Everyone agrees that data quality is important, but that doesnt make them willing to pay for it. Any manager asked to approve a significant data quality project will rightly want to understand its ROI. Sometimes the justification is a simple cost reduction: fewer duplicate mailings, fewer misdirected shipments, fewer customer service transactions to correct mistakes. But often the benefit comes from higher revenue: better data will allow more effective promotions or more precise (that is, higher) pricing. Data management professionals are frequently unfamiliar with the details of such analyses. Even the business counterparts who are supposed to provide the necessary input may not know how to structure them to satisfy financial gatekeepers.
The challenge usually lies with the value calculation - the R in ROI. After all, investment is not much different from any other systems project. But how do you estimate the value of incremental revenue - or, indeed, what that revenue might be? Following are several real-world projects that involve benefits from improved data quality. Although they cover just a handful of the possible situations, they may inspire insights that apply to your own business.
Higher profit per customer. An automobile dealer wanted to increase revenues from its service operation, the primary source of business profits. A proposed integration project would make the service history of existing customers available to salespeople while a new car purchase was being negotiated. The value came from helping salespeople to make the most appropriate service-related offer for each purchaser. Existing service department users would be offered a long-term contract to lock in their business, while sporadic users would be given discount coupons to encourage them to come back. In this situation, returns were measured in terms of increased profit per customer. This incorporated a company-wide view that included profit on the sale itself, profit on future service revenues and profit from financing activities.
Improved promotion effectiveness. A consumer goods manufacturer relied heavily on retail promotions executed through its distributors. The manufacturer was aware that it often paid for promotions that never reached the store aisles. A little detective work found that materials for these promotions were sometimes not delivered to the distributor or, more often, they were not placed in the store by the distributors field staff. Digging further, it turned out that the companys contact lists for distributor field staff were often outdated. As a result, distributors often did not receive notice of planned promotions or know who to contact when materials were received unexpectedly or expected materials were missing. The value of the project to fix these lists was based on a reduction in wasted promotion materials - which accounted for about half the total marketing budget - and the revenue gain from increasing the number of promotions that were actually executed.
Increased value per response. An online marketing organization used email and Web advertising to generate orders. For each response, the company had to decide whether to require payment in advance of shipment. This was a delicate balancing act because prepayment reduced the number of orders, but credit often resulted in bad debt. In theory, the company could identify likely no-pay customers based on previous behavior, but poor matching and disconnected fulfillment systems meant only a portion of this history was available while the order was being processed. The project to improve matching and data access was justified by the value of better credit decisions; orders would increase because more good customers were given credit, while bad debt would drop because more bad customers had to pay in advance. Access to previous purchase history also would allow the company to better recommend additional products to buy once the initial order had been accepted. The profits from higher add-on sales per responder might actually exceed benefits of the improved credit decisions.
Optimal return on promotion expenses. A direct-response marketer acquired customers at a loss in order to make profits on future sales. It had a wide variety of products to offer for the initial promotion and a wide range of channels to reach the customers. Each product group traditionally evaluated acquisition promotions based on cost per order and expected revenues for future sales within its own group. Obstacles to a company-wide measurement of each customer included multiple account numbers for the same customer and lack of accurate cost data for lifetime value calculations. The value from removing these obstacles would be measured by the increase in company-wide profit from reallocating promotion expenses to the most profitable acquisition product and channel combinations. This required calculating the lifetime value of customers acquired by increasing or decreasing promotion spending for each combination - a demanding but feasible task. Moving investment from the least to the most profitable options would result in a substantial long-term profit increase with no change in promotion expense.
In one sense, each of these projects is justified in the same way: by higher company profits. But identifying the specific mechanisms that will generate these profits yields credible, understandable ROI calculations. These are much more likely to result in project funding than a generic appeal to the value of data quality. Although details for your projects will be different, a similar approach should also serve the projects well.
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