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The Scalable Way: Unlocking
Data To Drive Great Customer Experience
and Conversion Uplift
Arpit Srivastava
Manager - Marketing Strategy and
Analysis, Publicis Sapient
Paresh Mandhyan
Director of Marketing,
VWO
2
Arpit has 12+ years of experience in digital
marketing, data analytics, & experience
optimization.
In his current capacity at Publicis Sapient,
he consults several large scale businesses
to help them drive ROI; using data-driven
marketing & optimization strategies.
Paresh has 12+ years of experience in
various aspects of the marketing mix with
a keen focus on demand generation &
conversion optimization.
In his current role as head of marketing at
VWO, Paresh has been instrumental in
optimizing the end to end inbound funnel
to grow it by over 30% in 6 months.
Your Speakers for today’s webinar
Arpit Srivastava
Manager - Marketing Strategy and
Analysis, Publicis Sapient
Paresh Mandhyan
Director of Marketing,
VWO
Journey to Human Happiness
3
Journey to Happy Customer Experience
Robust Analytics Foundation
+
Data Driven Experience Optimization
Your target audience expects
great CX at every touch point!
Including your website, landing
pages, product pages etc.
We all know why is great CX so important… Let’s recap!
Audience Expectations
Be Relevant or Lose Customers
There is no middle ground.
70%
of business buyers says it’s absolutely critical for companies to
provide a personalized experience.
74%
of business buyers conduct more than half of their research
online before making an offline purchase.
75%
of B2B buyers would purchase again from suppliers that
offered omni-channel capabilities.
People used to shop in a linear pattern.
Linear Click Based Model
But, that’s no longer the case.
Audience Behaviour
Your Audience now shops in more ways than ever before.
10*
Touch points on an
average to generate a
qualified lead. Event
more to generate a
purchase online!
*https://www.onlinemarketinginstitute.org
So What Does It Take To Convert more
Leads/Customers on your website with great CX?
“For every minute spent organizing,
an hour is earned”
Benjamin Franklin
Step 1: Organize Your Analytics via Robust Analytics Foundation
(Qualified TAM)
Impressions, Visits, % New Visits etc.
(TAM with some commercial intent)
CTR, PDP Views, form fill
(Ready to buy or bought)
Visitor Loyalty, Cart size, Cart Abandonment
rate, Profit
(Current Users with repeat purchase)
Likelihood to recommend, CLTV
Awareness
Consideration
Purchase
Loyalty
Structure Measurement Framework Across Buyer Stages and Objectives
Building the Right MarTech Ecosystem
Martech
33%
Other Marketing
Resources
67%
Marketing Budget
Source: Gartner CMO Spend Survey 2018-19
Too Many Choices in the MarTech Space
Collect Consistent Data Using Data Layer & TMS
User Actions
▪ Page views
▪ Button Click
▪ Form Submit
▪ Checkout
▪ Purchase
Data Layer on web
page
<script>
window.dataLayer
=
window.dataLayer ||
[]; dataLayer.push({
'userId' : ABC123',
'internalUser' :
true,}); </script>
TMS
▪ Properties/
containers
▪ Tags /
Extensions
▪ Triggers / Rules
▪ Variables / Data
Elements
Marketing &
Analytics Tags
▪ Google Analytics
▪ VWO Insights
▪ LivePerson
▪ Marketo
▪ Remarketing
Pixel
Data
Layer
TMS
Organized
Analytics
Instrumentation
“Prioritize data quality everything else
is a false promise”
Head of Analytics, Fortune X Automobile client
Tag Key Content in your CMS
Content Creation
(PDFs, Banners,
Videos etc.)
Upload/Publish SharedConsumed
Metadata
Content ID
Category
Name
Type
Purpose
Stage
Persona
Metrics & Metadata
Views
Clicks
Downloads
Visitor Tagged
Metrics
Ratings
Comments
Social Share
Metadata
Published Date
Author’s Name
Build a Campaign Data Governance Process
Define campaign
tracking code
taxonomy for each
channel and
streamline the
creation process
Define marketing
channel using
tracking code
structure
Grant access to
campaign code to
each of marketing
team / agencies
based on channel
they are
responsible for
Channel owners
generate code by
inputting key
values and append
tracking code in
campaign
destination url
Scaling the Audience by Breaking the Data Silos
Analytics CRM
Partner
Data
Social Media
Shared audiences across Channels &
Teams
DMP has become key component in Analytics Infrastructure
Analysis on advance segments &
distribution
Use audiences to trigger email
Audience Targeting with personalized
content.
