The potential benefits of using ML capabilities are vast. How do you know where to get started? This is part 2 of a 2-part series. You can read part 1 on building you ML business case here.
This article series was jointly written by Elizabeth Marshall, Director of Solution Consulting, and Katie Jarrell, Senior Customer Success Manager, with supplemental editing by Matt Parisi, Sr Product Marketing Manager.
Now that you’ve read Part 1 of our ML for Marketing Tips series on Building Your Business Case, it’s time to move on to picking the specific use cases where ML for Marketing will be applied.
Every organization has goals for their customer experience. And you probably have milestones that lead up to those goals. We find that it’s most successful to start by augmenting your existing targeting efforts using ML insights. As Gartner puts it, “Do not reinvent the wheel.” In our customer research prior to building our new ML feature, the #1 request for ML analysis was “Tell me what my customers are going to do next.”
This simple insight can have a transformational impact when leveraged to improve customer experience. This is where we advise starting with your own ML for marketing strategy— leveraging predictive insights on the prospects and customers who are likely (or unlikely) to achieve your goal.
These insights can be layered in with your existing targeting to augment your efforts. For example, you’re probably retargeting visitors to your website. How much more effectively would that campaign perform if you layered in predictive insights? For example, you could retarget only those visitors who have a high likelihood to buy.
Our new ML feature, Tealium Predict ML, built straight into AudienceStream CDP, is a powerful way to maximize the value of ML for marketing by injecting ML capabilities into our Customer Data Hub. By adding ML to a well-integrated, customer data-rich platform (more info: What’s a Customer Data Platform?), the value of ML is scaled across the tech stack and the biggest challenges inherent to ML are solved— namely wrangling customer data and integrating insights.
As organizations bring more and more data from disparate sources together within the Customer Data Platform, the CDP becomes a rich source of customer data. ML thrives with high volume, high quality data. Utilizing Machine Learning in the CDP can help automate the creation of attributes (insights) at scale and enable the business to better understand who the customer is and what they mean to the business to help drive a particularly important business outcome.
In order to follow this strategy, it’s critical that the system in place addresses some obstacles:
Depending on whether or not you’re already an AudienceStream customer will determine whether you can go straight to generating and using predictive insights, or if you need to establish a data foundation for your ML insights.
If you’re an AudienceStream, great news! You have a head start. You have already defined the important behaviors for your customers across the customer journey. Any badge or boolean you’ve defined, is now a behavior you can predict and use for targeting.
If you aren’t an AudienceStream customer, there are some different considerations because in order for ML algorithms to produce effective insights, they require a well-understood, high volume dataset. There is a wide spectrum of customer data solutions and all will not necessarily be able to inject ML capabilities. Some questions to consider:
Or, better yet, consider adopting AudienceStream CDP for the unique advantages and opportunities it presents for scaling the value of customer data, and ML, across your entire tech stack.
If you do decide to onboard a CDP as step 1 of your new ML for marketing strategy, here are some important considerations:
Once you have a customer profile tracking the most important customer behaviors, then you can start to think about the specific use cases where you will leverage predictive insights to augment your existing programs.
Here’s a list of the top 5 ML use cases we see emerging today to get your imagination started: https://tealium.com/blog/data-driven-marketing/tealium-predict-ml-top-5-emerging-machine-learning-use-cases-with-customer-data/
Predict ML will score all visitors for the goal you select on a scale of 0 to 1. Then, this score can be used to define audiences and used in rules to trigger CX actions (if likelihood to purchase is greater than .8, add to email targeting list). Think about which audience segments you have that could benefit from a predictive insight being added. Here’s how it works:
Break your Visitors into 3 Groups based on their scores:
0 -.50 → Suppress from advertising or do nothing
.51-.80→ Remarket to this group, offer promotions/coupons to persuade them to re-engage
.81 and above → No action required
There you have it! From just beginning to think about your ML for marketing use case to setting it up in our brand new ML feature, we hope that this blog post helped you think about how to approach adding ML capabilities to your marketing mix.
Should you be interested to see how our new Predict ML feature can help your specific situation, we’d love to hear from you.
If you haven’t yet read part 1 of our 2-part series on ML for Marketing tips, check out part 1 on how to build your business case here.