1. Data management (dmp, marketing cloud)

2. These are the data companies

3. Global data company view
We work with all of them
Most don’t have ground truth about given audiences
Sometimes compete with us based on business models
Geographic concentration. (Mostly US)
Platforms lack support of our clients markets or have limited service in certain markets
Data providers are unique to one or a few markets (Kantar is a major exception)
Channel concentration (mostly digital)
In most cases, see a fraction of what we see globally or in market
Adobe

4. …but Nearly every company in this picture generates data that we use
But because data management is hard This is the coal.
Scott Brinker (CTO of ion) Marketing Technology landscape
How many tech vendors are in this picture?
50% of all vendors have homegrown Martech solutions

5. But I need a dmp!! What is a DMP? I need a DMP!
Can I justify the cost?
Stop talking about the technology

6. DMPS Are
a small part of
The
ecosystem

7. Initial questions to ask the client and yourself about assessing DMPS
Does your client really need a Data Management Platform (DMP)?
How are they going to use it?
What are the most important functions?
Where will the clients business be in 1 year? Three years?
Are they willing to assume the total cost of ownership (TCO) of this technology for a period of 5 plus years?
REVIEW TEAM:
Do you and your team have detailed operational understanding of each of the functional areas you’ll be reviewing?
Are you and your team qualified to perform a vendor technology review for a multi market?
Are you and your team qualified to perform a review or structure a fair test of the advanced mathematics used in segmentation, modeling and scoring? 7

8. So…You want to do a DMP Review?
Technology vendor reviews are often feature box checking exercises. But, not all features and functions are weighted equally for each respective client.
Specific implementation for a given feature may be quite different from one vendor to the next.
Small nuances in technical implementations may make integration and implementation more or less difficult.
Vendor support to customers, data availability and integrations nearly always varies significantly by geographic market.
It is quite different to have the ability to do something vs having done it. Technical vendors will say, “Yes, I can do that.” What is more important is that they have done that thing and done it in production on more than one large client.
Beware of vendors who have many required/critical features that are “on the roadmap.” (We call this being “roadmappy.”) Any feature that is on a vendors roadmap unless it will be delivered in the next 30 days, should be considered a feature that doesn’t exist. While this vendor may have a perfectly good technology for other clients, they may not be right for this client.
Finally, trust but verify. Twice.
Product Design in GroupM Connect 8

9. During an actual DMP review (American version)
Vendor: We integrate TV data into the customer journey.
Us: Are you currently doing this with any clients in production?
V: None of our clients have started to do this, yet.
Us: What TV data would you onboard.
V: Either Nielsen or set top box. Depending on what you want to use.
Us: How do you do this?
V: At the individual user level
Us: Do you have a contract with Nielsen for the respondent level data with the ability to join that data to your data allowed in the contract?
V: We’re in discussions with Nielsen.
Us: What about set top box data. Where would you get that?
V: Rentrak
Us: Do you have a Rentrak individual user level feed integrated?
V: We’re in discussions with Rentrak to get the user level feed.
Us: How would you overcome the fact that Rentrak can only provide clusters rather than individuals in their feed?
V: We’re still working through some of the details on how we would integrate the TV data.
Product Design in GroupM Connect 9

10. Product Design in GroupM Connect
DMP Bingo
Watch for these phrases…
We can integrate ANY data. (Have they seen your clients data?)
360 degree view of the client. (What about user level inside Google?)
Complete view of the customer journey across ALL channels. (Including click by click inside the Facebook app?)
Infinitely scalable. (!)
The biggest ID graph in the world (Bigger than Facebook?)
The most accurate predictive analytics.
It’s free. (So, you give it away and make money on volume?)
It is better to work with a vendor who is more limited in feature set but honest and transparent.
Product Design in GroupM Connect 10

11. General business Size and location(s) Employees Sales
Account management
Writing code QA
Product management
Implementation
Design
Offshore vs onshore. Employees vs contractors
Local support
Pricing structure/costs
Volume?...
Data. Profiles under management
Per seat
Per integration
Some other metric
Combination 11

