frequency, Monetary Value)
- support for incentive projects and promotions
- cross purchasing analyses, cross-selling, up-selling, x-sell

•    Market research:

- industry-specific reports (Desk Research)
- analysis of the brand, company and product
- Mystery Shopping
- press reports for individual industries (Content Analysis)
- text and document analysis (Text Mining)
- pricing strategies

Within our analysis we focus mainly on the usefulness of the analysis - and we know those help:

•    to understand customer behavior better
•    to capture tendencies and purchase cycles
•    to understand customers profiles and their shopping strategies
•    identify profitable and unprofitable customers (including CLV modeling)
•    specify customer potentials
•    organize incentive campaigns for all customer groups
•    create predictive models for specific stages of customer life cycles

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