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Accenture helped a leading US pharmacy retailer build an innovative customer analytics capability that provides business users and suppliers with detailed customer insights.
This solution optimized the retailer’s loyalty marketing programs and enabled it to continually improve the service it provides for its customers. The Accenture Customer Insight analytics engine offers a uniquely accessible view of item-, store-, and customer-level data to create profitable marketing promotions and campaigns. Since taking the new solution live, this pharmacy retailer is positioned to achieve high performance, increasing sales among its loyalty program cardholders, improving transparency of its marketing investments, and enhancing the organization’s ability to make decisions that will drive cardholder value and growth.
With more than 65 million members, our client has the largest customer loyalty program in the United States. The success of this program is based largely on the company’s sophisticated customer-centric marketing capability. Identifying customer segments and understanding customers’ behaviors has allowed the company’s marketing intelligence group to create targeted promotions that meet the needs of its customers, maximize sales and bolster loyalty. Every second, 30 of its loyalty cards are scanned in the United States. Within 24 hours, those transactions are transformed into actionable customer insights.
Despite the program’s enviable success, our client believed it could further improve its promotional efficiency and effectiveness. One way to do this was by allowing non-technical business users easy access to more detailed insights into customers’ characteristics and purchasing habits. With such granular information, the company’s marketing workforce could not only refine promotions to drive additional sales within customer segments, but also create new micro-segments based on demographic information (such as gender, family composition, income and location) and purchasing patterns (such as average basket size and preferences for national brands over private label products). Equally important, greater access to more detailed information would allow the company and its vendors to better understand the effect of a particular promotion on specific product sales and category sales, in addition to overall sales. With those insights, promotions could be adjusted to maximize the return of every marketing dollar invested.
A team of Accenture Interactive professionals worked closely with the pharmacy retailer to develop a leading-edge, integrated customer insight capability based on the Accenture Customer Insight analytics engine. Our client leverages this solution for several reasons. Most notably, it was designed to put relevant information about customer behaviors in the hands of nontechnical users so they could continually improve the value of the program for customers. Additionally, it combined advanced reporting, intuitive tools and on-demand access to deliver powerful insights to decision makers across the marketing organization.
Accenture worked closely with the company to help ensure that the analytics solution would be used efficiently, effectively and in accordance with the client's needs.
The development team:
Interviewed their business users to understand their marketing objectives, business questions and data needs.
Designed and developed analytical requirements around their specific marketing objectives.
Implemented Accenture Customer Insight for use by decision makers and non-technical users across the marketing intelligence organization.
Identified integration points between Accenture Customer Insight and existing, complementary tool sets within the marketing organization. For example, the team integrated the analytics engine with the company’s existing campaign management system to produce insights related to marketing spend and promotion effectiveness.
Trained the users on how to understand and interpret the reporting content, as well as how to apply the insights to improve the customer experience.
Accenture’s ongoing High Performance Business research program shows that a key characteristic of high-performance businesses is their ability to create loyalty by delivering experiences that are tailored to customers’ preferences, expectations and intentions. One way they accomplish this is by using econometric and return-on-investment analytics to develop a deep understanding of customer behaviors across channels and throughout the life cycle. In fact, Accenture research shows that high-performance businesses are five times more likely to use analytics strategically compared with low performers.
With Accenture’s help, our client has established a cutting-edge, highly sophisticated customer analytics capability that:
Connects business users with customer-infused decisions to optimize their loyalty marketing programs, including offers at the register, e-mail, direct mail, among others. For example, with the new analytics capability, our client was able to offer special promotions to environmentally conscious buyers in select locations. This campaign not only satisfied the segment members’ desire to be more eco-friendly, but was also responsible for more than $100,000 in incremental sales increases. In another case, the company was able to target its highest-value, longest-tenured cardholders with a direct mailing. The campaign generated a 27 percent response rate, significantly lifted sales for this segment, and achieved a return on investment of more than 100 percent.
Offers the loyalty card program team members a uniquely accessible view of item-, store- and customer-level data that enables them to gain the level of insight they need—when they need it—to make customer-focused decisions.
Combines an intuitive user interface with self-service, on-demand reporting. The system's pre-built reports focus on understanding customer habits and identifying key drivers of shopper behaviors.
Offers segmentation templates, which allow the company to create refined groups of customers, based on unique shopping characteristics, value potential and demographics for deep-dive analysis.
2011