Part 55 - AI Buddy....How does product information help the customer make a decision?
Good product information should reduce uncertainty and make it easier to choose the right product.
A customer who visits an online store does not necessarily come to buy.
She might come to investigate.
Compare.
Understand.
Consider options.
Find out what she really needs.
And only when she feels she has sufficient information can she be ready to make a decision.
The most important task of product information is therefore not to fill the product page with as much information as possible.
It should help the customer move from:
"I don't really know what I need."
to:
“Now I understand the differences.”
and further to:
“This product fits my needs.”
In other words, good product information is decision support .
Product names, descriptions, images, specifications, sizes, comparisons, availability, delivery, warranty, return policies, guides, and questions and answers can all contribute to the same task:
to reduce customer uncertainty.
This applies to large online stores.
But it is at least as interesting for small and medium-sized businesses.
A smaller online store doesn't necessarily need the world's most advanced technology to offer good decision support.
It can start with something far simpler:
What do our customers need to know to make the right choice?
Magne & the Friend
Magne
Friend...
We've now spent two articles talking about product information.
The buddy
Yes.
Magne
I'm almost starting to wonder if we're making this more difficult than necessary.
The buddy
What do you mean?
Magne
The customer sees the product.
Reading the description.
Look at the price.
And buy.
The buddy
Do you always do that?
Magne
No.
The buddy
What are you doing?
Magne
Comparing.
The buddy
With what?
Magne
Other products.
The buddy
What else?
Magne
Reading specifications.
Checking reviews.
Looking at pictures.
Maybe I'll look for the product elsewhere.
The buddy
Why are you doing all this?
Magne
Because I want to be sure I'm making the right choice.
The buddy
There you have the whole article.
The customer is trying to reduce uncertainty
Magne
So the decision is really about uncertainty?
The buddy
Often.
Think about all the questions that may arise before a purchase.
Does the size fit?
Does the product work with what I already have?
Is the quality good enough?
Do I really need the most expensive model?
Will the product arrive before I use it?
Can I return it if I choose the wrong one?
Magne
And if the online store doesn't respond?
The buddy
Then the customer must find the answers elsewhere.
Magne
Or abandon the purchase.
The buddy
Yes.
Lack of information can become friction.
More information is not always better
Magne
Then the solution is simple.
We include absolutely everything we know about the product.
The buddy
Heh heh.
Then you can create a new problem.
Magne
Too much information?
The buddy
Yes.
The customer does not necessarily need all the information at the same time.
She needs the right information at the right time and in an understandable form .
Magne
So 87 technical specifications aren't automatically good product information?
The buddy
No.
If the customer doesn't understand which five actually matter to the choice, we've just moved the problem.
The customer needs help understanding the differences
Magne
Let's say we have three almost identical products.
The buddy
It's a classic situation.
Magne
Model A costs 3,990 kroner.
Model B costs 4,990.
Model C costs 6,490.
The buddy
What does the customer want to know?
Magne
Why on earth does C cost 2,500 kroner more than A?
The buddy
Exactly.
If the product pages only present each model in isolation, the customer has to find the differences themselves.
Magne
But if we compare them?
The buddy
Then the online store does part of the analysis work for the customer.
Magne
For example:
Choose A if...
Choose B if...
Choose C if...
The buddy
This is where product information begins to become advice.
We must separate the important from the unimportant
Magne
But how does the online store know which features are important?
The buddy
By understanding the customer.
Magne
There we are back again.
The buddy
We keep coming back there for a reason.
A manufacturer may have a hundred data points about a product.
But the customer might only consider five of them.
Magne
And different customers may care about different fives.
The buddy
Just.
For one customer, low weight is most important.
For another, battery life is crucial.
A third prioritizes price.
A fourth needs a certain compatibility.
Magne
So good decision support must connect product features to customer needs.
The buddy
Yes.
Pictures are also product information
Magne
We talk a lot about text.
The buddy
Good that you say that.
Product information is far more than text.
Magne
Pictures?
The buddy
Of course.
A good product image can answer questions that would require many words.
How big is the bag?
How does the jacket fit?
Where are the connections?
What does the material look like?
Magne
Video can show how the product is used.
The buddy
Yes.
And charts can explain goals.
Tables can make products comparable.
Illustrations can explain functions.
Magne
So we should choose the form of information according to what the customer needs to understand.
The buddy
Exactly.
Not everything should be solved with more body text.
Price is also information
Magne
Price is not product information, is it?
The buddy
For the customer, it is absolutely part of the decision-making basis.
Magne
True.
The buddy
And not just the price itself.
The customer may need to understand:
What is included?
Will there be any extra?
What does delivery cost?
Does the product need accessories?
What does it cost to use over time?
Magne
So a low product price may become less attractive if the customer discovers three required add-ons at checkout.
