Overview
From product search to distributed commerce
Answering engines are changing the logic of product search. Customers no longer have to search for specific products or individual keywords. Instead, they can describe what they need, which requirements they have or which problem they want to solve. AI can use this information to identify suitable products, compare them and make specific recommendations.
This is changing not only where product search takes place, but also how. Commerce extending beyond a retailer’s own online shop across different platforms and touchpoints is not a new development. Marketplaces, social commerce and other external channels have long been part of many commerce strategies.
Answering engines, however, add a new dimension: external systems are increasingly becoming part of product advice and selection themselves. Rather than simply displaying products, they can interpret requirements, compare offers and make recommendations. This changes the rules of Distributed Commerce.
Distributed Commerce: When commerce moves beyond a retailer’s own front end
Omnichannel Commerce
The focus is on creating a seamless customer experience across different touchpoints.
The retailer largely controls the Customer Journey across its channels.
The retailer’s own online shop generally remains a central anchor point.
Distributed Commerce
The focus is on making products available wherever customers discover, search, compare or buy.
On external platforms, retailers give up some control over presentation, visibility and customer interaction.
The online shop remains important, but does not have to be the starting point for product search or purchase decisions.
Omnichannel Commerce
Retailers connect different channels to create a Customer Journey that is as consistent as possible.
The focus is on creating a seamless customer experience across different touchpoints.
The retailer largely controls the Customer Journey across its channels.
The retailer’s own online shop generally remains a central anchor point.
Distributed Commerce
Products and commerce processes are distributed beyond a retailer’s own properties to external platforms and channels.
The focus is on making products available wherever customers discover, search, compare or buy.
On external platforms, retailers give up some control over presentation, visibility and customer interaction.
The online shop remains important, but does not have to be the starting point for product search or purchase decisions.
In short: Omnichannel Commerce focuses on connecting different channels. Distributed Commerce is about making commerce available beyond a company’s own digital properties.
Answering engines add a new dimension to this established principle. They change not only where product search takes place, but also how a customer need is translated into a product selection or recommendation.
What answering engines do differently from traditional search engines
With traditional search engines, product search usually begins with a specific query. The search engine returns relevant pages and product offers, which users then compare and assess themselves.
Answering engines are more conversational. Users can directly describe what they need and which requirements matter to them. For example:
“I’m looking for a lightweight, waterproof hiking boot for a multi-day trek. It should cost no more than €180.”
AI can derive relevant criteria from this request, compare suitable products, explain the differences and narrow down the selection further by asking follow-up questions. Users no longer have to translate their needs into individual search terms and filters first.
As a result, product search is shifting away from individual keywords towards specific needs and use cases. Increasingly, the central question is: Which product is right for my particular requirements?
Current Forrester data shows that answering engines are already being used to support specific purchase decisions: 62 per cent of regular answering engine users in the US and UK use them for product research and recommendations. Forty per cent use them specifically to discover new products.
The use of answering engines for product research and recommendations shows that this development has already reached the buying process. Answering engines are increasingly playing an active role in product selection and recommendations rather than simply helping users find information.
For retailers, this creates a new requirement: if AI is to select appropriate products, it needs to understand what distinguishes a product and which needs it is suited to.
If AI selects products, it needs to understand them
For an answering engine to determine whether a product matches a specific request, it needs more than a product name and short description. It requires information that clearly describes the product and indicates which requirements and use cases it is suitable for.
Depending on the product range, this may include technical specifications, materials, variants, sizes, prices, availability, accessories, compatibility information or use cases.
This raises an additional question for retailers: How should product information be structured so that external systems can correctly understand, classify and compare a product with other offers?
If important details are missing or information is contradictory, AI systems may find it more difficult to assess whether a product meets a specific need. Product data therefore increasingly affects not only how a product is presented, but also whether and in which context it can be considered by AI systems.
Product data is taking on additional responsibilities
Product data has long been the foundation of Distributed Commerce. It supplies online shops, marketplaces, price comparison sites and other external touchpoints. Answering engines, however, place greater demands on this data. AI systems need to do more than simply display product information. They must also interpret it, establish relationships between different pieces of information and assess products against specific customer needs.
Data quality is therefore becoming even more important as part of the underlying commerce infrastructure.
This is where PIM comes into play. A PIM system can provide the central data foundation from which online shops, marketplaces and other touchpoints obtain consistent product information. Answering engines add AI-based interfaces to this ecosystem.
This requires an infrastructure capable of making product data, prices, availability information and other commerce data flexibly available via APIs or feeds. The key factor is not any single new channel, but the ability to connect new systems without having to build complex bespoke solutions each time.
