Guided selling
7 min
reading time

✔ Why your filtering assistant is no match for Aiden

Written by
Marja Silvertant
Published on
27/2/2024

Guided selling comes in many forms and one decision aid is not the other. But where is that in it? To answer that question, we thought it would be useful to first take a look at the most common form of guided selling: the filter selection aid.

Guided selling comes in many forms and one product finder is not like the next. But where are the differences exaclty? To answer that question, we thought it would be useful to first take a look at the most common form of guided selling: the filter selection aid.

In this article, we share the advantages and disadvantages of a filter selection aid. As an example, we use Coolblue, the second largest e-commerce player in the Netherlands, with more than €2.3 billion in turnover in 2021. We take a look at their choice aid for televisions — the company's largest category, and a product that every consumer knows —.

So we're not making it easy for ourselves: it's one of the biggest e-commerce players, with virtually unlimited resources, it's about their largest category and a well-known product. We therefore expect this decision aid to work perfectly.

How does it work?

For each television, the manufacturer provides technical data to the webshop, also known as product features. These attributes are shown as filters on the lister page of each product category:

To help customers discover which televisions are relevant to them, Coolblue offers a filter selection tool that presents the characteristics in conversational form (question-by-question):

In the example below, customers are asked about their TV's preferred screen size and can choose from three answers: Small, Medium, and Large. Each answer is explained using some textual and visual content:

The answers are directly linked to the available product attributes, which means that each answer automatically selects the corresponding filter on the lister page. Once each question has been answered, the “product advice” is complete: the lister page is now filtered and only shows the televisions that meet the selected characteristics. In this example, there are 39:

In short, a filter selection aid asks the customer various questions about the available product features, and then applies them as filters to a lister page add.

Let's take a closer look at this. What works and what doesn't?

Advantage: It's easy

Asking a few quick questions is a pragmatic way to help your customers find the right product. It always provides more than no help at all.

Advantage: it is scalable (*)

With a filter selection aid, you rely on your product data as a source of help to your customers. The big advantage is (or should be) that your product database already exists. You've invested a lot in building and enriching it, so it makes sense to make good use of it.

So, in theory, relying on your product data should work and scale. When new products enter your store, the corresponding product attributes are uploaded to the database. The filter selection aid has direct access to those characteristics and can include these products in product advice.

However, there is one important condition. Having access to complete, accurate, and consistent product data is an ongoing concern for most retailers. If your product data isn't perfect, your filter selection aid won't be able to help your customers properly.

Disadvantage: it's not about people, it's about products

Product data contains the technical, objective characteristics of a product. For televisions, these include the number of colors, the refresh rate, the color depth, the sharpness, the weight and the year of introduction.

However, there is one problem. As a layman, these (technical) features don't mean much to you. And the features of a television may still partly appeal to the imagination (because more is better, right?) , but what about, for example, an iron? How many grams of steam production per minute do you need to iron your desired garments? Or, if you're looking for hiking boots, what's the implicit difference between a leather, synthetic, suede, and nubuck finish?

You must be (or become) a product expert to understand the technical features and their implications before tailoring them to your needs.

Another example. If you want to buy new headphones, you'll have to choose between features such as “wireless” and “noise cancelling”. As a non-expert, you just have to know that both characteristics will make your time on the train a lot more enjoyable.

So product features tell you what a product has, is, or can. But not why this is a good product for you.

Disadvantage: It's binary

Good product advice is nuanced. It is usually a trade-off between functionality, aesthetics and budget. For one customer, some of these factors will outweigh the others.

Filter decision aids lack nuance. This is because product attributes are binary: they are true or false. If a customer is looking for a “television” that costs “less than €1,500”, is “at least 55 inches” and has “ambilight”, a filter shows selection aid solo the products that meet this exact description. A perfect alternative that costs €1,501 does not appear in the results because it does not fit the filters used.

This type of binary data is also problematic when you're talking about preferences instead of demands. Whether it's the color of a device, the weight, or the brand: these are all preferences. Maybe you'll give the preferred on a Samsung TV, but you're still willing to try another brand if it meets your other requirements. Filter decision aids guide users through a funnel of tough demands that don't take such soft preferences into account. Even in the CoolBlue Selection Guide, there is no way to mark options such as a sound bar or ambilight as “nice to haves” or “optional”.

Finally, if multiple products meet a certain characteristic (e.g. supplied with a sound bar), a filter selection aid makes no further distinction between the products. Each product that appears in the advice has that specific option. So why does one television cost €1,339 and the other €2,569? Presumably (hopefully) there is a difference in the implementation of this functionality. However, these differences are not clear to the customer (after all: the lister page is only filtered without further explanation). The customer will therefore have to further investigate how the products differ from each other and which product exactly suits their needs. That is not veritable product advice.

Disadvantage: You're not solving your customers' needs

Imagine: you are in a store. You know, one of those built with bricks. You ask a sales representative to help you find the right television. She asks you a number of questions:

  • What are you going to use the television for?
  • What format of television are you looking for?
  • Do you prefer a brand?

“Thanks,” she says after you answer. “Give me a moment and I'll show you which TVs are right for you.” After a few minutes, she comes back and takes you to the warehouse, where she presents you with 75 televisions. “All of these are perfect for you!” When you then ask which ones to choose, she says she has sorted the televisions by relevance, and that you can use the characteristics to compare the products to make a decision.

This sounds like a laughable interaction, but sadly, it's still the best that 25 years of e-commerce has to offer. Here we see the completion of Coolblue's filter selection guide:

There are as many as seventy-five TVs selected, all of which are apparently perfect for “streaming movies”, have “excellent picture quality” and are “big” (>55 inches). The price difference between the cheapest and the most expensive is just €28,600 (2144%!). And here are the first few product results:

How does this help a potential customer? How can customers choose between these four, let alone the full seventy-five models? There is still a lot to investigate before it is clear which model best suits their needs and wishes.

What is the alternative?

Using a filter choice aid hardly helps solve your customers' most important problem: stress of choice.

Customers only buy once they are convinced that a product will work perfectly for them. That is why your goal should be to bring order to the chaos: reduce the amount of information, cut the number of products and thus prevent anxiety and stress of choice among your customers.

With only a filter selection aid, you will never achieve this.

This is because a filter selection aid is based on the needs of a webshop: “We have a dozen data is immediately available, and as long as our customers select the right features, we'll point them in the right direction and sell more.”

A real one guided selling app puts customer needs first. So:

  • Ask your customers questions about their intended uses and purposes. Such questions are much easier and more fun to answer than ticking off product features.
  • Provide a nuanced experience that distinguishes between “must-haves” and “nice-to-haves”, thereby avoiding the pitfalls of (binary) product databases.
  • Offer advice on which product features fit “perfectly” instead of just “good” (e.g. 5% over budget isn't great, but probably still good).
  • Limit the number of results and tell a customer how and why those products are a good fit for him. When you do this, you solve a customer's pain and problem.

You expect the above in a store, so why not in an online store?

We built Aiden to help make decision aids that lead to the best results - for the customer and for the webshop. And it works. In our case studies, you can read how our approach to guided selling results in conversion increases of up to 500%.

Stop losing customers to choice paralysis

Help doubting customers by automatically offering tailored advice, achieving 2.5 times more conversions.

We're happy to share our advice, completely free of charge