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Predictive Marketing Adds Value to Many Business Processes

Predictive marketing is a powerful tool, and the ability to understand its implementation and use is quickly becoming a mandatory requirement for all CMOs.

Matti Airas / April 10, 2019

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.
  • 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.

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.)

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.

Interested in hearing more? Contact us for a free consultation and let's see how Predictive Marketing Analytics can work for you.


The blog was originally posted on idBBN.com.

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Matti Airas
Lead Business Consultant, Marketing Science Team, Customer Experience Management

Matti Airas is an expert in customer feedback management, marketing automation, predictive marketing analytics, and how to use data and machine learning to automatically trigger customer interactions. Before joining Tieto, Matti worked for a customer feedback analysis company Etuma and before that Nokia in the U.S.

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Matti Airas

Lead Business Consultant, Marketing Science Team, Customer Experience Management

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