The fashion specialist’s customers particularly value the company’s product quality and service, which is lived out across all channels. Walbusch has successfully implemented email marketing for quite some time already. In the past, the main focus was primarily on the placement of sales-related offers and subjects as well as targeting branding issues. Aside from its general communication measures with continual purchasing incentives, new subscribers are also welcomed and dormant customers reactivated via email. Walbusch now wanted to expand its email marketing through the addition of highly personalised product recommendations. The company aimed to further increase its click and conversion rates in this manner.
Walbusch chose Episerver Campaign for its solution. It enables highly individualised recommendations to be generated for the email channel in real time forMarh successful cross and upselling. Dynamic customer profiles are the basis for the customised newsletter content. The click behaviour in the newsletter is used for the individual placement of the personalised purchase recommendations. Furthermore, the clicks made in the online shop as well as the recipient’s surfing behaviour are also taken into account. Episerver Campaign Recommendations then automatically inserts the tailor-made product recommendations in the newsletter. All content is only created and loaded at the moment when the newsletter is opened for the first time.
Walbusch is synonymous for high-quality, comfortable fashion. The company offers the complete services of a multichannel provider: besides a regularly printed catalogue, customers can make purchases via the online shop or in one of the approximately 40 retail outlets.
In a step-by-step manner Walbusch approached the numerous possibilities Episerver Campaign provides. Over several months, the fashion specialist included a placeholder in all newsletters for test purposes. The entire distribution list was then divided into three separate recipient groups for each new dispatch and the recipients were individually addressed according to the type of recommendation:
Customers that had viewed a blue summer shirt in the online shop also received comparable tips from the shirt category, which were then displayed beside the blue shirt. For this purpose, the recommendation algorithm drew on data from previous purchases of similar customer groups.
The product recommendations displayed were based on the dynamically created click and surf history of the recipient. In cases where there was no such previous history, top seller recommendations were used instead. At all times, only the clicks and purchases of products from the recommendation area of the corresponding newsletters were recorded. Walbusch also stipulated that items already purchased by the customer were not to be displayed again. Overall, the targeted recommendations were displayed in more than half a million opened newsletters.
The highly personalised purchase recommendations showed the best results within our test. The preceding development of accurate customer profiles used for the product recommendations requires continual tests and a little patience. I can especially endorse recommendations that are based on the click and surf behaviour of individual customers and that also relate to other shop visitors.
Bastian Reuss, Online Product Marketing Manager at Walbusch
The personalised recommendations based on individual click and surf behaviour proved to be particularly successful. With these types of individualised offers, the click rate was boosted by as much as 57 percent compared to the recommended top seller products – and the conversion rate skyrocketed by 135 percent. Significant uplift could also be recorded due to personalised product recommendations when compared to recipients that were shown the previously clicked products: the click and conversion rates were 9 and 27 percent higher, respectively. All figures express the change to the click and conversion rate compared to the other test groups and exclusively refer to the recommendation paragraphs. Walbusch therefore effectively exploited its cross and upselling potential.
The fashion specialist also gained a whole range of valuable insights. Among these was that the click rates were significantly higher in the cases where a customer history existed. It was also evident that the accuracy of the recommendations improved over time as Episerver Camapign “learnt” step-by-step from each individual customer as well as from the behaviour of other customer groups in the shop. In addition, the share of customers that could be associated with a click and surf history grew over time, which increased the pool of recipients for personalised recommendations.
Overall, it was clearly evident that Walbusch customers paid significantly more attention to the personalised, behavioural-based recommendations in newsletters than to top seller offers or previously viewed products.
A number of reasons could be identified for this:
Summary: Walbusch could further enhance not only the already high relevance of its newsletter – but also its close relationship to its customers.
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