Canadian Credit Union does smarter marketing with new analytics tool

Having more customers use your product or service is always great, especially in these tough economic times.

But wonderful as it is, a swelling client base can also be a challenge when trying to develop a direct marketing campaign.

Until recently, it’s something Coast Capital Savings had to contend with when trying to implement an effective cross selling strategy. The Surrey, BC-based firm is Canada’s second largest credit union managing more than $11.9 billion in assets for over 400,000 members in the West Coast.

The company says moving to a data mining and business intelligence (BI) tool from Cary, N.C.- based SAS Institute has worked wonders for its marketing campaigns.

Following the rollout of SAS Enterprise Miner, last October, Coast Capital managers can better understand customer data and make faster and smarter marketing decisions with very little technical assistance.

This is a huge improvement over what was possible with their previous BI system.

Enterprise Miner is SAS Institute’s flagship analytics product, specifically developed for data segmentation and modeling, according to Michael Turney, head of strategy and field marketing at SAS Canada.

Over the past four years, Coast Capital’s membership grew by more than 100,000 or 25 per cent.

“Our challenge was to make the most of our direct marketing budget by accurately targeting customers with the most relevant services,” said Jerome Lengkeek, the organization’s manager of product development and analysis. “We didn’t want to waste our client’s time by sending them irrelevant offers.”

He said the old system – a combination of a marketing database from Metavante Corp. and standard Microsoft Office tools such as Excel and Access – worked well in the 1990s, but had difficulty scaling up to the new demands.

For instance, the old database helped users understand the performance of existing marketing campaigns, but lacked the statistics analysis tools and predictive modeling capabilities needed by company executives to plan new campaigns.

Operation of the previous system also required extensive assistance from IT teams and senior marketing analysts.

“As a result, much of our planning was subjective – based on the past results and common sense approach,” Lengkeek said. “The main drawback was rather than being tightly targeted our campaigns still cut a pretty wide swath.”

All that has turned around since the deployment of Enterprise Miner, the Capital Coast executive said.

The SAS tool, he said, uses data cleansing and consolidation tools – as well as mathematical equations – to help users determine which services are appropriate and most likely to be used by certain Coast Capital customers.

This eliminated the shotgun approach to selling and made way for a more targeted technique, said Lengkeek.

He said the visual, intuitive interface also allows less technical managers and users to operate the system without IT help. Users now take advantage of simple drag-and-drop menus and easy-to-read charts and graphs rather than plough through complicated numbers-based reports.

Lengkeek said it took up to five marketing analysts operate the old system. Today Coast Capital uses only one. “Testing marketing campaigns used to take at least a week. Now we can put together a campaign in a single day.”

Organizations that experience sudden data growth – and the resulting urgent need for analytics tools – typically pass through four phases, says SAS’ Turney.

Phase 1 – Handling operational conflicts

Companies often hold conflicting or redundant data on the same client in separate departments. For example, a bank may have a banking account data of a client in its transactions department but that same client will be identified differently in a mortgage account held by another department. This makes it hard for the various departments to exchange information or target possible service opportunities.

Phase 2 – Data cleansing

The organization realizes its database needs to be “cleansed” so clients can be properly segmented into groups and identified for particular marketing campaigns.

Phase 3 – Modeling

A need is also felt for modeling tools to recognize predictive client behaviour. Knowledge gleaned from these models is then incorporated into marketing campaigns.

Phase 4 – Smart product development

Companies begin to optimize the use of analytic tools to develop and offer more precise products and services for individual clients. For example firms don’t just send out e-mail campaigns to certain age groups because they know this group is Web savvy. The firm actually determines which time of the day the e-mail is likely to be read and what sort of enticement is likely to get a positive response from the recipient.

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