Todays e-commerce companies are doing everything they can to optimize their conversion rate: In doing so, they concentrate, for example, on preventing purchase cancellations, facilitating navigation in the web shop or optimizing search results. However, there is a critical size whose optimization would mean enormous added value for retailers, but which is currently hardly being worked on, as there are simply few possibilities: The bounce rate.

The bounce rate is defined as the percentage of visitors who enter a page and leave it immediately (after about 5-10 seconds or on the first page). The reasons for "bouncing" are quickly found: Either the shop loads too slowly or the prospective customers do not feel well taken care of because their expectations were not fulfilled: Perhaps the hoped for products are not offered, perhaps the visitors do not find their way around well or are addressed incorrectly by the design and the tonality, so that they would rather look around in another shop. In the first case, it is clear what needs to be done. The second case, on the other hand, is considerably more difficult and is considered by most shop operators as hardly solvable, if at all. Because they still have no knowledge of the users who enter their shop for the first time. Optimizing the online shop for these unknown visitors seems impossible.

Classical analysis tools do not manage to "get to know" prospective customers before or during the first visit to the shop. The reason: You do not have enough or no data about the respective user and can therefore not react individually to him. They fail in particular because of an intensive comparison with previous buyers, who are very similar to the current user, because a) they do not carry out a real-time analysis and b) they do not include sufficiently many different characteristics (so-called dimensions) in their analyses.

As soon as a visitor enters an online shop, ODOSCOPE performs a high-dimensional real-time analysis: infinitely many different characteristics are included here, such as the day and exact time of the visit, the device used, the location, the current weather or the campaign that led the visitor to the shop. This reveals completely new relationships to previous purchases, which in turn allow conclusions to be drawn about the buyers themselves. For example, a visitor who enters the shop on a Wednesday morning with his PC via a targeted business newsletter will have different interests and intentions than someone who surfs on Facebook with his tablet on a rainy Sunday evening after the day's topics and is directed to the shop. These correlations in the data cannot be brought to light by conventional tools or manually. By searching for analogies to similar buyers from the past, the OI platform immediately identifies the current customer/buyer "type" and prepares the shop offer accordingly - adapted to the needs of the current visitor.

The OI system is based on classical web analysis tools and uses its existing, anonymised mountain of data (raw analysis data) as a wealth of experience. It has access to a huge pool of historical data and analysis results. If a new visitor enters the shop, all its relevant features are identified and compared with the historical analysis data. Through a real-time correlation analysis, the system finds out what, i.e. which content, which design, etc., has worked best for similar visitors in the past. For example, instead of displaying on the landing page what worked best on average for all visitors, the OI system displays as exactly as possible the content that is really relevant for the current user. With the OI solution, the shop offers the user exactly the incentives to stay. In addition to the product range, key visuals, images, sliders, main categories, bargain offers, content, etc. can also be adapted - elements that are usually already stored in the CMS or shop system.

An example: A user who is looking for a new computer is redirected by Google to the landing page of the shop "". As the online shop is very large and has a huge product range, it is important that the visitor is shown relevant products on the start page and that the page is designed according to his preferences. With the help of the OI platform, the shop directly displays the laptops that are most likely to please this visitor. Through real-time correlation analysis, the system immediately determined that the user was very likely to buy a laptop rather than a stationary PC. All characteristics such as manufacturer, price range, monitor size, performance and condition are taken into account and adapted to the possible interests of the user. In addition, the system also recognizes that this user is surfing with an iPhone and makes the shop correspondingly mobile-friendly. In this user- and customer-oriented way, the needs of the new visitor can be directly addressed - as in a stationary sales talk: The complete shop is designed in such a way that it offers the optimal shopping experience (customer experience) for each individual visitor. And because the interested party will be properly addressed, well advised and comfortable right from the start, he will stay and not bounce.

Mehr Insights zur CX-Optimierung?

Jetzt Dein Whitepaper sichern und von Top-Experten wie Zalando, konversionsKRAFT & Co lernen!

Kostenlos herunterladen

Subscribe to our monthly newsletter