AI-Assisted Consumer Targeting 101 – Part 2: Segmenting, Wrangling and ModelingAugust 28, 2017
It’s not news that modern sales and marketing teams are harnessing the power of predictive analytics and AI to stay competitive. Predictive modeling uses data to solve business problems and ensures that key insights about current and potential customers don’t go undetected.
Predictive analytics is one of the hottest growth areas in marketing and many marketers have an intuitive sense of what “predictive analytics” means, but for marketers to get the most out of their predictive models they need better insight. For marketers to improve campaign responses and acquire more customers they need to leverage AI to identify the best targets – in order to do this, marketers must have a deep understanding of who their best audience is so that they can create a brand experience that will compel them to take action.
Part 1 of this series provided an overview of the predictive modeling basics. Today, we’re going to dig deeper and take a how segmentation, wrangling and modeling can help solve your unique business problems.
Prioritizing Consumers – Segmentation
In order to get the most out of your AI Targeting, you need to ensure that you have very clear goals and that you are going to analyze is the most pertinent business wise. Your customers have different profiles, so why would you lemp them all together in one bucket?
Before uploading your customer data set, make sure that you have an idea of what you want to get out of your model. Many marketers prioritize their in-house data by breaking them down into segmented groups.
Segmentation divides your large customer base into clearly identifiable groups. Not every customer is worth reaching out to. So, if you’re focused on finding new repeat buyers to boost revenue growth, or trying to figure out how many of your lapsed customers are worth pursuing on a tight budget, segmentation identifies the customers that you want to find more of.
With the increasing amount of data and data sources, marketers often find themselves with more data than they know what to do with. In order get the most value out of their data, marketers must turn this data into something meaningful. Wrangling simplifies all of your messy, incomplete and complex data for easy access and analysis, but it can be a chore.
To get high-quality output, marketers must first manually prepare a high-quality input. Marketers collect messy data from multiple sources, cleanse it and consolidate it into one data file or table.
Wrangling is a more advanced data manipulation than data preparation and the Reach Analytics AI-assisted predictive platform automates it so that marketers get better results in minutes. The Reach platforms make it easy for you by taking your raw data from a marketing campaign or a customer list and:
- Cleaning your data
- Restructuring names and addresses
- Householding and deduping the data.
- Appending and enriching your data with demographic, psychographic and behavior data.
- Identifies prospects who look most similar to your best customers.
- Gives you a deeper understanding of your current customers.
- Maps the geolocation of your customers.
- Identifies where you have the most opportunity to grow.
- Uses AI-assisted targeting to rank or score the best people in your customer / prospect file.
- Identifies people within the population most likely to engage in a specific marketing campaign and become customers.
- Reduces marketing budget.
In seconds, your raw data will be ready to be for AI to analyze.
Once your data has been wrangled, it’s time to get down to business and leverage our AI. Normally, the modeling phase on top of the wrangling can take days or even weeks for marketing analysts or data scientists to apply methodologies to your data – that’s why modeling is often considered a tedious and time-consuming process as well as a costly endeavor.
At Reach, we use look-alike and response modeling to analyze large amounts of consumer data to predict likelihood using our self-service, predictive AI. Our platform automatically appends 600+ unique consumer variables to provide unique consumer insights that can be acted on in near real-time, or less time than it takes you to make another cup of coffee.
Clone your (best) customers – Look Alike Modeling:
Optimize your campaign – Response Modeling:
To keep your company a step ahead, make sure that your marketing team is in the know and has a winning predictive strategy. Rather than letting a long and cumbersome process of wrangling and modeling preventing you from leveraging data, optimize your business decisions with AI-assisted consumer targeting and get the most out of your data.
Make sure to catch our next post, where we’ll explore the importance of your output data: customer insights, prospect lists and model scores.
Get a good look at our predictive analytics infographic for more details and learn how you can start building your future with AI-assisted consumer targeting.