AI Shopping Assistants: Help Customers Choose Without Making Up Product Facts

The customer would like to buy a bag for a short business trip. The store provides lots of bags and the customer is confused about choosing the right bag that would fit a laptop. A useful assistant might simply ask some questions to provide some useful suggestions. A useless assistant might just invent the size needed, overlook the customer budget or suggest a bag that is out of stock.

An AI shopping assistant should give advice on choosing a specific item based on true data about the product. It should not simply invent some features just to be certain about its suggestions. Generative AI would allow making reasonable arguments, but, the store would still need reliable data about their products, prices and stock.

This guide uses an example of a bag shop for planning purposes. It shows how to create a useful shopping assistant using simple English language. The same principles can be used for other product types with their own set of guidelines. These are examples of planning and not any claim of a Digitonix customer. Begin with a smaller scope of work that the team will be able to test and keep.

Pick one shopping task

Decide on the task that the shopping assistant is going to help the customer complete. For example, our shopping assistant helps to find the most suitable bag for the trip and laptop of the customer. It doesn't have to deal with all kinds of products, payments issues and returns right from the start.

Provide the results using straightforward language. The customer ought to come up with some right bags and make comparisons on the important aspects. The assistant should make clear the point where the business lacks enough information. This goal is simpler to measure than a general statement of improving all sales. It also assists in determining the necessary information on the product.

Use the product record as the source

A product record contains aspects like the size, materials, features, among others that relate to the various variants. A variant refers to a version of a product with regards to size or colour. In this case, the assistant must provide the correct product record. This should not be a combination of information of one bag with images or costs of another.

Double-check your data before building the AI. The missing facts and unclear labeling may complicate normal purchases as well. Assign responsibility for product facts and its updates. An error-free reply provided by the artificial intelligence cannot correct the mistake in the source itself. It is necessary to use the latest facts from approved sources and show the useful product page.

Ask useful questions

But do not put any unnecessary questions in there. So, it could ask about the size of the laptop, the length of the journey and the budget that the customer has got. Clarify all concepts to the customer, if he has not understood some of them. Do not make the questionnaire at first, if just a few questions are necessary.

Let the customer change his mind. For example, the customer can begin with a request for a travel bag and after that conclude that he needs only a work bag of smaller size. Inform the customer about the possible options and do not make a mistake that the first request will be the final one.

Compare facts that matter

It is important to make the comparisons in relation to what is required by the customer. For instance, when the issue is the size of the laptop, compare the dimensions that have been verified or the claim of fit. Similarly, in the case of weight, compare the product's weight. Do not supply a made-up fact to fill in the blank spots.

Show the source page or a specific product. The customer should understand which version you are comparing. You may use straightforward language, such as this model has a listed width of this size, which is supported by the source. Do not make general statements like best for everyone. State why you suggest each option and what information is available.

Keep price and stock current

The cost or the stock may differ from one or the other. The assistant has to consider the current approved source of information from the store, but not the outdated source from the generated response. Choose the level of freshness of the information. If the assistant cannot check whether the thing is available, inform the customer about it. A valuable suggestion of the product must not become the commitment that the store cannot meet.

But, the order placement will require the same controls at the store. Simply adding the item to the chat will not mean that the product will be reserved for the customer. Show when the price or stock control will be made and what will be the next step of the customer to buy the product. Ensure that your language is aligned with the discount/variant policy of the store.

Explain missing information honestly

There might be situations where the store cannot give any answer to the customer based on its information base. Specifically, the customer wants to find out if the bag is suitable for some rare device. In this case, the helper will have to describe the situation and offer a solution. The solution might involve giving the dimensions of the item or suggesting the customer contact the support team.

All unknowns do not have to stop you from moving forward. The assistant may help the customer in identifying what is known about their situation. Show them the useful product page and possible support options. Record all inquiries related to unknown facts about the product for the product team. They may discover something interesting that should be included in the store regardless of whether the customer contacted an AI or not.

Separate advice from actions

Talking about a bag could be something else, but changing a basket or making an order is something else. Make sure that the application has an action, and the customer gets a chance to choose it. Make sure that the correct product and variant have been chosen before doing any action. Do not use unclear statements like "that sounds good" when fulfilling the request of a customer.

