Retail Analysis Objectives

The surface objective of retailers for a predictive analysis program is the identification, understanding and early reaction to market and consumer trends.  I see the secondary and probably more important objective for this predictive analysis is the development of collaborative relationships with consumers and the extended market partners.  This collaboration objective will support the retailers’ success and longevity in the omni market in a sustainable manner that eliminates the ‘me too’ practices that are currently prevalent in the market.  These ‘me too’ practices are practiced in place of the creative and collaborative practices and capabilities that are required for retailers to succeed in the future.  It is important though for sustained success to develop a personal relationship with consumers and retail partners that is built on collaboration.

Data is only valuable when it is used as a basis for analysis and confirmation of the analysis.  To this point data values volume in order to improve the analysis and the confirmation of hypothesis.  The challenge of the collection and analysis is that you really do not know where the analysis and the confirmation is going to take you and so you must collect a great deal of data. This is the basis of big data analysis and this is the types of activities at which big data collection and analytics excels and these are the activities that big data encourages.  The value produced from the analysis increases with the volume of data and also the analysis skills, in other words, there is a level of experimentation in this analysis that continuously drives the investigation for validation.  

Fortunately for retailers the omni market retail environment provides a treasure trove of shopping and purchasing data that was not previously available to retailers.  It is important for retailers to collect this information as a matter of course during the operational activities related to completing sales and filling delivery to consumers that can be added to other operational data related to receiving, transportation and forecast data that can be utilized in support of the retail operations.  There is a tremendous amount of additional data available to retailers as a result of the online shopping and purchasing operation which also should be added to the collection of data to provide additional data points for analysis.  This collection during the operational activities would best be supported via a control tower framework that would allow the data to efficiently flow to and from the appropriate containers and processes.

This change necessary now is to recognize both the availability of data and the value of this data in extending and growing collaborative relationships across the entire chain.  There is a treasure trove of data generated by online activities now that has grown in volume and potential value and considering the growth of the omni market in retail this data is continuing to dramatically increase in both volume and potential value.  This requires though that legacy retailers begin to collect the data at a minimum to support the collaborative opportunities that will result from the omni market.  Do not wait while focused on the operational impact of the omni market this will only delay the potential value.

And now for the audience participation portion of the show…

ECommerce will have wide ranging impacts on both the retail and manufacturing sectors.  How can you focus these abilities to improve the consumer's experience?  Improving the consumer’s experience will require a re-evaluation of the sales channels, the manufacturing channels and practices and the supply chain channels and practices from the raw materials to the consumers’ homes.  In order to ensure and maintain success in this new reality you must harness the tools and capabilities in many new areas.  How can you support these continuously changing requirements?