Retail has some of the tightest margins, and is one of the greatest beneficiaries of big data. Retail use cases can create a 360-degree customer profile, optimize stocking strategies, maximize pricing, and help retailers increase profits and cut costs. And retailers tend to know more about their customers. Target, for example, has gathered detailed data about their customers to use with big data analytics to shape new product lines and new marketing campaigns.
Big data can mean big money for retailers. McKinsey reports that using big data analytics a retailer could increase operating margins by more than 60 percent. And when big data use cases are used to support sales and marketing, the ROI averaged between 15 and 20 percent.
Case 1: A Retail Use Case for Predictive Analytics for Consumer Demand
Creating a use case is one of the first steps in any big data initiative. Retail use cases define the scope of the question you are striving to answer in terms that make it easier to define the scope of the data and the logic behind the analytics.
For example, using retail use cases Target was able to pinpoint when a customer is pregnant by the vitamins they purchase so they can market more maternity goods. Rather than asking a broad question such as “what do pregnant women buy?,” they defined the use case with a more narrowly formulated question, “Can you determine if a customer is pregnant, even if she didn’t know?”
Target marketers understood that getting new parents early, before the baby arrives, would make them long-term customers. Analyzing data from Target’s baby shower registry and customer profile information, Target marketers started to see emerging patterns and identified 25 products that were assigned a “pregnancy prediction” score. Eventually, Target was able to create a national database of tens of thousands of women who were likely pregnant based on their shopping habits.
The beauty of using big data to understand consumer behavior is you are not limited to analyzing the customer data you have in hand. Big data predictive analytics can include unstructured as well as structured data so it’s easy to include other revealing data streams, such as social media conversations, online shopping patterns, email traffic – anything relevant to consumer behavior.
Case 2: Predicting Stocking Demand
Profiling consumers is just one way retail use cases can drive profits from big data. Big data can also help predict product demand.
For example, a big box retailer is interested in predicting upcoming demand for video games. Consolidating data such as past game sales, social media buzz, movie ticket sales, gaming industry marketing spend, and other factors can be used to predict demand for new video games.
Using the video game use case and big data analytics, the retailer can create a predictive model that not only shows demand, but predicts online versus in-store sales and where demand will be greatest. The same data can be used for pricing optimization, competitive analyses, and other factors, and it makes it easier to optimize the supply chain.
Case 3: Pinpoint Marketing Strategies
Another popular retail use case is to refine marketing programs. Big data analytics can tell retailers where to spend their marketing dollars for maximum return, whether it’s in advertising, social media campaigns, direct marketing, in-store promotions, or some other channel.
Big data also is having a huge impact on retail personalization. Using big data analytics and technology retailers can deliver a highly personalized online shopping experience, directing visitors to personalized offers based on data gathered and processed in real time. A blog posted on the Harvard Business Review notes that personalization increases sales by 10 percent and delivers five to eight times ROI.
Many retailers are looking forward to Next Best Offer (NBO) technology. NBO uses real-time analytics to present offers to consumers via smartphone. In-store mapping is gaining popularity and when you combine real-time mapping with mobile offers the sky’s the limit.
Case 4: Better Security
With the recent data breaches at Target, Nieman Marcus, and Home Depot, data security is becoming a greater concern for retailers. How can they effectively protect consumer information? And how can they minimize loss due to fraud?
Retail use cases to identify fraudulent behavior are an excellent application for big data analytics. Using data from point-of-sale, sales projections, warehouse movements, return rates, and other sources can identify anomalies that could point to fraud. And big data analytics are an effective tool to protect the enterprise from a data breach.
These are just four retail use cases to consider. There are countless others. Retailers can both realize a substantial increase in revenue and protect themselves using big data analytics. What is the biggest big data opportunity for your retail customers?