One of the most difficult tasks for marketers is a personalized customer approach and that’s exactly where AI can help.
In marketing projects, customers or prospects are typically segmented into groups, such as gender or geographic region, and based on these segments automation links are created. However, a truly personalized approach isn’t possible that way, only recommendations, offers, or purchase proposals based on a cluster.
Not only does this practice have high waste coverage, it also regularly upsets customers when e.g. they are getting offers for things they have bought long since. Even though the necessary information for a truly personalized, individual customer approach is already available.
The problem is the lack of resources and tools for harnessing this data
The database is simply too complex to make a manual setup of personalized automation routes possible. Not to mention that the use of AI technologies can create completely new possibilities here.
Instead of manually segmenting by specific groups of buyers, creating the content with the appropriate fixed incentives, and then monitoring and controlling the effectiveness of the campaign, the marketer can focus on basic strategic decisions: Should the campaign generate rapid revenue or rather rely on sustainable customer loyalty? How should the topic of incentive recommendation be handled? Are these used aggressively or rather moderately? Etc.
The rest is taken care of by AI which decides strategically on patterns and individual customer data about the time, the channel, the content and the type of incentive.
Who to talk to when about what
For this, the AI algorithm uses existing data of the customer account. Ideally, a customer has already made orders in the past and can be identified based on the buying history so far. If this is not the case, the AI can also predict possible behavior, using e.g. information such as: How does the buyer deal with emails and what does he click? How does he navigate on the website or the online shop? Which products and product categories does he look at and which one does he put into the shopping cart?
Based on this data, the AI can identify patterns and decide on this basis which content is offered, via which channel the customer is contacted, or which products are recommended.
Set stimuli - only if necessary
Incentive recommendation is currently a highly discussed topic, dealing with questions like "Do you always use vouchers- and if so when? In which form do you use them? What impact does that have on sales margin development in the short term and what are the long-term effects?"
Due to lack of transparency, an "all or nothing decision" is often made. Either everyone gets the coupon or nobody. However, this does not do justice to the customers and unnecessary incentives are distributed, which could be saved.
An AI-based incentive recommendation engine can now calculate for each individual customer how likely that person will place an order within the next few hours or days. The algorithm decides whether an incentive is necessary, and if so, to what extent. If it is a customer who buys very regularly, you can assume that he will make an order very soon and an incentive isn’t needed.
Likewise, AI can help in reactivating lost customers by providing longer inactive contacts with a higher incentive - accepting lower margin in favor of the reactivation.
Not to mention that with the help of AI, it is not only possible to reactivate inactive customers via Facebook and Google, but to get new ones. For this purpose, ads are automatically imported into Facebook or Google, based on the behavioral and lifecycle data of users.
Based on this anonymous information (e.g., sign-ups, browsing behavior, purchase history, and product affinity), potential buyers, known as statistical twins, can be identified on Google or Facebook and contacted through ad campaigns. Pseudonymous data is used to ensure privacy. If the potential buyer then enters into a dialogue with the dealer, or responds by making a purchase, he/she enters a customer lifecycle and will be addressed in the future in a personalized way.
Strategist instead of operator
The role of the marketer will certainly continue to change because of the further automation options - away from operational execution towards planning and strategy development.
The customer approach will evolve from a “one-to-many” to a “one-to-one” communication. This next level of customer communication provides marketers with the ability to target customers and potential buyers, while significantly reducing wastage through ineffective communication and false buying incentives. Customers in turn benefit from suitable offers and special and tailored offers.
By Daniela La Marca