Last week, we introduced Lytics Predictive Audiences, which allows marketers to build lookalike audiences quickly and accurately based on the first-party behavioral data stored in the Lytics customer data platform (CDP).
For marketers, the value proposition is clear. With Lytics Predictive Audiences, they can target potential high-value customers (or any other segment of customers) with appropriate personalized experiences across all the channels in their marketing technology stack–from email to social media to their websites–and they can emphasize reach or accuracy depending on their marketing objectives, budget, and message content.
But data scientists are critical stakeholders in CDP projects too, and we wanted to make sure that they could get value from Lytics Predictive Audiences as well.
Data scientists can save valuable time with Lytics Predictive Audiences
Given the ability of lookalike models to identify similarities between groups of customers, it’s no wonder that marketers want to use them to do things like:
- Reduce churn
- Increase subscription and conversion rates
- Maximize customer lifetime value
In the past, building models like these required time–not just from the marketers, but also from in-house or consulting data scientists, who needed to construct and validate models’ assumptions and accuracy, and then oversee their outputs.
We’re not suggesting that Lytics Predictive Audiences will replace in-house data scientists. What it will do is help them manage the ever-increasing load of AI/ML data-driven projects making their way through marketing departments.
Lytics Predictive Audiences empowers marketers to create simple modal interventions–like subscription or registration pop-ups–in a few minutes without data science support. That way data scientists can focus their attention on additional high-value models or particularly complex situations like consumer credit applications.
How Lytics Predictive Audiences Supports Data Science Teams: Autotuning
Lytics Predictive Audiences won’t turn your marketing team into data scientists, but it can help them automate many of the models marketing teams rely on on a day-to-day basis to target customers effectively. One reason is the technology’s self-learning, or as we call it, autotuning capabilities.
If you’re a musician like Cher or T-Pain, autotune stabilizes audio inputs in a way that yields consistent, optimized results. If you’re a marketing data scientist, Lookalike autotuning yields consistent, optimized results by doing a couple of key things to stabilize model inputs.
First, Lookalike autotuning provides a key set of Lytics’ proprietary behavioral inputs — behavioral scores and content affinity. Next, it examines all data mapped on a user that correlates with the model’s target behavior and automatically includes those as candidate features. Lastly, it performs hyperparameter tuning to ensure that the underlying model algorithms are performing as well as they possibly can — because, well, who likes to tune hyperparameters manually?
With autotuning removing the guesswork and the hypothesis testing from model building, your teams can focus on activating those models in more of their efforts and on measuring the results while you focus on experimentation, advanced model building, and delivering insights.
Empower your marketing team with Lytics Predictive Audiences and Autotuning today. It’s already live, so they can get started now or sign up here to get a demo.
So get started building your lookalike models today–and see how they engage your customers across all your marketing channels.