AI News, Predictive Machine Learning — Behind The Scenes at Fliptop (and Predictions for the Future of MarTech)

Predictive Machine Learning — Behind The Scenes at Fliptop (and Predictions for the Future of MarTech)

In recent years, however, technology has made it easier to bridge the gaps between siloed data sources, and provide a unified view of data across platforms.

While predictive technologies may appear simple on the surface—input data, output predictions—anyone who’s attempted to manually derive insights from these enormous, disparate data sets knows that there’s a lot happening behind the scenes.

The Fliptop predictive platform, or as we call it, Darwin, automatically builds an array of machine-learning models and applies dozens of statistical measures to determine which model is most predictive at each stage of the marketing and sales lifecycle.

After appending external data signals, and pre-processing the data set to ensure high quality, we have a list of examples, each with a set of associated features.

This data, called “training data” is used as input to a learning algorithm, which outputs a model, as shown in the figure below.

A random forest consists of multiple decision trees (usually, the more trees, the better.), and a simplified example of what a predictive lead scoring decision tree might look like follows:

If we build the exact same tree multiple times, this doesn’t give us any more information than the original tree (since each tree will make the exact same prediction).

To solve this problem, random forests add some unpredictability to the equation, helping us understand which features will be most influential in driving conversion.

The final output is four predictive models—one for leads, contacts, accounts, and opportunities—to help our customers predict conversions at every stage of the lead lifecycle.

While every organization’s needs are unique, consider that following trends that machine learning and predictive marketing will enable in the very near future: Today, thousands of companies are already reaping the benefits of predictive marketing.

From pinpointing leads most likely to convert, to account-based targeting, to pipeline forecasting and campaign optimization, predictive has empowered functions across the organization.

We’ve entered the age of the predictive enterprise and the companies able to harness their data to make smarter, data-driven decisions will be the ones that win.

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