One specific example of this development is Google’s Universal Commerce Protocol. The approach illustrates how product information and commerce functionality can be made available in a structured way to AI-based applications.
Product data is therefore not taking on an entirely new role, but it is gaining an additional one. It forms not only the foundation for distributing products across existing channels, but increasingly also determines how external AI systems understand, classify and select products for specific customer needs.
Greater visibility also means less control
Answering engines can make products visible at exactly the moment when customers express a specific need. At the same time, retailers give up some control over how their products are selected, classified and presented in comparison with competitors.
This raises new questions: Which characteristics influence a recommendation? What happens if product information is missing or interpreted incorrectly? And how much influence remains over brand messaging and product presentation when the Customer Journey takes place within an external AI interface?
Distributed Commerce is therefore not purely a technical issue. Retailers also need to assess strategically which platforms are relevant to them, which dependencies these platforms create and how much control they are willing to give up in return for additional visibility.
The aim is not to be present everywhere. What matters is using new touchpoints where the additional reach justifies the effort and the loss of control.
What retailers can do now
Retailers do not need to overhaul their commerce strategy immediately. What matters more is putting the right foundations in place so that new AI-based touchpoints can be tested selectively.
Four areas are particularly relevant:
Review product data
Product data should be complete, unambiguous and written with typical customer questions in mind. It is important not only to provide technical specifications, but also information that makes clear which requirements and use cases a product is suitable for.
Keep the infrastructure flexible
Product information, prices and availability should be made reliably accessible to new systems via APIs and feeds.
Experiment selectively
Not every new touchpoint is suitable for every product range or business model. A sensible approach is to begin with selected products and specific use cases to test which forms of product search and advice are genuinely relevant.
Assess effort and value
Retailers should examine how products are presented, which information is required, which queries lead to relevant recommendations and how much operational effort is involved. This makes it easier to assess whether a new touchpoint is worthwhile in the long term.
Companies that prepare their product data and systems so that new touchpoints can be connected without complex bespoke solutions, and then test these touchpoints selectively, create a stronger basis for informed decisions in Distributed Commerce.
Conclusion: The online shop is here to stay, but it has company
The online shop remains an important part of the Customer Journey. But product search and purchase decisions are increasingly beginning at other touchpoints. Answering engines can become the interface between customer needs and a product range, influencing which products make it onto a customer’s shortlist in the first place.
For retailers operating in Distributed Commerce, the front end is therefore only part of the picture. The product data, APIs and systems behind it are equally important, ensuring that information can be provided consistently and new touchpoints connected flexibly.
From novomind’s perspective, this places particular emphasis on the technical foundation. Commerce is becoming even more distributed, making it all the more important for the underlying infrastructure to work together reliably. A powerful PIM system, flexible commerce systems and open interfaces create the foundation for making products available accurately and in context within new AI-based environments.
Further reading
For anyone looking to explore Distributed Commerce in greater depth, the latest Forrester report, Distributed Commerce Strategy: Algorithms And LLMs Become Sellers, provides further insights into new commerce channels, answering engines and the strategic questions they raise for retailers and brands.
Frequently Asked Questions about Distributed Commerce
What is distributed commerce?
Distributed Commerce describes a commerce strategy in which product discovery, advice and, in some cases, purchasing processes take place outside a retailer’s own online shop. External platforms and AI-based interfaces therefore become independent touchpoints within the Customer Journey.
How are answering engines changing product search?
Answering engines enable product search based on specific needs rather than individual keywords. Users can describe requirements or use cases, while AI identifies suitable products, compares them and makes recommendations.
Why is product data becoming more important for answering engines?
Answering engines can only classify and recommend products reliably if relevant product characteristics are available in a complete and unambiguous form. Depending on the product range, these may include technical specifications, materials, sizes, compatibility information, prices or use cases.
What role does PIM play in distributed commerce?
A PIM system can serve as the central data foundation from which online shops, marketplaces and AI-based applications obtain consistent product information. This enables product data to be managed in a structured way and made available across different touchpoints.
What is the difference between omnichannel and distributed commerce?
In Omnichannel Commerce, a retailer connects its own channels to create a Customer Journey that is as consistent as possible. In Distributed Commerce, parts of product search, advice and the purchase decision also take place on external platforms that the retailer does not fully control.
What should retailers prepare for distributed commerce?
Retailers should focus on reviewing the quality of their product data, creating flexible interfaces and selectively testing new AI-based touchpoints. The key is to ensure that product information can be provided reliably while regularly assessing effort, reach and the degree of control being relinquished.