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Wij leven in een wereld waarin consumenten vele verschillende kanalen en media gebruiken om tot een aankoopbeslissing te komen en hun klantervaringen voortdurend evalueren en delen. Voor bedrijven is het een uitdaging om binnen dit steeds veranderende klantgedrag te navigeren, om waarde uit enorm veel data te halen en om de Customer Experience van hun klanten te begrijpen en te verbeteren. Rogier leidt de samenwerking tussen McKinsey en CX-technologieleverancier ClickFox. Hij zal een sneak preview geven van de ‘winnaars’ in CX en hoe zij technologie gebruiken om een 360 graden view van Customer Experience te bouwen en ervoor te zorgen dat deze beschikbaar wordt op elk moment van de dag voor de hele organisatie.
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Customer Experience Analytics Solutions
Nyenrode, June 5
Overview document
| 1
#1 The WorldCustomer behaviors
change rapidly, navigating across channels, evaluating the full
experience not just one part of it
| 2
| 3
| 4
#2 Customer Journeys For customer experience, touch points matter, but journeys matter more;
journey-led transformations deliver
great impact
| 5
Web IVR Agent Store Mobile
Traditional customer service approaches focus on touchpoints or “moments of truth”
of all Companies still “work” with a silos based approach>70%
| 6
Customer journeys are different than “moments of truth”
OnlineFAQ
Techagent
Storefor phoneupgrade
Activation app
IVR “team issue”
A journey is …
… an event typically across time and channels, and therefore, cross-functional in nature
… anchored on how customers experience about it, not the way functional silos do
of all customer interactions happen during multi-event, multi-channel, cross-time journeys>50%
| 7
Journey performers win in Customer Experience ...and Growth
R2 = 53% vs. 14% for single touchpoints satisfaction
R2 = 53% vs. 14% for single touchpoints satisfaction
R2 = 53% vs. 14% for single touchpoints satisfaction
SOURCE: McKinsey U.S. multi-industry survey
Example: PayTV – Journeys winners are overall customer experience winners
Example: Auto Insurance, a 1/10 of a point in journey sat worth a full point of revenue growth ($200M on average)
Overall CSAT2011
8.4
8.0
7.6
7.2
6.8
6.4
6.0
Journey satisfaction2011
7.87.67.47.27.06.86.66.46.2
A
6.0
I
H
J
G
FE
D
L
K
CB
-2%
0%
2%
4%
6%
8%
Revenue growth2011 vs. 2010
9.2
Journey satisfaction2011
9.4
N
9.08.88.68.48.28.0
M
EK
C
B
A
| 8
Journey-led transformations deliver impact across multiple dimensions
Fuel revenue growthChurn, upsell, acquisition
Improve customer satisfaction
Engage employeesLower cost to serve
20 to 30%20 to 30%
15 to 20%15 to 20%
20%20% 10 to 15% 10 to 15%
| 9
#3 Big Data There’s ever more data …but companies often don’t know what they or
how to drive value from it
| 10
Mobile
Everyone, everything, every interaction generates “exhaust” data
Transactions
Social Audio/video
Scientific/engineering ‘Internet of things’
| 11
Most companies store more data than the size of the entire collection of the library of US Congress…
>500=WalMart data warehouse in 2004
US EXAMPLE
235 = Library of congress collection in 2011
Average stored data per firm with more than 1,000 employees (Terabytes, 2009)
SOURCE: IDC: US bureau of labor statistics; McKinsey global institute analysis
1,800
231
278
319
370
536
697
801
825
831
870
967Discrete Manufacturing
Utilities
Healthcare Providers
Securities and Investment Services
Banking
1,312
1,507
1,931
3,866
Education
Retail
Wholesale
Construction
Resource Industries
Process Manufacturing
Insurance
Communications and Media
Transportation
Professional Services
Government
| 12
…however in a very fragmented and not consistent ways
Structured and
unstructured
HadoopMassive parallel
processing,XXX
500m to 5bn
touch points per year
Disparate customer data
sourcesagent call, IVR, Web,
Mobile, Social media, transactions,
retail/stores, segmentation
data
XX TB of data
Complex calculation, predictive algorithm, immediate
access
Recent research: 0,5% of available data is actually used!
