Managing unstructured text data for forecasting purposes

1. Managing unstructured text data
for forecasting purposes
Using 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 purposes
Using 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 2012
Pagina 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 program
D/IMRS 2012
Pagina 4

5. An Example
How 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. Twitter
D/IMRS 2012
Pagina 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 Example
How 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 used
D/IMRS 2012
Pagina 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 before
Existing 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) (%) R2
Existing Model 94 7% 0,06
Alternative Model 1 78 6% 0,69
Alternative Model 2 81 6% 0,83
Alternative 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 set
1/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 Example
How 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 2012
Pagina 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
results
D/IMRS 2012
Pagina 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 2012
Pagina 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.nl
D/IMRS 2012
Pagina 14