Retail intelligence combines technology and analyses to help retailers optimize their business functions and increase profitability. It uses customer data and predictive analytics to inform business decisions, get insight into the competition, and identify new opportunities.
Retail data is big data
Customer interactions happen in a lot of ways for retail businesses. Picture a big whiteboard in your mind. Over on the left are people; lots of them. Those people have a range of devices connected to different networks, systems, channels, platforms and apps.
Over on the other side of all that technology, fiercely competing for consumers attention, are retail brands in every conceivable market. These retailers want to access those customer data sets, blend them with each other, and ingest the data into their own systems. Why? To study the totality of customer interaction and turn it into insights that inform decision making around things like new site selection and supply chain management.
Of course, this is a deceptively simple picture of a very complex process. All that technical variance at the beginning of the chain makes stuff hard enough. But it's the sheer volume of retail industry data and understanding how to use it that makes finding a comprehensive BI solution so hard.
Data sets for retail intelligence are of varying quality
The starting point is to understand what kind of customer data you want to work with and where that comes from. The most valuable type of data is first party data a.k.a. data you own. You can also get first party data through a willing telco partner.
Second party data belongs to someone else. It's your supplier's, or your partner's. Maybe it comes out of an app or a digital marketplace. It's useful because it helps create a fulsome picture of customers, but it comes at a cost, supply issues are common, and data quality can vary.
Third party data is collected by companies that do not have a direct relationship with the consumer. On the upside, third party data is always available. On the downside, it can be less reliable than first party and second party data. That's why Unacast has a diligent focus on accuracy, with our team of data scientists spending years perfecting our methods of extrapolating data into meaningful aggregated data sets. Read more about our methodology.
Retail data analytics is a fragmented market
When markets are large, growing and fragmented, as the retail data analytics market is, that's good for investors, entrepreneurs and, in theory, clients. The idea is that all that investment and innovation results in competitive market conditions, improving products and the deals to be had.
Unfortunately that same state of fragmentation can be an impediment for retailers on the buy side of the retail intelligence and predictive analytics market.
There's a lot out there. Some of it will work with your internal systems, some of it won't. Is the most important thing the data that feeds your analytics, your ability to visualize it and report on it, or something else? Do you need a data analytics solution specific to your retail niche?
Answering these questions before you start looking for data analytics solutions will help shorten runways to sourcing, implementing and commercializing the technology.
Interested in how location data can be added to the equation?
Footfall data and a good understanding of mobility patterns in catchment areas are some data inputs in retail intelligence that can add a lot of value for retailers. Unacast can help you get started.