12. Key DMP functional areas
Use cases
Primary reason for use
Secondary reasons for use
Proof
Tech infrastructure
System architecture
Key technologies
Hosting
Infrastructure best practices
Tech team
Support
Deployment
Deployment process
Support for multibrand or multidomain deployments
System hosting
Domains
Data collection and normalization, consumer profiling
Tagging system
Tag-based first-party data collection
API connections
Log file data ingestion
Data ingestion from external sources
Web analytics data
Ad server data
Search data
ESP data
Social data
Mobile data (web-based, in-app)
Offline data
TV Data
Other data streams
Data normalization
Refresh schedule
Third-party data integrations
Third-party data provider integrations
Pricing models
Incorporation of new third-party providers
Segmentation and user profile management
User versus segment orientation
PII to non-PII data conversion
Anonymous to known data linking
Segment development
Flexibility of taxonomy
Dynamic segment updating
Scoring and modeling
Scoring capabilities
Look-alike modeling
Decisioning
Decisioning capabilities
Decisioning syndication
Activation
Data syndication to external activation platforms
Desktop display/video media
Desktop CMS
Mobile CMS
Social platforms
Mobile advertising (web-based, in-app)
Offline
Other
Process for adding new activation partners
Data ownership and security
Client data ownership
Client data firewalling
Data leakage protection
Data rights management
Privacy and security practices
Consumer privacy functionality
Raw data access and portability
Raw data availability
Client data portability
Reporting and analytics
Standard reports
Report flexibility
User interface/User Experience (UI/UX)
Availability of self-serve
Client service
Implementation and consultative services
Training
Technical support
Product Design in GroupM Connect 12

13. 0. Use Cases Area Requirement Questions Guidance Use cases
Area
Requirement
Questions
Guidance
Use cases
Why should people want to use your system?
0.1
Primary reason for use
What is the primary reason your customers choose to use your sytem?
Planning vs targeting vs activation
0.2
Secondary reasons for use
What are the secondary reasons your customers use your system?
0.3
Proof
What do you and your system do and show to prove your value?
Does this map to the primary and secondary reasons. Your job is to look for proof. Anything "on the roadmap" is fiction. Assume that this functionality will not be delivered, ever. Verify all functionality is being used by clients in production systems. Speak to clients and partners. Have a bon fide technologist on the review team who can validate wheter or not
0.4
Key technologies
Support for structured data
What technologies are used within the system? What is proprietary vs. licensed vs. open source?
Beware of over reliance on expensive proprietary systems (teradata, netezza). Be mindful of unproven technologies/.
0.5
Tech team
Who built the system? How much is outsourced vs. insourced? If outsourced, what companies provide the outsourced services?
0.6
Support
Who provides tech support (insourced vs outsourced? 13

14. 1. Tech infrastructure 1 Tech infrastructure
What does the technology stack actually stack?
Need documentation. Look for single points of failure, outdated methodologies and technologies, non-standard practices, lack of controls, small engineering teams tackling large scale problems. Signs of lack of reliability or scalability.
1.1
System architecture
The system will be architectected in a modular way such that each component speaks to other components via an API.
What's the architecture of the system?
1.1.2
Access to each component will be made available via API to External systems
The system will be extensible such that new modules may be added without rearchitecting the system.
1.1.3
The system will be extensible such that new data types may be added without rearchitecting the data schema(s).
How are new data types added?
1.3
Hosting
Support for redudunancy intra and inter data center of all critical modules.
Where are data centers located? How many? If global, what regions (countries) are served by each data center?
Beware of single region
1.4
Infrastructure best practices
Support for standard practice off site disaster recovery.
Are systems redunant intra data center vs extra data center? What is the disaster recover setup?
Replication intra and between data centers. 14

15. 2. Deployment 2 Deployment
How and where do deployments occur and are hosted?
2.1
Deployment process
Do you have a standardized deployment process? If yes, what is it? What is the average time for deployment (from contract sign to live)?
Over 12 weeks is a fail. (Ex of delay on client side for items such as tag deployment) Rollout should happen within 4 weeks.
2.2
Support for multibrand deployments
Support for multi-brand deployments.
Do you support multibrand and/or multidomain deployments? If yes, please provide specific examples of live client deployments of this nature. How does this occur?
Support either a rigid hierarchy of parent-child-grandchild relationships or a more fluid "tag" based association of entities. The later is more flexible but can create more issues with reporting and
2.3
Support for multidomain deployments
Support for multi-domain deployments.
Data collection should be done
System hosting
The system will suppport local (client) hosting of system components.
Where is the system hosted? Can a client deploy the system locally (e.g., within its own environment)? If yes, please give live examples.
No desire for local hosting. Clients could ask for this. Availability of local hosting of entire stack could signal lack of focus. Supporting licensed software simultaneous to SAAS is difficult particularly for small companies
2.4
Domains
The system will have the ability to operate in both first and third party domains.
Does your system operate in your domain or your customer's domain (e.g., third-party versus first-party domain)?
Must support first and 3rd party deployments. Method for linking first and third party data sets is ideal. 15

