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Increase customer knowledge through better data use and organisation
Businesses collect a lot of information in the course of a regular customer-supplier relationship. Telecom operators, more than any other businesses, have data loads that would be complex to manage without a well thought-through organisation
It all begins with the acquisition of new customers: the operator registers the customer’s first and last names, the different offers he/she subscribed to, in addition to a record of orders, bills, calls and other relevant information. Althougth customer data is more widely available in the telecom sector than anywhere else, this sector remains very competitive, covering various domains such as fixed line, mobile and internet. With the change of business models and the development of new technologies, rich and sophisticated offers are being delivered to the market.
Given this context, operators need to answer the following two questions: How can we manage offer performance? What are the market expectations? Traditional analysis used for market studies, though they are necessary, only provide a snapshot and fail to offer a dynamic view of the customer’s purchasing behaviour. When it comes to managing offers, it is necessary to have simple, reliable and available reports within an acceptable time scale.
Marketing and commercial executives must realise that access to data and its use are essential not only to industrialize their reporting, but also to control their offers and better understand their customer behaviour. Implementing analytical CRM solutions meets operational needs. It also opens fields for many applications: segmentation, churn control, performance analysis, etc
Data warehouses, data marts and marketing databases are technical answers to the growing number of requirements from marketing departments. If a datawarehouse is designed to meet the needs of the entire company, then a particular “view” (data mart) must be designed in order to answer the specific needs of marketing departments. Business intelligence or analytical CRM free users from technical dependencies and allow the development of numerous applications:
- geomarketing analysis: optimization of a sales network set up, reduction of offers cannibalisation, competition management, market share increase, business and demographic characteristics overview, adaptation of the product / geomarketing characteristics offer.
- predictive churn scores: identification of churn criteria, probability calculation of customer departure, dynamic monitoring of predictive scores, implementation of preventive targeted actions, optimization of retention actions.
- lifetime value: calculation of current and potential value of each customer, life estimation span, identification of priority targets, adaptation of actions and investments / value segment.
- cross-selling & up-selling: optimization of additional margins on campaigns, building of hypothesis regarding customer / offer appeal, maximization of customer additional margin, optimization of campaign frequency, understanding the customer’s life cycle, monitoring of the impact of solicitations and optimization of their frequency.
- loyalty programme: identification of priority targets, adaption of loyalty offers / segments, taking into account customer characteristics (product affinity, personal data, customer’s seniority, etc.), optimization of loyalty investments, customer segments, monitoring of targeted offers’ impact on churn levels.
A customer knowledge project is at the crossroads of marketing, statistics and information technology. It requires a joint management from the three entities. As in any project, the technical aspect (choice of solutions, tools, data format, etc.) is important. Yet, the management aspect should be undertaken by the marketing department, since it is the only one which masters its requirements and data
Each phase is important and contributes to the project’s overall success, from strategic framing of the requirements to the final approval. However, given the nature of these projects, experience shows that special attention must be given to the following three points:
- the entity that will manage the project
- the data audit
- the writing of the data dictionary
Let alone the technical details, the key to a customer knowledge project is the creation of a storage space (database or data mart) fed by inputs from different data sources from operational systems:
billing, orders, CRM, etc. Special attention must be paid to the audit of data provided by these operational systems. A data inventory should be led so as to feed the database and assess its reliability and usability.
Often, data dictionary definition is done hostily or is undertaken by the IT department, although it should be done both by the marketing and IT departments. The data dictionary is the detailed description of the database’s entire content. It stands as the reference document for new users and new developments, and ensures fast adoption of the tools by new users.
The files feeding the database are to be described, as well as the frequency of the inputs and the fields with which they are described. All fields must have understandable names and the associated management rules must be indicated for fields that are modified or which result from calculation.
A customer knowledge project is not a ‘‘big bang’’ project. It is led by lots and iterations. Therefore, it is wise to plan an initial lot on an “operational reporting” perimeter, focusing all the efforts on the fast delivery of a reliable and user-friendly solution. The implementation of the following lots will then be greathly facilitated.
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