Two fundamental shifts are occurring in the commercial world: the data environment is integrating and gravitating to the center of every business, and the roles of the analytics practitioner, the business analyst and the IT specialist are fusing. The emerging skill set is a multidimensional one, and the role might be described as that of “analytics power player” or one who knows how to best exploit a company’s dynamic information assets for competitive advantage-while serving as a catalyst and bellwether for teams of his or her associates.
The new analytics power players are hard to find; there are not enough of them to go around. They have specialized knowledge that makes them as rare as Stradivarius violins and almost as valuable. They know that the best analytic solutions are integrated with sophisticated marketing communication tools to ensure that messages sent to customers are relevant, timely and arrive at the right moment. This engages customers with meaningful dialogues and helps to drive profitable relationships.
For these reasons, retailers must invest in data warehouse-driven analytics to uncover opportunities to reduce costs and drive revenue growth. Companies have uniquely valuable information assets which, when examined and leveraged through detailed analysis, can contribute dramatically to positive performance. So, specifically, on what type of analytics should retailers focus? The following are ten important types:
1. Frequent shopper programs
Current economic pressures are an opportunity to refocus the retailer’s loyalty toward the customer, with targeted offers and benefits. Most retailers under-use the card database and leave opportunities “in the box”, when they could be mining for opportunities that are most meaningful to their best shoppers today. For instance, retailers should be using analytics to ensure that promotional funds are directed at the right customers (the big spenders or most profitable) and not the wrong ones (the “cherry pickers” or least profitable).
2. Multi-channel marketing
Being able to engage the shopper with a consistent experience across all channels of business requires that all channels leverage the same customer information. For example, shopper card programs-where most shoppers see benefit when shopping in the store and getting discounts at the register-could also segue nicely into e-retailing, e-mail marketing and even mobile marketing. This could allow for targeted offers such as e-coupons to be directed inexpensively to shoppers based on their shopping behavior, demographics or need. Tracking these promotions electronically offers a wealth of insights that can be plowed back into subsequent promotional plans leveraged by all channels.
Mobile Marketing for Convenience
3. Mobile marketing for convenience
Delivering promotional offers when the shopper is most likely to be interested in the product or service is the goal of most marketing campaigns. The proliferation of mobile phones provides retailers with an opportunity to extend an offer or other relevant communications at a time and place when the consumer is more willing to respond. Technology now exists for retailers to target a shopper approaching a store, for instance, with an offer designed specifically for them based on their previous shopping behavior and interests.
4. Sales analytics support highly effective customer engagement
Technology cannot supplant a salesperson, but it can help. By providing retail staff with better tools to engage the customer, the customer is more apt to buy as the retailer wants them to buy. Among the tools being deployed are PDAs with access to customer history and mobile POS systems that reach customers fast. The wild card factor in this could be smart phones loaded with custom applications that support store-level service.
5. Fresh take on space planning
Shelf space is prime real estate and needs to be treated that way. New tools permit retailers to optimize category and section space on a macro level, delivering greater return per foot while allowing for store-level differences. Tighter item assortments may follow, where they boost section productivity without disappointing target shoppers. This is a great opportunity to improve “customer centricity” and tailor store assortments at local level.
6. Pricing optimization analytics
Price management and optimization software helps retailers decide what cost increases to pass along to shoppers and when. In some instances, it may be less injurious to the retailer’s bottom line to hold a price or pass along only part of a cost increase on high-profile items.
Improve Profitability Analytics
7. Improve profitability analytics
Create more accurate measurements of profitability by calculating a behavioral-based, enterprise-wide view of value-by customer, product, sales channel or organization. Now more than ever, retailers must see the whole profit picture! Learn which relationships to fight for, which to grow and which, if any, may be futile.
8. Use integrated analytical and operational CRM tools
Integrate your customer relationship management (CRM) programs so that analytical insight is actionable in near-real time for extremely relevant, customer-facing operational communications. Get the software integrated and connected to POS, Web, kiosk, e-mail, mobile and call center, so that you can not only respond to customer selling opportunities, but the information you get from the interaction can be captured and transformed for additional insight and leverage.
9. Improve forecasting and demand chain management
Use more detailed, integrated and timely data to more accurately forecast demand. Having a more accurate view of demand at the store level enables retailers to reduce out-of-stocks (preserving sales), while also minimizing inventory (reducing costs). The result is stronger, more collaborative relationships with suppliers while improving customer interactions and relationships.
10. Remember: analytics run on data!
We strongly encourage business evolution to an enterprise data warehouse, so that your analytical intelligence is based on a complete view of the business, not a partial view. An enterprise data warehouse improves accuracy and reduces the time to deliver knowledge across all organizations. However, even more to the point, the quality of your analytical intelligence depends on the quality and completeness of your data. The data is the heart of a company’s knowledge assets and must be integrated for a holistic picture.
Data environments vary widely as to the quality, scope and freshness of data they hold. Data sources for analysis can range from a single data mart to a motley collection of data marts-and extend upward to include a centralized, cross-organization reservoir of detailed, dynamically-refreshed data: the enterprise data warehouse.
The environment chosen by the analytics power players of tomorrow is profoundly important to the quality, scope and freshness of intelligence that they can deliver and act upon. It matters immensely. That’s why the analytics power player wants rapid access to enterprise-class intelligence, which is integrated from across the whole organization and centralized, for a complete and accurate view of the core business and its many constituencies. It is fed continuously from data portals, transactions and countless sources, and includes deep histories of data detail while also absorbing new data in real time.