Improving the customer experience using big data customer-centric measurement and analytics
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Transcript of Improving the customer experience using big data customer-centric measurement and analytics
How may we [email protected] 2013
Improving the Customer Experience Using Big Data, Customer-Centric
Measurement and AnalyticsBob E. Hayes, PhD
TCE: Total Customer Experience
Copyright 2013 TCELab
1. Customer Experience Management
2. Customer Loyalty
3. Optimal Customer Survey
4. Value of Analytics
5. Big Data Customer-Centric Approach
For more info on book:http://bit.ly/tcebook
Copyright 2013 TCELab
Customer Experience,Customer Experience Management
and Customer Loyalty
Customer Experience Management (CEM)
The process of understanding and managing your customers’ interactions with and perceptions of your brand / company
Copyright 2013 TCELab
Copyright 2013 TCELab
Optimal Customer
Relationship Survey
Customer Relationship Surveys
Copyright 2013 TCELab
• Solicited feedback from customers about their experience with company/brand
• Assess health of the customer relationship• Conducted periodically (non-trivial time period)• Common in CEM Programs
– Guide company strategy– Identify causes of customer loyalty– Improve customer experience– Prioritize improvement efforts to maximize ROI
Four Parts to Customer Surveys
Copyright 2013 TCELab
1. Customer Loyalty – likelihood of customers engaging in positive behaviors
2. Customer Experience – satisfaction with important touch points
3. Relative Performance – your competitive advantage
4. Additional Questions – Extra value-added questions
Customer Loyalty Types
The degree to which customers experience positive feelings for
and engage in positive behaviors toward a company/brand
Emotional(Advocacy)
Behavioral(Retention, Purchasing)
Love, Consider, Forgive, Trust
Stay, Renew, Buy, Buy more often, Expand usage
Copyright 2013 TCELab
Customer Loyalty Measurement FrameworkLoyalty Types
Emotional Behavioral
Measuremen
t Approach
Objective
ADVOCACY• Number/Percent of new
customers
RETENTION• Churn rates• Service contract renewal rates
PURCHASING• Usage Metrics – Frequency of
use/ visit, Page views• Sales Records - Number of
products purchased
Subjective(Survey Questio
ns)
ADVOCACY• Overall satisfaction• Likelihood to recommend• Likelihood to buy same product• Level of trust• Willing to forgive• Willing to consider
RETENTION• Likelihood to renew service contract• Likelihood to leave
PURCHASING• Likelihood to buy different/ additional
products• Likelihood to expand usage
1 Using RAPID Loyalty Approach - Overall satisfaction rated on a scale from 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied). Other questions are rated on a scale from 0 (Not at all likely) to 10 (Extremely likely). * Reverse coded so lower rates of these behaviors indicates higher levels of Retention Loyalty. Copyright 2013 TCELab
Customer Experience
Copyright 2013 TCELab
• Two types of customer experience questions• Overall, how satisfied
are you with…
Area General CX Questions Specific CX Questions
Product 1. Product Quality
1. Reliability of product2. Features of product3. Ease of using the product4. Availability of product
Account Management
2. Sales / Account Management
1. Knowledge of your industry2. Ability to coordinate resources3. Understanding of your business issues4. Responds quickly to my needs
Technical Support 3. Technical Support
1. Timeliness of solution provided2. Knowledge and skills of personnel3. Effectiveness of solution provided4. Online tools and services
0 1051 2 3 4 6 7 8 9
ExtremelyDissatisfied
ExtremelySatisfied
Neither SatisfiedNor Dissatisfied
Customer Experience
Copyright 2013 TCELab
• Overall, how satisfied are you with each area?
1. Ease of doing business
2. Sales / Account Management
3. Product Quality
4. Service Quality
5. Technical Support
6. Communications from the Company
7. Future Product/Company Direction
0 1051 2 3 4 6 7 8 9
ExtremelyDissatisfied
ExtremelySatisfied
Neither SatisfiedNor Dissatisfied
CX Predicting Customer Loyalty
Copyright 2013 TCELab
Company A Company B Company C Company D0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
74%
42%60%
85%
0%
4%
2%
4%Specific CX QuestionsGeneral CX Questions
Perc
ent o
f Var
iabi
lity
(R2)
in C
us-
tom
er
Loya
lty E
xpla
ined
by
CX Q
uesti
ons
General CX items reflected areas (e.g., product quality, ease of doing business, tech support) and additional specific CX items reflected specific aspects of the general items (product reliability, tech support knowledge, account management’s ability to respond quickly).R2 reflects percent of variance of customer loyalty that is explained when using general items in regression analysis . ∆R2 reflects the additional percent of variance explained above what is explained by general items when using general items and specific items in a stepwise regression analysis.
