Classic retail business design involves choosing a product, determining the proper price through supply and demand analyses, and setting up a distribution channel.
One of the earliest examples of a famous retail operation is the Sears, Roebuck, and Company. They are famous for their longevity, as well as their famous catalog, which has been published for over 100 years.
Interestingly, modern marketers are stupefied when they realize that Sears delivered their voluminous catalogs free of charge, even to customers that rarely made purchases. They immediately imagine Sears’ retail profits being eaten up in their catalog-publishing department.
Although the market environment once allowed this type of operation, modern companies must streamline every part of their marketing, including catalog advertisements.
Does this mean that Sears could have categorically stopped their catalog publications, expecting customers to transition smoothly to online catalogs? Probably not, unless they were prepared for the kind of revolts the Greek government is experiencing.
Some Sears customers only use paper catalogs, and others still prefer it even though they have online access. How can Sears know how to design a transition to online catalogs? The answer lies in business intelligence for retail.
Traditional Methods for Retail Analysis
If Sears were trying to decide whether to abandon hardcopy catalogs, they might start by asking their customers through surveys. Although this might be a good strategy, customer surveys are biased to the imagination of the customers.
Many will simply say they want catalogs because they might somehow need it. It costs them nothing in any case. For these reasons, a survey is not a good way to determine the true customer priority of whether they would prefer a catalog to having lower prices.
The sure way to determine customer action is to observe the customer behavior after a change. For instance, Sears could decide to discontinue the catalog for one month and observe a single parameter: revenue during that month.
However, to keep other parameters from affecting the outcome, they would have to freeze all other marketing campaigns, including sales, promotions, or changes in product offering.
Even if they could freeze the controllable parameters, their study would still be subject to uncontrollable factors such as seasonal buying patterns, the changing economy, and changing customer demographics.
Using BI Platforms for Multivariate Analysis
The solution is to use a business intelligence (BI) platform. These platforms use statistical analysis techniques that can take into account multiple parameters that are changing at the same time. The analysis platforms can use all of the big data from the retail outlets, and determine the sensitivity of revenue to catalog availability.
The bottom line is that some customers will miss the paper catalogs, but Sears may still be justified in discontinuing catalog publication for the best overall customer satisfaction and the best profitability. The big data can even determine the effects of a tradeoff solution, such as limiting catalogs to customers with recent purchases.
Product shelf space is also a critical decision in modern retail. In decades past, stores would stock any items that customers requested. This was sufficient for a low competition environment.
However, modern companies cannot wait for a customer to request an item. If the industry is already supplying the item, an individual store cannot risk having a customer come to the store, then leave in disappointment to buy the item from a competitor.
Conversely, if a customer asks for a specialty item that no one else will buy, a store may need to disappoint the single customer in order to maintain low prices for remaining customers.
How can a store make such a complicated decision? Business intelligence platforms supply the necessary algorithms to optimize profitability through product offerings.
In fact, they can accommodate a third dimension, where a particular product may have too low a demand or too big a footprint to keep in local inventory, but can alternately be offered online.
These are just a few of the examples where business intelligence for retail can take advantage of the great amount of available data to yield critical decisions. Modern companies can use business intelligence to help optimize all of the facets of retail business design.
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