The buddy
And then we have created uncertainty just when the customer should feel safe.
Delivery can decide the entire purchase
Magne
The same applies to delivery, right?
The buddy
Absolutely.
Imagine that the customer needs the product for a birthday on Saturday.
Magne
Then the best product in the world may be irrelevant if it arrives on Monday.
The buddy
Exactly.
Therefore, availability and delivery are not just logistics.
For the customer, it is decision-making information .
Return policies can make it easier to dare to buy
Magne
This is interesting.
Because returns are about what happens after the purchase.
The buddy
But knowledge about returns influences the decision before purchase.
Magne
Of course.
If I'm unsure about the size, I want to know what happens if it doesn't fit.
The buddy
And if that information is hard to find?
Magne
Then the risk I experience increases.
The buddy
There you see how broadly we need to understand product information and decision support.
What do customers ask customer service about?
Magne
Now comes the SMB question.
Where does a small online store that doesn't have a large analytics system start?
The buddy
I would start very practically.
What are customers asking about?
Magne
Customer service.
The buddy
Yes.
Email.
Telephone.
Chat.
Store employees.
Returns.
Questions on product pages.
Magne
If ten customers ask the same question...
The buddy
...maybe the eleventh customer is also wondering about it.
Magne
Then perhaps the answer should lie on the product page.
The buddy
Exactly.
Customer service can be a goldmine for understanding what information is missing before purchase .
Returns can tell us what the customer misunderstood
Magne
And the returns?
The buddy
They may be at least as interesting.
Why is the product being returned?
Magne
Wrong size.
The product was smaller than expected.
Did not fit the equipment the customer had.
The customer thought it had a function it didn't have.
The buddy
And what does that tell us?
Magne
That some of the returns may also be information problems.
The buddy
Yes.
Not all returns can be avoided with better information.
But some people might be able to.
Now AI is starting to get really interesting
Magne
I think I know where you're going now.
The buddy
Try.
Magne
Traditionally, the online store must decide what information all customers should see.
The buddy
Yes.
Magne
But if the customer can tell you what is important to her...
The buddy
...a digital solution can help to a greater extent with finding and explaining the relevant information.
Magne
So the customer can say:
"I need a laptop for studying. It has to be light, the battery has to last all day, and I won't be gaming."
The buddy
The customer has given us three important criteria.
Magne
And the system may be able to use the product information to find relevant models and explain the differences.
The buddy
This is precisely where AI-based decision support becomes interesting.
But AI needs something to decide based on
Magne
Let me guess.
Product data.
The buddy
Yes.
And product knowledge.
Magne
If your online store only has product name, price and generic text...
The buddy
...the digital advisor also has limited knowledge to work with.
Magne
So again we come back to the groundwork.
The buddy
We do it.
Good AI-based trading doesn't necessarily start with AI.
It starts with good knowledge of products and customers .
Academic specialization
Product information reduces information asymmetry
The business normally knows more about the product than the customer.
This can be described as information asymmetry .
The seller or manufacturer knows the product's characteristics, limitations, variants, and uses.
The customer only has access to the information the business makes available, combined with their own experiences and information from other sources.
Good product information reduces this difference.
The goal is not for the customer to know everything the business knows.
The goal is for the customer to have sufficient relevant information to make an informed decision .
The customer has different types of uncertainty
Uncertainty before a purchase can take several forms.
Functional uncertainty: Will the product actually do what I need?
Financial uncertainty: Is the product worth the price?
Compatibility Uncertainty: Does it work with what I already have?
Size and fit uncertainty: Will the product fit me?
Quality uncertainty: Will it last and work as expected?
Delivery uncertainty: Will I get the product when I need it?
Post-purchase uncertainty: What happens if something goes wrong or the product doesn't fit?
Different products create different forms of uncertainty.
Therefore, product information should be developed based on the specific decision the customer is going to make .
Information must have a hierarchy
When all information is presented with the same visual and linguistic weight, the customer must figure out for themselves what is important.
Good information design establishes a hierarchy.
The most important thing should be easy to discover.
The details should be available when the customer needs them.
Technical specifications can be structured.
Complex differences can be visualized.
Immersion can be made available without blocking basic understanding.
This can be described as progressive immersion :
First, what the customer needs to orient themselves.
Then the information needed to compare.
Finally, the details for those who need them.
Comparison reduces the customer's cognitive work
When products are evaluated in isolation, the customer must remember information from one page and compare it with information from another.
It requires mental capacity.
Comparison tables, clear differences, and consistent product attributes can reduce this burden.
The online store then does part of the structuring work for the customer.
This is particularly important when the products are:
technical,
expensive,
very similar,
or has many properties that must be considered simultaneously.
Product information must be consistent
Decision support works poorly if the information contradicts itself.
