Sometimes we are not aware of the effect that a small error repeated repeatedly in the stores of a retail chain can have. That is why it is essential to detect points of improvement that guide the company to a continuous improvement based on the analysis, detection and proactivity of our teams.
The most important thing is to understand the importance of any detail in the stores.
According to Edward Lawrence and his Chaos Theory, there are systems sensitive to small variations that generate large unpredictable changes. And stores are precisely these systems where constant changes occur, internal and external, to which customers are sensitive.
That is why, sometimes, invoicing can go down for no apparent reason. Where is it failing? Is it really a failure, or is this a seasonal bump that could be expected? Has anything changed recently that has induced this change, or is it due to an external factor? Can we estimate when it will revert? And above all: Is there anything that can be done now to change it?
All these questions demonstrate the importance of measuring, tracing, detecting and acting on the data fluctuations that may occur in our store.
But this task is as important as it is subtle, and often requires measurement and evaluation tools for each store, as well as specialized algorithms to detect correlations that are not obvious or intuitive to the human analyst.
The first thing, of course, is to have the correct metrics defined. This is something that, more or less, every company has. These metrics usually fit into the following classification:
Objectives: One, two or, at most, three indicators that mark the final data. They are usually highly related to turnover and customer loyalty. They act as a digital beacon, the guide towards which all the actions of each level of our company should be directed.
Other KPIs: Beyond the objectives, there are a multitude of additional metrics that we
help shape the health status of our company and each of its stores. These are
data that must be defined by the company itself, showing those aspects of added value that are most important to the company’s philosophy.
The definition of these metrics and objectives is key. Once they are defined, measured and calculated on a daily basis, you will begin to know if things are going well or if they are getting worse. This is where data analysis comes into play. Objectives can go up or down, but why do they do it? This is discovered through the different algorithms that find correlations, sometimes unthinkable for the human analyst.
The solution to optimize and improve the performance of each of the stores of a company goes through the application of the latest developments in technology of Big Data and Artificial Intelligence. This is not just a trend that everyone is joining: this data science is optimizing many businesses in all imaginable economic areas, since it detects precisely these correlations that escape traditional analysis.
The necessary steps to implement this type of solution go through:
These solutions are reasonably expensive to implement, but bring many more benefits than they might cost. On the other hand, fortunately, there are some solutions in the market to provide this service of applied Artificial Intelligence without the need to develop it in the structure of your company, which can be costly both in time and money.
We recommend studying the options that exist in the market and choosing those that adjust to your needs in an agile way, without the need of long developments, but that at the same time offer a personalized data analysis and that contribute value to the company.