Over the past decade leading-edge marketers have optimized their marketing functions and significantly improved their bottom line by collecting, mining, and analyzing customer data. Many companies, however, have settled for one-off applications and have missed out on the opportunity to optimally target the right customers with the right products at the right prices.
Most marketing managers are sitting on a virtual gold mine of untapped customer, product, price, and channel information. At a time when consumers are exposed to arguably thousands of messages a day, the only way in which marketers can differentiate their products and appeal to fickle consumers is to understand segment level consumer behavior. And the only way to analyze s behaviors, needs, and wants is through an integrated data asset which stores the right data and enables sophisticated analysis. Just look at the results from companies that heavily invested in leveraging data and analytics to optimize their marketing functions. Wal-Mart, perhaps the best-known example, collects more data about its products and shoppers purchasing habits than any other retailer in order to increase operational efficiency and maximize product sales. Wal-Mart recently used predictive modeling analytics on its data to determine the top-selling items before hurricanes in Florida stores. Analysis revealed that the top-selling items stretched beyond expected items like flashlights and water/beer was the top-selling prehurricane item, while sales of Strawberry Pop-Tarts increased sevenfold during these periods. As a result of this analysis, Wal-Mart is able to preorder and stock the optimal quantities of these items during hurricane seasons. Such extensive, but efficient, use of data analytics has made Wal-Mart the world s leading retailer. A similar example is Best Buy. After mining its data, Best Buy determined that as many as 20 percent of its 500 million annual customers were not profitable. These customers bought products, applied for rebates, returned the merchandise, and then bought them back at returned-merchandise discounts - all of which affected the company s bottom line. Analytics gave Best Buy the insight to remove these customers from their mailing lists and scale back promotions that attracted them to their stores. Companies across all industries can learn from the Wal-Marts and the Best Buys of the world to make robust analytics work for their businesses. Here s how you start: Prioritize to Improve Life-Cycle Analytics - Conduct a strategic review of the consumer life-cycle areas and focus on areas that will generate the maximum returns. The prioritization will depend upon a host of factors - competitive situation, strategic goals, etc. Improve Cross-Organizational Data Collection - Once the key focus areas have been identified, collect cross-organizational data from different internal systems. A lesson to keep in mind is that getting the right data is just as important. Many companies have lost millions of dollars trying to gather data that is irrelevant and low value-add for the analytics required, such as investing millions of dollars in creating a data asset to store daily information when customer level aggregated monthly information will suffice. Augment External Data - Supplement internal data with external information, such as credit bureau data, demographic information, and/or sector-specific statistics, to improve the accuracy and effectiveness of marketing models. Analyze Data - Develop analytical models to generate specific lists, guidelines, and rules that the frontline staff should act on, e.g., retention lists and cross-sell prospect lists. Build an Analytics Capability--In order to harness the full potential of analytics across the customer life cycle and create a company-wide culture of analytics-driven decision making, companies must build significant capabilities. A basic framework includes:
- A well-defined strategic imperative to create clear company-wide communication and manage change. A comprehensive data gathering and storing process, and technology to transform, clean, and link internal and external data at minimal investments. Efficient and defined processes for analytics and best practice sharing. An analytical engine and a test-and-learn culture, which thrives on detailed iterative analysis. Establishment of guidelines for data/model access and use is essential. Ongoing monitoring and refinement of the engine. An appropriate governance and funding mechanism to ensure appropriate resources for these tasks. C-level sponsorship is critical. ol> By building capabilities to conduct analytics for improved segmentation and decision-making, marketers can significantly increase the effectiveness of their operations, thereby driving shareholder value and ROI. Source: