A supermarket sends their customer an e-mail indicating his/her brown sugar is about to finish and they ask if he/she needs some more. The customer thinks: HOW do they know? Well at least, we know they are not reading minds yet; it is just Predictive Analytics algorithms processing Big Data patterns.
Retail industry, by its nature, is usually the pioneer of utilizing Big Data and its sub-disciplines. It is no different for Predictive Analytics. Predictive Analytics leverage big data and enable retailers to plan their inventory, replenishment management and marketing strategies in addition to minimize risk and uncertainty. Let’s see in what ways Predictive Analytics can be used in the Retail Industry;
Improving Engagement through Personalization
When you collect data at every single touch point, smart algorithms enable you to obtain insights on customer purchase history, pattern, preferences, page views, interests and other forms of engagement to create a single customer view. Predictive Analytics adds another value; you can foresee what your customers’ next actions might be and make recommendations about relevant products based on their behaviour.
This might be a well-known online tactic but clichés are clichés for a reason. The customers appreciate relevant suggestions tailored to their taste; therefore these recommendations will increase customer engagement and brand loyalty.
Building Targeted Campaigns
Predictive algorithms collect and analyze data from various sources such as demographics, market insight, response rates and geography in addition to customer insights together. By determining what campaigns would be more successful based on these analytics, marketers can pinpoint the most effective message/product for a single customer. Targeted campaigns will lead retailers to accomplish higher conversion rates.
McKinsey report also shows us that targeted campaigns can deliver 5 to 8 times the ROI on marketing spend and lift sales 10% or more.
Predictive pricing analytics collate demand, product pricing history, competitor activity and inventory levels. And automatically set optimal prices in order to respond to market changes in real-time.
On the contrary to the traditional “season sale” approach (when the demand has already gone), Predictive Analytics determine the optimum time when prices should be dropped and has shown that a gradual reduction in price generally leads to deliver maximum profits.
Don’t we all love to see the listed results when we type just a few letters? Predictive site search will allow users to have a user-friendly shopping experience based on customer history, behavior and preferences. It can predict what your customer is looking for, effortlessly. As customer experience is one of the most important assets for retailers, predictive search should also be prioritized to gain customer satisfaction and loyalty.
Retail Location Selection
Choosing a store location is one of the most strategic long-term decisions in the retail industry. Predictive Analytics can be used to forecast the potential revenue for a selected store location based on demographics, property market, competitive activity, market conditions, customer purchase power, purchase behaviours etc. before investing. These algorithms can also be used to analyze and manage the existing locations.
Anticipating Demand and Inventory Management
In addition to customer behaviours, Predictive Analytics also track economic indicators, promotions, discounts and allocation between stores to optimize stock management and supply chain. This allows retailers to allocate the right products to the right store at the right time and avoid product waste.
Predictive Analysis can be applied to many other areas in the retail industry. It can be a huge benefit to retailers and enables organizations to plan their business in every aspect and response quickly to the market changes. It is important that organizations should integrate Predictive Analysis into both online and offline channels for the big picture and practice omni-channel strategies.