Converting Retail Big Data Into Predictive Insights
Converting Retail Big Data Into Predictive Insights
IT Data Solutions
People tend to talk about big data as one, all-encompassing technology, but the truth is, you can perform big data analysis in different ways. As a retailer, you are probably familiar with descriptive analysis, which gives you a summary of past data to describe what has happened. You likely use descriptive data analysis to track sales, inventory, and key performance indicators (KPIs). Data analysis, however, can do so much more.
Predictive data analysis uses the data you have from descriptive analysis as well as from other sources to make predictions of probable outcomes. Even if you’ve closely tracked your business’ performance and based your decisions on data, there is still a significant amount of guesswork and risk as you plan for the future.
Big data analysis can help you move forward more confidently with predictive insights delivered quickly and efficiently in areas such as the following.
1. Consumer behavior
If you knew which touchpoints customers would choose next to engage with your business, when they would engage, and which items they’d find most appealing, you’d have a head start on converting those sales. Predictive data analysis can’t tell you with 100% certainty where to meet each client to nudge them forward on a shopping journey, but you will have greater insights into, for example, whether clients are responding to social media posts at certain times of the day or week, shopping on their smartphones, or making in-person shopping trips.
Predictive data analysis can also reveal consumer patterns that can guide retailers’ decisions about the feasibility of offering products on a subscription basis, pay-per-use, or other model based on consumer preferences.
2. Personalized experiences
Insights from predictive data analysis can also delight customers with personalized service and product recommendations both online and in-store. As soon as a customer logs onto your website, they can be greeted with offers and deals that will resonate with them.
In-store, your team equipped with mobile devices can access that information anywhere on the sales floor to personalize service. These insights are based on more than just customer histories. They combine that information with data from online browsing, loyalty program participation, in-store beacons, social media, and other sources to create an “omni-picture” of your customers, understand them better, and perfectly tailor experiences to what matters most to them.
3. Sales forecasting
Retailers walk a tightrope when it comes to forecasting sales, ordering inventory to meet demand, and reserving capital to invest in other parts of their businesses. Predictive analytics can replace time-consuming manual methods of retail sales forecasting – and analyze larger volumes of data from diverse sources to produce a more accurate forecast.