| 13
…but those that are able to master Big Data outperform their peers
Revenue 1999-2012Percent; 10-year CAGR
EBITDA 1999-2012Percent; 10-year CAGR
13
19
13
11
18
6
6
8
6
7
Healthcare
Retail
IT
FinancialServices
Telecom
12
9
20
10
13
6
6
10
4
5
Other companies
Big Data leaders
SOURCE: Bloomberg; Datastream; annual reports; McKinsey analysis
| 14
#4 CE = BDWinning in Customer
Experience is winning in Big data, uncovering never ending flow of
highly specific opportunities
| 15
1 E.g., demographics, products owned, segment, CLV
Connecting Customer Journeys across hundreds of millions of touchpoints …
… to accelerate CSAT, retention, up/cross-sell improvements and cost
Increase retention and up/cross-sell, by several bps e.g.,
▪ Path to churn/cross-sell
▪ Channel preferences
▪ Micro segment campaigning enablement
Email Social andchat
Stores
Web
Call center
Customer data-base1
Field
Mobile/SMS
Check minutes balance
Change phone
Repair wireless router
Churn
Upgrade data plan
Drive up CSAT/NPS by 5-10 pptsacross Journeys, e.g.,
▪ Pain points across channels
▪ “Ideal Journey”
Reduce customer service cost by 10-25% across channels, e.g.,
▪ Call / field service deflection
▪ Shift to self-serve
McKinsey and ClickFox connect touch point data to visualize Customer Journeys and drive CSAT, cost and revenue improvement
| 16
At the heart of Journey Analytics is the powerful ClickFoxplatform that connects data into Journeys
Tasks
Completions & Departures
Raw DataEvents
Instant Transform
Paths
All Events Connected
Journeys
Paths to Outcomes
Failed Web Pay Enrollment
Web Pay Confirmed
IVR Call
Transfer to Agent
Web Auto Pay
Success
IVR Pay By Phone
Agent New Account Info
Enrollment JourneysUnstructured IVR Logs
Structured Agent Logs
Retail Desktop Data
Web Logs
Retail Steps
Cross Channel Outgoing Paths from IVR promise to
pay event
Cross Channel Outgoing Paths from web online
payment event
Churn Journeys
IVR Prompts
Web Pages
Agent Steps
| 17
Journey visualizations help to uncover first order cross channel insights to drill down into
Example of Journeys leading to low CSAT in cards – card activation example
Second order insight #1-Website asking clients to place a phone call to finalize card activation
Second order insight #2-IVR not recognizing card; customer is forced to ask for an operator
Second order insight #3 – IVR navigation not simple; customers often follow the ‘contract information’ leading to internal call transfers and increasing frustration
| 18
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
24 27 30 33 36 39 42 45 48 51 54 57 60 63 66
Responsespread
Average NPSof customerscompletingjourney
replacement card
lost stolen
enroll
search
offers
application
mobile features
travel notification
help
balance transfer
modify payment
error
authorized users
rewards
locked out
collections
fraud
disputes
activation
manage payment accounts
statements
message alert
authenticationmanage account
profile
recent transactions
account summary
make schedule payment
Use case 1 – For the card division of this bank, ClickFoxallowed to prioritize Journeys driving to high / low CSAT …
Source: McKinsey / ClickFox, client data
Highest
Lowest
Worst Best
Most polarized consumers
Mixed consumers
Similar sentiment across all customers
Spread of Response vs. NPS
Source: McKinsey / ClickFox
| 19
…which was then used to “fix” key leakage points for prioritized journeys
Source: McKinsey / ClickFox
Mobile Journeys leading to frustration …▪ 42% of users accessing
Mobile Rewards (700k customers) check rewards status multiple times < 72h
▪ These customers drive 750k calls annually to the call center ($2-4M cost)
Example mobile app journey
… Driven by inconvenient back and forth between Mobile app screens
Path: Mobile Rewards ! Mobile Transactions ! Mobile Account Summary
▪ 69% of detractors who reach mobile transactions page return to mobile rewards page
▪ 90% of detractors who reach end of journey return to mobile rewards page
| 20
#5 Management shiftThe new management paradigm is about test
and learn at ever increasing scale, where
details matter
| 21
The new management paradigm is about test and learn at ever increasing scale
▪ Tool-enabled generation of insights, where typical business analysts can drive analyses in a dynamic, organic way
▪ Daily, weekly flow of customer opportunities and risks with potential action to take – independent of any IT constraint
FutureHistory
▪ United data sets, as all customer data connected, in one place (interactions, segmentation, transactions)
▪ Discrete data sets, as customer data sitting in disparate systems and is hard to pull together
▪ Discrete dashboards which provide silo-ed views on business performance
▪ Pilots takes months, requiring ad-hoc, hand-made measurements to understand business impact of implemented improvements
▪ Need for scarce Subject-Matter-Experts with advanced analytical skills and deep industry/BT knowledge to make sense of data
▪ Limited to static and infrequent analysis of customers behaviors, taking weeks or months – often highly dependent on IT
▪ Based on 3600 transparency on E2E customer journeys, and related customer profiles
▪ Near real time feedback, across channels, on multiple dozens of test-and-learn activities,
How are data made available?
How are insights generated from data?
How do insights inform business decisions?
| 22
Example – Create a “Journey lab” to quickly test and refine improvement ideas before scaling them
Redesign test
BEFORE
Before
Test
Multi-channel tracking
Clicks on Send a Temp Password
Clicks on Answer Secret Questions
Success
58%
Answer secret ques-tion and reset pass-word
Secret answer error
41%
Other error
1%
Other disposition
84%
Reset password
16%
▪ Education codes▪ General info▪ Features
related assistance▪ Payments
released assistance
Did not to Web <30 days
51%
Did not return to Web <30 days 49%
Success
19%
“Cannot remember answer”
2%
Call agent <1 day
15%
Click to chat
~0%
IVR and hang-up <1 day
5%
“Password reset” failure issue
100%
▪ 15% issues ending up in agent calls
▪ … of which 84% issues not solved on the phone
▪ … of which 50% do not go back on online in 30 days
Redesign of web page
Real time tracking of results with ClickFox▪ Web usage▪ Calls related to
password issue
| 23
Example – Machine-enabled decision making leverage churn prediction models to implement trigger-based retention actions
Data processed through ClickFoxand modeling engine
Customer data collected on a daily basis
Probability of churn updated for each customer
If churn probability above threshold, retention action is triggered
Recommender engine selects optimal retention offer for customer at risk
Information is provided to outbound systems / front-line employees for immediate response
Next best action
▪ Retention action:email sent to customer with 50% discount on annual fee
▪ Timing: Immediate response
‘Static’ profile
Gender : Male
Age: 28
Region: Andalucía
Tenure: 16 months
Product: Fixed tel, Mobile, Internet, Cable TV
Pricing: €67/year (mispositioned)
Payment history: all bills paid on time
Churn likelihood
An example of a real customer journey leading to churn…
… and what happens behind the scene to prevent it
Jan 1st Jan 31st
Existing model
1
0.8Retention Action Threshold
0.6
0.4
Billing dispute
Product downgrade
Jan 13th
Mobile port outQuestions about disco. process
| 24
OUT