Dimrs (2012) Managing Unstructured Text Data (Schijns)
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Dimrs (2012) Managing Unstructured Text Data (Schijns)

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Managing unstructured text data for forecasting purposes

Managing unstructured text data for forecasting purposes

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Dimrs (2012) Managing Unstructured Text Data (Schijns) Presentation Transcript

  • 1. Managing unstructured text data for forecasting purposesUsing webcare data to optimize a contact center‟s capacity by Jos Schijns Ivo Temmink and Gonneke Zwennes (Cendris)
  • 2. Managing unstructured text data for forecasting purposesUsing webcare data to optimize a contact center’s capacity• What is the paper about? – More a case study than a research paper – A (pilot) project – Done by Ivo Temmink & Gonneke Zwennes (consultants analytics at Cendris) – Asked by a client to optimize the forecasting of the number of seats needed in their CC – Included unstructured text data in the model besides structured, historical data on customer contacts (time series forecasting)D/IMRS 2012Pagina 2
  • 3. Problem statement• Why do we think this is of interest? – Unstructured data is often reported to represent around 80% of all data that is available to an organization – “True leaders are those who will unlock the potential of unstructured data and translate it into business value” (Confirmit, 2010)• But, how to deal with unstructured data and analyze text from various sources in a meaningful and measurable way? D/IMRS 2012 Pagina 3
  • 4. An Example• How did we tackle this problem? – How did we deal with unstructured data and analyze text from various sources in a meaningful and measurable way? – In order to optimize the forecasting of the number of seats needed in the clients’ CC• Generally, we used a 4-step approach: – Step 1: Listen to the (voice of the) customer – Step 2: Interpret data to extract meaningful information – Step 3: Deliver actionable insights – Step 4: Continuously improve the programD/IMRS 2012Pagina 4
  • 5. An ExampleHow did we tackle this problem?• Step 1: Listen to the (voice of the) customer – Proactively besides reactively – Using unstructured data already acquired and analyzed by the clients‟ webcare team • Websites • Blogs • Social media, e.g. TwitterD/IMRS 2012Pagina 5
  • 6. An Example How did we tackle this problem?• Step 2: Interpret data to extract meaningful information – The webcare team collects all the online (text) messages containing one or more pre-defined words – The webcare team reads, labels and archives the (text) messages in the „Inbox‟ of the webcare application • Labels are pre-defined (e.g., „complaint‟, „compliment‟, „recommendation‟) • Itemized each customer quote and analyzed it for positive, neutral or negative sentiment • Determining impact factor using an automatic algorithm, 5 categories, anchored by „very low – very high‟ D/IMRS 2012 Pagina 6
  • 7. An ExampleHow did we tackle this problem?• Step 3: Deliver actionable insights – Three alternative models were developed, besides the existing model of the CC – All three alternative models included webcare data – But differed with respect to whether or not time series data were included – And the timeframe of the webcare data usedD/IMRS 2012Pagina 7
  • 8. An Example How did we tackle this problem? • Step 3: Deliver actionable insights – What model does best? Text data included Time series included 2-7 days before 24H beforeExisting Model √ - -Alternative Model 1 √ √ -Alternative Model 2 √ - √Alternative Model 3 - - √ D/IMRS 2012 Pagina 8
  • 9. An Example How did we tackle this problem? Average deviation* Average deviation* (absolute) (%) R2Existing Model 94 7% 0,06Alternative Model 1 78 6% 0,69Alternative Model 2 81 6% 0,83Alternative Model 3 188 14% 0,17* Average difference between forecast and actual number of calls D/IMRS 2012 Pagina 9
  • 10. An Example How did we tackle this problem? Alternative model 2 Total number of calls 2011 Training set Validation set1/jan 1/feb 1/mrt 1/apr 1/mei 1/jun 1/jul 1/aug 1/sep 1/okt 1/nov 1/dec D/IMRS 2012 Pagina 10 Number of calls Own forecast by client Model 2 (24-h webcare data, including historical series)
  • 11. An ExampleHow did we tackle this problem?• Step 4: Continuously improve the program: do‟s and don‟ts – The sources taken into account by the webcare team • E.g., more websites? Other websites? Other sources? – Labels • More labels? Better/more specific? • More consistency in labeling – More messages classified by sentiment – Including other variables • E.g., „volume‟D/IMRS 2012Pagina 11
  • 12. Conclusions• Adding webcare data to historical series of contact data results in a better forecast of the number of inbound calls• Unstructured data should not only be used in a reactive way, as a service, but also in a proactive way, as a tool for forecasting and planning purposes, e.g. Workforce Management (WFM)• Companies can benefit from taking what customers share, for example in social media, driving actionable insights and business resultsD/IMRS 2012Pagina 12
  • 13. Contribution to Direct/Interactive Marketing• The power of text analytics• An application of unstructured data analysis that helps driving organizational performance, i.c. more efficient WFM• Results, however, can also be used for – Improving the list of FAQ‟s – Adapting the scripts for agents – Preparing agents for emotions – Reduce average time spent on calls• “The future success of companies and organizations will increasingly be based on their ability to unlock hidden intelligence and value from unstructured data, and text in particular” (The 451 Group, 2005)D/IMRS 2012Pagina 13
  • 14. For further information Contact Details Dr. J. M.C. (Jos) Schijns, MBA Assistant Professor Open Universiteit School of Management T +31 (0)45 – 576 21 96 E jos.schijns@ou.nlD/IMRS 2012Pagina 14