AI-powered Recommendation Engine

AI empowers human staff to offer highly tailored experiences

By meticulously analyzing detailed customer profiles in a person-to-person setting – encompassing not just purchase history but also expressed preferences, past engagement patterns, communication styles, and even subtle behavioral cues – AI empowers human staff to offer highly tailored experiences. This goes far beyond generic "customers who bought X also liked Y" recommendations.

Recommendations



Scenarios:


  • A person walks into a store, and is buying a suit. The CRM understands that he has already bought a pair of shoes the previous time he visited the brand, but in another city. The AI will now tell the clerk that the customer should be offered to buy cufflinks and a tie because. The AI should also predict what this customer is interested in based on data from similar profiles purchase behavior.

  • A woman has made reservations at a restaurant. As they arrive, the server sees that she had her birthday only three days ago and since she has entered that she’s lactose intolerant in her preferences, the AI algorithm suggests that they should offer a complimentary vegan tiramisu to celebrate the birthday.

In a retail environment, a sales associate, having this information pushed to their device thanks to PREMIND, might be instantly cued into a customer's prior purchases from another branch of the same company, their stated interest in sustainable products, or even their recent browsing activity on the company's website. This deep level of understanding allows for proactive suggestions that genuinely anticipate customer needs and desires.

Suggestions



AI's analytical prowess extends to identifying intricate patterns and predicting future customer behavior with remarkable accuracy. This predictive capability allows PRE:MIND to drive proactive customer engagement strategies, moving beyond reactive problem-solving.

Scenarios:

Preparing staff and customer ahead of a visit
Depending on customers' visit interval and purchase behavior the AI should be able to predict a visit and a product that is demanded at a given time (seasonal, but not only). This can be used to reach out to customers before the visit, enticing them with exciting personalized offers, and also prepare the staff at the venue so they’re well prepared for the upcoming visit.

When a profile should be linked up with complementary brands
AI is constantly monitoring trends, transactions, and engagement data to better the customer journey—also between brands. The client will receive information about which other clients their customers are visiting thus better understanding which brands are trending/important within the clients’ target group.

When a profile hasn’t committed an action in a while
Imagine the CRM system flagging inactive customers who haven't engaged with the brand in a specific timeframe. Instead of a generic re-engagement email, AI can suggest a highly tailored promotional campaign based on their past interests or a personalized offer that specifically addresses their potential pain points. This proactive approach is instrumental in nurturing customer relationships, preventing churn before it occurs, and ultimately leading to a significant increase in customer lifetime value (CLV).

Furthermore, AI can identify opportunities for complementary brand collaborations. For example, if the CRM identifies a segment of customers interested in luxury travel and eco-friendly products, AI might suggest a partnership with a sustainable tourism company.