In last week’s post (here), we looked at what’s driving machine learning use cases in marketing. We also reviewed what are the prevalent and upcoming ways in which machine learning is ramping up the possibility of getting more consumer eyeballs. Continuing in that steam, today we’d discuss the ways AI is opening up possibilities of user engagement and re-engagement. Every marketeer would concede the growing significance of user conversion and churn prevention in their scheme of things. Let us look at some of the ways that can be achieved with the help of AI.
Predictive lead scoring
This is merely an extension of applying the consumer behaviour analytics models on to sales. The intent is simple – focus on qualified leads that are more likely to convert. And past data trains the models. An example of such a startup is Mintigo.
Within targeted advertising, machine learning has varied applications across ad platforms, ad intermediaries and ad-tech companies. For ad platforms, artificial intelligence will help optimise their clients’ ad campaigns. For agencies, there is ample scope for campaign management and optimisation. And most importantly, for technology companies, there is an increased demand today for better targeting algorithms.
Virtual (And Augmented) Reality
Experiential marketing, as the term is popularly known, will see new light with VR and AR capabilities. Both AR and VR technologies are dependent on AI learnings to make the user experience interactive and immersive.
An AI system can create user cohorts, serve ads, assess interaction and keep on improving the model to ascertain which user segments can and should be retargeted and when.
As businesses continue to mine their own data, they will begin to automate more of their process and become more interesting and relevant to their best customers. While chatbots today are still clumsy, they are getting better and will eventually become an expected tool for solving problems. The spirit of growth hacking will remain strong as businesses look to do more with less, leading to more technology-driven solutions to common business problems.
Deploying machine learning models on datasets of user buying patterns, data from purchase behaviour from elsewhere and leveraging that information, along with methods from dynamic pricing algorithms, can significantly increase user conversion habits. A startup engaged in building such a solution is PerfectPrice.
This term is new but an old trick that no longer requires a human seller behind the desk. Remember, when you went to buy a product on a shop and when you tried to bargain on the price, the seller insisted this was the last piece on sale urging you to buy immediately. Some of us are exposed to that psychological trigger every time we visit an app to buy a flight or a hotel room ticket. AI can determine, at an individual level, how impulsive buyer you are and how to best target you. An interesting startup working in this arena is DataSine.
Churn prediction – You should see greater customer retention. ML should enable you to more quickly and accurately identify customers who will churn and proactive engage those customers to mitigate the risk. With enough data, ML may even predict the type of offer most likely to re-engage the customer.
Predictive customer service – This might seem like a little far fetched to some but in this age, where competition is intense, quality of customer service often stands out as the major difference between those who succeed and those who don’t. Input information for predictive customer service can be tenure driven, user communication driven or alerts driven to begin with. And with time, this could lead to a paradigm where customer grievances are the least and positive feedback becomes a norm.
In the next post, we’ll have a look back and talk about other interesting use cases.