Content synchronization across devices
Buy advertising based on audience
intelligence
Unified Data
Analytics
DSP / Ad
networks
ExO platform
Email
Engine
Qualitative
Quantitative
▪ Web analytics data
▪ CRM Data
▪ Heat maps
▪ Form analytics
▪ Session Replays
▪ Customer Interviews
▪ Sales & Client Services Interviews
▪ User Testing
▪ User Feedback on Why they are or aren’t
doing something
▪ VOC
Outcome: Architecture to support scalable optimization program
CRM
Marketing
Automation
DMP
Experience
Optimization &
Ad platforms
Web
Analytics
See the experiment associated
with a lead and a contact in
CRM
Lead, contact and account
attributes
TMS
Data
Layer
Segmented audience
originally created in Analytics
Recognize user before EXO platform serves the experience
Optimized experience based
on data from other systems
Website (CMS) Audience discovery, segmentation, and enrichment
Report performance of the experience
Onboard Analytics & CRM data to be used for media
Content Meta Data
Session recording &
Heat map
Second and Third
Party Data
attributes
Understand Audience Behaviour
Tracking metrics and identifying what parts of
conversion funnel need fixing
Hypothesis
Constructing educated hypothesis, based on your research
Prioritize
Planning and prioritizing your hypothesis
Test
Testing your hypothesis against the existing version of
the website
Measure/Learn
Deploying the winning hypothesis and/or gathering
learning for subsequent tests
Key Deliverables:
Improve Audience CX
Improve
Conversions/Revenue
Step 2: Implement an Experience Optimization Program (ExO)
Why ExO?
Bridge the Gap
What you
offer?
What
Customer
needs?
Customer
Satisfaction
Understanding Audience Journey –
Use the Framework to Follow the Trail
Build a structured Hypothesis – VWO Hypothesis Framework
Based on Observations
I believe Solution
will address Problem
for Audience
and impact Goal by uplift %
Evidence or
Research that
identifies a pattern
or a problem that
needs solving
If you are just
starting to test, then
Audience is
probably all visitors.
But as you mature in
optimization your
audience is a subset
Brings in
accountability on
what your team
was trying to
achieve
Example Of A Data Driven Hypothesis
Build a structured Hypothesis (another approach)
Identified Problem Qualitative Proof Quantitative Proof Change/tactic Key Metrics Define Hypothesis
Form Submission Rate
is below industry
average for Quote
lead form
Feedback from
usability testing
Visitor to Lead is 2% for
Pricing form
Breakdown long "Get a
Quote" form into multi
step form
Quotation
form submit
rate can be
improved by
50%
If we breakdown long "Get a
Quote" form into multi step
form then it would reduce
friction & we can improve form
fill rate by 50% because
currently Quote form
converts@2%, less then site
average of 3%.
Measuring Quality & Quantity of ExO program
Measurement
Quantity:
o # of test ideas
o # of experiments live
Quality:
o # of experiments that reached
significance level
o # of positive results
o # YTD Revenue impact
Knowledge Management Framework
Experiment Story & Hypothesis Metrics that Matter Learning
If we remove generic lead/Demo forms with eBook offers for
pages that have content targeted to enterprise clients then it
would improve Form Completion Rate by 50% because current
form is showing conversion rate of less then 1% compare 2% for
other forms
Form Completion Rate
Enterprise prospects need to
be dealt differently with some
content marketing tactic & not
regular demo or generic form
in order to increase
conversion e.g. eBooks &
starter kit has shown
improvement in conversions
rate by 50%
VWO Prioritization Framework
Confidence Importance Ease
How confident are you of
achieving the uplift in
conversion rates from this
test?
How valuable are the
audience you are testing for?
How Easy is it implement
this test?
Generally higher confidence
can be achieved with high level
of research
Not just effort for design and dev,
but also take into account how
easy is it to get all stakeholders to
agree to doing this
For example a test conducted
on bottom of the funnel could
be more important than a test
on top of the funnel
Targeting and personalization
One to
most
One to few
One on
One
Majority sits here e.g.
Geo Based
experience
Up your Game Strive for
this e.g. targeting based
on content
consumption, customer
attributes
& persona driven
Auto personalization.
AI and ML play.
Targeting & Personalization Framework example
Audience Data by Analytics, CRM, DMP, & ExO platform
Page /Screen Type
e.g. Home Page
User Who
downloaded but
never requested
demo
Customers
Attributes like
Enterprise Plan
Subscriber for
Product X
Anonymous Use
from Industry X
Returning User
from a city where
you are hosting an
event
Section/compone
nt/Ads/Emails
Hero Image
& H1
Hero Banner
CTA
Side Bar
Header Links
Center Right
Sticky CTA
Customer Type Targeting
Goal: Make the re-ordering
process easier
Cookie returning customers and
speak differently to segment with
re-order messaging
8% increase in overall
conversion
Control
Variatio
n
Wrap up
Don’t just focus on increasing the
number of times you roll the dice, but
focus on increasing the odds of winning!
