There's a whole new world of marketing out there folks. The old ways of reaching prospects and reeling in customers are no longer enough. TV, radio, print and even internet ads are the price of admission for companies trying to reach and engage customers. Successful marketing professionals now have to contend with social media of many different forms, in-app mobile advertising, and community development and management.
Fishing for Leads
The increase in the complexity of the marketing environment has yielded new software tools such as marketing automation and community management. Social media management and analytics help deal with the scope of social media marketing while campaign automation tools have proliferated like seaweed in a warm ocean. Big data and analytics tools have also become a more common part of the marketers’ toolkit. These tools try to bring actionable insights to marketing professionals.
The core problem though is scale. There are just too many prospects to reach affordably. It is too expensive to target the world. Social media, which casts as wide a net as possible, yields too many responses or too few good ones. Too few channels restricts who hears the message and too broad a message will attract people who have no intention to buy.
For marketing professionals, consumer product marketers especially, the ocean is too big and they need to trawl only where the right kind of fish are grouping. The answer to this is predicative analytics. Predictive analytics applied to customers is a big data approach to understanding and predicting behavior, including buying behavior. This propensity to buy is usually expressed as a customer profile or audience segmentation with a scoring mechanism that allows marketing professionals to concentrate on those consumers most likely to buy.
Predictive Analytics Hones Messaging
Most predicative analytics systems use data from CRM systems, census and other demographic data, sales data, responses to previous marketing campaigns, social media analytics and other relevant data about customers. This data is then used to create targeted lists of customers with scores that represent a propensity to buy. This type of customer profiling helps marketing professionals to target awareness campaigns. Even more important, predicative analytics allows marketing professionals to understand customers and prospects to the point where they can provide personalized experiences and offers engineered to draw in consumers rather than waiting for them passively.
A few years ago, this type of analysis was “Minority Report” style technology — more fiction than science. Thankfully the tools have progressed with most major marketing automation vendors adding these capabilities including Adobe, IBM, Oracle and Salesforce.com. There are also a number of other companies marketing standalone products in this space such as Reunify and IDInteract.
The complexity of the data models and difficulties in reconciling the disparate types of information that go into these models, along with the big data infrastructure, makes this a costly product category. Given the high expense, using predicative analytics to target customers for marketing will be the domain of the multi-brand, multi-national company for the time being. It is, however, reasonable to expect that predictive analytics will become a less expensive service over time so that almost any marketing professional can tap the technology for brand management and lead generation.
Predictive analytics provides ways to drive awareness and demand at lower costs by raising the response rate on a smaller number of prospects. In other words, it allows companies to target fewer potential customers with better results. This positively affects both the cost and revenue side of the business. In a world of near continuous, worldwide marketing, it may someday be a requirement to see a return on investment on marketing.
Title image by projectonephotography (Shutterstock)
Editor's Note: To read more from Tom, see his Bring the Internet of Things to Customer Service and Support
About the Author
Tom Petrocelli is Research Director, Enterprise Social, Mobile, and Cloud Applications at Neuralytix. He is an experienced marketing, technology, and business executive with 29 years in the computer technology industry.
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