Top 5 Emerging Machine Learning Use Cases with Customer Data
Seeing the ways that machine learning and customer data can drive revenue through improved customer experiences is fascinating, the only real limit on how you can use ML-driven predictions is your imagination Depending on your role, here are some of the uses we think are worth highlighting (and are explored in-depth below): For Marketers: Machine […]
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Seeing the ways that machine learning and customer data can drive revenue through improved customer experiences is fascinating, the only real limit on how you can use ML-driven predictions is your imagination
Depending on your role, here are some of the uses we think are worth highlighting (and are explored in-depth below):
For Marketers:
- Machine Learning-Powered Segmentation
- Increase Purchase and Conversion Rate
For CX Strategists:
- Customer Retention and Reduce Churn
- Funnel Optimisation (Customer Journey)
For Analytics Pros and Data Scientists:
- Predictive Analytics, Customer Insights
Also read: SEMrush Announces a New Content Analytics Tool: ImpactHero
Proactive Marketing: Machine Learning-Powered Segmentation
Machine Learning enables you to anticipate the behaviours that you’re tracking in AudienceStream CDP. Behaviours like purchase, loyalty sign up, views, conversions, renewals, combinations of behaviour, etc.
These predictions come in the form of a number between 0 and 1 reflecting the likeliness of that individual customer to complete that behaviour.
For example, a score of .99 means that the user is extremely likely to complete that behaviour in the given timeframe. This score is added to the customer profile at the end of each and every visit.
The score can immediately be used to create an audience (no separate deployment headaches). And the audience triggers actions to target those customers across all integrated channels in real-time— using their likelihood (or non-likelihood) of completing a certain behaviour.
Some interesting examples include:
1. Identify users who are likely to sign up for a loyalty program, and add them to campaigns with increased bidding
2. Anticipate users who will likely sign up for the newsletter, and personalise the website with an offer
3. Discover users who aren’t likely to purchase, and suppress them from advertising to save costs
Marketing Efficiency: Improve Purchase or Conversion Rate
For any important action your users take on your website (or any digital property), like a purchase, you can pinpoint the likelihood of this event and then take proactive action to encourage your goal.
For example, one of our customers is trying to increase credit card applications on their website. By using Machine Learning, they can score the likelihood of users to apply for the credit card and then target those who are most likely with advertising and also with on-site personalisation.
As a result, they are able to focus only on the best prospects and can drive overall better performance.
Customer Retention: Reduce Churn
With estimates ranging from 5x to 10x the cost, it’s much cheaper to keep a customer than to acquire a new one. This makes churn reduction a primary goal for any marketer working with a product that has a recurring purchase.
However, without machine learning, it can be hard to tell the future. With machine learning capabilities, it’s possible to forecast renewal events and then take proactive action if warning signs present themselves.
In this way, machine learning capabilities can help identify the highest value, lowest cost opportunities to maintain revenue streams.
Customer Experience: Funnel Optimisation
If you have a series of milestones in your customer experience strategy, machine learning-powered scores can be used to determine the likelihood of a customer making the next milestone.
Then, these scores can be used to guide the action you’ll take to encourage your customer to achieve the next milestone. In this way, you can take proactive action at every step of the customer journey to better progress customers through the funnel.
If the likelihood is low, you may need to take drastic action, whereas if it’s high, then your action might be more minor. You can also analyse these groups for insights into what action would be appropriate.
One example from our customers involves predicting the following milestones, with actions tied to these predictions to take different actions for low or high likelihood scores:
1. Interest Form fill – Initial conversion
2. Return to site – Continued interest
3. On-site search – Seeking a product
4. Application/registration – Product purchase
Predictive Analytics: Get Customer Insights and Validate Assumptions
Many machine learning solutions are a black box, with no control. You get a score, and you use it, or you don’t.
Tealium Predict ML was built on the principles of transparency and control, so our customers not only get to pick the behaviour they want to predict but can also see the data that is used to inform a prediction, along with controls to include or exclude certain data points and time ranges to tune the model.
Also read: Cooler Screens Provides Consumers Better In-Store Experiences By Taking A Privacy-First Approach
The model automatically runs through all available data, picks the data points that are predictive and weighs them based on significance. All of this is presented transparently in the UI, supplying customer insights for technical and non-technical resources.
Conclusion
Those are some of the best stories we’ve seen so far, but there are even more where that came from. If you could predict any customer behaviour that you’re already tracking, what would you do with that information? We’d love to see if we can help.
Having deployed data layers for literally thousands of companies, we can help you get the machine learning promised land whether your ML initiative is just getting started or has been in place for years.
With Predict ML built on top of AudienceStream CDP, there’s never been a lower cost and lower risk way to start or amplify ML projects!
