Intershop Communications AG | Press Release |
Dynamic Personalization from Intershop increases revenue
Intershop's Recommendation Engine enables online retailers to provide personalized product recommendations and content
- Online businesses utilizing personalized recommendation show increase conversion rate and basket size
- Intershop Recommendation Engine combines existing customer data with current customer activities in real time
Jena, October 20, 2009 – The dynamic Recommendation Engine from Intershop enables online retailers to provide personalized product recommendations and content to their visitors. Using this tool, they increase conversion rate, customer retention, and the value of the average online shopping basket. The solution is able to analyze a variety of customer profile information and adapts recommendations in real-time using a self-learning algorithm. Because there are no complex business rules to implement and manage, the administrative effort is minimal and product changes can be automated within the system.
Individual Recommendations for your full range of users
Intershop’s Advanced Recommendation Engine can provide a personalized shopping experience for all users, from registered customers to anonymous visitors as well as predefined customer segments, such as employees.
When an existing customer enters the online shop, the Recommendation Engine accesses all available data from the customer profile including: browse, search and order histories, product evaluations and recommendations, downloaded materials and wish list items. Based on these criteria, the solution makes personalized recommendations for products, contents, and alternate search options.
In order to quickly convert anonymous visitors into buying customers, the engine utilizes their browsing behavior in real-time. How long a visitor stays on a certain page, which search terms are entered, and which recommendations are followed are all examples of data that are used by the system to further adapt its suggestions. The application can also be used for special customer segments, such as employees, and can incorporate company policies within recommendation rules for further configuration.
Connecting dynamic analysis in real-time with existing data
By using real-time analysis in combination with existing profile data, the accuracy and relevance of the recommendations is further improved. Enhanced recommendations lead to a better overall customer experience and in doing so increase sales and customer retention.
Dirk Lauber, Head of E-Commerce at Baur, a daughter of the Otto-Group, states how the solution lives up to its promises for their online-fashion store in every day live: "By using the Intershop Recommendation Engine we have completely uplifted the personalization capability of our site and achieved a considerable increase of the conversion rate and the size of the average online shopping basket."
Intershop Communications AG (founded in Germany 1992; Prime Standard: ISH2) is the leading independent provider of omni-channel commerce solutions. Intershop offers high-performance packaged software for internet sales, complemented by all necessary services. Intershop also acts as a business process outsourcing provider, covering all aspects of online retailing up to fulfillment. Around the globe more than 300 enterprise customers, including HP, BMW, Würth, and Deutsche Telekom run Intershop solutions. Intershop is headquartered in Jena, Germany, and has offices in the United States, Europe, Australia, and China. More information about Intershop can be found online at www.intershop.com.
This news release contains forward-looking statements regarding future events or the future financial and operational performance of Intershop. Actual events or performance may differ materially from those contained or implied in such forward-looking statements. Risks and uncertainties that could lead to such difference could include, among other things: Intershop's limited operating history, the unpredictability of future revenues and expenses and potential fluctuations in revenues and operating results, significant dependence on large single customer deals, consumer trends, the level of competition, seasonality, risks related to electronic security, possible governmental regulation, and general economic conditions.