Getting in Harmony with Customers

The challenges of single customer views and managing a master record of customer information are not new issues. Although the introduction of enterprise and CRM suites greatly improved the available data, functionality and processes, it did not totally solve the problem of a single view of the customer. Most enterprises have heterogeneous environments extending well beyond the capability of a single vendor. The need for integration of customer information remains.

Clean Data

For Allianz the business case for the single customer view project revolved around the ability to improve customer service levels and promote brand loyalty, while also enabling Allianz to cross-sell its products and communicate with its customers based on a true understanding of their needs.

To achieve that goal Allianz integrated Harmony with its main insurance application, CSC’s Polisy. Unlike traditional data cleansing solutions, Harmony ensures that Allianz can maintain its single customer view without duplicate identities creeping back in.

“The benefits that Harmony provides us are threefold,” Coles says. “Firstly, there is the ability to cleanse our existing customer information. Secondly, Harmony facilitates the identification of customer duplication before consolidation occurs. And, thirdly, Harmony provides a mechanism to assist in maintaining data accuracy. This is where inconsistencies arise, particularly when you have as many different sources of customer information as we do.

“We also wanted to be smarter at marketing and campaign management, so that we could use the information about our relationship with customers better in order to target-market particular products aligned to their needs,” Coles says.

Coles says Allianz took a significant risk in leading the charge on implementing the new version of the Harmony software, but was pleasantly surprised that a software product it had expected to be immature was able to be implemented so smoothly. “In fact, when I asked the implementation team if they would have done anything different in the light of hindsight, they reported they had found implementation fairly mechanical,” Coles says.

“The big challenge we had — and we were pleasantly surprised that there were so few challenges overall — was more from a business perspective in really being clear about the business rules because we needed to mechanically cleanse the data.

“The issues we had didn’t involve the technology itself, or the quality of the data, the issues were around being absolutely crystal clear about the business rules for cleansing and matching data, and then testing those to the utmost degree. It wasn’t something that we wanted to get wrong.”