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AI-Assisted Consumer Targeting 101

Part 1: What You Need to Know about Predictive Models 

The discourse on predictive analytics and AI is prolific these days, especially in the realm of sales and marketing. The truth is, sales and marketing teams can no longer afford to miss out on important insights about existing and potential customers. They need the ability to solve business problems using data, lest they be pushed aside by competitors who are taking the leap.

The good news here is that AI-assisted consumer targeting doesn’t need to be mystical or overly complicated. In fact, by understanding the first part of the process – building predictive models – marketers can start using their data to acquire new customers or improve campaign responses. Building predictive models is a critical piece of the experience; it not only determines the quality of your insights, but also influences your metrics, your output, and your answers going forward. In short, your predictive models empower you to make informed decisions faster, change course at a moment’s notice, and refine strategies as data shifts over time.

Let’s take a look at how you can build predictive models to solve your unique business problems.

Gather Your Data

Marketers inevitably have loads of data at their fingertips, which makes for some incredible “model fuel.” When you start to build your predictive models, you’ll want to gather all of your inputs and data sources – even if it’s data that doesn’t directly affect you. The more data you can acquire, the better. Don’t worry about the volume right now, because in the next step you’ll be parsing it down.

Prepare Your Data

Of course, your inputs and data sources are going to be messy. You might have large and dirty files of millions of data points. In order for your predictive models to be accurate, you’re going to need to wrangle all that data. Cleansing, de-duping and householding are just some of the treatments your information will need before it can be used for building a predictive model. Without all this preparation, AI won’t be able to deliver answers or help you drive actions.

Build the Models

Now that your data is ready to be presented, you can start building predictive models that help you answer specific questions or define the best campaign approach. For example, let’s say you want to grow your fundraising efforts. You need to find more donors like the ones you’ve had in the past. So, you upload a sample of your donor data to a predictive platform, and the program builds look-alike models that you can use for your upcoming campaigns.

Of course, marketers don’t usually build the models, but a solid predictive marketing platform can deliver more than 50 models instantly and transparently. It can append hundreds of variables to your data, identify specific customer traits using predictive algorithms, and find winning prospects accurately in real time.

Receive Output

Finally, the last piece to the process is receiving ultra-refined output you can immediately apply to the sales and marketing efforts. Marketing teams can use output to drive efficiency, measuring how effective communications are at each stage of the funnel – and instantly tweaking strategies with real-time data. Sales teams can use output to score leads, based on the best prospects or keeping an eye on “stale” prospects that start showing interest again. The output is key to solving business problems with AI and predictive analytics, because it gives you direction right away and continues to do so over time.

When building predictive models, these four steps will help you optimize your experience with AI-assisted consumer targeting. It’s a critical path to acquiring new customers and improving campaign responses, and it's how businesses are gaining a competitive advantage.

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Bruno Delahaye
Bruno Delahaye
Bruno is President of Data Science with 15 years of experience in enterprise software coupled with deep expertise in predictive analytics and data mining. Prior to the Data Decisions Group merger, Bruno served as the CEO of Reach Analytics and Vice President of Sales-SAP( KXEN). Earlier in his career, Bruno managed the database marketing department and data mining technology of Urban Science in London. Bruno has earned an Executive MBA from HEC School of Management, a post-graduate degree (DEA) in Economics Methods from University EVE / ENSAE, and a Masters in Quantitative Methods from Paris IX Dauphine University. Bruno’ passion is his three explorer children, one who loves to build robots which occasionally pursue Bruno at high speed.