Thank You!
Q&A
Checkout upcoming webinars
https://vwo.com/resources/webinars/

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[Webinar] The Scalable Way: Unlocking Data To Drive Great Customer Experience And Conversion Uplift

  • 1. 1 The Scalable Way: Unlocking Data To Drive Great Customer Experience and Conversion Uplift Arpit Srivastava Manager - Marketing Strategy and Analysis, Publicis Sapient Paresh Mandhyan Director of Marketing, VWO
  • 2. 2 Arpit has 12+ years of experience in digital marketing, data analytics, & experience optimization. In his current capacity at Publicis Sapient, he consults several large scale businesses to help them drive ROI; using data-driven marketing & optimization strategies. Paresh has 12+ years of experience in various aspects of the marketing mix with a keen focus on demand generation & conversion optimization. In his current role as head of marketing at VWO, Paresh has been instrumental in optimizing the end to end inbound funnel to grow it by over 30% in 6 months. Your Speakers for today’s webinar Arpit Srivastava Manager - Marketing Strategy and Analysis, Publicis Sapient Paresh Mandhyan Director of Marketing, VWO
  • 3. Journey to Human Happiness 3
  • 4. Journey to Happy Customer Experience Robust Analytics Foundation + Data Driven Experience Optimization
  • 5. Your target audience expects great CX at every touch point! Including your website, landing pages, product pages etc. We all know why is great CX so important… Let’s recap! Audience Expectations
  • 6. Be Relevant or Lose Customers There is no middle ground. 70% of business buyers says it’s absolutely critical for companies to provide a personalized experience. 74% of business buyers conduct more than half of their research online before making an offline purchase. 75% of B2B buyers would purchase again from suppliers that offered omni-channel capabilities.
  • 7. People used to shop in a linear pattern. Linear Click Based Model But, that’s no longer the case. Audience Behaviour
  • 8. Your Audience now shops in more ways than ever before. 10* Touch points on an average to generate a qualified lead. Event more to generate a purchase online! *https://www.onlinemarketinginstitute.org
  • 9. So What Does It Take To Convert more Leads/Customers on your website with great CX?
  • 10. “For every minute spent organizing, an hour is earned” Benjamin Franklin
  • 11. Step 1: Organize Your Analytics via Robust Analytics Foundation (Qualified TAM) Impressions, Visits, % New Visits etc. (TAM with some commercial intent) CTR, PDP Views, form fill (Ready to buy or bought) Visitor Loyalty, Cart size, Cart Abandonment rate, Profit (Current Users with repeat purchase) Likelihood to recommend, CLTV Awareness Consideration Purchase Loyalty Structure Measurement Framework Across Buyer Stages and Objectives
  • 12. Building the Right MarTech Ecosystem Martech 33% Other Marketing Resources 67% Marketing Budget Source: Gartner CMO Spend Survey 2018-19
  • 13. Too Many Choices in the MarTech Space
  • 14. Collect Consistent Data Using Data Layer & TMS User Actions ▪ Page views ▪ Button Click ▪ Form Submit ▪ Checkout ▪ Purchase Data Layer on web page <script> window.dataLayer = window.dataLayer || []; dataLayer.push({ 'userId' : ABC123', 'internalUser' : true,}); </script> TMS ▪ Properties/ containers ▪ Tags / Extensions ▪ Triggers / Rules ▪ Variables / Data Elements Marketing & Analytics Tags ▪ Google Analytics ▪ VWO Insights ▪ LivePerson ▪ Marketo ▪ Remarketing Pixel Data Layer TMS Organized Analytics Instrumentation
  • 15. “Prioritize data quality everything else is a false promise” Head of Analytics, Fortune X Automobile client
  • 16. Tag Key Content in your CMS Content Creation (PDFs, Banners, Videos etc.) Upload/Publish SharedConsumed Metadata Content ID Category Name Type Purpose Stage Persona Metrics & Metadata Views Clicks Downloads Visitor Tagged Metrics Ratings Comments Social Share Metadata Published Date Author’s Name
  • 17. Build a Campaign Data Governance Process Define campaign tracking code taxonomy for each channel and streamline the creation process Define marketing channel using tracking code structure Grant access to campaign code to each of marketing team / agencies based on channel they are responsible for Channel owners generate code by inputting key values and append tracking code in campaign destination url
  • 18. Scaling the Audience by Breaking the Data Silos Analytics CRM Partner Data Social Media Shared audiences across Channels & Teams
  • 19. DMP has become key component in Analytics Infrastructure Analysis on advance segments & distribution Use audiences to trigger email Audience Targeting with personalized content. Content synchronization across devices Buy advertising based on audience intelligence Unified Data Analytics DSP / Ad networks ExO platform Email Engine