1. General CX questions explain customer loyalty differences well.
2. Specific CX questions do not add much to our prediction of customer loyalty differences.
3. On average, each Specific CX question explains < .5% of variability in customer loyalty.7 General CX 5 General CX 6 General CX 7 General CX
0 Specific CX 14 Specific CX 27 Specific CX 34 Specific CX
• Customer experience questions may not be enough to improve business growth– You need to understand your relative performance
• HBR study (2011)1: Top-ranked companies receive greater share of wallet compared to bottom-ranked companies
• Focus on increasing purchasing loyalty (e.g., customers buy more from you)
Competitive Analytics
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Relative Performance Assessment (RPA)
• Ask customers to rank you relative to the competitors in their usage set
• What best describes our performance compared tothe competitors you use?
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RPA Predicting Customer Loyalty
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
69% 72%
18% 16% 14%
1% 2%
8% 7%1%
1 RPA Question
7 General CX Questions
Loyalty Questions
Perc
ent
of V
aria
bilit
y (R
2) i
n Cu
stom
er
Loya
lty
Expl
aine
d by
Gen
eral
CX
Que
stion
s an
d Re
lativ
e Pe
rfor
man
ce A
sses
smen
t (R
PA)
What best describes our performance compared to the competitors you use?
1. General CX questions explain purchasing loyalty differences well.
2. Relative Performance Assessment improved the predictability of purchasing loyalty by almost 50%
3. Improving company’s ranking against the competition will improve purchasing loyalty and share of wallet
Understanding your Ranking
Copyright 2013 TCELab
1. Correlate RPA score with customer experience measures
2. Analyze customer comments about the reasons behind their ranking– Why did you think we are better/worse than the
competition?– Which competitors are better than us and why?
• What to improve?– Product Quality was top driver of Relative Performance
Assessment– Open-ended comments by customers who gave low RPA
rankings were primarily focused on making the product easier to use while adding more customizability.
Additional Questions
Copyright 2013 TCELab
• Out of necessity or driven by specific business need• Segmentation Questions
– How long have you been a customer?– What is your role in purchasing decisions?– What is your job level?
• Specific topics of interest to senior management– Perceived benefits of solution (What is the % improvement in
efficiency / productivity / customer satisfaction)– Perceived value (How satisfied are you with the value
received?)• Open-ended questions for improvement areas
– If you were in charge of our company, what improvements, if any, would you make?
Summary: Your Relationship Survey
Copyright 2013 TCELab
1. Measure different types of customer loyalty (N = 4-6)
2. Consider the number of customer experience questions in your survey (N = 7)– General CX questions point you in the right direction.
3. Measure your relative performance (N = 3)– Understand and Improve/Maintain your competitive advantage
4. Consider additional questions (N = 5)– How will you use the data?
Copyright 2013 TCELab
Big Data, Analytics and Integration
Big Data
• Big Data refers to the tools and processes of managing and utilizing large datasets.
• An amalgamation of different areas that help us try to get a handle on, insight from and use out of large, quickly-expanding, diverse data
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Big Data Landscape – bigdatalandscape.com
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Three Big Data Approaches
1. Interactive Exploration - good for discovering real-time patterns from your data as they emerge
2. Direct Batch Reporting - good for summarizing data into pre-built, scheduled (e.g., daily, weekly) reports
3. Batch ETL (extract-transform-load) - good for analyzing historical trends or linking disparate data
Copyright 2012 TCELab
Value from Analytics: MIT / IBM 2010 Study
Top-performing organizations use analytics five times more than lower performers
Copyright 2013 TCELab
http://sloanreview.mit.edu/the-magazine/2011-winter/52205/big-data-analytics-and-the-path-from-insights-to-value/
Number one obstacle to the adoption of analytics in their organizations was a lack of understanding of how to use analytics to improve the business
Value from Analytics: Accenture 2012 Study
Copyright 2013 TCELab
1. Measure Right Customer Metrics - only 20% were very satisfied with the business outcomes of their existing analytics programs