16. 3. Onboarding and normalization
Data intake and normalization
How does the vendor collect and ingest first-party data?
3.1
Tagging system
The system will provide it's own tag management system.
Is there a proprietary tagging system? If yes, please describe it and list core components. If not, what do you use to collect tag-based first-party data? Does the tag have to sit directly on the page? Does it have conditional data routing capability to other sources? Is there a tag object model? If so, please provide. Can it route both first and third party domains (data collecdtion) to same destination? Who has certified the tags to run embedded into creatives?
Self serve tagging required with documentation and UI. The tag function and UI
3.2
Tag-based first-party data collection
The tag will support multiple collection schemes.
What is the range of tag-based first-party data the system can collect (1) binary yes/no visited or did not visit a page; 2) page structure 3) unstructured data such as user comments)? Must elements passed be predefined and classified? How is classification (reclassification) done after the fact?
3.3
API connections
Support for collection of data via API.
What API standards are supported? What data exchange can happen via API?
3.3.1
Support for responsed to push data via an API.
3.3.2
Support for pulling data via API.
3.4
Log file data ingestion
Support for log file ingestion
Is log file ingestion supported? For what platforms? What is the ETL/ELT process?
3.5
Data ingestion from external sources
Support for the ingestion of data from external sources.
What data streams does your system currently pull from to inform user profiles and/or client segmentation in the system?
Some combination of logs, embedded tag (direct calls), app calls and api calls. Must define method of ingestion, cost, engineering and non-engineering level of effort to bring new data online. Need to measure our own internal resource impact. For example, how does this affect DataMart and DataMart staff?
3.6
Data normalization
Support for User Interface configured data normalization rules.
For each of the above channels, how is the data normalized for incorporation into a client's user profiles and/or segmentation schema? What is modeled vs direct match?
Provide schema to link to common ID.
3.6.1
Support for machine automated data normalization.
3.7
Refresh schedule
The system will provide configurable refresh times for individual attributes.
How often are individual attributes updated? What's the round trip time between data collected and model updated? What is the round trip time between model updated and made available for targeting?
Some combination of direct write to profile and stored for modeling.
3.8
Automation
All system data ingestion functions will be automated.
3.8.1
Support for scheduling of data ingestion. 16

17. 4. Onboarding and intake: 3rd Party
Support for 3rd party data intake
How does the vendor manage third-party data?
4.1
Third-party data provider integrations
Support for third party data ingtegrations configurable via user interface.
Which third-party data providers are integrated into the system and with which do you have server to server integrations? Which vendors are tag level or cookie domain integrations done? Do you normalize data across third-party provider taxonomies? If yes, how?
4.1.1
Support for 3rd party tag type integrations.
4.1.2
Support for 3rd party server to server data integrations.
4.1.3
The system will suppport cookie syncing with 3rd parties
4.1.4
Support for normalization of third party data taxonomies via user interface.
4.2
New data integrations
Support for onboarding new data providers via the user interface.
What is the process for onboarding new data providers? How many data providers are integrated in total
4.2.1
Support for onboarding of CSV files via the user interface.
4.3
Data Pricing models
Support for multiple price structures for each individual data set.
How is third-party data priced? What variety of pricing/data payment models are available to a client?
4.3.1
Support for account based price control of individual data sets.
4.3.2
Support for CPM based data pricing configurable in the user interface.
4.3.3
Support for volume tier based data pricing configurable in the user interface.
4.3.4
Support for outcome (CPA) based data pricing configurable in the user interface.
4.3.5
4.4
Data Quality
Support for automated quality assurance processes.
How is data quality assurance automated on inbound and outbound requests?
Adhoc + time vs engineering + time
4.4.1
Support for automated discovery of missing data upon ingestion
4.4.2
Support for the automated discovery of errors in data upon ingestion.
4.5
Data Type
Support for the import of multiple data types
4.5.1
Support for the ingestion of structured data.
4.5.2
Support for the ingestion of semi-structured data.
4.5.3
Support for the ingestion of unstructured data. 17

