Mobile Attribution Modeling

7/28/2019 Mobile Attribution Modeling

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The Channel-less

Consumer

TALK ABOUT US USING

#FUTUREM

Identifying Attribution within Mobile


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Michael KaushanskyAnalytics and Insights, Havas Media,

[email protected]

@kaushansky

2

New Approach to ConsumerCentric Modeling
mailto:[email protected]:[email protected]


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Deterioration in key performance metrics (leads &sales)

We looked for answers: why wasnt our existing

media mix working? Whywasnt TV, search anddisplay across screens generating the samevolume of leads?

Simulations of existing media mix models pointed

to a shift to digital though unclear where. Currentmodels were not reflective of emerging trends inmedia.

Our global Auto client had experienced a

recent deteriorating trend

3


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Second level

Third level

Fourth level

Fifth level

Media Consumption The Path to Purchase

The Role of the Consumer Consumer Expectations

What we experienced was a shift in marketing

dynamics


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Adding to the shift - the emergence of the

multi-channel marketplace

2 explosive and complementary markets

Local Advertising: $35B in 2015

Mobile Commerce: $39B in 2016

Location, Location, Location

70% of mobile revenue tied to location by 2015

40% of mobile search has local intent1) BIA/Kelsey, U.S. Local Media Forecast, 03/2012

2) eMarketer, US Mobile Commerce Forecast: Capitalizing On Consumers Urgent Needs, 01/2012

3) Forrester Report, US Online Sales Forecast, 2009

4) BIA/Kelsey, U.S. Local Media Annual Forecast (2010-2015), 06/2011


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Cross Channel Shopping will be 6X of Online

Retail alone

Source: Paypal Media Network


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Multi-channel brings a new complexity to the

purchase funnel


Nearly 1/3 of retailers credited smartphones

with driving traffic to physical stores in 2011,

up from 1/5 in 2010-Retail Systems Research, The 21stCentury Store: The Search for Relevance, 6/2011

15% of US online shoppers made a holiday

purchase via their mobile, growing 3X over

Holiday 2010- Baynote, 2ndAnnual Baynote Holiday Shopping Survey, 1/2012

- IBM, Benchmark December holiday report, 1/2012


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The purchase funnel becomes the purchase

pretzel



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Existing model failed to identify the shift

towards digital

Investment

Sales

Black

Box

Consumer journey is unique

Journey metrics are

essential to understand the

impact of media


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Our objective was clearupdate the model!

Enhance our existing model to address the

unexpected digital shift

Approach divided the journey into stages, defined

success metrics across each stage & linked the metrics

to show progression throughout the journey

Methodology developed 4 models which explained

key drivers at each stage, i.e. what drove search, site

traffic, leads and sales


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Of Course it

will work


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We modeled the consumer journey across the

5 stages

Stage Success Metric

Awareness Organic Branded Search

Engagement Page Views

Consideration Digital Leads

Shopping Showroom Traffic

Purchase Unit Sales

1

2

3

4

5


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Consumer Stages

Awareness

Engagement

Consideration

Shopping

Purchase

Hierarchical Modeling explained impact at

each stage

4 Independent

ECONOMETRIC

Models

Known Strong

Existing Correlation

Media Mix

TV

Print

OOH

Display

Search

Mobile

Online videoPaid social

Social


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We identified 12+ data sources across 52 weeks

Structured

Equation

Source Data Begins Timing Data Descriptions Metrics Available

Digital Media (DFA) 2009 daily actual media spend by channel (OLA, SEM) impressions, cl icks, hvts

Kantar Evaliant 2010 weekly competitor web impressions and spend impressions, spend

Kantar Stradegy 2011 weekly offline GRP & spend GRP, spend

Sysomos 2009 weekly social media buzz data buzz volume, sentiment

Hitwise/Compete last year daily site visitation and duration visitation, duration

Google Insights 2004 daily total search trends (paid & organic) search trend index

Autodata/Wards 2008 monthly monthly sales data by brand/model/category sales figures

Google Analytics/ Omniture 2009 daily Website/model/configurator/RFQ pageviews visits, configs, RFQ, Model PV

Harte-Hanks 2001 weekly owners, prospects, email/mail campaign, etc.

Urban Science 2009 weekly leads by channel by week leads

Manufacturer 2009 weekly dealer visits dealer visits

Third Party Sites 2009 monthly Comp model PV, day/week/month model pageviews

Brand Tracker 2010 Semi attitudinal data by month consideration, intent

Census 2000 monthly economic data by Geo economic data

Yt= + 1Mt +2Et +3St +4Tt +t , Y (represents th e dependent variable, e.g.,sales)

M (medi a), E (engagem ent), S (search es), T (stor e traffic), etc .


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Results

As suspected results pointed media

consumption varying at each stage

We identified 5 key insights


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1. Brand equity dominates & TV is king of paid

(36% contribution from TV)

Stage

Awareness

Engagement

Consideration

Shopping

Purchase


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2. More channels at play in driving site traffic,

digital media is the workhorse (33%

contribution)

Stage

Awareness

Engagement

Consideration

Shopping

Purchase


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3. Paid Search was essential in driving leads

influenced by DRTV (paid/Mobile search +

DRTV @ 46%)Stage

Awareness

Engagement

Consideration

Shopping

Purchase


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4. Leads were the most significant driver of

showroom traffic (40% contribution)

Stage

Awareness

Engagement

Consideration

Shopping

Purchase


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5. Showroom traffic was the best proxy of

units sales

Stage

Awareness

Engagement

Consideration

Shopping

Purchase

3500

4000

4500

5000

5500

6000

6500

7000

7500

8000

2400

2600

2800

3000

3200

3400

3600

3800

SalesUnits

Show-rooming was responsible for 82% of sales

Unit

Sales

Showroom

Traffic


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Moving the consumer further down journey

increasingly relies on paid media

0%

20%

40%

60%

80%

100%

Media

Base

1 2 3 4 5 sales


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The new models made our simulations more

true-to-life (what-if scenarios) identified

dimensioning returns & ROI


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What weve learned

1. Consistently / frequently assess the journey of your consumers

2. Digital media is significant in the middle of the journey

3. Mobiles role was strongerthan previously thought

4. Promotional messaging is effective if consistent at each stage

5. Exogenous data (weather & holidays) have minimal impact

6. Going dark beyond 5-weeks would be detrimental

7. Optimizing media at each stage points to a 12% improvement


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Ramifications and things you should consider

Do Your

Homework

Determineconsumerbehavior bystage/funnel

Weight socialinfluence

along journey

Set up for

measurement

success

Establish KPIsalongpurchase path

CRM lists

Paid, owned

and earnedmedia

Unify 3

screens

Creative +Media

Merge screenidentifiers andinteractions

Crunch data

What does it

tell you?Weightactivityagainstconversioncontribution

Optimizeaccordingly

Future

Consideration

Weight socialinfluencealong the

journey

Invest inapproach &

tech to unifyscreens

Documents

Mobile Attribution Modeling