On its online platform asambeauty presents a dream world of over 500 high-quality beauty articles of its own brands. But such a variety of products can also quickly be overwhelming and make it difficult for the user to find the right article. Experience in our Best Practice how situation aware recommendations - the next generation of recommendations (RECOs) - enable asambeauty to guide shop visitors through the product jungle with really relevant offers. And thereby increase shopping basket values and sales enormously.
Starting situation: The customer journey through the product jungle
As a traditional family business, Asam Kosmetik has reinvented itself several times in recent decades: After starting as a drugstore production company in the 1960s, the company began to take over the production of active ingredients in its own hands in 1983. After intensive research and development, the company founded the M.Asam brand in 2002, which it quickly increased in popularity on the German market, primarily through teleshopping. Later, the range was expanded to include further high-quality product lines, which can also be ordered online since 2011. In 2017, the online shop expanded and transformed into the beauty platform asambeauty.com, which offers a variety of products of all own brands. Today, more than 500 products from various areas of the beauty world can be found there.
The large assortment offers a dream world to browsers and returning customers - full with high-quality beauty articles exclusively from private labels. But for existing customers in particular, this new variety of products can also be overwhelming and make the search for the right article more difficult. So what is the best way to show online shoppers a way through the product jungle and offer them individually tailored products?
The beauty platform asambeauty offers a high-quality product variety for every age and care need.
Problem: When conventional recommendations reach their limits
For this purpose, asambeauty has so far relied on conventional product recommendations. These are based on the shopping behavior of historical customers who viewed or bought similar products to the current user. Through offline clustering, they are played out in the style of the well-known slogan "Customers who were interested in this product were also interested in...": On the product detail page, the add to basket page and in the shopping cart. For many visitors, however, these classic RECOs were of little relevance.
As an example, imagine a young lady looking for inspiration for a new, stylish face lotion. Her customer journey begins after she has been transferred from the social media platform Instagram to asambeauty.com. On the home page, she is now suggested the new products and bestsellers of all customers - individuality looks different. If she navigates to the product detail page of lotion. Again the actual preferences of the young woman remain unconsidered. If she adds an item to her shopping basket, she discovers the next recommendations, which again do not meet her interest in a new, stylish lotion: Instead, the traditional RECO tool only suggests products that customers with identical shopping baskets have also bought.
In fact, the woman may only find what she is looking for when she actively searches the skin care products by leafing back and forth through a few pages. Not every visitor is willing to do so. Instead, a considerable proportion of users are more likely to jump off and switch to the next beauty platform. In order to prevent this, the beauty platform was looking for a solution that would enable each individual visitor to be recommended the most suitable products
Blind by choice. Large product ranges often make it difficult for online shoppers to find the items they are looking for.
Solution: Customers who are like YOU
For this asambeauty relies on the next generation of recommendations: situation aware recommendations by ODOSCOPE. This approach enables the beauty platform to tailor the product suggestions to the individual needs and the current shopping situation of each individual user.
This is possible because ODOSCOPE makes use of the company's existing data and uses it profitably: The Operational Intelligence Platform (SaaS) combines product and category feeds with historical tracking data from the beauty platform. If a current visitor enters the shop, ODOSCOPE determines an individual peer group of historical users on the basis of this database and at the moment the page is accessed. These are similar to the current user in terms of their personal needs (e.g. skin, hair or body care) as well as their situational characteristics (e.g. Sunday evening on the couch with the tablet via Instagram vs. Thursday morning with the desktop PC from Google). Based on the surfing behavior of this peer group, individually relevant product suggestions for the current visitor are determined and played out - fully automatically and in less than 20 milliseconds. In contrast to conventional personalization or classic RECOs, the current shopping situation plays a major role here: ODOSCOPE therefore calls the process "situationalization".
Situation aware product recommendations can influence the entire customer journey of the current online shopper within seconds and lead to a better shopping experience.
The situation aware recommendations correspond with much higher probability to the wishes of the current shopper, so that he is accompanied on his Customer Journey with individually relevant contents and advised personally: Drawing on the example of the young lady, she is compared with historical users and their surfing behavior within a few milliseconds. Her location in Hamburg, the log-in via WLAN, the use of an Apple system, her tablet as a device, the access time on Sunday evening and finally the forwarding of instagram (referrer) all represent relevant situation data. These are available even for unknown users, can be linked to their surfing behavior and finally allow the classification into a peer group of users who are very similar to the young woman.
Thanks to this situationalization, the lady is already made aware on the start page of articles purchased by similar customers in a similar shopping situation. On the product detail page, the desired lotions in a stylish design could then be suggested directly, because these were also considered by the individual peer group. When the shopping basket is opened and during check-out, articles are finally recommended that customers have bought in a similar situation. Last but not least, our woman also saves time-consuming searches and filter settings because the articles on the product lists are also sorted according to the individual relevance for her specific peer group.
Different recommendations for each situation: The interests of the young lady in our example probably differ greatly from a user who enters the beauty platform on Thursday afternoon with Firefox and a desktop PC via Google and clicks on hair care products. Asambeauty responds perfectly with situation aware recommendations.
Success factor: Situation aware recommendations for EVERY customer
Situationalization differs from conventional RECO-methods especially by two factors: First, not only personal, but also situational data are made available for the calculation of suitable product recommendations, which guarantees a more extensive database. Secondly, the disruptive OI technology in the background ensures truly customized results: Thanks to In Memory Grid and real-time clustering, the diverse database neither needs to be simplified nor modeled.
This means that existing customers can immediately find the most relevant products for them with the help of situation aware recommendations. This guarantees a better Customer Experience! In addition, inspiring suggestions encourage users to make more spontaneous purchases. Because the usable situation data is available even from first-time visitors or anonymous users, asambeauty can even present situation aware recommendations to them - before their first click and absolutely GDPR-compliant. asambeauty uses the product recommendations at various touchpoints of the Customer Journey to offer an individual product range with relevant product content that appeals to each individual user right away.
Enthusiastic customers are our best reference.
Pioneers of the online trade choose ODOSCOPE and are convinced: by best results, less work, customer-centered service.
Project success: Significant increase in shopping basket values and revenue
As a result, shopping basket values and sales on the beauty platform could already be significantly increased during the test phase. This was particularly noticeable on the product detail page: Compared to classic RECOs, situation aware recommendations provided an uplift of around 4% for the products purchased and the profit per visit. For tablet users, sales there even increased by 11%. Because of this outstanding performance, asambeauty rolled out the situationalization solution for the entire shop after the test phase, so that the previous RECO tool could be completely replaced.
Would you also like to generate 20% and more turnover uplifts? Get to know the potentials of situationalization for your individual shop. Request your Live Demo here!