Personalization does not have to begin with an AI recommendation engine. A simple rule—show spare parts to owners of a particular product—may be more useful and easier to explain than a model trained on thin data.
A parts supplier knows which machine a customer owns. Showing compatible replacements is a useful rule. Training a complex recommendation model might add little value unless there are many equivalent parts and enough behavior to learn from. Starting simple makes the value of additional sophistication measurable.
Match method to problem
Use rules when logic is clear, audiences are small or the choice needs a straightforward explanation. Consider learned recommendations when there is enough behavior, many choices and a way to evaluate outcomes. Neither method compensates for poor product data.
Cold-start situations deserve attention. A new customer or product may have little behavioral history, so an AI system can become repetitive or unreliable. A simple contextual rule and a diverse default set can serve that case well. Evaluate how quickly the experience improves with new information, and let customers correct an irrelevant recommendation rather than trapping them in one inferred interest.
Establish a baseline
Compare the personalized experience with a sensible general experience. Look beyond clicks to completed tasks, margin and unwanted repetition. A model recommending something a customer just bought needs business context regardless of its score.
Keep control with customers
Respect preferences and monitor whether recommendations narrow choices unfairly. Start with one limited use case and expand only when extra complexity produces a meaningful benefit.