  • 20. Qualitative Quantitative ▪ Web analytics data ▪ CRM Data ▪ Heat maps ▪ Form analytics ▪ Session Replays ▪ Customer Interviews ▪ Sales & Client Services Interviews ▪ User Testing ▪ User Feedback on Why they are or aren’t doing something ▪ VOC
  • 21. Outcome: Architecture to support scalable optimization program CRM Marketing Automation DMP Experience Optimization & Ad platforms Web Analytics See the experiment associated with a lead and a contact in CRM Lead, contact and account attributes TMS Data Layer Segmented audience originally created in Analytics Recognize user before EXO platform serves the experience Optimized experience based on data from other systems Website (CMS) Audience discovery, segmentation, and enrichment Report performance of the experience Onboard Analytics & CRM data to be used for media Content Meta Data Session recording & Heat map Second and Third Party Data attributes
  • 22. Understand Audience Behaviour Tracking metrics and identifying what parts of conversion funnel need fixing Hypothesis Constructing educated hypothesis, based on your research Prioritize Planning and prioritizing your hypothesis Test Testing your hypothesis against the existing version of the website Measure/Learn Deploying the winning hypothesis and/or gathering learning for subsequent tests Key Deliverables: Improve Audience CX Improve Conversions/Revenue Step 2: Implement an Experience Optimization Program (ExO)
  • 23. Why ExO? Bridge the Gap What you offer? What Customer needs? Customer Satisfaction
  • 24. Understanding Audience Journey – Use the Framework to Follow the Trail
  • 25. Build a structured Hypothesis – VWO Hypothesis Framework Based on Observations I believe Solution will address Problem for Audience and impact Goal by uplift % Evidence or Research that identifies a pattern or a problem that needs solving If you are just starting to test, then Audience is probably all visitors. But as you mature in optimization your audience is a subset Brings in accountability on what your team was trying to achieve
  • 26. Example Of A Data Driven Hypothesis
  • 27. Build a structured Hypothesis (another approach) Identified Problem Qualitative Proof Quantitative Proof Change/tactic Key Metrics Define Hypothesis Form Submission Rate is below industry average for Quote lead form Feedback from usability testing Visitor to Lead is 2% for Pricing form Breakdown long "Get a Quote" form into multi step form Quotation form submit rate can be improved by 50% If we breakdown long "Get a Quote" form into multi step form then it would reduce friction & we can improve form fill rate by 50% because currently Quote form converts@2%, less then site average of 3%.
  • 28. Measuring Quality & Quantity of ExO program Measurement Quantity: o # of test ideas o # of experiments live Quality: o # of experiments that reached significance level o # of positive results o # YTD Revenue impact
  • 29. Knowledge Management Framework Experiment Story & Hypothesis Metrics that Matter Learning If we remove generic lead/Demo forms with eBook offers for pages that have content targeted to enterprise clients then it would improve Form Completion Rate by 50% because current form is showing conversion rate of less then 1% compare 2% for other forms Form Completion Rate Enterprise prospects need to be dealt differently with some content marketing tactic & not regular demo or generic form in order to increase conversion e.g. eBooks & starter kit has shown improvement in conversions rate by 50%
  • 30. VWO Prioritization Framework Confidence Importance Ease How confident are you of achieving the uplift in conversion rates from this test? How valuable are the audience you are testing for? How Easy is it implement this test? Generally higher confidence can be achieved with high level of research Not just effort for design and dev, but also take into account how easy is it to get all stakeholders to agree to doing this For example a test conducted on bottom of the funnel could be more important than a test on top of the funnel
  • 31. Targeting and personalization One to most One to few One on One Majority sits here e.g. Geo Based experience Up your Game Strive for this e.g. targeting based on content consumption, customer attributes & persona driven Auto personalization. AI and ML play.
  • 32. Targeting & Personalization Framework example Audience Data by Analytics, CRM, DMP, & ExO platform Page /Screen Type e.g. Home Page User Who downloaded but never requested demo Customers Attributes like Enterprise Plan Subscriber for Product X Anonymous Use from Industry X Returning User from a city where you are hosting an event Section/compone nt/Ads/Emails Hero Image & H1 Hero Banner CTA Side Bar Header Links Center Right Sticky CTA
  • 33. Customer Type Targeting Goal: Make the re-ordering process easier Cookie returning customers and speak differently to segment with re-order messaging 8% increase in overall conversion Control Variatio n
  • 34. Wrap up Don’t just focus on increasing the number of times you roll the dice, but focus on increasing the odds of winning!