In the information economy, companies focus on the dynamic use of data derived from the extended enterprise. In the retail sector, businesses must identify market segments containing the most profitable customers and prospects and deliver promotional messages that will affect their behavior.
Through the capture, storage, management, distribution and analysis of data, retailers can recognize patterns to predict customer-purchasing behavior and can liberate their marketing from the “rearview mirror” syndrome.
The move to segmentation. It is well known that 80 percent of a retailer’s profits come from the top 20 percent of its customers. Therefore, retailers must realign their marketing strategies from the one-message-fits-all mind-set to personalized promotional campaigns. Segmenting customers – not markets – is the most profitable marketing approach.
For example, through customer analysis, marketers can identify and target top switchers. If your switchers are price-sensitive, you can attempt to change their preference structure by raising their awareness of and preference for specific brand/product attributes. By instilling the concept of better value, you may alter the perception of price-sensitive purchasers to your advantage. Through analysis of behavior, you can identify this segment and target it with an appropriate message.
You might assume that, with the emphasis on one-to-one marketing and customer personalization, numerous customer relationship management applications could handle these marketing objectives. That is not the case. Data analysis is the weak link in the customer intelligence chain. Problems with integration, accessibility and scalability have resulted in information barriers, including:
• Valuable customer information residing in data silos – such as point of service, point of purchase, loyalty and reward programs, demographic database, clickstreams and customer history – is not integrated within the enterprise and the extended value chain.
• Merchandisers and marketers lack the expertise to interface with complicated analytical tools sold by business intelligence vendors.
• Current retail data analysis requires extensive resources to develop, maintain and use.
• Data are growing at an exponential rate, overwhelming the capacity of existing data warehousing and analytic tools.
• The selection of current analytical applications is too simplistic and universal for specific, customized analysis.
Emerging technologies that address vast data volumes and distribution of this data have paved the way for a new breed of intelligent analytical applications. Retailers that use scalable, Web-enabled data will understand their customers better and will emerge as the true players in the information economy.
Demand chain optimization. Until now, retailers relied on mass marketing and one-on-one sales to increase market share and acquire new customers. Existing customers often became neglected stepchildren. The emphasis on CRM has radically changed that perspective.
Today, ineffectual mass marketing is slowly being replaced with highly targeted segmenting and clustering. This strategy has only recently been attainable with the availability of business intelligence applications and analytical tools that are Internet accessible, scalable and intuitive. The current technology trend allows retailers to perform ad-hoc queries from any location and to receive the results as actionable data, not canned reports.
As decision making becomes more decentralized and a democracy of decision makers contributes to the overall marketing strategy, accessibility to business intelligence becomes a prerequisite for successful CRM strategies. Marketing is no longer the sole domain of an insulated marketing department. Decisions that affect the marketing of products and services are being assumed by people throughout the extended enterprise.
Armed with easy, dynamic access to data and a simple platform in which to share and manipulate it, retailers can work in tandem with their partners to develop highly focused co-marketing campaigns. By giving merchandisers and manufacturers access to a common view of their customers and the ability to manipulate that data actively, they could gain a mutual understanding of key shopping habits and brand preferences.
This information, when accessible and scalable, can drive strategic retail marketing decisions, allowing businesses to build loyalty and increase profits. This can result in:
• Direct marketing campaigns targeting specific segments receptive to a “mass customized” message.
• Rewards programs that provide intelligent incentives to increase a portion of a customer’s spending.
• Loyalty programs that analyze customer history and behavior, resulting in increased purchases and strategic inventory adjustment.
• Identification of your most valuable and profitable customers – that 20 percent responsible for 80 percent of a retailer’s profits.
• An increase in sales to moderate shoppers and move them up the value ladder.
• Optimized trade areas and improved assortments store by store.
• Improved customer segmenting to specific geographic, demographic and psychographic traits.
• Cross-selling of the most profitable products and increased average market basket size.
• Maximized return on investment from promotional campaigns, including accelerated marketing cycles.
• Improved customer segmenting and clustering through granular analysis of customer data.
Through the sharing of enterprisewide business intelligence, retailers can enhance their partnership relations and improve their return on investment. Working in cooperation with their partners, retailers can develop tailored direct marketing campaigns that target brand switchers, cross-sell their most profitable products, recover declining loyal customers and upsell to their best customers.
As we begin a new century, intelligence is becoming a highly valued commodity. In the information economy, businesses are drawing on their vast stores of data to increase their competitive edge. At the click of a mouse, retail marketers and their partners can access mission-critical customer data, allowing them to make intelligent marketing decisions. In the process, they can leverage this valuable data to retain, segment, target and upsell customers while increasing their direct marketing ROI.