If the product title says one size, the specification table another, and the body text a third, uncertainty increases rather than reduces it.
Consistency is therefore a fundamental quality dimension.
This applies, among other things:
product name,
units of measurement,
sizes,
properties,
compatibility,
price,
stock status,
delivery,
and other key product attributes.
When information is to be used by multiple digital systems, this consistency becomes even more important.
Customer service is a source of content development
Traditionally, customer service and content work can be treated as different functions.
But the questions customer service receives provide direct insight into where the information on the website is insufficient.
If customers keep asking:
“Does this part fit the Model X?”
it may be a sign that the compatibility information should be improved.
If many people ask:
"Is the size normal?"
size information may be insufficient.
Thus, customer service can act as a continuous feedback loop to the product content .
Return data can reveal knowledge gaps
Return reasons can similarly be used as insight.
If many people return a product because:
it was bigger than expected,
the color was perceived differently,
the product was not compatible,
or a function was misunderstood,
the business can investigate whether the product information contributed to the expectations gap.
This does not mean that better information eliminates returns.
But the data can help the business identify where pre-purchase expectations and post-purchase experiences don't match .
Good decision support is also about what we leave out
A common misconception is that better product information means more product information.
It doesn't necessarily.
Too much irrelevant information can make the decision more difficult.
Good decision support therefore requires editorial choices:
What does the customer need to know?
What should the customer know?
What do only certain customers need?
What could be in the recess?
What is irrelevant to this decision?
Information architecture is thus also about prioritization .
From static information to dialogue-based decision support
Traditional product pages present largely the same information to everyone.
AI-based interfaces can open up a more dialogue-based model.
The customer can express:
need,
priorities,
limitations,
budget,
experience level,
and usage situation.
The system can then attempt to link these criteria to structured product data and documented product knowledge.
It may make it possible to explain:
why a product is suitable,
what options exist,
and
what compromises the customer must consider.
This is a far more interesting application of AI than simply generating new sales copy.
Human knowledge remains crucial
Although AI can make information more easily accessible, someone still has to decide what is actually true and relevant.
An experienced employee may know that a shoe is technically waterproof, but that it will be too hot for certain activities.
A bicycle mechanic may know that two components are formally compatible, but that the combination works poorly in practice.
A photographer can understand that one technical specification looks impressive, but means little to the relevant customer group.
This is contextual subject knowledge .
When documented and combined with good product data, the business can build far better digital decision support.
What does the customer need before making a decision?
Product information should answer the uncertainty that stands between the customer and the choice.
Customer's question | Type of information | What the information does |
What is this? | Product name and description | Identifies the product |
Does it suit my needs? | Application and recommendation | Creates relevance |
What is the difference? | Comparison | Make options understandable |
Does it suit me? | Size, measurements and fit | Reduces the risk of making the wrong choice |
Will it work with what I have? | Compatibility | Reduces technical uncertainty |
Is it worth the price? | Features, quality and differences | Provides a basis for valuation |
What does it actually look like? | Photos and video | Reduces sensory uncertainty |
When will I get it? | Stock and delivery information | Provides predictability |
What if I choose wrong? | Returns and warranty | Reduces perceived risk |
How do I use it? | Guidance and documentation | Provides security before and after the purchase |
From information to decision
The product information helps the customer through several mental steps
Step | Customer's thought | The task of product information |
Need | I need something | Help the customer define the need |
Briefing | What is there? | Present relevant options |
Understanding | What do the differences mean? | Explaining properties and concepts |
Comparison | Which option is best for me? | Make relevant criteria comparable |
Risk assessment | What could go wrong? | Reduce uncertainty with precise information |
Prioritization | What is most important to me? | Connect characteristics to needs |
Choice | This suits me best. | Confirm product relevance |
Buy | Am I confident enough to do it? | Provide clear pricing, delivery and terms |
After purchase | How do I get value from the product? | Support usage, maintenance and problem solving |
What can small and medium-sized online stores do?
