How to use guided search to manage choice overload

By the end of this post you will know why most sites find it very difficult to design navigation and filtering strategies for their users, why it’s such a valuable problem to solve, and one possible approach to starting to solve it.

It is proven that humans are entirely overwhelmed by too much choice. On the other hand, limiting choice too much is also a disaster, meaning it increases the chances you don’t find what you’re looking for at all. It is important to have something to compare to, but we must be able to hold that comparison in our head, and we cannot do that with many options at the same time1.

That is why web navigation and filtering proves to be one of the most difficult things to get right, but equally, has the most potential to increase revenue. You must make an accessible path to all available choices, while giving the user the control over which paths they take to reach a given subset of choices. Your job is to get the user on the path they need to be, to the product/s they want, with as few possible activities/clicks as possible, giving just enough choices along the way.

So what can we do to keep enough choice but reduce the burden of the consumer so it’s not overwhelming? One option is to offer a guided shopping experience. This isn’t new in the offline world. In any physical store, the sales associate is a guide. If it were economically viable, having a helpful salesperson for each individual customer should theoretically increase the number of happy customers you have. If you visit a new city, a taxi driver can be your guide. If you are in a restaurant, the waiter is your guide.

The principle is always the same. Someone has knowledge of most possible choices, at least more than you. You give them signals, which they interpret to reduce the choices slowly to a manageable subset for you, and then you can comfortably choose. What is the guide actually doing? They actively help you to find what you are looking for. The word active is important. It differentiates a guide from something like a map. A map is passive. It gives you all the possible options, and it is up to you to navigate through them on your own. Here is a more complete example: Imagine you enter an enormous flea market all by yourself. You are looking for a rug for your living room, and there are over 500 stalls in this market. What do you do?

The first step is illustrated below, it is the “type” problem decision tree, going down the path of each type to the depth which can help you find the single type you need.

guided discovery flow

You reached the rugs. The emphasis now changes completely. You are no longer refining by the type of product. You are now refining by the attributes of a single product type, meaning – the “kind”. This is an important stage. The heuristics have moved from looking for things which meet the definition of “rug”, to the heuristics of a “suitable” rug. Again, the “kind” of rug. Put another way, a type is more akin to a category, in that categories should be mutually exclusive, they should not overlap too much. Otherwise, it creates difficulty for the user. Kind is more akin to a set of filters, they can be used in combination with each other.

The next step begins, the kind problem. You work backwards from your definition of suitable, to attributes or characteristics that rugs possess. Once that map has been made, you can match up as many of those attributes in one rug as possible. If it is the right price, then perhaps that is your rug.

search sorting
Image via rugs.com

So what actually happened here? There were three distinct stages in your search:

  1. Reducing the breadth of products to search through, by limiting to one type i.e. rugs
  2. Translating your preferences into attributes of that type i.e. what kind of rug
  3. Using comparison to try and find the rug meeting as many attributes as possible

Why is this made easier if it is a guided experience, and what does that mean in this sense?

The approach we just illustrated is entirely passive. The site in question did nothing active to help the customer, leaving them to do all of the work. You used the site’s static functionality to slowly reduce the number of products shown, by giving it ways of filtering the overall number of products for you.

How can it be improved by being made active, or put another way, guided? The easiest way to think about this is to try and find ways to reduce the cognitive load or overall effort on the part of the end user, in the areas where they have choices to make.

When it came to calculating choices, what do you think were the hardest parts of that journey in terms of effort? Typically, it’s mapping customer preferences to attributes, and the process of trying to find the subset of products that most meet that combination of attributes.

Therefore we can infer that the overall navigation to reach the “type” is not the first thing we need to optimise. It is the “kind” problem which causes the most difficulty. This is where guided experiences shine. It is also where they are really hard, because if you get it wrong, it’s useless.

There are a small number of steps you can take to design a guided experience optimally:

  1. Track. What attributes of each type of product are people refining by the most? For example, if you’re selling rugs, you should track what % of users in the rug category refine by size, pattern, material. You should track what combinations they refine by, right before a product is clicked on. You should track in what order refinements are generally chosen.
  2. Interview. Let’s say size is the first commonly refined attribute. Ask people what determines the size they choose. Track the most popular determinants. This helps you build your map.
  3. Design. Give the user a modal, with a list of the most commonly used determinants from across the commonly chosen attributes. For example:
    1. Style
    2. Maintenance
    3. Budget

      Ask the user to choose which determinants matter to them and if they wish to add others. Grab the attribute linked to each chosen determinant, and ask them to choose relevant options from each one.

    4. Let the user know as they are choosing, how many products will match their selections, as well as other possible attribute combinations. Provide different paths i.e. if you increase budget by X, you will have Y more options. The Airbnb price slider is a good example of this.

    5. Make the process as visually stimulating as possible with rich imagery and also rich text descriptions. Ensure it feels like the user is crafting their perfect rug rather than digging it out from a big pile.
    6. Feedback. If you find that the vast majority of people prioritise one thing in their map, consider adding it as a primary step in the navigation instead. For example, if you sell leggings, and you find most people first refine by sport, consider adding sport as a subcategory of leggings, as seen on Sweaty Betty (picture below). You can also see a similar concept in action on Rugs.com with the second picture below, where they combine different kinds of attributes of rugs into a menu of the most popular refinements i.e. modern, washable, Persian etc.

Why do these points help? We said that guided experiences are hard. It’s true. You have taken over the burden of the search, you are now the consultant. Therefore, if you don’t give the user enough flexibility to give you enough/the correct information, or if you get those possible options wrong, the user may as well not have bothered. These points ensure the experience is flexible enough to receive enough data from the user, but rigid enough to ensure it can actively reduce the load on that user by helping with their map, and their combination troubles.

To sum up, we looked together at:

  • How there are two distinct modes of searching: by type and by kind, where type involves high level navigation and kind involves specific attributes of any particular type
  • That all search is a balance of recall (breadth) and precision i.e. with each consecutive signal of intent from a user, the precision increases, and the breadth should decrease
  • That the cognitive load is present most in the by kind search stage and therefore the opportunity for guidance exists the most in that stage
  • How implementing good guided experience involves tracking, interviewing, designing, stimulating and feedback loops

If you have built your own guided experience, please, tell me how it went. What am I missing here? The more we talk about this, the better we can build for each other as consumers.

This blog post was inspired by an article from Baymard research.


1. Footnotes:

About the authorMatthew Foyle

Matthew Foyle

Solutions Engineer @ Algolia

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