18. 5. Segment and user profile management
Segmentation and user profile management
How does the vendor build and manage user profiles and segments?
5.1
Profiles and segments
Support for individual, anonymous consumer profiles.
Is your system architected to build, manage, and target individual profiles or to assign users (cookies, profiles) into buckets for segment targeting?
Need to support both user directed and automated profile capabilities. How are anonymous vs non-anoymous PII data profiles handled.
5.1.1
Support for automated building of individual, anonymous consumer profiles.
5.1.2
Support for the addition of metadata to N consumer profiles via the user interface.
5.1.3
Support for a non cookie based unique identifier per user profile.
5.1.4
Support for access of individual anonymous consumer profile records via the user interface.
5.1.5
The system will maintain a single user profile across all media.
5.1.6
The system will simultaneously maintain the consumer state within a single profile across multiple media.
5.1.7
Support for rule based segmentation.
5.1.8
Support for segment construction based boolean expressions of combinations of any data point in a consumer profile.
5.1.9
The systems will support real time access to any data point in any consumer profile via a 3rd party system. (<20ms round trip)
5.1.10
Support for real time access to any segment via a 3rd party system (<20ms round trip)
5.1.11
Support for export of N consumer profiles to external systems.
5.1.12
Support for segmentation triggers based on recency.
5.1.13
Support for segmentation triggers based on frequency.
5.1.14
Support for segmentation triggers based on actions.
5.1.15
Support for segmentation based on a combination of recency and/or frequency of a given action.
5.1.16
The system will maintain a map of all user ids across all data providers and devices.
5.2
PII vs non-PII
Support for privacy compliant PII to non-PII conversion.
Do you have a defined/standardized process for converting PII to non-PII? If yes, what is the process? What external partners are involved? Who owns the relationship with those partners? What is your level of experience with this? How would this work in markets where privacy restrictions are different (e.g. Netherlands and Germany)?
5.2.1
Support for privacy compliant PII to non-PII via a 3rd party provider.
5.2.2
Support for stripping of non-privacy compliant data upon ingestion and prior to storage.
May be requirement for certain geographic markets.
5.2.3
Support of the stripping of non-privacy compliant data upon ingestion will be configurable by geography.
5.2.4
Support for black box component managed by 3rd party to strip non-privacy compliant data.
5.2.5 18

19. 5. Segment and user profile management (Con’t)
5.3
Anonymous to known data linking
Support for linking anonymous data to known user profiles.
Does your system have a defined process for linking anonymous and known user profiles? Please describe it.
Direct match vs data fusion
5.3.1
Support for direct match of anonymous data to known user profiles.
5.3.2.
Support for data fusion of anonymous data to known user profiles. (eg. Clustering, Mahalonobis Distance, etc)
5.4
Segment rules
Support for user interfaced defined rules for segment construction
What is the process by which you/your client develop audience segmentation rules which dictate how segments are constructed?
5.5
Taxonomy
Support for a hierarchical relationship of data entities within a taxonomy.
Is your system segmentation taxonomy pre-baked, built, and customized on a client by client basis or some combination therein?
Need some flexibility here. Rigid hierarchies generally don't work.
5.5.1
Support for free form linking of data elements via metadata association.
5.9
Dynamic segments and counts
Support for updating of consumer segment membership in <1minute after trigger occurs.
Does your system offer dynamic segmentation (e.g., real-time updated segments based upon interactions of users with channels)? Are the segment counts shown in the UI actual or modeled? If modeled how does modeling occur?
Look for hamsters running on a wheel in the background.
5.9.1
Support for realtime count of all segments when segment counts appear in the user interface. 19