Start with the questions customers are already asking
Source | What can we learn? | What can be improved? |
Customer service | What customers can't find answers to | Product descriptions, FAQs and guides |
Store employees | Which explanations actually help? | Digitize employees' professional knowledge |
Returns | Where expectation and product do not match | Size, pictures, application and explanations |
Search the website | What customers are trying to find | Terminology, navigation and product data |
Product comparisons | What differences are difficult | Consistent attributes and comparison tables |
Reviews and feedback | What customers value or misunderstand | Highlight relevant features and limitations |
Product data | Where information is missing or inconsistent | Data quality and structure |
Employee experience | What the datasheet doesn't tell you | Context, advice and practical recommendations |
Technical terms
Key concepts when product information is intended to support the customer's decision
English technical term | Explanation |
Decision Support | Information, tools or advice that helps the customer understand options and make an informed choice. |
Information Asymmetry | A situation where the business and the customer have different access to relevant information about the product or purchase. |
Perceived Risk | The customer's subjective assessment of uncertainty and possible negative consequences of a purchase. |
Information Need | The information the customer needs to understand a situation, solve a problem or make a decision. |
Cognitive Load | The mental capacity required to understand, remember, and process information during a task. |
Comparability | The extent to which products and features are presented so that the customer can easily compare alternatives. |
Information Hierarchy | The organization of information by importance and level so that the most important things are easy to discover and the details can be found when needed. |
Progressive Disclosure | The principle of presenting necessary information first and making more detailed information available as needed. |
Product Attribute | A structured characteristic of a product that can be used for search, filtering, comparison, and decision support. |
Compatibility | The extent to which a product works with other products, systems, or requirements. |
Expectation Gap | The difference between what the customer expects before the purchase and what the customer actually experiences afterwards. |
Feedback Loop | A process in which experiences and feedback are systematically used to improve product information or services. |
We have only just begun.
Magne
Friend...
Now I see product information in a slightly different way.
The buddy
How then?
Magne
Previously, I thought we had product information because the customer needed to know something about the product.
The buddy
And now?
Magne
Now I think that each information element should have a task.
The buddy
Seam?
Magne
Reduce an uncertainty.
Explain a difference.
Answer a question.
Make products comparable.
Or help the customer understand what fits their needs.
The buddy
We've come quite far then.
Magne
But I also see how much work this can be.
Imagine a smaller online store with several thousand products.
Product data must be quality assured.
Descriptions need to be improved.
Customer questions must be analyzed.
Comparisons should be built.
Information should be updated.
The buddy
Yes.
Magne
Then we need people.
But maybe humans can also get some help?
The buddy
Now you open the door to the next part of the story.
Magne
Artificial intelligence?
The buddy
Yes.
So far, we have primarily talked about what the business needs to know and what the customer needs to understand .
Now we can ask how AI can help employees make this knowledge available.
Magne
So it's not AI replacing the store employee...
The buddy
...but AI can become a new work tool.
Magne
Then I know what question we should ask.
The buddy
Run.
Magne
How will artificial intelligence become the store's new employee?
The buddy
There we continue.
For friend...
We have only just begun.
Academic background and further reading
This series also builds on my own professional journey through the Web Design study , the eMarketing study , Innovation and Commercialization and professional seminars in San Francisco and Oxford . Here you will find the background, professional environments and experiences that have followed the development from the early years of the web to today's digitalization.
Recommended literature
Developments in artificial intelligence are moving faster than perhaps any other field of study in our time. No single book can provide all the answers, but good books can provide a solid foundation for understanding the technology, the opportunities, and the challenges.
In the KI-Kompis series, we therefore recommend a selection of books that illuminate artificial intelligence from different perspectives – technology, strategy, management, innovation, ethics, digitalization and practical application. Together, they provide a broader understanding of how artificial intelligence affects people, businesses and society.
Click on the book icon to see the full literature overview with recommended books on artificial intelligence.
Recommended books from our library
Human Compatible: Artificial Intelligence and the Problem of Control
Author: Stuart Russell
Short review
Human Compatible is considered one of the most important books on artificial intelligence and the future of humanity. Stuart Russell explains in an easy-to-understand way why the development of artificial intelligence must be based on human values, ethics and responsibility. The book combines research, philosophy and practical examples, and gives the reader a deeper understanding of both the opportunities and challenges that artificial intelligence represents.
Why we recommend the book
This is a book that anyone who wants to understand artificial intelligence should read. Stuart Russell is one of the world's leading researchers in artificial intelligence, and the book provides a unique insight into how humans and artificial intelligence can develop together in a safe and responsible way.
You Look Like a Thing and I Love You
Author: Janelle Shane
Short review
With humor, warmth, and a host of entertaining examples, Janelle Shane explains how artificial intelligence actually works. The book shows why language models and other AI systems sometimes make surprising mistakes, and how these mistakes help us understand both the strengths and limitations of the technology.
Why we recommend the book
One of the most easy-to-read and entertaining introductions to artificial intelligence. Perfect for those who want to understand how AI works without having to read heavy technical books. A book that makes complicated topics easy to understand.
A Brief History of Intelligence
Authors:Max Bennett
Short review
This fascinating book takes the reader on a journey through the history of intelligence – from the first simple organisms to today’s advanced language models and artificial intelligence. Max Bennett combines insights from biology, neuroscience, psychology and artificial intelligence and shows how understanding the human brain can help us develop better and more responsible AI systems.
Why we recommend the book
A unique book that places artificial intelligence into a larger historical and scientific perspective. Perfect for readers who want to understand what intelligence really is, and why the development of artificial intelligence is a natural part of human technological history.
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