There’s no arguing that big data is helping drive efficiency and insights in various industries. And retail is no exception to the rule. There are certainly challenges involved, but overcoming them can lead to huge gains in increasing revenue and reducing costs.
Retailers can use big data and analytics to collect and analyze structured and unstructured data at every stage and aspect of their business process and optimize everything from spotting popular products to predicting sales, demand and finding best prices. The correct use of big data translates into increased sales and reduced costs.
Understanding customer behavior
Big data enables retailers to monitor and understand customer behavior. No more need to make educated guesses based on gut instinct. The right data collection practice will help retailers to understand and predict customer’s buying habits and preferences.
Behavioral analytics is the science of studying customer actions and finding patterns and actionable insights. The data in this category encompasses everything that a customer does, including clicking ads, viewing items, queries, adding items to the shopping cart (as well as removing them), and making a purchase.
Having visibility into the shopper’s journey will enable you to discover pain points, such as which search terms aren’t providing customers with the results they need. It can also give you a better picture into what actions customers perform before making a purchase. These data can help you improve your conversion rates by optimizing your platform for better user experience and providing product recommendations that are more likely to fit the needs of your customers.
A handful of tools offer customer behavior analytics, with some being more efficient than others. If you’re using Salesforce, the platform recently launched Einstein, an AI engine which uses customer behavior analytics to show shoppers more relevant products. Another option is to use specialized business intelligence and behavior analytics platforms such as CoolaData, for fully managed data warehouse and advanced analysis. After all, you need to provide insights-driven shopping experience for your customers.
“Shoppers crave a particular experience when they shop, whether offline or online. An attractive display of product items and information, a good price and easy checkout experience, a feeling of a personal relationship by receiving a personalized discount or personalized offer — each action is a vital step in the shopper journey to purchase and feel that we’ve received a pleasant experience that leads to results,” wrote Tsahi Levy, the CMO of CoolaData.
Predicting trends and forecasting demands
Today, retailers have a wealth of tools that can help gather data and understand what shoppers are looking at, discussing and buying.
E-retailers in particular will have access to a richer dataset, which includes which products the customers viewed or added to their carts before changing their mind. They can also gather information from the Internet and social media about what products are being most talked about. This can help them make precise decisions about which items should be presented as “trending” and which should be introduced as up-sells.
Thanks to artificial intelligence and the Internet of Things, brick-and-mortar retail can also make its own foray into big data and analytics. Computer vision algorithms, connected cameras and sensor technology help retailers to gather information about areas and products that are most visited and get better insights into how customers interact with their wares.
Enterprise social media analytics platforms such as Crimson Hexagon gather data from social media platforms to glean insights on specific products, a retailer’s brand in general, competitors, as well as sentiments on different aspects of the retailer’s business. These insights can be beneficial to both brick-and-mortar and e-retailers.
Taking the insights a bit deeper, retailers will be able to find out which one of their products are performing better, which are performing poorly and they should get rid of. Predictive analytics will give them insights into how they should pre-order to have the right amount of items in stock without overbuying and making inefficient use of storage space.
Such decisions can be critical in times such as the holiday shopping season when customers indulge in shopping sprees and a wrong or right move can be decisive for the success of a business. The insights big data provides can also be crucial in finding general seasonal habits and getting the stock prepared in advance.
Platforms such as Halo use predictive analytics to help retailers optimize their procurement and supply chain management. An interesting case study is Stage, an American store chain that used big data and predictive analytics to speed delivery, personalize customer service and improve its bottom line.
Big data helps adapt dynamic pricing schemes instead of sticking to longer periods to make decisions. In fact, big data played a crucial role in optimizing product prices for retailers in the previous Black Friday sale. The results were impressive in customer numbers and sales figures.
Where to start
Despite all its benefits, big data is not a magic wand, and collecting data without planning and knowing what to do with can end up doing more harm than good. Having access to the right knowledge and tools is key to success. A good place to start is KDNuggets, a website that is dedicated to data science and analytics trends and a good source for both introductory and advanced material on big data. NGData’s has also compiled a list of useful resources about big data analysis.
Big data is a big deal. Its correct use can shed a much needed light in the dark tunnel of retail business.
This post is part of our contributor series. It is written and published independently of TNW.