Customer Experience Management

@ 2015 BRIDGEi2i Analytics Solutions Pvt. Ltd. All rights reserved

6 May 2015

Customer Experience

Management

Customer experience management shifting from

reactive to proactive model

How do I improve customer experience for high

potential customer?

What type of customer do we have? Who are our

loyal customers?

Questions asked yesterday Questions asked today

What is the NPS trending at and how can we

improve the same? What are the key issues customers are facing &

how do I get a single view of all customer issues?

How are customer responding to satisfaction

surveys? What is their feedback? What are the key topics that customers are

talking about in social media?

How to reduce service cost?How to improve customer satisfaction with

optimized spent?

How can I improve support center productivity?How can I equip support center better to improve

customer satisfaction?

Using valuable data across customer lifecycle

Purchase

Receives emails Registers

product

Have support interaction

Respond to surveys Post in social media

Direct marketing interactions Repurchase

Transaction Data Marketing Interactions Data Registration Data

Survey Data Support Data ( Chat, Phone, Email)Social Media Data

Visits site Browse

Web traffic Data

Pertinent questions which analytics can help answering

Measure & track

customer

satisfaction

Identify key drivers

to customer

satisfaction

Recommend

actionable insights

• Who are the dissatisfied customer?

• What are the main pain points faced by customers?

• How is customer experience trending over time?

• What is the preferred channel to reach out to customer?

• What is impacting customer experience?

• Who are the best customers?

• Which customers are at risk and needs immediate attention?

• What are customer sentiments, needs, and preferences?

• How to optimize service cost?

• What product features to enhance?

• How to improve service agent productivity?

• How to sale at the point of service?

BRIDGEi2i platform intends to create a 360 degree view…

5

Mobile

Page views

Source

Time spent

Web data

Survey response

Reviews Blogs

Transactional

data

Marketing

response

2. Product

Purchase

3.Marketing

efforts

4. Repeat

Purchases

5. Customer

servicing

cost

6. Customer

Churn

1. Contact

Acquisition

Customer

Transactional

data

Support data

Social media

Summarize &

Visualize key

customer

experience metrics

Discover

correlation

between different

events and KPIs

Identify

immediate action

items to work on

Integrate customer touch-points data and create metrics to…

ExTrack – Create a quantitative dashboard to relate various

metrics…

6

Which geography needs more attention? What are the top features discussed by customers?

What is the channel preference for customers? How many reviews generated over past months?

ExTrack – Deep dive & discover areas that needs immediate

attention…

7

Open ended customer reviews……converted to structured data

identifying key topics ……to deep dive into specific topic

Ex-Track – Provide a search and text mining mechanism for

various facets of experience…

Co-Relative Features Discussed Sentiments Expressed over Features

Mine open ended customer reviews

for search keywords……to identify sentiments related to associated terms & act on it

Perf

orm

an

ce

Speed

Wifi

Camera

Apps

Heat

3G

Charge

Replace

Service

Warranty

Updates

Refurbish

Support Battery Life Performance

Batt

ery

Su

pp

ort

Negative Positive

Technology

We are already supporting few clients on customer

experience

9

Supported a sports

entertainment company

assess pain points in

customer experience

through analysis of social

media posts and

comments

Supporting a high tech

company in measuring

and reacting to IT user

experience across email,

chat and surveys

Supported a leading

group insurance provider

in US assess key drivers of

volume & dissatisfaction

with support instances

Supporting a large retailer

periodically track various

engagement indices and

correlate with visit , spend

share and social media

trends

Social Media Feedback

analysis

User Experience from

emails and surveys

Customer Support Log

analysis

Customer Loyalty Tracking

Understanding Pervasive User Experience For A Fortune 100

Networking Client

1010

• From the various data

sources available, each user

feedback is mapped to a

particular client service

• The service sub-

categorization mapping is

done based on the keyword

correlation between user

feedback and the services’

associated keywords

• The sentiment of each of the

user feedbacks pertaining to

a Service are classified

based on the sentiment

analysis algorithm and are

assigned one of the three

categories: Positive/

Neutral/ Negative

• Each of the user feedback

content is then analyzed to

identify the issue that it

relates to

• A noun based algorithm is

developed to understand

the theme/ issue that a user

informs about. This helps

the service owner to take

appropriate actions

• Categorizing

user feedbacks

to a particular

client

service/tool

• Understanding

the sentiment

of the feedback

• Identifying the

issue and help

the service

owner address

those issues

Data Key Features Outcome

Tools & Services Sentiment Analysis Thematic AnalysisEmail

Remedy

Client feedback tools

Survey

• To have a single presentation layer to ensure that near real-time user feedback methods are in place in combination with

the existing client’s feedback programs offering new centralized services, robust analytics and an active response

process.Objective

Tools &

Services

Sentiment

AnalysisThematic

Analysis

Identification of key reasons for calling contact centre and

drivers of low first call resolution based on agent notes

1111

• Agent notes are textual, and

contains many spelling

issues. Publicly available

lexicons & dictionaries are

used to enable this

• The history of conversations

were broken into single

discussions to allow

separate analysis

• Semantics based text

mining methods are used to

segment discussions into

various topics which allowed

to identify various reasons

for calls.

• Analysis of service instances

needing multiple calls

provided insights on key

bottlenecks in the claim

management process.

• Analysis of time taken to

resolve various issues

helped to set right

expectations with customers

• Insights obtained

from the exercise

helped the insurance

company identify key

bottlenecks in the

process and

conceptualize

measures to

significantly decrease

the service instances

as well as improve

customer

satisfaction.

Data Sources Approach Outcome

Internal unstructured

query logs from call

centre Agents

The Client is a large provider of group insurance especially in the US. Hundreds of queries regarding claims and other

related service are made by customers at the company’s contact centre every day. The objective of the project was to

analyse the huge volume unstructured texts in contact centre agent comment to assess the key drivers of service call

volume and other related aspects .

Objective

Report:

Summary

Statistics

Data Structuring Identify Key topics Analyze drivers of delay

Report:

Identify

Problems

and trends

Assessing customer pain points from social media feedbacks

1212

BRIDGEi2i identified 3 sources of

information towards the Client’s

objective

• Yelp to mine consumer

sentiments. Slow data but with

profound insights

• Facebook to understand key

event response metrics.

• Twitter to understand rate at

which Client is engaging

patrons

• Share of voice across social

media – mentions in SM vis-à-

vis competitors

• Level of engagement –

enthusiasm across patrons and

prospects towards an event

• Feature level sentiment –

across all offerings vis-à-vis

competitors

• “Attention required” – on key

areas of offerings

• Use text categorization

algorithms to identify set of

words that describe the new

classification

• Associate a class match with

a probability to assess its

trustworthiness

• For the first few iterations, a

feedback loop will help the

learning algorithm

The client has been

able to use the

metrics to identify

focus areas in terms

of a brand presence

or perception

improvement.

Analysis is now

being rolled out

across all new sites.

Data Sources

Approach Outcome

Yelp

Twitter

Facebook

The Client is one of the world’s largest golf entertainment companies with assets in 11 cities across US and UK. As an

initiative to improve their brand presence and perception, The Client is interested in (a) understanding the reach of its social

media promotion activities and (b) innovative methods to identify & manage consumer sentiments as soon as a negative

event has been triggered.

Objective

Dashboard:

Monitor

Metrics

DATA GATHERING AND

MININGCreation of Metrics Delivery Mechanism

Dashboard:

Driver

Analysis

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