Enabling communications companies by delivering actionable insights from big data - to enhance customer relationships, reduce costs and develop profitable new products and services.
Although it was the dominant player in the booming cell phone sector, a major telco was struggling to get a handle on its Key Performance Indicators (KPIs) and approached Datamine for help.
Datamine identified several reasons for the operator’s ineffective KPI reporting - key amongst them being disparate systems, issues with data cleanliness, wide user needs and different reporting methods.
- Datamine’s solution to remedy the issue included:
- Consulting on what KPI’s should be across all business groups
- Identifying all possible data sources and data issues
- Building a data mart within the data warehouse (a data mart is a subset of a data warehouse that is specifically designed to service the needs of a specific user group or business division)
- Developing reporting on new KPI’s which Datamine audited and ran for 3 months
- Delivering the solution in-house and training the internal team to manage it
Datamine was able to successfully bring very large and disparate systems together and assisted a diverse range of organisational stakeholders in defining the company's reporting needs – the result being much improved monitoring of KPIs. Datamine’s training of systems administrators also equipped the company with an in-house resource capable of effectively managing its cell phone data mart.
A large provider of home phone connections requested Datamine’s assistance with a segmentation study of its entire fixed service residential customer base. While the company had already developed segments that were meaningful to its business there were, nonetheless, some problems.
- First, the segments that had been developed relied on information held for only one-third of the company's residential customers. This left two-thirds of the customer base unable to be placed in the correct segment.
- Second, the company was unsure whether the segmentation it had performed was statistically valid. In particular, were the segments actually different from each other, or phrased another way, were the customers within each segment actually similar?
Datamine was able to solve both problems. The first using a solution called ‘reverse engineered clustering’, a technique that begins with clusters (segments), and then identifies the structure and reliability of the clusters using the data.
Solving the second problem required drilling down and further differentiation of the segments the company had previously created. Ultimately some segments were amalgamated, some were split, and some customers with significant similarity were moved between segments.
For the provider, the outcome of the project was statistically valid customer segmentation that made ‘sense’.