It wasn’t that long ago that consumer packaged goods (CPG) companies relied primarily on advertising and promotions and then let the basic principles of supply and demand control pricing with consumers.
But much has changed in recent years as today’s mobile consumers have become empowered by immediate access to information through their smartphones and instant access to the web and social media channels to guide their product purchases as well as dictate pricing via showrooming.
These changes are creating massive disruption for CPG companies struggling to determine the most effective techniques for attracting and retaining today’s discriminating customer.
Still, the flood of data that consumers are sharing across the digital spectrum is also creating immense opportunities for CPG companies to connect directly with end customers and provide them with personalized and relevant offers.
According to the following TIBCO Spotfire infographic, 53 percent of U.S. consumers already trust and buy directly from CPG companies. Meanwhile, 40 percent of CPG companies expect to sell products directly to consumers, up from 24 percent in 2012.
To help take advantage of these direct sales opportunities, organizational leaders for CPG companies can use predictive analytics and customer data in a number of different ways to engage with and convert would-be customers.
For instance, product owners and business leaders at CPG companies can analyze consumer sentiment in social media channels as well as survey results and traditional feedback mechanisms to help determine the characteristics that matter most to customers and prospects such as price, quality, value, etc.
Organizational leaders can then use these insights to guide decision-making on product pricing, selection, messaging, and other strategies.
Because consumers use a variety of digital channels – including web, email, mobile, and social – CPG leaders can also use predictive analytics to determine the most effective messaging to use and the channels in which to deliver that messaging, based on behavioral data.
For example, a manufacturer of household cleaning products is able to determine that a high percentage of the target customers it’s trying to reach (females from 34 to 59 years of age) are most likely to respond to SMS-based offers that are presented while the consumers are in-store.