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The number of emails is soaring. The need for automated email processing systems is also growing rapidly. Currently available email response solutions, however, are less than perfect. A key problem can be seen in categorisation?the process of sorting emails automatically?where only a combination of different methods will lead to a satisfactory solution. A young Saarbr?cken-based company, XtraMind, has developed a promising software package incorporating language understanding, machine learning and professional workflow management to come up with a solution. By , independent journalist The request for email response management systems is booming as email emerges as a popular means of communication between customers and companies. Customers send their inquiries quickly and simply to suppliers and ... wait and wait. A survey by Emagicon AG of Germany has found that, in the summer of last year, it took an average of 95.38 hours for a reply to come back, if it was returned at all. At the same time, the International Data Corp (IDC) documented about 10 billions emails per day worldwide for the year 2000. This number is expected to rise to 35 billion a day by the year 2005. According to Frost & Sullivan, American companies alone will invest more than two billion US dollars in 2007 to control this influx of email. Customer relationship and cost reductionAs a result of this email boom, a new field has recently emerged in customer relationship management ? called email response management (ERM). ERM is developing automated methods, based on language technology processing techniques, to speed up email handling as well as making it more efficient. Already 150 to 200 incoming emails a day could justify a properly deployed ERM system, from the perspective of retaining customer loyalty and reducing personnel costs. ?The software industry has risen to this challenge as well as it could,? says Hans Uszkoreitco-founder and Board Member of XtraMind and professor of computer linguistics at the Saarland University in Saarbr?ckenin a statement about the market situation. ?Often hasty and less-than-perfect solutions have been developed, which reduce the costs for email responses, at least to some extent.? But in Uszkoreit's opinion there are very few satisfactory solutions around today. ?The big challenge lies in the correct categorisation of email, for which humans use their language skills.? The basic problem lies in categorisationThe categorisation of most ERM systems is currently based on either statistical-mathematical techniques, or pattern-based ('pattern matching') procedures. Using the statistical approach, a system 'learns' the statistical characteristics of the different categories through an analysis of example texts. The relative frequencies of single words play a central role in this approach. This is why the procedure usually requires large amounts of text for 'training' the system, to provide acceptable results. This is not the case, however, with pattern matching categorisers, where searching by keywords is much more relevant. Keywords are either specified in advance or can be acquired through a machined learning procedure. Their relative frequencies are, in principle, of no relevance. It turns out that one successful strategy leading to acceptably high quality of results is through the use of combinations of different language technology techniques. A young Saarbr?cken-based company?XtraMind Technologies?is one German company working along these lines. In addition to an integrated ERM solution, XtraMind is offering single technologies as part of a construction tool set, which improves different functions within the ERM. Speech recognition improves ERMThe categorisation software XM-XtraClass is at the core of Xtramind's modular product range. In addition to the statistical and pattern matching procedures already described, it integrates three language recognition methods:
Where several methods are equally suitable, these are made to cooperate with each other, leading to better results through mutual reinforcement. With its optimal blend of language technology and statistical procedures, XM-XtraClass claims to achieve the enviable combination of precision, abstraction, robustness and dynamic adaptation. XtraMind owes its development head-start in part to the know-how of its founders, who come from such diverse backgrounds as language technology, software engineering, and IT consulting. Another unique factor contributing to the company's success is the fact that it is the first commercial spin-off of the Deutsches Forschungszentrum for K?nstliche Intelligenz (DFKI) - the high-achieving German Research Centre for Artificial Intelligence. The results of ten years of top-level research at DFKI have, therefore, been incorporated into the development of Xtramind's products, ensuring a sound footing in the future market place.
?Our physical proximity to Saarbr?cken-University enables the continuous exchange of ideas with its scientists. None of our 34 members of staff wishes to forego the high-level creative environment,? says CEO Dr. Klaus Netter, a ex-DFKI researcher himself. He also values the world wide network of technology and research partners, such as its recent association with Materna GmbH. XtraMind is quick to capitalise on these benefits through an ongoing expansion of its product range. The XM-MailMinder addresses and solves this email problem. It either forwards incoming mails automatically to the right expert (and supports his answer by offering alternative solutions), or it returns an reply message itself ? depending on the selected mode. ?The precision level of our classifier is greater than 90%.? Dr. Netter is proud of this achievement, which scores better than competitors' products in all benchmark tests. The learning procedure the XM-MailMinder uses is anything but simple. Small amounts of around 50 emails already produce excellent results and a stable learning model, which is capable of being scaled up semi-automatically. It is possible, threfore, to achieve significantly greater classification differentiation, allowing greater precision in responding to customers' inquiries. This also has the effect of shortening the response time to changed inquiries, as might be the case after a special advertising campaign. Deployment of XtraMind-products in call centres
Call centres with a volume of up to 1,000 emails per day will value the XM-MailMinder as a very welcome support tool. Installation effort of the system is not too heavy. ?With the clustered and tailored customer data we can compile a model within two or three days. This will subsequently be tested by the customer for a period of one to two weeks. Then starts the operative deployment?, says Dr. Netter on the process of implementation. This is one reason why many of Germany's largest call centres can be found on on XtraMind's user evaluation list. The clustering of data is essential in the preparation leading up to the installation of XM-MailMinders. XM-Gravity is a XtraMind system component for the automatic grouping of documents. Without any human intervention, it identifies thematic coherences as well as similarities and is able to form corresponding categories. Thus a system emerges which is based on relevant terms and formulations in texts. New markets might developXM-Finder can search associatively for unstructured information within a variety of document-, information- or knowledge management systems. ?Large companies often store between 10 and 20 percent of redundant documents.? says XtraMind's CEO, who sees a potential market for cost saving, with data storage costs amounting to an average of 0.80 EUR per megabit per month. With XM-Finder, identical documents can be found and deleted quickly and efficiently. In particular the XM-Finder has good potential in new, smaller niche markets. ?We have developed a DNA search engine with our finder which is proving to be very promising in tests,? says Dr. Netter. ?This product can be applied in all sectors with comparable data structures?. XtraMind, for now at least, intends to concentrate on its core market - the development of language technologies and products aimed at call centres.
is an independent journalist based in Munich, Germany. Her main focus is on technology and economy matters within IT and the Internet Economy. The editors of HLTCentral would welcome any feedback on the article.
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Email Response - EN
A survey by Emagicon of Germany has found that it took an average of 95.38 hours for a [business] email reply to come back, if one was returned at all. 
