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Predictive Analytics

Why Predictive Analytics?

While not a foolproof look into the future (we wish), predictive analytics is the process of using information from existing data sets to find patterns that provide insights about what is likely to happen in the future. For businesses, analyzing how a message or product will resonate with customers before going to market creates an advantage, one that allows for nimble adjustments of decision making that can make or break a marketing campaign or product launch.

Predictive analytics is not a new concept, not by a long shot. Still, creating the algorithms and models necessary to predict outcomes was once a craft reserved for a few skilled data scientists. With the emergence of new AI and machine learning tools, not to mention a growing interest in data analytics generally, access to the once-exclusive world of predictive analytics is now accessible to any data-savant willing to ask the right questions. Even more transformative, a predictive analytics process that used to take weeks or months now requires only days or hours with the right tools.

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DDG CEO Mike Hail - Artificial Intelligence Then and Now

And, yes, while the power of predicting the future is self-evident considering its impact on business decisions, another important reason that predictive analytics has pushed to the forefront is the rising popularity of predictive analytics itself. The question to ask yourself isn’t who uses it and who doesn’t, but who uses it and who uses it better. People are hopping on this bandwagon fast. 

The key differences for businesses in this predictive analytics arms race will come down to the following: 

  • What is the quality of your predictive models? 
  • What is the quality of your data? 
  • Do you have the tools to gather insights faster than the competition? 
  • Are you asking the right questions?

Historical data is helpful, of course, but predictive analytics takes the insights offered by data analysis to an entirely new level.

Your Guide to AI-Assisted Consumer Targeting

AI has opened the door for not just data scientists, but business analysts and marketers to accurately predict consumer behavior. Learn more about leveraging AI to quickly and effectively gather insights about your target audience.

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Consumer Targeting 101

An introduction to consumer targeting and predictive models.

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Consumer Targeting 102

A crash course in segmenting and modeling.

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Consumer Targeting 103

An analysis of profiling and scoring.

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Reach is Cloud-Based Machine Learning

Reach is DDG’s proprietary machine learning platform for predictive analytics and is especially useful for marketers and businesses looking to release a new product or service quickly. 

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Request a Response Model Audience

By loading in current customers or campaign responders, then Reach can quickly identify (in minutes) the audience that is most likely to respond to a marketing campaign or a product launch. Instead of guessing who your best customers might be, don’t just use a data-driven approach, but a predictive approach. 

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Request a Lookalike Audience

You probably have an idea of your target audience or at least the profile of your best customers. We’ll use that data to create the ideal audience for your next campaign.

Request a Lookalike Audience

Market Segmentation and Primary Research

DDG’s approach to predictive analytics for marketing campaigns or product launches begins in a controlled, survey-based environment. Using “modadic experimental design,” consumers are asked an array of survey questions designed to determine their affinity for specific marketing messages or products.   

These primary research surveys create the groundwork needed for future predictive modeling. The consumers are separated through needs-based segmentation, which consolidates people in clusters of similar marketing or product needs. These can be leveraged to determine the probability of any one customer in any demographic of their responsiveness to a given marketing choice or product.

Predictive Models DDG uses include:

  • Response Models
  • Churn Models
  • Nested Ensemble Models
  • Time Series Models
  • Win-Back Models
  • Survival Models

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Nested (Ensemble) Modeling

Nested models are a form of ensemble modeling techniques that help determine the maximum likelihood of an action, such as someone buying a product or responding to an ad. 

Using a national consumer file with hundreds of variables (thousands of values) data such as location, age, income, gender, and other variables, we can create models that can predict the responsiveness of different groups of customers to specific products. Nested models are especially useful for companies whose products will be adopted by different groups of people or are configured and priced differently depending on certain criteria.

In order to determine whether or not your company is a good candidate for nested models, we can meet with you (virtually or otherwise) to understand how your products are configured, what has worked best for you historically, and where you’ve previously struggled. If nested models make sense, we’ll compare your current performance to your expected lift using a nested model.

Request a Predictive Analytics Consultation

Want to test out nested modeling for yourself or want to learn how to make the most of predictive analytics for your organization? Schedule a no-cost one-hour consultation with us.

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