What is predictive marketing?

Many businesspeople like you and me probably think of “marketing” as a set of processes that promotes our products and services with a view toward generating revenue and margin. What’s not to like about it?



Most consumers (also like you and me, when off-duty) probably think of “marketing” as a blur of information spamming our inboxes, mobile phones and brains. It’s an altogether more negative connotation of a concept as ancient as “Adam, can I tempt you with an apple?”

With “predictive marketing,” businesses can better convert the negative into a positive perception of their brands, products and services. Put simply, predictive marketing injects a deep understanding of the customer’s needs and preferences into marketing processes. It helps marketers define tactics that address the needs of the individual, rather than using a shotgun approach to which many consumers have grown tired.

A Closer Look at Predictive Marketing
Predictive marketing is based on advanced models and algorithms that analyse data and generate insights. “Big data’” generally provides more nuanced insights, but even “small data” (incomplete, irregular, few sources) can make a positive difference. Importantly, any available data point that can provide some insight about the customer should be utilized, whether behavioural, interaction, descriptive — sourced internally or externally. The analytics will help make sense of the data, developing multifaceted profiles of individual customers and microsegments. 

One point of note — the more granular the profile is, the more likely it is to change frequently, so continuously refreshing the data becomes more important. Conversely, if your segmentation is based purely on demographic data, updating data is less critical since a person’s position will obviously not change frequently.

Armed with deep customer insight, companies can begin to understand cause and effect. For example, a tactic that’s been successful under specific circumstances in the past (purchase sequence, pricing, channel, browsing history, etc.) is more likely to be successful again under the same circumstances than in a random scenario.

Further analysis of the relative importance of the drivers of “success” helps extrapolate the learnings into scenarios that share the most important characteristics. Similarly, analysing affinities between product categories and offer types sheds light on potential tactics even where the individual customer is unknown.

The final stage is to put these predictive insights into action by using marketing campaign tools to inform each customer touchpoint.

What Predictive Means to the Marketer
The relative sophistication of marketing tools is not the key point here – although highly automated, large-scale, omnichannel marketing systems allow you to better amplify the business value. Whether the predictive analytics involved is a Chaid decision tree, K-means clustering or Cox regression model shouldn’t be the marketer’s concern either. In fact, it will most likely take a combination of these and many other types of algorithms running simultaneously and feeding off each other to reach an objective.

The differentiator, and what the marketer cares about, is the ability to turn data into predictive insights that optimize actions.

Predictive marketing will give the marketer a higher degree of certainty that his or her marketing tactics will be successful. It means knowing:
* where your customers are in their relationship with your brand,
* how valuable they might be to you over their lifetime
* what makes them both more engaged and more valuable

This allows you to take actions that reflect the individual’s situation, reinforcing the experience that you want them to have.

Predictive Marketing is an Essential Part of Marketing Strategy
An organization deploying predictive marketing is more likely to define a marketing strategy that’s stretching yet realistic. Predictive marketing works beyond the tactical level, optimizing engagements with each individual. Working with predicted variables provides CMOs with the guidance required for optimizing their investments in specific customer segments and channels. It helps drive alignment from the organization’s overall objectives to acquire, grow and retain customers, through the marketing strategies, to the tactics deployed in each engagement. 

Advanced analytics bridge the gap between an abundance of data and successful marketing. These solutions provide the succinct, dynamic and actionable insights that feed into marketing systems and other systems of engagement, be it sales or customer service systems. 

Customer Experience Use Cases
Coming back to my original point about how we – as consumers – have mixed perceptions...

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