Converting Retail Big Data Into Predictive Insights

Wednesday, June 10, 2020

By Kurt Ramcharan

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.

4. Promotion ROI predictions
Running a promotion can be risky. Even if you take past promotions’ effectiveness into account, there’s no guarantee that your next sale will bring in the traffic you need along with the sales and margin you’re looking for. Promotion-optimization software that analyzes data from your business and external sources can provide you with better intel on whether your next promotional idea is likely to be a winner.

5. Operations and supply chain
The customer experiences you provide reflect how well your operation runs. Predictive data analysis will help you make smart business decisions, such as timing shipments so that your shelves aren’t bare but your storerooms aren’t overstocked. Or, you can access the information you need to optimize labor schedules to support excellent customer service but avoid having staff sit around idly during slow-traffic times.

Running business smarter
To benefit from predictive data analysis, you need to implement the right tech tools. Onsite data centers and in-house data scientists aren’t feasible for many businesses, but cloud applications put predictive data analysis within reach. These easy-to-use solutions analyze data from online and business platforms, as well as your customer and sales data.

Choosing to add a predictive data analysis solution to your business toolset can save you much of the time you currently spend on forecasting, planning promotions, and making operational decisions. Moreover, you can have instant insights when you need them to enhance customer experiences and more fully understand your customer base.

Do your competitors know something you don’t?

Article published in Digitalist Magazine: Converting Retail Big Data Into Predictive Insights

About Kurt Ramcharan

Kurt Ramcharan is the Marketing and Communication Director of Beyond Technologies. As an experienced marketing executive, he is passionate about developing go-to-market and demand generation strategies that blend traditional techniques with emerging technologies. Kurt has over 15 years of experience with B2B tech companies and a deep understanding of the shifting business dynamics within the wholesale, distribution and retail segments. Such expertise enables him to work leading retailers and wholesalers across North America. This experience allowed him to develop a strong knowledge of the supply chain, customer experience, omnichannel operations, and pricing and promotion business processes. With a people-first mindset, Kurt’s approach is about combining human behavior knowledge with the latest technologies to create meaningful digital transformations and engaging customer experiences.