Big Data and Mobile Analytics - MMA SF Jan13

LEVERAGING BIG DATA IN
MOBILE MARKETING
Jennifer Veesenmeyer
Merkle Analytics

Mobile Marketing Association

Merkle, Mobile & Me

• Merkle’s mobile division • VP in Merkle Analytics, Digital
• Award-winning mobile web, app, Analytics Practice
tablet and SMS solutions (I own Mobile, Social and Site)
• Innovative Responsive Design • 175 statisticians and analysts,
services >95% with a master’s degree +

Mobile Marketing Association 2

What we’ll cover today

Context & Level-Set Application

Deadly Table Big Data
Sins of Stakes for Ideas for
Mobile Mobile Mobile
Analytics Analytics Marketing


7 Deadly Sins of Mobile Analytics

1. Lumping smartphones and tablets together
2. Measuring mobile marketing in a silo
3. Ignoring the context of purchase process
4. Measuring usage rather than impact
5. Reporting on mobile as a channel
(when you’re using it as a platform)

6. Guessing instead of testing
7. Settling for subpar analytics


DEADLY SIN # 1 :
Lumping smartphones and tablets together
Research confirms what many of us have seen in our own data –
smartphones and tablets are used differently.

Smartphones are an
on-the-go device, but
tablets are more for
media consumption.

A 2012 study by Flurry
Analytics also showed that
67% of time spent on
tablets was on games, but
only 39% on smartphones.


DEADLY SIN # 2 :
Measuring mobile marketing in a silo
Research has proven what we have suspected –
mobile marketing improves conversions in other channels.

Mobile ads increased
website conversions Impact of Cross-Media (mobile and web) Ads
(Ghose, Han and Park 2012)
by 36%.

In another study, Ghose
et al found that 33% of
mobile site visitors
made a store purchase
within 1 week.


DEADLY SIN # 3 :
Ignoring the context of purchase process
Consumers take a multi-device path to purchase…
and they use mobile devices throughout the consumer lifecycle.

Google Multiscreen Study (August 2012)


DEADLY SIN # 4 :
Measuring usage rather than impact
Measuring usage is important for improving user experience
and understanding adoption, but it isn’t a measure of success.
Does your organization have
Getting to ROI is difficult a method for quantifying ROI
from mobile marketing programs ?
and many companies
don’t try.

This is an opportunity for
mobile marketers to gain a
competitive advantage.


DEADLY SIN # 5 :
(when you’re using
Reporting on mobile as a channel it as a platform)

If your mobile site and pc/laptop site have the same objectives,
put them in the same report so you can compare performance.

When mobile has a
unique strategy, then it is
a channel to be reported
separately.

But, if it is an add-on to
your other marketing
initiatives, then it is a
platform to be compared.


DEADLY SIN # 6 :
Guessing instead of testing
Testing is important both pre and post launch.

What mobile optimization efforts were
Optimize mobile utilized by your organization in 2012?
performance post Did not perform any mobile
testing or optimzation
launch by starting
Optimization without testing
with the easiest first
– messages and Not applicable/don’t know

landing pages. Data analysis of past
campaign metrics

Customer feedback/survey

Testing (A/B split,
multivariate)


DEADLY SIN # 7 :
Settling for subpar analytics
Limitations in mobile analytics should be acknowledged
but not accepted.

Have a plan (and budget) for
evolving your mobile analytics
capabilities.

Mobile marketing budgets
are increasing dramatically,
but they would increase even
faster if you had good analytics.


5 Table Stakes for Mobile Analytics

1. Tools: Get the right analytics tools in place
2. Metrics: Know what is important to measure
3. Process: Establish an end-to-end analytics process
4. Research: Understand your mobile customers
5. Plan to Integrate: Have a plan to integrate
mobile into your overall measurement strategy

These are the bare minimum capabilities for
mobile analytics. Seriously. If you don’t have
these in place, you’re behind.


TOOLS:
Get the right analytics tools in place
Determine what the right analytic tools and capabilities
are for your organization.

Infrastructure Components Sample Decision Types
 Behavioral  Incumbent or niche partners

 Attitudinal  Appetite for learning curve
and/or decentralized
 Competitive systems

 Acceptance of nascent
ecosystem


METRICS:
Know what is important to measure
Separate KPIs and diagnostic metrics from attributes.
You need both for analysis, but not for reporting.

250+ attributes can be collected, depending on the analytics vendor chosen.
Only a handful are measures of success. Fewer still provide a valuable basis
for segmentation in dashboard-level reports.

BUT, getting your data at the user-level opens up many possibilities for
providing micro-targeted messaging. Thanks to Big Data technologies,
sometimes more data is more actionable.

It’s ironic that sometimes the cure for
drowning in data is more data.


