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Enterprise Marketing & Promotions Management

Predictive Analytics: Business Intelligence and Forecasting for Retailers and Grocers

By / June 2016


To remain competitive, retailers and grocers need to have keen insights into product assortment, quantities, sizes and pricing, to name just a few. Big Data that’s organized and analyzed properly can be leveraged for predictive analytics, providing business intelligence to maximize customer satisfaction and sales.

Predictive Analytics for Business Intelligence
Predictive analytics is the use of data mining, statistical algorithms, modeling and machine-learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond what happened in the past to provide a best assessment on what will happen in the future. Predictive analytics streamlines decision making and produces new insights that lead to better actions. An example of predictive analytics in action is Amazon.com’s carefully curated recommendations based on predictive algorithms encouraging visitors to buy more or add to their wish lists.

With faster computers and interactive, user-friendly software, more organizations are turning to predictive analytics to increase their bottom line and competitive advantage. Top uses for predictive analytics include identifying trends, understanding customers, predicting behavior, improving business performance and driving strategic decisions.

Up to now, transactional data and information from past purchases have driven promotional marketing and targeted email campaigns. When this historical information can be combined with behavioral data, predictive analytics can forecast outcomes that are important to retailers, such as cross-sales opportunities.

The issue is that the behavioral data is unstructured. Much of the information is text-based, found in social media posts or blog articles. The data describes more than simply what happened but looks at why it happened. Scrubbing, formatting and organizing the data is time-consuming and labor intensive but critical for retailers to make reliable predictions.

Another component that’s vital to the success of predictive analytics is for retailers to define a goal and work backwards.

RetailPro, a retail management software provider, gives an example of a retailer with a goal of selling more sweaters. The retailer must determine how it can add value by learning which of its customers or look-alike customers are buying sweaters, which types and when.

The campaign will leverage predictive analytics by segmenting shoppers. Each day in the fall as winter approaches, a different set of customers will receive a personalized email based on preferences inferred from past purchasing, or even browsing experiences. Simultaneously, the system tracks which customers have bought sweaters to offer additional rewards or to avoid spamming.

For supermarkets and grocery stores, Express Analytics, a cloud-based marketing analytics firm, describes top five uses of predictive analytics for food retailers.

1. Promotions
Through their online and offline activities, shoppers give details about themselves to retailers. Based on website clicks, coupon usage, frequency of visits and payment methods, grocers can anticipate customers’ needs, building marketing messages that include past purchases. Thanks to predictive analytics, retail grocers can better understand customers, their spending power, and shopping habits to align with promotional campaigns.

2. Shopper Targeting
Shopping is an activity that forms a pattern over time. By including more granular data such as customer demographics, retailers can create focused, customized offers, aimed specifically at a particular shopper or target audience.

3. Campaign Management
Predictive analytics arms grocers with vital data to develop efficient marketing campaigns that produce sales with more fine-tuned budgets. Supermarkets can better target consumer segments to improve results of each marketing program.

4. Pricing
Predictive Analytics can answer key questions that are critical for retailers using value pricing as a tool to pull in customers:

  • What is the correct price-point to gain the most sales?
  • How often should we launch price-based promotional activities?
  • What would be the impact of competitive pricing on sales?

According to Express Analytics, “Pricing is one area where the use of predictive analytics starts to show results in about six months, helping a retailer achieve about 5% increase in margins.”

5. Inventory Management
Predictive analytics helps food retailers remove the uncertainty from inventory management. Eliminating decisions based on hunch or past sales, predictive analytics accurately anticipates demand and suggests better replenishment strategies. In addition, grocers often find that a particular product was missing from their stores, and when added, benefit from greater sales. Inventory imbalances are removed and the overall results are a decrease in inventory costs and an increase in sales.

Grocers and retailers can leverage predictive analytics in many areas of their operations, both customer-facing and at the back-end. These analytics can be used to develop more relevant promotions, price, product assortment, and ultimately result in happier customers.


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