2. Focus on Strategic Issues - only 39% said that the data they generate is "relevant to the business strategy"
3. Integrate Business Metrics - Half of the executives indicated that data integration remains a key challenge to them.
Disparate Sources of Business Data
1. Call handling time2. Number of calls until
resolution3. Response time
1. Revenue2. Number of products
purchased3. Customer tenure4. Service contract
renewal5. Number of sales
transactions6. Frequency of
purchases
1. Customer Loyalty2. Relationship satisfaction3. Transaction satisfaction4. Sentiment
1. Employee Loyalty2. Satisfaction with
business areas
Operational
Partner Feedback
1. Partner Loyalty2. Satisfaction with
partnering relationship
CustomerFeedback
EmployeeFeedback
Financial
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Data Integration is Key to Extracting Value
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Linkage Analysis
Operational Metrics
Transactional Satisfaction
Relationship Satisfaction/
Loyalty
Financial Business Metrics
Constituency Satisfaction/
Loyalty
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Customer Feedback Data Sources
Relationship Survey
(satisfaction/loyalty to company)
Transactional Survey
(satisfaction with specific transaction/interaction)
Social Media/ Communities
(sentiment / shares / likes)
Business Data Sources
Financial(revenue, number of sales)
• Link data at customer level
• Quality of the relationship (sat, loyalty) impacts financial metrics
N/A
• Link data at customer level
• Quality of relationship (sentiment / likes / shares) impacts financial metrics
Operational(call handling, response time)
N/A
• Link data at transaction level
• Operational metrics impact quality of the transaction
• Link data at transaction level
• Operational metrics impact sentiment / likes/ shares
Constituency(employee / partner feedback)
• Link data at constituency level
• Constituency satisfaction impacts customer satisfaction with overall relationship
• Link data at constituency level
• Constituency satisfaction impacts customer satisfaction with interaction
• Link data at constituency level
• Constituency satisfaction impacts customer sentiment / likes / shares
Integrating your Business Data
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Customer Feedback / Financial Linkage
Customer(Account) 1
Customer(Account) 2
Customer (Account) 3
Customer(Account) 4
Customer(Account) n
Customer Feedbackfor a specific
customer (account)
Financial Metricfor a specific
customer (account)
x1
x3
x2
xn
x4
y1
y3
y2
yn
y4
yn represents the financial metric for customer n.
xn represents customer feedback for customer n.
.
.
.
.
.
.
.
.
.
Copyright 2013 TCELab
Determine ROI of Increasing Customer Loyalty
Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10)
Customer Loyalty
Pe
rce
nt
Pu
rch
as
ing
Ad
dit
ion
al
So
ftw
are 55%
increase
Copyright 2013 TCELab
Operational / Customer Feedback Linkage
Customer 1Interaction
Customer 2Interaction
Customer 3Interaction
Customer 4Interaction
Customer nInteraction
Operational Metricfor a specific
customer’s interaction
Customer Feedback for a specific
customer’s interaction
x1
x3
x2
xn
x4
y1
y3
y2
yn
y4
yn represents the customer feedback for customer interaction n.
xn represents the operational metric for customer interaction n.
.
.
.
.
.
.
.
.
.
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Identify Operational Drivers of Satisfaction
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Identify Operational Standards
1 call 2-3 calls 4-5 calls 6-7 calls 8 or more calls
Number of Calls to Resolve SRSa
t with
SR
1 change 2 changes 3 changes 4 changes 5+ changes
Number of SR Ownership Changes
Sat
wit
h S
R
Copyright 2013 TCELab
3 Implications of Big Data in CEM
1. Ask/Answer bigger questions
2. Build company around the customer
3. Predict real customer loyalty behaviors
Copyright 2012 TCELab
[email protected]@bobehayesbusinessoverbroadway.com/blog
How may we [email protected] 2013
Improving the Customer Experience Using Big Data, Customer-Centric
Measurement and AnalyticsBob E. Hayes, PhD
For more info on book:http://bit.ly/tcebook
RAPID Loyalty Measurement
Index Definition Survey Questions
Retention Loyalty
Index (RLI)
The degree to which customers will remain as a customer/not leave to competitor (0 – low loyalty to 10 – high loyalty)
Likelihood to switch to another company*
Likelihood to purchase from competitor*
Likelihood to stop purchasing*
AdvocacyLoyalty
Index (ALI)
The degree to which customers feel positively toward/will advocate your product/service/brand (0 – low loyalty to 10 – high loyalty)
Overall satisfaction
Likelihood to choose again for first time
Likelihood to recommend (NPS)
Likelihood to purchase same product/service
Purchasing Loyalty
Index (PLI)
The degree to which customers will increase their purchasing behavior (0 – low loyalty to 10 – high loyalty)
Likelihood to purchase different products/services
Likelihood to expand usage throughout company
Likelihood to upgrade
1 Overall satisfaction rated on a scale from 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied). Other questions are rated on a scale from 0 (Not at all likely) to 10 (Extremely likely). * Reverse coded so lower rates of these behaviors indicates higher levels of Retention Loyalty.
• Assesses three components of customer loyalty
Copyright 2013 TCELab
Financial Metrics / Real Loyalty Behaviors
• Linkage analysis helps us determine if our customer feedback metrics predict real and measurable business outcomes
• Retention– Customer tenure– Customer defection rate– Service contract renewal
• Advocacy– Number of new customers– Revenue
• Purchasing• Number of products
purchased• Number of sales
transactions• Frequency of purchases
Relationship Satisfaction/
Loyalty
Financial Business Metrics
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Operational Metrics
• Linkage analysis helps us determine/identify the operational factors that influence customer satisfaction/loyalty
• Support Metrics– First Call Resolution (FCR)– Number of calls until resolution– Call handling time– Response time– Abandon rate– Average talk time– Adherence & Shrinkage– Average speed of answer (ASA)
Copyright 2013 TCELab
Operational Metrics
Transactional Satisfaction