Reduce agent churn
It’s imperative that an agent you’ve won as a customer stays a customer. Even if you have thousands of agents, a machine can apply patterns to each unique user, identify an unhappy agent who might churn, and let you know early – up to 60 days in advance – so you can take action to keep that customer.
Increase conversion rates for targeting campaigns
Machine learning creates profiles for your users to tell you which products they’re most interested in right now, and you can use that information to target those users on other sites. Show them the right ad, at the right moment, and watch your conversion rates increase.
Better services for third-party advertisers
Machine learning offers a solution that targets users based on the complete picture of their behaviour, not just one or two key factors. Optimised targeting means your advertisers will see a jump in their conversion rates, and you’ll be able to sell your ad space for higher prices.
Increase conversion rates for consumer monetisation campaigns
Targeting is only as effective as the algorithm you use to select potential customers from among the millions of users on your site, and only a machine can optimise that process. One of our clients makes close to one million € in additional revenue per year simply by using machine learning to more effectively target users.
Build a better product & better customer experience
Optimising features like automatic recommenders on your portal means that the machine analyses the complete behaviour history of all your users over time and makes more intuitive recommendations that feel more natural to the individual user. The results? A higher number of users, a better user experience, an uptick in user satisfaction, and ultimately higher conversion rates.
Use all the data you collect
There are never enough human resources to turn all the patterns in big data into something you can use for targeting. Machine learning is the only way. The results can be almost magical: three times the amount of revenue from the same campaign, with the same amount of users, just because the algorithm selected different users to target.
