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A more personal customer approach: 5 tips for a better online shopping experience

By Regina Henkel


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Due to the Corona crisis, purchases are increasingly shifting to the internet. This increases the competition among online retailers. So how can the shopping experience be designed in such a way that customers are happy and shops are successful?

The current situation contributes to the digitisation of retail at an enormous rate. Online shops must be prepared for the fact that the online competition will continue to increase in the future, and this will also raise customer expectations and the standards when being approached. Munich-based technology company trbo specialises in automated customer approaches and optimises online shops for fashion and sportswear companies like the Otto Group, Engelhorn, Lodenfrey, Sport Schuster, Keller Sports, etc.

With the AI-based platform technology, content and product selections of websites can be customised individually - adjusted in real time to the situational needs of customers on their customer journey. A self-learning algorithm analyses user behaviour on the basis of about 50 visitor characteristics and subsequently delivers various targeted contents. We have spoken to Kira Schirl, chief operating officer of trbo and expert for AI-supported onsite personalisation, about how fashion retailers can offer their customers an even better online shopping experience. We have put together five tips.

1. How does a user come to the website?

The correct approach starts with the channel through which a user comes to the online shop. Because via the click-in channel, one can already deduct different user interests. Users who come from price comparison sites are mainly interested in offers - they are the classic bargain hunters. Therefore, they should see the best offers directly on the site; the sales category should be clearly highlighted. If they enter directly on the product page, they should also immediately see the savings - the display of strike through prices is particularly effective here. These users may also be convinced to purchase through an incentive - for example, a newsletter registration could be combined with a voucher.

When users come to the site from image-heavy platforms such as Instagram or Pinterest, they are more likely looking for outfit inspirations. They should be able to reach the inspiration pages of the website as quickly and easily as possible. Here, different complete outfits for various topics and occasions can be presented. If the user likes one of it, he or she can put the complete outfit with just one click into the shopping cart and does not have to go through long lists of black jeans, for example.

2. Where does a user enter the online shop?

Not only the channel through which the user comes to the site but also the entry page itself can be optimised to better meet user needs. For example, in many online shops, special promotions are often visible only on the home page. But not every user enters from the home page, but for example from a product page. Accordingly, promotions should be integrated on these pages as well. Depending on the channel or user type, the promotion can then be placed dynamically and accordingly, in a more or less flashy way.

Product pages especially can often be optimised, particularly when the online store uses Google Shopping ads because these have to lead the user to a product page. If the user does not like the displayed product, he or she leaves and the paid-for traffic does not even pay off. For this reason, product pages should be enriched with alternative product recommendations. Thus, the user might find what he is looking for among those alternatives and end up purchasing after all. With this measure alone, trbo has been able to increase the user value for one client by 23 percent.

3. What is of interest to a user?

When it comes to product recommendations, there is often a need for optimisation. Especially here, many shops do not use the full potential of an approach that is available to them. Many online shops show the same recommendations to every customer; mostly these are the top sellers from the entire selection or a particular category. But even more can be achieved here when user interests are taken into account. On the one hand, product alternatives to the jacket that was just viewed make sense. Those recommendations become truly personal when they are based on a user’s customer journey. An intelligent algorithm can then decide what suitable products to recommend. For this purpose, previous customers are divided into groups and their purchasing behaviour is analysed. Appropriate recommendations for other users are then based on this.

For returning users, recommendations and the content of the page can be further customised. If a user has already searched for different dresses and blouses among the womenswear category, the start page can be adjusted accordingly when entering the page next. Then menswear products will take a back seat and the user will directly find the best inspirations and recommendations for dresses.

4. When does a user visit the online shop?

The time of the day when a user comes into the online store can be used for a more individual approach. Regardless of whether seasonal events or the weather, there are many possibilities to use such triggers. For example, suggested articles or teasers on the start page can be matched with the current weather conditions in a user’s location. If one knows that the weather in the user's region will be fine on the weekend, one can, for example, create a choice of swimwear to increase the user’s anticipation of the weekend. If the user is in Hamburg and there, it is raining again, one can pick this up on the website and package it in a nice message: "Cloudy and gray again? Then bring some colour into your life with our colorful blouses!".

Seasonal events are just as suitable for a special customer approach. For Black Friday, users are looking for the best deals - these can then be provided with countdowns, for example, to increase the pressure to buy. Or if users are searching for gifts during the Christmas season, one can make their shopping easier with gift advisors - answering a few questions will narrow down the immense selection for users - until the perfect gift ideas for their loved ones have been suggested.

5. Where in the buying process has a user reached?

For each placement, the step of the buying process a user has reached is crucial. Users who just started searching can find inspiration with product recommendations that match the season or earlier purchases. A user who has been looking at a certain leather jacket for days is more likely to be convinced through a (time-sensitive) discount.

A step in the buying process that worries online retailers most is the cancellation at shopping cart level. A user views different products, even adds some to the shopping cart - and then leaves the site without completing the purchase. To prevent this from happening, it is advisable to remind users of their shopping cart again when they exit. Combined with a small incentive, they might still be convinced to buy. But even a complete shopping cart cancellation is no reason to bury one’s head in the sand. The next time the user visits, the forgotten shopping cart can be displayed again - maybe the user was merely looking for cheaper alternatives and did not find them.

Conclusion: There are many different possibilities for online fashion retailers to optimise the customer approach and thus the purchasing experience. It is important, however, for online retailers to know their customers - in order to then be able to fulfill their needs. It does not even have to be the first name - the click-in channel alone helps with personalising the approach. Which way is the right one varies from store to store. To that effect, online fashion stores should focus on A/B and multivariate tests, according to the expert, in order to gain valid insights into which type of approach works best.

This article was originally published on FashionUnited DE. Edited and translated by Simone Preuss.

Photos: Pexels.com; screenshots trbo

Artificial Intelligence
customer approach