Predictive marketing is a powerful tool, and the ability to understand its implementation and use is quickly becoming a mandatory requirement for all CMOs.
Related blog post: What is Predictive Marketing
If you have systematically collected data from your contacts and customers, you can use predictive marketing to improve almost any marketing or sales-related business process. The basic requirement is to define one dimension in your dataset that the machine learning (ML) model can use to learn and predict patterns in the data. These dimensions are called Labels. Labels can be, for example, churn/not churn, became a customer/did not become a customer, bought a lot/bought a little. Predictive marketing can also be used to cluster contacts or customers who have similar behaviour patterns.
Predictive Marketing can improve almost any of the marketing, sales, and loyalty programs listed in the chart below.
Graphic 1: Marketing Program Portfolio
Here are some ways Predictive Marketing is already being used to improve existing business processes:
- LEAD GENERATION: Generates a customer lookalike list that is used in Data Management Platforms (for advertising) and programmatic advertising in order to drive traffic to your website.
LEAD SCORING: Estimates the likelihood of converting a lead to a customer and then triggers the appropriate marketing and/or sales activities. (Graphic 2)
Graphic 2: Lead-scoring matrix and appropriate sales efforts
ACCOUNT-BASED MARKETING: Uses identity association to identify buying teams.
SEGMENTATION: Automatically groups customers based on their firmographics, persona, communication channels and web and content behaviour.
CROSS-SELLING AND UPSELLING: Clusters contacts and companies based on similarities in purchase behaviour in order to target those with the highest potential for buying additional products. Watch this webinar, to discover how predictive marketing helped ATEA Finland to develop their cross-sell programs >>
DATA ENRICHMENT: Completes data rows for analytics purposes by automatically populating missing information using lookalike companies or common behavioural patterns.
RETENTION MANAGEMENT: Reduces churn by identifying customers who are not likely to renew their contracts or are likely to stop buying your goods and then triggering proactive sales and marketing activities aimed at these customers. Watch this webinar to discover how Visma Software used their customer data in churn prevention >>
Predictive Marketing works well only if you have enough data for the machine learning algorithms to gain an understanding of your customers’ behaviour. In practice, this means that there need to be at least a thousand rows of information in your CRM and/or marketing automation databases. Each row of data represents one company or contact. And, of course, the more data you have, the more accurate the conclusions drawn by the algorithms.
One good thing about machine learning is that the data doesn’t have to be complete or perfect. Machine learning can tolerate incomplete data. (See DATA ENRICHMENT above.) If you aren’t sure whether you have enough data or the right setup, take this survey to find out.
There is no doubt that predictive marketing improves the efficiency and efficacy of marketing automation. But just like marketing automation, predictive marketing isn’t for everyone. (I mean, you probably don’t need machine learning to help you manage your customer portfolio if you sell one or two nuclear plants every decade.) But if you’re managing even hundreds of customers and already have a marketing automation system in place, predictive marketing could be the missing factor that’s keeping you from reaching your sales targets. More about who can benefit the most from predictive marketing in our next blog post.
Download the Predictive Marketing e-book, which explains the topic in plain English.
More on the topic:
- Download the e-book: Get to know predictive marketing
- Blog: Predictive marketing is within your reach
- Watch: Webinar series on predictive marketing
About the writer:
Matti Airas is a consultant in predictive data-driven marketing and customer experience. He has previously worked for the customer experience feedback analysis company Etuma and for Nokia in the U.S. His passion is figuring out how to use data to solve business problems.
Matti enjoys writing, podcasts (especially on U.S. politics), golf, long walks with his wife and Jack Russell Terrier, and any kind of skiing.