Benefits of Predictive Analytics for The Retail Industry

Running a business in 2020 comes with a considerable number of challenges. Especially when it goes about retail, with huge pools of data that don’t always provide successful results. While retailers try to transform this information into unique ideas to attract customers, competition continues to grow.

The good news is that there is a workable solution you need — predictive analytics.

It has captured different organizations’ support, with a global market that will reach about $10.95 billion by 2022. It is used in banking, healthcare, transportation, and any industry where customer satisfaction plays a crucial role. Netflix and Spotify are among companies that fully rely on data analytics. The retail industry appreciates these tools as well. Amazon and Walmart learn everything they can about their customers to optimize their marketing campaigns and get more profit.

What if we say you can put your name along with these world-famous brands using predictive analytics for retail?

What Is Predictive Analytics?

Brands have conducted a lot of research interviewing customers or promising a discount for several insights. However, predictive analytics for the retail industry will show you: there’s much more to discover. It is because your audience doesn’t know it yet as well. As Steve Jobs once said, “Our job is to figure out what they’re going to want before they actually do.” This is when predictive analytics comes in.

While a lot of companies work on demand, your task is to create this demand. All the ways to succeed boil down to analysis. The analysis of the market, trends, and economic situation — retailers gather all this data and come up with new offers. That is how predictive analytics works. It makes predictions using data mining and modeling tools.

How to Use Predictive Analytics in Retail in 2021

Customer Journey

Algorithms determine how long the client will remain with your company and how you can retain them. From the moment of the first purchase, you already receive valuable information about the client: their email, demographic and geographic characteristics, etc.

Comparing a new customer with regular ones, you can accurately predict one’s value throughout the life cycle. This kind of information helps to make strategic marketing decisions. In this case, predictive analytics in retail is like a sales funnel with efficient stages for your business. The main task is to strengthen all links in the chain.

Customer Behavior

This algorithm analyzes how customers behave when making a purchase. Thanks to it, you will know:

• If your customers use a website or call center;
• If they buy more during promotions;
• How often they shop;
• How much money they are spending;
• The time between purchases.

Analyzing customer behavior, you can choose the right way of targeting and communicating with the client. For example, people who buy products with discounts only are more likely to be interested in all-out sales at extremely low prices. And vice versa, customers who typically buy at full price are perfect for new product promotion.

Big Data for a Personalized Experience

Predictive analytics uses cumulative data to predict what a customer is likely to buy next. Based on this, it generates appropriate recommendations, suggesting a sleeping bag or a set of camping tools when you are looking for a good tent.

• Up-Sell RecommendationsRetailers can offer a larger package or a roll-on promotion for a particular item at a more attractive price. “Order now and get -10%” is a great example of this strategy. Most up-sell recommendations are tied to a specific SKU and offer related products along with the key ones.A classic example is Apple Online Store, which always suggests you better versions while looking for a new gadget.

• Cross-Sell Recommendations

Unlike up-sell recommendations that offer a more expensive option to the chosen one, cross-selling recommendations are formed to show other products bought with popular items.
Your clients will see the notifications saying, “People who bought this item also bought …” or “Hot price! Today only!” Take a look at Zalando, the European eCommerce company. Looking for a new T-shirt, you’ll note a line with “How about these?” suggestions.

• Next-Sell RecommendationsThese recommendations are defined after the customer makes a purchase – they come as a thank-you letter or a letter of confirmation of the purchase by email. Next-sell recommendations are personalized for each client and consider all the information about them, not just their last purchase.
  Mark Jeffrey has described an excellent example of next-sell recommendations in his book Data-Driven Marketing. A hypermarket found out that customers who build summer terraces or gazebos consider buying a grill or barbecue next. Their marketers launched a follow-up campaign, inviting these customers to buy a grill after buying the supplies to build their summer terraces. Using predictive analytics in retail stores, you can get a drastic increase in sales.

Supply Chain Management and Product Distribution

Back-office operation is an area that is often overlooked. Operational efficiency is impossible without optimizing supply chains. Use predictive analytics in the retail sector to answer questions like what to store, when to store, what to discard. You should always have control of long-term storage goods and a stock of popular ones. With this tool, you won’t make purchases based on guesswork.

Trade Promotion Effectiveness

This algorithm determines the brands and products preferred by customers. When a brand launches a new product, it is essential to understand how to promote it to boost your sales and satisfy the customers’ needs.

The analysis of trade preferences provides in-depth data about products from other manufacturers that may interest your audience. For example, people who prefer brand A are also willing to buy products from B or C, but will not show interest in brands like D or E.

Benefits of Predictive Analytics in Retail

At Fayrix, we work with different clients to exceed their expectations. We’re collecting success stories to show that there is nothing impossible with the wise approach. Below are some examples of predictive analytics in retail:

Grupo Bimbo, Mexico

Grupo Bimbo is the worldwide leader in baking, with over 100 brands represented in 2 million stores. Our team has developed a model predicting product demand at POS’s using the stats for prior periods and on different combinations (location, product, group, etc.) As a result, the returns of unsold products were decreased by 40%.

Milavitsa, Belarus

Milavitsa is the biggest and the most famous lingerie producer in Eastern Europe that works with hundreds of dealers selling over 10 thousand catalog items in more than 25 countries.
Fayrix developed a model predicting product demand based on the history of purchase plans and action purchases by dealers. The main challenge was connected with a lack of values and history for some dealers, so our team came up with a model based on similar companies’ statistics. Consequently, the number of slow-moving items decreased by 150-200%.

Final Thoughts

Implementing predictive analytics solutions for the retail industry can take your business to the next level. Moving from raw data to one useful forecast can take months of work. You need to import data from different sources, clean it by removing outliers, and combining data sources. The next step is to develop an accurate predictive model based on the aggregated data using statistics, curve fitting tools, or machine learning. As it’s a complicated process, you need someone to have your back.

Fayrix can provide your business with invaluable forecasts in a few clicks. We automate all data preparation and engineering tasks while you can start learning unique insights. Contact us to provide you with the best retail experience ever.