Consumer packaged goods (CPG) companies are underestimating the growth potential for e-commerce with consumers, according to a recent study by Deloitte.
Both CPG executives and consumers expect online sales of food, household products, and personal care products to grow over the next year as well as the next three years, the study notes.
However, consumers’ intent to purchase these products online far outpaces executives’ expectations for both time periods.
Predictive analytics offers CPG executives tremendous opportunities to learn more about the specific types of products that consumers are interested in purchasing online.
This includes recognizing and acting on the changing behaviors of customers across a variety of age groups and income ranges.
For instance, when it comes to making purchase decisions, price is equally important to both higher earners (those with incomes above $56,000) and those who earn less (less than $28,000), as 89% of consumers in both salary groups rate price as the most important factor, according to a study by Ernst & Young.
But perhaps more telling is how the growth and maturity of e-commerce is shaping consumer behavior in other ways.
The growing use of e-coupon and price comparison sites has led 60% of consumers to view online shopping as a “competitive” sport, compelling them to find better prices than their peers, notes a study by Yahoo and Universal McCann.
CPG companies that can uncover behavioral insights about specific customer groups they’re targeting, can leverage this data to help craft strategies on their own as well as with retailers and other players in the supply chain that play off these behavioral characteristics.
Meanwhile, predictive analytics can also be used by CPG firms to help them better understand specific customer segments.
Predictive analytics can also help them identify the right products to offer to the right customers at the right time through the right channels based on demographic information as well as purchasing habits, social media sentiment, survey data, and other available information.
For example, a customer who shops frequently (once or twice per week) with a particular supermarket chain may demonstrate a tendency to purchase the same brand of laundry detergent – but only when it’s discounted by a certain amount.
The CPG that manufactures that detergent could apply the customer’s transaction data – along with other data about customers with similar characteristics – and then use these insights to craft offers for customers in this target group to purchase the detergent online at a specific price that’s calculated to drive a high conversion rate.
This information can be further tested by combining this with other offers, such as a discount that’s based specifically on purchasing a laundry detergent in tandem with a fabric softener.