Over the last decade, companies working in the artificial intelligence (AI) space have attempted a myriad of applications, experienced thousands of successes as well as failures, and started to fine-tune what AI along with machine learning can do. In many cases, retailers have seen a return on investment in in-store applications–which product lines may be successful where, how much of a certain product to stock when, or where to place items within the store that encourage spontaneous buys. The challenge moving forward is how to go beyond that by using sophisticated data intelligence to find the “devil in the details.”
Although certain data points in and of themselves may not signal anything important, used in combination with other internal and external knowledge, those data points may help a retailer know what not to do as well as what to do. They may solve puzzling legacy riddles that lead to lost profitability, identify completely new opportunities, or support counter-intuitive strategies for future initiatives.
Looking beyond the macro trends
Even as far back as five years go, the experts were already warning companies not to forget about the exceptions when analyzing data. An article in CIO Magazine reported that “businesses often focus on the trends and then shape solutions, products and user experience based on trending customer preferences and desires. The problem is that a customer trend or a majority percentage doesn’t represent everyone. There are always exceptions and those ‘exceptional insights’ can often show businesses new ways of doing things or open a door to a surprising opportunity.”
For example, at the advent of the omnichannel movement, retailers locked into trend data may only have seen that a certain number of customers purchased in-store while another percentage purchased online. Assuming that those customers were siloed in their respective channels, retailers may have focused resources on the single growing channel alone.
However, once retailers understood that an increasing number of shoppers were actually browsing in-store and then researching and purchasing online, that created a shift in priorities to support that unique customer behavior and capture additional profitability that may have been lost without that insight.
Today’s data intelligence systems can provide a much deeper look into the data, helping to identify those things that are compromising a business’s path to profitability. Not only can this data help organizations find solutions to existing problems, but opportunities where bleed exists at everything from the production level, through stocking, and to constant availability.
Expanding scope to broader insights from third-party data
Although many businesses are improving their ability to collect and analyze internally generated data such as sales performance, customer preferences, and purchasing behavior, few have figured out a way to refine insights using third-party information. This oversight can lead to missed opportunities for greater profitability.
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For example, data gathered from a single store may help managers determine whether to stock more or less of two items it already carries based on past sales, how many cashiers are needed on a weekend vs. a weeknight based on customer traffic, or where to place seasonal items based on shopper behavior.
Expanding the data repertoire to include information from multiple locations within a single chain introduces opportunities to test different products in separate markets and then offer the highest performers in all markets. It creates an internal supply that can rebalance excess supply at one location with excess demand in another.
Incorporating external, third-party data reveals opportunities that cannot be seen at all with only internal data. For example, consumers’ rising interest in sustainability and the growing trend for a zero-waste living may fuel a retailer’s interest in establishing a zero-waste or sustainability zone that offers customers easy access to products that match their desired lifestyle.
Disposable income purchasing patterns, weather data, and a host of other external data sources can potentially accelerate a retailer’s path to profitability if incorporated effectively within a data intelligence solution.
Spotting cultural and behavioral cues
Having the right data intelligence can help retailers recognize cultural and behavioral cues before they become prevalent–and obvious to the competition. Take a deeper dive into detailed consumption patterns and discover counter-intuitive models that can then be effectively navigated to generate profit.
For example, the recent global pandemic obviously resulted in consumers staying closer to home. Restaurant delivery and curbside pick-up of purchased meals increased as well as the shift to preparing meals at home.
However, sophisticated data intelligence noticed that demand for gourmet, international, and hard-to-find ingredients began to rise in certain markets as boredom increased and home chefs began exploring complex and exotic meals to satisfy both their culinary curiosity as well as a desire to hone cooking skills. Retailers who respond quickly to such cues can not only have these ingredients on hand as demand increases but market to that growing desire.
Creating the right internal data narrative
According to McKinsey, 70 percent of all major transformation projects fail. Often, data-led transformations experience this fate due to the lack of sufficient buy-in from internal stakeholders. From defining investment needs to laying out a strategic direction, data-led transformation must begin with the right “data culture”. While data can reveal and guide, the finer details of execution must reside with the individuals who are attuned to the business objective.
To learn more about how to use data to spot the spoilers and uncover those details that may be hampering your productivity, reach out to us.
Hypersonix is the leading AI-driven Actionable Intelligence Platform for Commerce. Our operational intelligence infrastructure for commerce enables decision-makers with rapid visibility, predictive insights, and suggestive actions—leading to actionable decisions and increased business agility.
