At Rise of AI 2018 in Berlin, Dominique Ziegelmayer, Director Product & Technology at Trusted Shops, argues today's personalized shopping is mostly superficial, using an online shirt purchase where a retailer asked for gender but kept showing colors already rejected by clicks. Ziegelmayer proposes three hypotheses about retail's future: stores will know shoppers' personal details before they arrive despite privacy laws, since retailers already try to infer demographics from behavior; machines will generate products tailored to individual preference rather than just help customers search for one; and human vendors will be replaced by AI that has read every product sheet and review and can ask a few clarifying questions instead. Present-day evidence includes Google Duplex, an early kiosk piloted at Media Markt, and facial recognition linked to emotion detection seen at trade shows. Ziegelmayer then presents Trusted Shops' own approach: deriving approximate demographic signals from customer feedback text through author profiling rather than mandatory profile questions, classifying review text to learn which product aspects customers like or dislike, and using progressive chatbot-style follow-up questions to turn vague feedback like "everything was perfect" into specific, actionable detail. The core argument is that structured feedback, not just click data, makes real personalization possible.
Key takeaways
- Ziegelmayer argues real personalization requires acting on customer feedback, not just click or order data, citing Alexa ignoring a negative product review.
- A German retail group's checkout cameras that inferred customer age and gender for advertising drew heavy criticism, but showed the underlying capability already exists.
- Trusted Shops derives approximate demographic signals from customer feedback text through author profiling rather than requiring customers to state age or gender directly.
- Progressive chatbot-style follow-up questions can turn vague feedback like "everything was perfect" into specific, actionable detail about delivery or product quality.
- Ziegelmayer predicts human vendors will be replaced by AI that has read every product specification and review and can ask a few targeted clarifying questions.
Detailed notes
How personalized is online shopping today?
Ziegelmayer opens with a personal shopping story: a leading online retailer asked whether the shopper was male or female, correctly inferring male from past purchase history, but after the shopper clicked only white shirts, the product list kept showing pink and blue shirts regardless. The conclusion drawn is that personalization today works only partially, since retailers rarely act on the feedback a customer already gives through clicks and choices.
Three hypotheses about the future of retail
Ziegelmayer offers three predictions. First, stores, whether online, offline or conversational, will know a shopper's personal details before they arrive, despite data privacy laws such as GDPR; as evidence, a German retail group had installed checkout cameras to infer customers' age and gender for personalized advertising, which drew heavy media criticism, showing the underlying capability already exists. Second, machines will move from helping customers find an existing product to generating the product itself, illustrated by a Nike shoe configurator where a machine that already knew a customer's preferences could skip manual configuration entirely. Third, human vendors, who today consult customers by reading product sheets and offering advice, will be replaced by AI that can read every product specification and review in existence and ask a few targeted questions, for example about budget, to narrow down a recommendation.
Early signs: Duplex, Media Markt and emotion-aware kiosks
Ziegelmayer points to Google Duplex, shown at a recent Google keynote, as evidence that natural, human-like conversations with machines are becoming possible, and describes piloting an early interactive kiosk at Media Markt, calling it a limited first step rather than the vision being described. The imagined future store has a showroom that renders or projects only products likely to fit the shopper, a conversational robot clerk that opens by reading mood from facial expression, technology Ziegelmayer says already exists at trade shows, and narrows down preferences through a short series of questions before a drone delivers the chosen product.
Why click data alone is not enough: the Alexa and ketchup examples
Ziegelmayer describes asking Amazon Alexa to reorder razor blades that had been tried once and reviewed negatively for a slimy strip on the blade; Alexa offered the same product based on order history alone, ignoring the written feedback, and the reorder was declined. A separate example, Heinz's purple and green ketchup, which customers disliked, is used to argue that companies fail when they act without first understanding customer needs and expectations through feedback.
Trusted Shops' approach: feedback-driven personalization
Ziegelmayer presents Trusted Shops' own work using natural language processing to derive approximate demographic signals, age, gender, potentially income, from customer-written feedback text through author identification or profiling, avoiding the legal problem in Germany of requiring customers to state age or gender directly; accuracy is not 80 to 90%, but is enough for a first step into personalization. A second technique uses multi-class text classification on reviews to identify which specific aspects of a product category customers like or dislike, which Ziegelmayer says could eventually be used to design an "ideal" product from aggregated preferences. A third technique uses chatbot-style progressive questioning to convert generic feedback, such as German customers typically writing "everything was perfect," into specific detail about delivery timing or product quality by asking targeted follow-up questions, and the same questioning approach could guide a customer to the best-fitting product across millions of existing reviews within a few exchanges. Ziegelmayer closes urging companies to start collecting feedback broadly, including non-textual signals like which products a shopper physically picks up and puts back, as the foundation for genuine personalization.
Quotes
Feedback is the key to understand your customers.
Dominique Ziegelmayer
If you don't understand your customer, you will never be able to do something personalized.
Dominique Ziegelmayer
Vendors will be replaced by AI.
Dominique Ziegelmayer
Questions answered in this talk
Why isn't online shopping personalization as good as it could be?
According to Dominique Ziegelmayer at Rise of AI 2018, retailers often collect signals like clicks but fail to act on them, illustrated by a shirt retailer that kept showing colors a shopper had already ignored, and by Amazon Alexa recommending a reorder despite a negative written review of the same product.
How can companies infer customer demographics without asking directly?
Dominique Ziegelmayer explains at Rise of AI 2018 that Trusted Shops uses author identification, a natural language processing technique, to derive approximate age and gender signals from customer feedback text, avoiding the legal issue of mandatorily asking for such data.
How can businesses get more useful customer feedback than generic praise?
Dominique Ziegelmayer describes at Rise of AI 2018 using chatbot-style progressive follow-up questions to turn generic feedback such as "everything was perfect" into specific detail about aspects like delivery timing or product quality.