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Insuring Accurate Data
Insurance.com cleans up its customer data -- and cleans up, period.
For the rest of the January 2008 issue of CRM magazine please click here
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Tell me about your organization. We're an insurance agency. We primarily, as far as the agency is concerned, focus on [auto insurance]. We do sell other products, but most of those are through our partners. We're giving accurate, bindable rates from multiple carriers and you can purchase that policy either directly online or through the sales center from us. The company started in 2001. We have multiple types of relationships: We have B2B2C, B2B, and B2C; it's a pretty sophisticated business model, I suppose. What problems were you facing? [We] wanted a standardized enterprise solution for customer matching, data quality, and data profiling. [DataFlux] allowed us to reduce the number [of] redundant technologies within our business while increasing the accuracy and effectiveness of our CRM processes. We work with hundreds of external sites and if you're actively shopping for insurance, you might come from one site, and another site, and then another site. Well, it's still the same customer, but the way that they're interacting with us is from three different venues. So we're using DataFlux to understand that this is the same customer just coming to us from three different sources -- it changes our behavior on how we interact with them. Why did you select DataFlux? You're trying to prove your return on investment, and you're trying to prove speed-to-market -- typical decision processes that have to take place within an organization. The thing we initially were using DataFlux for was customer matching, because we're a portal. We [also] use it in our data quality processes, for profiling standardization. It's continued to expand. We just wanted to enhance our CRM capabilities, essentially. There was always some hole that was left in the [other] products, either in the profiling, or the customer matching, or standardization -- or as far as performance would go, trying to use it in a run-time environment. DataFlux really covered all those needs. It was something that we could use in a transactional system, and it was something we could use in a batch system.
How many vendors did you test out? We do a lot of research internally. We get that down to around six or seven vendors, send out a pretty extensive [request for proposals], and do an extensive interview and demo with each of the vendors. Then at that point, we get it down to the final two where we do a proof of concept with each vendor. Even though that may extend the decision-making process, we find that's how we have the most success, because you never know what you're going to get until you get your hands on it and actually start to develop something with it. The selection process probably took us between two and three months. How long did it take to get into full production? We started using it internally, immediately. Within the first month, we were doing customer managing and we had that integrated in our reconciliation process for commissions. We actually took somewhere around four to five months before we had that integrated in with our transactional production application. How did you measure your return on investment? We have seen a continuous improvement in both customer acquisition and reduction in time-to-market with new solutions [as well as] conversion rates and retention rates. Basically, we have a standardized product that we can use in multiple environments and multiple use cases and that reduces our time-to-market, it reduces our overhead costs, and it really allows us to add a lot of transparency within the system. Instead of using multiple people with multiple skills for various scenarios, those same people can use standardized approaches across the board. It's increased our productivity. 5 Fast Facts
  • Age of the Initiative? Two years
  • Who Was Involved The director of IT, myself, and the database development manager
  • Best Idea? Using a standardized solution to address current and future needs
  • Biggest Surprise? Efficiencies gained through data cleansing and standardization solution
  • Biggest CRM Mistake Made? The lesson learned is that you can never plan enough to identify all obstacles when implementing enterprisewide solutions
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