Digital marketers should consider online data co-ops for three reasons:
1) First-party data alone lacks scale while third-party data can be costly and inaccurate. Data co-ops provide a scalable and validated alternative.
2) Data co-ops allow marketers to gain insights from aggregated first-party data from multiple brands and publishers, providing more reach and granularity than any single source.
3) Case studies found marketers who used a data co-op approach saw improved marketing ROI, customer acquisition, and customer experience versus relying solely on their own first-party data or third-party data.
4 REASONS DIGITAL MARKETERS SHOULD CONSIDER AN ONLINE DATA CO-OP
1. 4REASONS
DIGITAL MARKETERS
SHOULD CONSIDER
AN ONLINE DATA
CO-OP
Webinar with:
Jacob Ross
PRESIDENT ADROIT DIGITAL & Fatemeh Khatibloo
PRINCIPAL ANALYST CUSTOMER INSIGHTS PROFESSIONALS FORRESTER
2. Jacob Ross became president of Adroit in October 2014.
He has previously held leadership positions at Metamarkets,
Criteo, Demand Media and Right Media (now Yahoo!).
Jacob graduated with honors from Reed College.
Follow him @jacobaross
Fatemeh Khatibloo serves Customer Insights Professionals at
Forrester with a focus on the shifting consumer data ecosystem.
She has previously worked at Times Two Marketing, Binger
Catalog Marketing, Epsilon and Haggin Marketing
(now SolutionSet). Fatemeh has a B.A. from George
Washington University.
Follow her @fatemehx2
About Our Speakers
3. CHAPTER 1:
Everything Old is New Again:
The Modern Data Co-op
CHAPTER 2:
Better Together:
How Marketers Can Benefit
from Digital Data Co-ops
CHAPTER 3:
Making the Case for Digital Co-op Data:
A Tale of Two Brands
5. How do digital marketers stay
ahead in the current landscape?
Note: digital display ads transacted via and API, including everything from publisher-erected APIs to more standardized RTB technology; includes advertising
that appears on desktop/laptop computers as well as mobile phones and tablets; *includes banners, rich media, sponsorship, video and other
6. To make predictions about
what customers will buy
in the future you need to
have good data on what
they have bought in the
past Harvard Business Review
A Predictive Analytics Primer, September 2014
7. Your 1st-party data
is powerful
but doesn’t offer scale
3rd-party data is vast
but can be costly, inaccurate and
often opaque
23. Data co-ops are
now being used
in the digital
world
This transaction data is:
Scalable
Validated
Secure
Actionable
24. ADROIT DIGITAL SHOPPER COOPERATIVE
Customer Success | Case Studies
Digital data co-ops drive better:
• ROI • Customer acquisition • Insights
GOALS
TACTICS
RESULTS
Online-Only Children’s Furniture Retailer
• Prospect for new customers
• $6 Return on Ad Spend
• % of New Customers at 40%
• Media: Display
• Strategies: Prospecting Only
• Targeting: Shopper Co-op model, Private Marketplace,
Whitelist, Goal-Based Optimization
• Exceeded primary metric goal by 33%, delivering
$8 Return on Ad Spend
• Delivered 50% new customers
25. ADROIT DIGITAL SHOPPER COOPERATIVE
Customer Success | Case Studies
Digital data co-ops drive better:
• Remarketing • Full-funnel results
GOALS
TACTICS
RESULTS
Multi-channel Men’s Clothing Retailer
• Prospect for new customers and grow
remarketing pool to drive purchases
• $5 Return on Ad Spend goal across both strategies
• Media: Display, Social, Mobile
• Strategies: Prospecting, Remarketing
• Targeting: Shopper Co-op model, Co-op Segments,
Private Marketplace, Goal-Based Optimization
• Delivered $7 Return on Ad Spend, 40% higher than goal
• Adroit was consistently the top-performing partner
Back in the catalog heyday of the ‘90s
And it’s here, in co-ops, that we first encounter the notion of “second party data” with any scale.
But obviously, most digital marketers see “shared data” and can’t help but
As a result, over the past five years, consumer awareness and concerns around data collection and use have skyrocketed. And people are worried.
There are some businesses that have figured this out, and have used data as not just an insights engine but also an innovation engine. Here are some examples.
Input to the analytics you rely on: What is your customer’s context at her moment of need?
Second, baked into the output you deliver: The touch point needs to be contextually relevant and appropriate.
Input to the analytics you rely on: What is your customer’s context at her moment of need?
Second, baked into the output you deliver: The touch point needs to be contextually relevant and appropriate.
Input to the analytics you rely on: What is your customer’s context at her moment of need?
Second, baked into the output you deliver: The touch point needs to be contextually relevant and appropriate.