PROCESS:
Establish an end-to-end analytics process
Mobile analytics starts upstream with insight and measurement
strategy. Post-launch begins a continuous improvement process.


RESEARCH:
Understand your mobile customers
Internal research can help you understand the current need
and forecast the opportunity.
• Are your consumers engaging with content
differently now by each of the channels?
• Are your consumers changing how they
visit your web site?
– Review current trends over the past
few years. Is the trend accelerating?
• Are more consumers using search from
their mobile devices?


PLAN TO INTEGRATE:
Have a plan to integrate mobile into
your overall measurement strategy
You need an integrated measurement strategy –
even if you don’t have an integrated marketing strategy yet.

An integrated measurement strategy includes:
• KPI framework with Global KPIs that apply
to all channels in addition to channel-
specific metrics
• Technology architecture for managing the
data and connecting multi-channel
behaviors to customers
• Reporting strategy that encourages cross
functional and multi-agency collaboration


Examples of Big Data in
mobile marketing:
• Multichannel CRM
• Location
• Social

Two keys to successfully using
Big Data in mobile marketing:
• Get personal
• Go real-time
Image from WebOptimeez (http://bit.ly/VqJda9)


Big Data Ideas for Mobile Marketing

Multichannel CRM Data
1. Enriching customer profiles with mobile data
2. Targeting the omnichannel customer
3. Integrating mobile display, mobile web and email

Location Data
4. Testing coupon value vs. proximity
5. Predicting traffic patterns and using “behavioral fencing”
6. Researching cross-shopping, mobile-instore dynamics, motivators, etc

Social Data
7. Experimenting with Social MRI


MULTI-CHANNEL CRM DATA:
Enriching customer profiles with mobile
In ConnectedCRM we bring mobile data into the customer
database to build toward an omni-channel view of the customer.

The analytics behind
ConnectedCRM is
explained in our
upcoming book:


MULTI-CHANNEL CRM DATA:
Targeting the omnichannel customer
Case Study Example: 5th Finger used customer purchase history
to create personalized coupons for a major retailer.
Data used:
• Product purchase history
• Average purchased product value
• Average monthly spend

Execution:
• Personalized, on a 1-1 level, SMS and In-App
Coupons Available on a weekly basis. Delivered
at strategic times of day and day of week.

Results:
• Mobile coupons had a 35% redemption rate (5-10% is typical)


MULTICHANNEL CRM DATA:
Integrating mobile display, mobile web and email
Case Study Example: Merkle used targeted mobile display to
acquire new email opt-ins for Motorola Mobility.
Data used:
• Device information
• Mobile site behaviors
• Email address
Execution:
• Targeted display ads on the individual device level
• Users were directed to different creative experiences (and value props) for
different devices
• Mobile landing pages encouraged users to sign up for email newsletter,
when they did, we entered them into the database with device information
• Email retargeting


LOCATION DATA:
Testing coupon value vs proximity
Ghose and Span found that you need to offer a 10% discount
for every 1km between the user and retail store.

Research: Disentangling Coupon Value vs Distance Proximity
Using Randomized Experiments (Ghose and Span, 2013)


LOCATION DATA:
Predicting traffic patterns and using
“behavioral fencing”
Behavioral fencing is the new geo-fencing.

Research is being done to
leverage predictive analytics
to model the probability a
customer will take different
actions based on historical
proximity and behavior.

Image from MartketingEasy.net (http://bit.ly/MU9d95)


LOCATION DATA:
Researching cross-shopping, mobile-
instore dynamics, motivators, etc
New vendors are providing new ways to research.

Example: Locately
• Customer Opt-in: In exchange for gifts cards,
consumers opt-in to share location data by
installing app or by enabling location sharing.

• Targeted Surveys: Consumers earn rewards
by filling out surveys capturing their thoughts.

• Location Analytics & Processing: Places
consumers drive past are tagged as missed
opportunities.


SOCIAL DATA:
Experimenting with Social MRI
Behavio’s open-source Android platform turns phones into
smart sensors of people’s behaviors and surroundings.

Behavio's Social MRI technlology was born at the MIT Media Lab,
as part of research into understanding social and behavioral dynamics.


Let’s Review



Or if you prefer Haiku…

hear mobile marketing sing
a big data song
CMOs will weep with joy


Recommended Resources

MARKETINGSHERPA
2012 Mobile Benchmark Report
http://bit.ly/UeEIrO

@marketingsherpa

ANINDYA GHOSE
Director, Center for Business Analytics
Associate Professor Stern School of Business
New York University

@aghose


JENNIFER VEESENMEYER
Vice President, Digital Analytics Practice

443.542.4611
612.356.4191 (cell)
@ @jenveese
jveesenmeyer@merkleinc.com


Big Data and Mobile Analytics - MMA SF Jan13

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