Store brand sales can grow to existing customers. However, investments to attract new customers may pay large dividends. Learning how to reach new customers will not be found in store sales data. Serious marketing research is needed.
If you mix useful information with garbage, the results often stink. Advocates of Big Data often forget that they need to consider the quality and potential value of all the data they include in their analyses. Collecting non-probabiliy samples, adding unrelated observations and mining the set for value can put firms on the wrong paths.
Category management helped identify some low-hanging fruit for store improvements. The process has some fundamental errors that will produce misleading recommendations when it is pushed to its limits. Academics have listed many problems with the process, but the industry still needs to apply the fixes. We need to move to category management 3.0 to get those improvements into the system.
Where personalized pricing (i.e., negotiated prices) makes sense, sellers will probably need higher base prices. Moving base prices higher and making them credible will make a varying discount strategy attractive to buyers. However, online price comparisons will limit this to only a few industries and markets. Customers will share transaction price information and will move toward sellers with lower quotes, driving transaction prices down.
Banks initially gave people incentives to use ATMs. Then some tried to charge extra for seeing a live person. Now ATM fees are common and online banking is being incentivized. Self-checkouts fit into the service mix for many grocers. People may perceive it as faster to scan their own purchases instead of having a professional scan them. Issues of technology breakdowns, higher shrink, lower front-end sales, costs and customer acceptance should limit adoption of incentives to use self-checkouts.
Good marketing research is hard. Many analysts are unaware of methodological problems or take unwarranted shortcuts. Often the most useful information comes from exploratory research surprises (i.e., "data science"). Any "Big Data" findings need traditional research verification. Thinking "insights" should be reserved for fact-based strategic recommendations which may go beyond the backgrounds of research analysts who know the methods involved. Managers with a strategy focus usually have limited technique training. They should heed the advice of those trained in research design. Information from researchers can be turned it into insights but strategists should not do the research themselves. Data can only be stretched so far (e.g., what "insights" can a localized, non-random survey of 100 shoppers suggest?). A shopper marketing team needs to divide labor (researchers and strategists) and respect each member's role. Over time, researchers probably can learn strategy easier than strategists can learn methodology. Conflicts between predictive analytics and survey/experimental methods tend to deal with method biases and costs to address a question. The best technique varies by question.
Developing and executing surveys is very, very difficult. Too many people think they can do it themselves and can properly interpret the results. To get useful insights from survey results costs more and too often retailers try to economize.
Hopefully Macy's conducted extensive surveys about customer reactions to this technology. There still are many who do not want RFID to be used in this way. The privacy implications of tagging may concern enough people to hurt Macy's sales.
True loyalty (not purchase frequency) is influenced by many things. A weakness in any one area will hurt loyalty. Consistent messages are important, customer experiences interacting with staff and store are important and many others must be maintained at high levels. One weakness will become the LLF (loyalty-limiting factor).