Finding Customer Signals –Powering Advanced Analytics with Person-Based DataJohn Gim

SVP, Advanced Analytic Solutions

David Popkin

Head of Data Strategy for Brands

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David PopkinHead of Data Strategy,

Brands

John GimSVP of Advanced Analytics

About Us

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◦ Recap: The Cookie Problem

◦ Identity Resolution Unlocks Advanced Analytics

◦ The Transition from Segment to Signal

◦ Case Study – Identify Predictive Signals by Connecting Online & Offline Data

Agenda

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COOKIENc92 87bc

COOKIE5Bmy 76Bc

Challenge: Devices

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IDFAHa27 0285

COOKIENc92 87bc

COOKIE5Bmy 76Bc

JOHN SMITH42 Greenwood Ave

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Attempts to measure impact make the limitations apparentKNOWN UNKNOWN

Social• Liked on FB• Tweets about

experience

See online or mobile ad • Impressions

and clicks• Publisher site• Campaign

Search• Search words• Referring URL• Site Activity

Email Capture• Open and clicks• Campaign• Refer a Friend

Purchase• Name, Address• Email Address• Products

Service Call• Phone Number• Issue• Disposition Status

Received Mailer• Birthday reminder• Offer tailored to me

See TV Ad• Impressions• Show• Campaign

UNIFIED VIEW OF CUSTOMER ACTIVITY

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Omnichannel customer experience is the priority

3%

6%

10%

11%

40%

7%

14%

16%

18%

14%

7%

14%

17%

19%

9%

0% 10% 20% 30% 40% 50% 60% 70%

Programmatic buying

Mobile

Cross-channel marketing

Data-driven marketing

Customer experience

Please rank these five areas in order of priority for your organization in 2017 first

second

Econsultancy / Adobe Digital Intelligence Briefing

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What challenges senior marketers

46%Integrating online and offline

“We’ve had to deploy and change our ways to make sure that we were present where the customer was getting inspired, because inspiration doesn’t always happen in the physical confines of a store.”

President of Online

US Retailer

51%UnderstandingCross-device behavior

Source: Forrester's Q1 2016 Cross-Channel Campaign Management Survey

48%TransformingReal-time interactions

BI Director

Global Bank

“Our interactions are mostly in the services area, but we’re looking to mine that data for cross-selling.”

Ninety-four percent of marketing professionals report that lacking “people-based” marketing capabilities contributes to their struggle with creating a complete view of cross-media exposure. Without this ability to link that complete view marketers aren’t able to accurately access customer insights.

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94%

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The Reality: No one has a complete picture

AGENCIES

?

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Identity Resolution

Age: 21Male

Eli SimonJoe Glass

Elias J Simon143A 55th

New York, NY

Eli Smith(650) 307-2131

E. Simon55 Post

San Francisco(650) 307-2131

Eli Simon10 Main StArkansas

Eli Simon$75, Shoes

Device ID4500d

Cookie ID 5699zk7fb42

Cookie ID 9O582Lk3

ID G59x55Term: skinny jeans

Cookie ID 4978d8lk

Cookie ID: 89f73kpn

Sentiment

Sentiment

Cookie ID 48y9lk76

Cookie ID

Cookie ID

Cookie ID Cookie ID

Cookie ID

impression

impression

IDFA4500d

Identity Resolution

IdentityLink: Xj5m9b

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Consumers are addressable

Data is actionable Technology is connected

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Now, a unified customer view is possible

Unified Customer View

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Accumulate engagement history and data with Identity

Airline Website Search Interest

Frequent Traveler Segment

Hotel Booking Partner Activity

Trigger Data (e.g. Weather)

IdentityFirst Party

Second Party

Third Party

Data Segment: “Golfers”

What type of signals can form the basis of a segment?

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Carrie made a large purchase at Golf Galaxy

Adam installed RangeFinder

App Rachel watched

The Masters

Ben visits a golf course frequently

Emily subscribes to Golf Magazine

James bought sneakers

Alex installed the ESPN app

Sarah bought a TV

Chris went to Starbucks

Sample high-quality signal data sources available

Location Transaction Online Behavior

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◦ Availability: Through data sourcing and advanced analytics, properly assess which data is available and useful

◦ Transparency: Proactive approach to knowing what data is driving composite scoring and avoiding algorithmic bias and black box solutions

◦ Competitive Advantage: Not relying on the same segment targets that are available to all competitors, but leveraging data associated to known and unknown user base

Root Data For Brands

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Data Utility, Data Quality, Data SourceThe usefulness of data is highly dependent on the quality, which in turn is highly dependent on the source. With a more digitally connected society, there are opportunities to focus on more factually accurate signal data which can lead to higher data quality and utility.

