15-Jayakrishna Venkatesh - CRM With Text Analytics for Public Service Organizations
Transcript of 15-Jayakrishna Venkatesh - CRM With Text Analytics for Public Service Organizations
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CRM for Public Service Organizations
A Text Analytics Approach
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Why Public Service Organizations
Large population of over 1.17 billion
Close to one-third population BPL
Unmonitored/growing prices of private/national brands
Highest need to opt to low cost subsidized essentialcommodities
Corporations, Municipalities, Water supply and
sewage boards etc., would also come into this bucket
since they are places which a citizen cannot afford to
avoid
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Why CRM for PS Organizations
Total customers = Total Citizens in India
So as companies, PS organizations are perhaps the
ones with the largest customer bases
No current system to monitor CRM methodically and
systematically
Faulty/Negligent CRM gives rise to illegal activities,
corruption and also unsatisfied citizens who are the
end users of either the service or products offered
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What is Text Analytics
Analysis by Specific Techniques for any business
reasons would be called Analytics
This analytics done on unstructured data is Text
Analytics which is the hottest approach to CRM these
days
Customers text inputs, be it in-store feedback,
comments, survey responses, call centre
conversations etc ., are the most important sources of
CRM inputs
These can be used for R&D, product development,
brand/category management etc, and mainly CRM
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How can Text Analytics help CRM
CRM is customer centric and it thrives on ³What
Customers Want´
Sales data, customer data etc give a variety of
responses about the performance of a store but never
tells us if an organization¶s customers (or end users)are satisfied and what they really want from the store
Customers¶ feedback is the most important source of
such data where customers
- Tell us what they want
- What are the problems they face
- Why would they hate/adore the public service organization
- And many more CRM relater business questions
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A general illustration of Text Analytics(from a real life example)
I walked into a TUCS store, but the store assistants did not even attend
me. They made me wait for about 20 minutes and asked me to come in
the evening since they would close for lunch. This is really hurting for old people like me aged close to 55, to visit the stores multiple times a
day, wait for hours and still not get what I want.Would want a better
system/monitoring to ensure they work properly.
**W ords uttered by my neighboring apartments¶ watchman, translated to English
The actual root cause of the
problem
Intention of the customer:Complaining (Negative
Feedback)
Suitability of the
complaint- Answers the
question where does thisapply?
Suitability of the
complaint-
Answers the
question to
whom does
this apply?
What does thecustomer want
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What is needed for Text Analytics
Text Analytics needs Text (from the customer
responses) which needs to be collected from every
customer, at-least from the customers facing problems
Collection of customer feedback can either be
- Manual with the help of printed forms- Automated with input kiosks, which would get inputs for
various buckets like questions, complaints, escalations etc
The advantage of getting it through kiosks are that
- The comments can be analyzed at the time of entry itself
- The status of complaints and analysis results can beretrieved at any point of time for any purpose
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CRM is a Routine ProcessCustomer SentimentAnalysis/
Text Analytics
Positives andNegativesidentified
CRMIdentifiesAreas of
improvement
IncorporatesThe changes in
The product/service
Put backIn placeFor the
customer To use again
Customer Responses
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Other Possible Sources for
Customer responses
Consumercomplaints.in, a forum where complaints on
various consumer related issues are recorded
Social Networking sites where public conversations
happen about Public Service organizations. These
include
Blogs are another rich source of information which
would give us information about such customer
experiences
In any of the above cases, specialized and
sophisticated natural learning BOTs needs to be
involved in the process to pick the consumer
complaints and decide what it talks about, what is the
problem, who is affected, reasons etc.,
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Typical Customer Response Reports
Parameter of Concern Negative Neutral Positive Parameter of Concern
Representative¶s Behavior 92% 0% 8% Representative¶s Behavior
Prices 0% 12% 88% Prices
Merchandise 20% 40% 40% Merchandise
In-store experience 76% 15% 9% In-store experience
In-store service 96% 4% 0% In-store service
In-Stocks 88% 4% 8% In-Stocks
Timings 100% 0% 0% Timings
Reliability 100% 0% 0% Reliability
Return Policy 12% 64% 24% Return Policy
Accessibility 0% 12% 88% Accessibility
Biased Services 36% 44% 20% Biased Services
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Advantages of CRM with Text Analytics
over Conventional CRM Methods
Systematic and methodical goal oriented analysis with
structured results, which can lead to actionable
insights and thus changes the business model
The change is triggered by none other than the end-
user himself
Sentiment rich customer responses are used for the
analysis which makes the decision makers well
informed about what the customers need, rather than
assuming those information from other sources of data
Text Analytics can also be used for building predictive
models. For e.g., if areas A and B suffer from the
same problem and if in A, a business model change
worked positive, it is also expected to work in B
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Advantages of CRM with Text Analytics
over Conventional CRM Methods (cont¶d)
Systematic and methodical goal oriented analysis with
structured results, which can lead to actionable
insights and thus changes the business model
If this system is automated, it can be applied to any of
the institutions, irrespective of the suitability, providedthe parameter of concerns are tailor made to the
institution using it. For example an IT system for text
analytics created for a TUCS chain of stores can also
be used for a General Hospital some changes in the
parameters loaded in
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Thank You!!!!