This year, Altis was a finalist in the 2012 IBM NZ Business Partner Awards.
We were nominated in the area of Smarter Analytics for delivering a single customer view and propensity model used for client marketing campaigns for one of our New Zealand based Insurance clients (confidentiality required).
Our client had a common problem ? creating a single customer view so that they could communicate across multiple lines of business. The client had multiple customer databases across three business lines ? Insurance, Health and Life and Managed Funds. As a result, the client did not have a single view of their customers across the enterprise. This situation made it impossible for our client to tap into their existing customers for revenue growth through cross-sell and up-sell of their products and services.
Happily, Altis were able to assist.
First, we worked on a proof of concept, aimed to create the opportunity to grow revenue and use their marketing budget more effectively, by:
? Establishing the first iteration of a single customer view that consolidates the many different business line databases
? Meeting Marketing?s immediate requirement to identify customers who are targets for a cross-sell campaign using predictive analytics
? Developing a Business View to provide a reporting capability for the campaign list, enabling the list to be profiled and segmented further to create sub-lists
? Building a platform able to be ported into the client?s development environment at a later stage
? Developing our client?s strategic in-house capability to generate campaign lists.
Altis used Infosphere Quality Stage to deduplicate and match customer data across their disparate systems. Our team used the Australian rules set for Quality Stage to achieve a percentage of matches, but due to unique naming conventions in Maori, Hindi, and Chinese we had to develop a number of rule sets ourselves. After the customer data was matched, we applied data-mining algorithms to select cross-sell opportunities based on products those customers had in their portfolio.
These cross-sell opportunities were then flagged in the Single Customer View datamart and a campaign list was generated in Excel. Marketing then applied customer segmentation and other filters to the list to generate campaign sub-lists for their marketing campaigns. The Phase 1 solution was meant to have a short ?life span? with Phase 2 and Phase 3 cementing the technology and processes into the business.
As of this writing, Phase 1 is still live and being used to benefit our client and increase their sales to existing customers. The business benefits of the project included:
? Creating an in-house capability and saving costs. Our client previously worked with external agencies to cleanse and match customer data on a one-off basis at about $20K per list
? Further cost savings. Even by using cleansed customer data, our client still had only 70% accuracy of customer data which means 30% cost wastage on marketing materials and postage
? Preparing our client?s business and IT for the use of predictive analytics as part of the campaign process
? Establishing repeatable processes from the single customer view through to campaign list selection. This improves campaign response rates by using lists that are statistically optimised for response ? Increasing customer satisfaction by targeting customers with relevant product offers
? Improving customer experience by not sending the wrong communication to the wrong customer
? Placing our client in a position to accelerate the porting of a proven solution to their production environment
? Introducing master data management ? an imperative for getting the single customer view data prepared for analytics.
Devin Deen Altis Regional Manager New Zealand
Related posts:
- Do you need a plan for Master Data Management (MDM)?
- Altis Consulting Recognised in University of New England Award
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