Concept:
Pitney Bowes Business Insight is introducing the Portrait Uplift Optimizer, designed to evaluate the effectiveness of customer communication. Through communication analysis, retailers are able to create targeted offers directed to specific consumer segments.
Portrait Uplift Optimizer separates consumers into four distinct target groups, organized by likelihood to buy: Sure Things, Persuadables, Sleeping Dogs and Lost Causes. The Uplift solution seeks to define the most important 30% to 40% of the merchant’s target audience, called Persuadables. By focusing marketing strategies on this highly “persuadable” segment, retailers can increase the possibility of purchase.
Team:
Headquartered in Troy, New York, Pitney Bowes has been in the business of business growth for more than 90 years.
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The Portrait Uplift method was created and developed by Senior Vice President and General Manager, Mark Smith, PhD, who works and consults directly with businesses in the financial services, retail and telecommunications markets on analyzing customer data in order to improve business performance. Smith’s team includes Product and Solutions Director Neil Skilling, PhD, who focuses on customer relationship management and data mining; Patrick Surry, PhD, who acts as Global Solutions Owner of customer analytics; Group Marketing Officer David Newberry, who has experience within the technology and marketing sectors; and Jeff Nicholson, Vice President of Product Marketing, who has worked with several industry analysts including Gartner, Forrester Research and Aberdeen Group.
Market Relevance:
Overall, Uplift modeling has had the most success in the areas of large volume B2C organizations in the financial services, telecommunications and retail industries, with applications in customer retention and renewal, demand generation, cross-sell and up-sell, where the value of an incremental response is relatively high.
The goal of Uplift is to help businesses target ideal customers with the right incentives, coupons and loyalty programs that will drive them to purchase. This creates an opportunity to reduce marketing expenditure and prevent customers from opting-out due to unnecessary contact.
Delivery:
Rather than merely measuring the incremental impact of a campaign based on the overall difference in purchase rate between a treated group and equivalent control group, Uplift modeling is designed to find patterns in behavior variation of an individual customer, using a control group as reference. In the end, the solution’s goal is to predict how each customer’s probability of purchase will change given a specific treatment or offer, such as sending a piece of mail, making a call, or changing a service.
Adoption of the Uplift model is most effective with the use of randomized control groups for a company with a customer base of more than 100,000 people, and a multi-channel market and communication path.
Proof Points:
Retailers that have utilized the Uplift modeling include Dell and BlueStem Brands, formerly Fingerhut, a leading catalog retailer. Dell’s adoption of the method allowed the company to optimize effectiveness of consumer catalog mailings.
Portrait Software was named a winner of the DMA Future Innovator’s Award in 2009.