Data Utility

Variable X is not very

useful

Data Quality

How good is the data?

Data Source

Where is the data coming from?

“The mind knows not what the tongue wants. […] If I asked all of you, for example, in this room, what you want in a coffee, you know what you’d say? Every one of you would say ‘I want a dark, rich, hearty roast.’ It’s what people always say when you ask them what they want in a coffee. What do you like? Dark, rich, hearty roast! What percentage of you actually like a dark, rich, hearty roast? According to Howard, somewhere between 25 and 27 percent of you. Most of you like milky, weak coffee. But you will never, ever say to someone who asks you what you want –that 'I want a milky, weak coffee.’”

– Malcolm Gladwell

The Challenge Of Capturing Actual Customer Preferences

The New Coke is one of the most famous research failures. Despite

thousands of sip tests and countless efforts to fine-tune the

taste based on the customer feedback, the New Coke was a

huge disaster.

The now acclaimed Aeron office chair received very low ratings in early tests.

Despite the ratings, the company decided to go on with manufacturing.

The rest is history: Aeron became one of the most iconic and best selling chairs in

the history of office furniture. And the irony: once the chair became famous, people started rating it much favorably.

It cost Walmart $1.85 billion to listen to what their customers

say. “This is the peril of listening to what your customers say

instead of what they actually did.” - Walmart Declutters Aisles Per

Customers’ Request, Then Loses $1.85 Billion In Sales

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Experiment: Replacing or augmenting historically survey based, non-transactional data points with signal data

The Unsurprising Strength And Usage Rate Of Transactional Data In Statistical Models

52%of predictive modeling variables,

utilized across a variety of modeling types and targets, are derived from

transactional data

25% 11% 12%

Transactional (1st Party)

Demographic (Age, Income, etc)

Lifestyle, Preference

Other (Macro)

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You know a lot about your customers/outcomesYou send us PII and customer attributes, we strip out PII and replace with IdentityLink. We send you 3rd Party Data tied to the same IdentityLink.

YOUR CRM DATA

FootballFan

Customer who Bought Car

Sports Fan

Browses Real

Estate

Buys Junk Food

Coffee Drinker

Drives a Minivan Customer who

Bought Car

Goes to the Movies

Drives a minivan

Browses Real Estate

Coffee Drinker

Buys Junk Food

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IdentityLink lets us connect 1st and 3rd party attributes

Term Length Unsubscribed Service Complaint

Competitive Shopping

Drives a minivan

Goes to the Movies

Browses Real Estate

Coffee Drinker

Buys Junk Food Sports Fan

Service Appointment Email Open

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Data science to determine which signals influence positive or negative known outcomes

Term Length Unsubscribed Service Complaint

Competitive Shopping

Drives a minivan

Goes to the Movies

Browses Real Estate

Coffee Drinker

Buys Junk Food Sports Fan

Service Appointment Email Open

v

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Online-to-Offline Data Analysis

Customer who Bought Car

MODELING ENVIRONMENTCRM + 3rd Party data both

mapped to IdentityLink

Compare known outcomes from 1st party data to attributes in 3rd party data, all keyed off of IdentityLink. Use data science to determine which 3rd party data attributes are predictive to power future marketing and models.

Goes to the Movies

Coffee Drinker

Sports Fan

Browses Real Estate

Buys Junk Food

Drives a minivan

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Top 10% more likely to convert

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Case Study - Quantifiable Assessment Method

Firewalls & Privacy

Privacy Agreements

No 1 to 1 targeting

Technical Restrictions

Signal Data

5,000+ Files

1+ Terabyte of Data for test

7 Direct Sources

Data Value

Evaluating which sources and which data

points are most valuable

Power ScoreUtility in modeling

and prediction

Inform Score

Utility for planning, creative, and

strategic vision

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• Aggregated age/income & other demographic data can fill gaps in standard appends (7-40% of fields can be NULL or unknown)

• Many confirmations and contradictions found across sources for ‘lifestyle’ attributes. TBD on creating an aggregated view across sources for model viability.

Case Study - Assessment Insights

• Aggregated geo data improves estimates of event traffic by understanding ‘true travel radius’ vs a generic radius assumption

• Combination of 3rd party web visits with competitive geo data improves defection/loyalty/imkpredictions where 1st party data is sparse or dated

• App level preferences, usage allow for more granular ‘product recommender’ engines

• Old customer records now have data associations that allow for identification of their journey stage by plotting timestamped data across sources. Primarily led through geo data and web activity.

• Plotting aggregated app-genre connections allow for an understanding of pairing/partnerships across verticals.

Model Improvement Comparative Data Interconnectivity

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Fin.