Predictive analytics can make every marketer more impactful and every marketing dollar go further. It's just one benefit of the digital age.

The non-digital age was filled with opinions, perceptions and a lot of hope. Marketing has always been about right messaging and reaching its intended audience. This fact about marketing has never changed.

During the non-digital era, marketers took messages and delivered them to their audiences (at least they claimed so). Businesses on the other end of that messaging waitedfor customers to walk through the doors or ring those 1-800 numbers.

newspaper classified ads

But if the response was weak or nonexistent, it always left marketers guessing what went wrong. Because of the lack of measurable feedback between the spend and purchase journey, marketers were shooting in the dark, hoping to hit the bullseye.

Enter the Digital Era

The digital age is filled with experiments, measurements and learnings. It has brought free flowing data, often in real time.

In measurable respects, it is leaps and bounds beyond print and television media.

For the first time, marketers can see how prospects are reacting to their campaigns. Lead analytics are key inputs for marketing spending decisions, and marketing dashboards replaced the intuition of creative teams.

predictive analytic data

The introduction of data into the marketing field fundamentally changed the industry to half art and half science.

Now measurement and analysis is the norm, revealing significant industry issues and challenges. As studies like The Digital Disconnect show, “Companies are ineffectively spending between 40 percent and 60 percent of their digital budgets. Looking at this on a global level, up to $38 billion of worldwide marketing budgets is being wasted on poor digital marketing performance.”

The Evolution of Predictive Analytics

In today's predictive analytics era, marketers can see the future impact before spending a dime.

As customers, we are leading increasingly digitized lives across social and mobile channels. Every day, every hour, we are leaving a growing digital footprint behind.

This is opening up the doors for data scientists to analyze and crunch massive stores of information. Smart algorithms identify patterns and correlations, and those algorithms are in turn getting smarter at predicting the next steps customers are likely to take.

Predictive algorithms used across predictive models are helping data scientists and statisticians dig out the stories beyond the analytics.

They shine light on insights in deeper and more meaningful ways than analytics ever could before.

It is the in-depth understanding of the story that reveals the complete customer journey — and it enables data scientists to put together a model, which is capable of predicting the future.

Learning Opportunities

Marketers can expect to know how likely it is for a certain event to occur in the life of a customer. Events could be search, view, click, try, purchase, repeat or add-on purchase, share, recommend, refer. And this is where the magic begins to happen.

Customers are targeted based on scientific predictions, which is based on their digital footprint.

From: Abstract levels of X Percent of Conversion Rate of Campaigns.

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Every prospect is individually analyzed based on data points provided by the model. Then personalized predictions are generated for every one of them.

As an example of predictive analytics, my company, Promoto, predicts the likelihood of a customer converting into a brand advocate. We analyze such things as historic purchase history, help desk data and social interactions with the brand to compute the likelihood of someone becoming a brand promoter.

The goal is to empower marketers to channel marketing dollars for maximum impact. Even before a dollar is spent on engaging with customers, markers know the chances of success beforehand.

To: Computing Predictions for each Individual Target.

Promoto data

Advantages of Predictive Analytics

So why should CMOs embrace predictive analytics?

  1. It maximizes return on marketing dollars
  2. Allows marketers to focus spend on prospects with higher chances of conversion
  3. Helps achieve goals faster, without losing time on experiments and testing
  4. Learning feeds right back into the model to make it more accurate with every run
  5. It increasingly builds on the understanding of the underlying parameters that drive behaviors, and in turn opens additional opportunities to optimize market plans.

In short, it ends the days of scattershot marketing and helps refine, define and target objectives with care and accuracy.

Title image "Doing the hard work" (CC BY 2.0) by Pedro Nuno Caetano

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