Predictive analytics with Red Olive

Control Room: Harnessing the Power of Data

The aim of predictive analytics is to predict future behaviour or events using the data you hold about things that have already happened. Predictive analytics has been around for some time, but it is only now becoming more mainstream and many businesses are unsure of what it is or where it fits into an overall information strategy. This is partly because it relies on having access to good quality data in a format suitable for business analysis, such as that available from a well designed data warehouse. The techniques involved are also more sophisticated than query and reporting and the degree of business understanding is higher, requiring well-established information and data governance.

Working in partnership with businesses, Red Olive helps its clients develop a predictive analytical capability using a framework that identifies patterns in historical data using data mining, while looking for new opportunities to increase profitability and decrease costs.

We use our Total Enterprise Modelling (TotEM™) roadmap to work with your organisation as a whole. We create a collaborative environment across teams and departments and build support for innovative ways of turning your data into profitable initiatives.

Red Olive’s framework takes the best of the industry-standard CRISP-DM (CRoss-Industry Standard Process for Data Mining) and applies an ‘agile’ overlay to make it more responsive for internet-paced market change. Our flexible approach ensures models remain current and effective, even for near real-time analytics. This avoids the trap of spending too long on building models in concrete only for them to quickly degrade as the business world around them changes.

Companies in a wide range of markets including energy, utilities and mining, telecoms, pharmaceuticals, retail, healthcare and financial services that fail to use their data proactively are missing out on a whole range of benefits. These include steady and incremental profitability improvements through promotion and pricing optimisation; the quick creation of new data models to support a single view of the customer; and more successful up-sell, cross-sell and retention through personalised consumer engagement. Using predictive analytics, companies can grow revenues and reduce outbound marketing costs by improving response rates through refined targeting with offers tailored to specific customer needs.

You can read more about Total Enterprise Modelling (TotEM™) on our blog.


What’s involved with Red Olive and predictive analytics?

From the start, Red Olive works directly with your business people and your data. Particularly for predictive analytics, it is vitally important to develop a clear understanding of the business meaning of the data, because inferences will later be drawn from it and potentially far-reaching action taken as a result. Often Red Olive consultants bring fresh eyes to help client staff members clarify their own understanding and make sure the most valuable business questions are being tackled. This fundamentally helps the client company on its journey to becoming an analytical competitor.

After this initial review we apply our data mining expertise to produce predictive models, using our statistical knowledge to select the most appropriate modelling tools and techniques for your specific needs.  As with all of our engagements, our focus is to deliver tangible business performance improvements quickly, with benefits seen by everyone in the business, not just the data team.

Suitable for:

  • Businesses who have good data management in place and who want to benefit more from it, for example exploiting their ‘single view of the customer’
  • Enterprises looking to create more refined customer segmentation with product portfolios tailored to them
  • Organisations looking to improve business performance and decrease costs through greater control of the customer relationship
  • Companies looking to increase profitability by improved up-selling, cross selling and customer retention