As with numerous hype cycles that preceded our current preoccupation with artificial intelligence, the time arrives where the rubber must meet the road. That is, businesses come to the conclusion that this ‘magical’ solution needs to be applied to real challenges and issues that the enterprise faces every day.
A recent article by Dr. Mark Chrystal, founder and CEO of Profitmind, a leading retail SAAS solution, succinctly outlines the imperative for retail. Data in and by itself is not useful. Without a clear idea of the problem to be solved, the effectiveness of data analysis is limited. It takes Action Analysis to understand how best to apply machine learning and AI to the retail enterprise.
Data Guides Action
Action Analysis, as a concept, is derived from a scientific methodology called “Action Research”. This is a highly pragmatic approach that aims to simultaneously investigate and solve an issue. In other words, as its name suggests, action research incorporates data analysis to simultaneously achieve and take action at the same time.
A highly interactive method integrating people, technology, and process, action research exemplifies a common-sense way to do effective business evaluation and analysis. Profitmind extends this paradigm of an AI/Machine Learning decision support engine that provides not just data insights, but guides action taken by reflected analysis of the data. But there is more that can be done to address retailer’s business challenges.
Action Principles
The next stage of Action Analysis can be reinforced and supported by integrating complementary and related Action Research principles such as:
- Operational/technical analysis most directly supports Action Analysis and consists of planning, acting, observing, and evaluating actions taken as a result of an expert/decision support tool’s analysis of large purposeful arrays of Retail data and its outputs and recommendations.
- Collaborative analysis is a deliberate effort to compile learnings, insights, and results from Action Analysis across different divisions of the retail enterprise (store, shopper media/experience, logistics, stocking, demand/pricing) using iterated feedback cycles. This is a form of meta-analysis that is frequently overlooked in the retailer’s world, but extraordinarily powerful at a strategic level to achieve improved store efficiency and success.
- Strategic action analytic reflection serves to contextualize systematic integrated processes that are ongoing, using the results of Action Analysis, coupled with strategic reflection of the holistic shopper experience and how the store/retailer can improve and be more efficient.
This varied approach differs sharply from simply ‘analyzing data’ and hoping to draw conclusions or support hypotheses of the business. Action analysis is forward-looking and formative, not summative, which means it becomes a dynamic tool to be used in the real-world flow of the business, to use data and analysis to drive improved actions in an iterative way.
As such, this new paradigm differs in purpose, context, and significance from the data analytic status quo of many retailers, and is a boon for those seeking to implement systematic change and improvement using the power of technology and AI.
