The online shopping process has undergone a structural transformation. The starting point hasn't changed: consumers research, compare reviews and prices across websites and comparison sites, and form their own opinions about what to buy and where. For years, this process was manual and tedious. Now, conversational assistants allow users to ask questions in natural language and receive a curated selection of pre-compared options, along with their prices and where to buy them. The research itself hasn't disappeared; it's simply delegated to these assistants. And Black Friday amplifies this behavior, because it's when the most people research and have the strongest purchase intent. While new conversational systems were already redefining product discovery, Google's recent removal of organic product carousels in the EU, which has reduced the visibility of direct listings by 90% to 100% in several European countries, has accelerated this transition. The traditional storefront is fragmenting, and organic commercial traffic is seeking new avenues. According to data from NIQ and Kearney, 74% of consumers already use conversational tools at some stage of product discovery. Furthermore, Adobe Digital Insights indicates that traffic from these environments increases conversion rates by 42% compared to traffic from other channels. In addition, only a third of consumers arrive at sales with a brand and product already decided; two out of three remain open to other options. This scenario is further complicated by the emergence of new advertising formats and sponsored responses on platforms like ChatGPT. By displaying direct visual comparisons with the competition within the interface itself, having competitive prices becomes even more crucial: an unattractive offer can be dismissed before the user even clicks. The question for any retailer or brand is straightforward: when an assistant researches and recommends, do you appear among its top choices, or are you not even considered? The 3 ways users look for bargains on Black Friday Conversational assistants don't process purchase intentions like a rigid keyword search engine. Depending on the stage of the funnel the consumer is in, the system can generate responses by citing specific offers and URLs: Discovery search (exploration stage): For example, "What are the best value running shoes?" The user researches the category. The assistant analyzes the available options, recommends top models, and can suggest where to find them at a discount. Specific product search (high-intent stage): For example, "Find the best deals and discounts on Hoka Clifton 9 running shoes." This is a direct search for a specific item. The system compares prices and can provide the URL with the best final price, as well as show alternatives. Search by technical attributes (expert stage): For example, "Find deals on running shoes with high-cushioning EVA foam soles." The shopper looks for specific features across different brands. The system cross-references technical specifications with current prices and discounts to show the most competitive options. Indirect competition: it's no longer just your product that's being compared, but the entire category. As these three search modes show, assistants can offer alternatives even when the user requests a specific model. And that's one of the biggest changes. Ranking on Google has always been difficult, but the playing field was broader: there was Google Shopping, dozens of results, and answers directly related to a specific search. Now, the funnel can be reduced to a much shorter list of recommendations. To enter that space, it's no longer enough to answer a keyword: the context of the query and how the offer compares to other options on the market also matters. For a specific search like "the best deals on Hoka Clifton 9," the assistant can break down stores and product variations, but if it detects significant price differences, lack of stock, or a superior offer within the category, it can suggest an equivalent model from the competition. For a more general query, like "what are the best running shoes for a marathon?", it will directly generate a range of possibilities. Therefore, many searches end up with a set of comparable, but not necessarily identical, products.

The change is a double-edged sword. On the one hand, competition is no longer limited to those selling the same product: any substitute with a better price-quality ratio can be included in the comparison. On the other hand, if a retailer offers the best price on several alternatives, it can appear multiple times within the same search result. Recommending three pairs of shoes doesn't mean they have to be the same model or brand. And this is where it all comes down to data: recommendation systems need to accurately read the actual price, availability, and product attributes. Without reliable and up-to-date information, an offer can be excluded from the comparison. The data from the original source gains importance. For years, product comparison sites have functioned as large digital storefronts. Appearing well in Google Shopping or on an aggregator could be enough, and observing their prices served as a reasonable benchmark for the market. This model is changing, especially after the removal of organic carousels from Google Shopping in the EU and the shift of listings to blocks on comparison sites. While traditional search in Europe fragments the product landscape and introduces more intermediaries, conversational assistants follow a different logic: they can cite and link directly to the seller's online store. This dynamic reinforces the importance of information published directly by each retailer and reduces the weight of indirect data from aggregators. Being cited depends, among other factors, on offering a proposition that can be correctly interpreted. The problem arises when the pricing strategy relies solely on prices from comparison sites or secondary feeds that may be outdated. The price and promotion published directly on the retailer's website thus become a particularly relevant signal. This is the new terrain of price positioning: carefully managing the price, promotion, and availability signal so that it can be interpreted correctly. The quality of the data then depends, above all, on its reliability in the original source. The Technology Stack for Competing for Visibility on Black Friday Gaining visibility in conversational responses during Black Friday requires an infrastructure capable of converting accurate data into fast and continuous pricing decisions: Reliable Data Monitoring: capturing the price, promotion, and real-time stock availability as they appear on the seller's website and app. Promotion Intelligence: evaluating the Black Friday promotional ecosystem. It's not enough to simply review the retail price; it's also necessary to monitor applicable coupons in the shopping cart, bundles, marked prices, and shipping costs to understand the final offer. Smart Attribute Matching: allows you to associate products using technical attributes to also monitor the behavior of comparable alternatives within the same category. Price Optimization with Reactev: connects Minderest data with optimization tools to implement continuous price changes and improve the competitiveness of your offer. In a European market without organic product carousels and with new ways of discovering products, Black Friday isn't won solely by those with the largest catalog, but by those who work with reliable and up-to-date data from the source. Only when price, promotion and availability are correctly reflected does the possibility of entering into comparisons and not being left out of the purchase decision increase.