While many retailers look at the environmental and experiential factors to determine their strategies, there aren’t many who look at data. Data holds enormous potential for retailers to improve outcomes but for so many, data is a hindrance. They have all the data they need, they understand the value of having it, but they just don’t know what to do with it.
Data overload and limitations of in-house teams
The idea that retailers are suffering from data overload stems from the fact that they don’t have in-house teams with adequate knowledge and capabilities to process the data and apply it to the business strategy. And it’s easy to see where the thoughts about overload come from – there is all types of data coming from many different places including weather forecasts, social media, competitors, in-store and online channels. For many retailers, it’s much easier to just maintain the status quo than to look at making improvements in data warehousing and management. But when agility is limited because of cumbersome data management and legacy technologies, keeping their heads above water will become more and more of an everyday challenge.
When retailers are faced with this challenge, they tend to react in one of two ways. They either misunderstand the opportunity and therefore don’t do anything about it, or they allocate more resources or headcount to process the data and make it work for them. But that doesn’t work. Simply adding more people into the equation is not going to help, mainly for the following reasons:
- Speed and time: Retail today is running at digital speed and it takes too long for analysts to find all of the answers needed. When they do get the right information, it is already out of date. This then needs to be followed by evaluating the data to determine the best course of action.
- Scale: Decisions in retail have to be made quickly and considering the speed it takes to get to the right and actionable insights, there is an uphill battle before the process even begins. An army of people would never be able to manually – or through traditional algorithms – make decisions at scale to keep a retailer’s business competitive.
- Cost: Requesting metrics from internal teams doesn’t just deplete time – it’s also costly financially. Recruiting, hiring, training, and continually sourcing people is an expense that can be better allocated to other areas of the business.
- Accuracy and aptitude – Even the best data scientists and analysts make inaccurate predictions due to limited understanding of factors. Building a bigger data team, or hiring non-grocery analysts with the right know-how takes time, and time is something that retailers can’t afford to lose.
What retailers actually need is the data warehouse, technology, intelligent applications, and know-how. That’s the only way that they can make sense of the wealth of data that is available to them.