Follow the regular flow of payments and ordering for the final action. The assistant may help the user get there, but it shouldn't skip the store’s check process. For an action timeout, the application needs to clarify the uncertainty and return safely. Multiple notifications should not automatically generate multiple basket items or multiple actions leaving the customer confused.

Keep private details out of casual chat

The customer has the freedom to compare publicly available products without giving full account information. Ask for only that data which is necessary for that action. Avoid giving away your payment details in regular talk. If the assistant offers the private order in the future, then the application must check access to the customer's order. Order number does not imply access to someone else's order.

Review what is stored in the chat history and logs. Inquiries about the product may be viewed as public data, but the name, address, or problem related to the order of the customer may be private. Decide the purpose and follow the privacy policy rules set by the store. There must be a support strategy so that productive problem solving does not involve revealing personal data of the customer in a group chat.

Design an easy conversation

Answers should be short enough to skim through. Provide a few options that can be helpful rather than giving a large amount of information about all bags available in the store. Product links should be easy to access. Customers should be able to leave the chat and continue shopping. It is better for the assistant to help customers shop rather than keeping customers trapped in the chat with no way out.

It is necessary to test the features using a phone and a keyboard. The customer must be able to view the answers, make selections, and receive error messages. The status icon will appear while the request is being processed. If the feature is unavailable, then use the traditional product search and assistance for the customer. This solution will let the customer shop without knowing how the system operates.

Test ordinary and difficult questions

Provide real-world-like questions based on the store's product category. Do testing on common requests, lacking information, out-of-stock products, and changed budget. Provide an example question that goes beyond the initial scope. See what kind of facts does the assistant use and if it explains the limits. The response to one correctly formed question is not enough for test.

Have the product team check these comparisons. Check whether the item, variant, dimensions, price, links match. Plus test the context actions like clicking on the product page or allowing an added item. For the customer it is the answer and action that matter, testing just the answer can overlook an error in the real process.

Measure help rather than chat volume

Select measurements which are related to the task you do. Useful measures include product visits, completed comparisons, questions routed to support and fixes for incorrect facts. Ask users and staff whether the help improved the decision process for the customer. Extra communications do not always mean added value. It might be because the initial message was misunderstood.

Include the process of keeping the products up-to-date, checking responses and dealing with any issues. The AI assistant requires an owner after its release. Decide who will deal with new questions related to the products and change the scope of support. A tiny helper, providing valuable and honest suggestions, is preferable to a wide-ranging assistant whose claims can’t be verified.

How Digitonix can help

E-commerce Development and AI Solutions services of Digitonix will serve as an effective place to talk about the shopping assistant. Bring the product facts, needs of the customers, sources of stock and possible actions on the store. Ask for a narrow scope at first, references to the products, useful testing and problem solving strategy. See ecommerce development for related information.

But, the already created Shopify store can require another way of dealing with it rather than the custom Node.js application. Everything depends on the store and its policies. In communicating through Digitonix, pay attention to the preference of the customer, correct data, and proper actions. The main point is to offer help in shopping with a definite owner after launching it.

Frequently asked questions

Can the assistant recommend a product with missing facts?

It should be mentioned what is known for sure, but it cannot make up some facts which are not true. Mention the fact which cannot be proved and give a proper suggestion according to the preferences of the customer and data, but do not forget about the limit of the data provided by the store.

Does an AI answer guarantee the current price?

What is required is the proper source of pricing data, which at the same time will suit all the needs of the store. One sentence which is generated is not enough for evidence. Mention the date of your checking of the value. Mention the last-minute check at the stage of checking out. Discount, variant, and unavailability can lead to different results.

Should the helper place orders automatically?

Do not mix important processes with regular advice. The customer needs to clearly check the product, variant, and the action taken in the right shopping flow process supported by the store. Follow the usual process checks of ordering and making the payments. Treat any chat reply that is general as not permission to buy or make a change.

How do we test a useful first version?

Choose a limited set of products and customer queries. Make sure that it includes the information gaps, needs changes, not available variants and unreasonable requests. Let the owners check the sources and let the customers complete the shopping task. Check the links and the actions of the product as well. The initial version should provide useful assistance and show its limits.

What should we prepare before discussing a project?

Gather information on products, inquiries, prices, source, supported actions, and access policies of the store. Identify people who would be responsible for updating the product details and customer support. Give an example of the first shopping task that would be useful for the AI and its testing criteria. Such information is necessary to turn the general idea of AI into a concrete feature with criteria and responsibilities.