Retail and ecommerce trends 2022

In this interview, Nicolas Mathon, Chief Strategy & Innovation Officer at Attraqt, explores the major retail and ecommerce trend of the year: Product Discovery

Retail and ecommerce trends 2022
Posted by Attraqt | 14 February 2022
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In this interview, Nicolas Mathon, Chief Strategy & Innovation Officer at Attraqt, explores the major retail and ecommerce trend of the year: Product Discovery

Attraqt is doing a lot of work in product discovery, can you explain what it is in more detail?

Product discovery includes everything that enables the discovery of new products on an ecommerce website. It applies to e-tailers who have large catalogues and can therefore benefit from recommending relevant products to their shoppers.

There’s a variety of ways this discovery can take place. The first and most obvious way is of course the search engine, which gives access to a list of items based on keywords. Another way is through merchandising. This requires website categories to be coherently organised with logic applied to promote certain products.

Lastly, via product recommendation. There’s the famous “people also bought…”, but there’s other elements that liven up an ecommerce website and demonstrate the diversity of products to each visitor in the best possible way. This is called the product discovery journey.

A well-constructed product discovery journey not only benefits the customer in helping them discover, find, and have a better experience but also the e-tailer by driving higher average order values and better conversions. We work on this concept with all types of local and international ecommerce players.

How does it work for e-tailers who want to implement your solutions?

Sometimes the entry point is a technical one, for example if a company wants to replatform or add functionality to their site. But more often, it’s about merchandising, in which case we talk to the people running the catalogue and sometimes the marketing/ acquisition teams.

As our platform is based on a lot of Artificial Intelligence (AI) functionalities with the option for clients to develop their own algorithms, the data science teams of our most mature clients also get involved.

Once the platform is deployed, all those people work together.

Technically, there are three main parts to the implementation. The first part is the product catalog. We have APIs that can be used to send it out, as well as integrations with common ecommerce platforms such as BigCommerce and Shopify. The integration is either native or requires some specific development if the client sends us their catalogue directly.

The second part is related to tracking and the collection of data on website users’ behavior. Either a JavaScript tracker is placed on the customer’s website or an API can be used on mobile applications. Offline data can also be passed on from shops relating to sales, shopping carts or receipts. These are feeds that can go through APIs or can undergo native integrations with the ecommerce platforms on the market. We’re also tag manager partners with systems such as Google Tag Manager.

Finally, the third part (and the most important) is the implementation of functionalities. Our APIs enable a website’s search, recommendation and merchandising functionalities to be activated. It’s usually the customer who implements our APIs to test our platform and find out what to show, to which user, on a particular touch point, on the website or through apps. If the customer has few or no internal resources, this can also be done by our own teams or by partners who help us provide support to our customers in implementing their solution.

What are the main techniques to best help the shopper on their journey?

I think it’s best to differentiate two types of shoppers, or at least the two states of minds shoppers might have. Sometimes they know what they want, and we help them find the right product, in the right place, at the right time.

The shopper usually goes through the search engine using expressive search terms. In fact, 43% of users on retail websites go directly to the search bar. And as many as 68% of shoppers would not return to a site that provided a poor search experience. Therefore, we use relevance technologies to ensure the product that appears is an exact match to what the user is looking for at that moment.

On the other hand, sometimes the shopper is not sure what they want and they the site to do some virtual window shopping. Personalisation has a great impact here because it suggests a range of relevant products to the individual user from a rich inventory, either to complement a previous purchase or because of their behaviour on the websites.

Our job is therefore to identify which state of mind the shopper has and respond accordingly.

What sorts of criteria can you use to personalise the experience?

The criteria is very broad and the configurations are different for each customer. However, there’s certain criteria that will reoccur such as the products in the shopping cart, the products that have already been purchased, or the products viewed by the shopper.

We also consider the user’s characteristics that we know from the CRM. Is it a man? Is it a woman? Are they urban? More of a city dweller? More of a provincial person? What is their geographical area? How old are they? Do they have a loyalty card? How many points do they have on their loyalty card? How often do they make purchases? There’s alot of things that can be used to understand the shopper so we use various algorithms. Some are better for what we call anonymous data. We might not know who the visitor is, but we can see how they behave on the website in real time and adapt our recommendations. Other recommendations are much more focused on segmentation and really understand the typical profile of the person and react accordingly.

What would be your advice for an optimal user browsing experience?

In everything we do, throughout the product discovery journey, the first objective is to serve the end user. The user experience is key so must be the main objective, even before business performance. I think that’s one of the most important aspects for the user to have a frictionless and omnichannel experience.

For example, imagine someone opens Instagram and sees an ad that redirects them to the mobile app, where they then start browsing a certain type of product. When they return an hour later using their Mac at home, we need to be able to understand their journey, because they may have wanted to make the purchase from their laptop at home but not from their phone while on the metro.

We need to be able to offer a seamless and coherent experience. This means showing the same products and highlighting products they have recently seen. We must avoid thinking in terms of functionalities, but rather try to leave behind silos and touch points and think horizontally: I have this type of customer in this state of mind, with this path. This is the experience they should have throughout their journey. This is what makes for a successful experience.

What are the advantages of creating a product discovery journey?

The first KPIs are very straightforward and easy to calculate. They are the increase in turnover, the average cart, the conversion rate… Then there’s secondary but no less important KPIs that emerge during a more advanced analysis phase. This relates to customer loyalty and everything that makes up a web user, because when they have a good experience, they come back to the website to repeat the experience.

We don’t think about these aspects as much, but they still have a strong impact on a business. For example, the product return rate can be affected. If the personalisation or the list of related products is poorly done, there is a greater chance the customer will be unhappy or make a mistake and return their package. AI can also help reduce this return rate by ensuring that the suggested products are as relevant as possible to the shopper.

Another secondary KPI, but one that has a significant impact for retailers, is the time humans spend on organising catalogs. Product ranking can be partially done by AI so more time can be spent on areas that need significant human creativity. We think this mix between automation and creativity, known as human control, is a crucial issue.

In your opinion, what will be the major challenges and areas for improvement for retailers in 2022?

The end of cookies is set to be a major challenge for retailers. All the technologies we just talked about were 100% cookie-based until two years ago. Without them it will reduce the ability to analyse the customer journey. This means we won’t have access to the same data to fuel learning and AI.

But we still have solutions and a range of use cases that can be deployed despite this issue. This involves creating a much stronger relationship of trust with shoppers. They are now the real decision-makers on what features they do or do not want to activate and what they do or do not want to share. They will require much more encouragement to open an account on a website and log in, which will lead to a rethinking of the structure of websites in order to add features that bring real added value.

So, to stay ahead of customer expectations, retailers will have to invest in alternative methods of obtaining shopper behaviour and preferences data in 2022.

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