SAS helps TalkTalk hold on to customers with big data
TalkTalk has unearthed new customer insights in just a few months of using SAS Analytics
TalkTalk is using business analytics software from SAS to model and predict customer patterns, trends and behaviours, with the aim of improving customer retention.
TalkTalk has over 4.8 million UK broadband, home phone and mobile customers but, in a saturated market, customer churn can be a problem. The company is keen to harness the vast amounts of data it holds in order to better understand its customers and increase opportunities to up-sell its products.
TalkTalk had been modelling its customer data for many years. However, the company believed it had yet to unlock the full wealth of patterns, insights and trends that existed in the data. It therefore began working on a new big data analytics initiative with SAS in the first half of 2012.
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SAS has helped TalkTalk to aggregate data from 12 separate sources – including CRM, transactional, network and dynamic usage data – and integrate them to create a single view of the data. The result is a 10 terabyte pool of customer data.
Using SAS Analytics, TalkTalk has been able to apply more sophisticated models and segmentation techniques to the data to reveal new triggers, as well the impact of multiple levers of behaviour on customer churn.
These are significant steps not only in terms of improving customer retention, but also in improving marketing and promotional efforts, according to SAS.
“Even though we are just starting on the project, we have already unearthed new customer insights and as we apply more rigorous models and predictive analytics, we expect to really unlock the value of their big data,” said Mark Wilkinson, managing director, SAS UK & Ireland.
As TalkTalk becomes more confident in its analytics and modelling it will look at how its product portfolios interact with each other to identify trends and patterns across broadband, telephone, mobile and, in the future, TV.
The company also hopes to integrate more data sources – such as social media and unstructured data from TV – and introduce predictive analytics capabilities from SAS to get better answers and insights from its customer data.