20. 6. Scoring and Modeling 20 6 Scoring and modeling
What scoring capabilities does the vendor have?
6.1
Scoring capabilities
Support for scoring of data points in consumer profiles.
Does your system provide scoring on user profiles or segments based upon predictive analytics? Please describe and give examples.
6.1.2
Support for scoring of confidence level of match of anonymous data to known data.
6.1.3
Support for confidence level of each individual data point.
6.1.4
Support for scoring of confidence level of segment membership.
6.1.5
Support for decay models in segement membership based on time.
6.1.6
Support for configurable date range for decay models.
Support for probabalistic modeling segment membership based on outcomes.
6.1.7
Support for configurability of all models in the user interface.
6.1.8
The system will provide all confidence levels when providing segment information in the user interface.
6.2
Look-alike modeling
Support for look alike modeling.
Does your system offer any look-alike modeling based on existing user or segment data? If yes, please describe. How are clients using this? Does your system provide transparent confidence levels of modeled segments?
6.2.1
Support for look alike models based on increase in size of segment in increments of 20% to 500% segment size.
6.2.2
Support for look alike models based on decrease in probability of generating outcome in increments of 10% to 100%
6.2.3
The system will display acutal count of look alike model membership in the user interface in <3 seconds from system user entering query.
6.3
Fraud
Support for rules based event fraud detection
6.4
Reconciliation and attribution
Support for automated linking of related activities (i.e. site visit and product purchase)
6.4.1
Support for tracking of multi touch attribution 20

21. 7.Decisioning 7 Decisioning
What decisioning capabilities does the vendor have?
7.1
Decisioning capabilities
Does your system have decisioning capabilities? If yes, are they manual (e.g., based on business rules) or automated (e.g., based on predictive analytics)?
7.2
Decisioning syndication
Where are the decisioning capabilities applicable (e.g., in what data syndication vehicles)? Please provide live examples. 21

22. 8. Activation 22 8 Activation
How and where does the vendor's system activate the data collected and modelled?
8.1
Activation via external activation
Support for activation via external systems.
With which external platforms is your system integrated?
Divide the world into media (ads) and content (CMS). Data can be used for customizing experiences in advertising and content. How does this occur
8.1.1
Desktop display/video media
Support for activation via display and video ad servers and DSPs
With which display/video media platforms (ad servers, DSPs) is your system currently integrated? Are clients actively syndicating data here? Please provide examples and specifics.
8.1.2
Desktop CMS
Support for activation via web content management systems.
Is your system integrated with desktop CMSes? Are clients actively syndicating data here? Please provide examples and specifics.
8.1.3
Mobile CMS
Support for activation via mobile app content management systems.
Is your system integrated with mobile CMSes? Are clients actively syndicating data here? Please provide examples and specifics.
8.1.4
Support for activation via management systems.
Is your system integrated with systems? Are clients actively syndicating data here? Please provide examples and specifics.
8.1.5
Social platforms
Support for activation via social platforms.
Is your system integrated with social platforms? Are clients actively syndicating data here? Please provide examples and specifics.
8.1.6
Offline
Support for activation via offline (TV) systems.
Is your system integrated with any offline channels including TV, print, point-of-sale, call center, etc.. Are clients actively syndicating data here? Please provide examples and specifics?
8.1.7
Integration
Support for automated push of profile (cookie.non-cookie lists) into activation platforms for targeting.
8.1.8
Support for enrichment of ad/content management system calls in real time via http.
8.1.9
The system will make available all integrated systems in the user interface.
8.1.10
8.2
Process for adding new activation partners
Support for the addition of new activation partners via the user interface.
What is the typical timeline and specific process for integrating with new syndication platforms? Can this be done upon request by the client and what associated costs are there? 22

23. 9. Data ownership and security
How does the vendor protect client data and facilitate client-specific data relationships?
9.1
Data access
Support for role based data access
Who has rights to client data? How and where is that outlined? How is it policed?
9.1.1
Support for CRUD (Create, Read, Update, Delete) functions by role.
9.2
Client data firewalling
Support for client data firewalling to prevent access to any client data.
How does your system manage and/or firewall client data as part of your system's broader cookie or profile pool?
9.3
Data leakage protection
The system will provide standard protections to ensure no data leakage. (i.e. no inserting plain text segment names into URL responses. No inserting taxonomy elements into tag calls.)
How do you protect client's owned data from leakage?
9.4
Data rights management
Support for sharing of data with second parties.
What data rights management capabilities does your system offer? How does it facilitate data partnerships with second parties (e.g., media partners) and third parties?
9.4.1
Support for sharing of consumer profiles across accounts configurable via the user interface.
9.4.2
Support for sharing of consumer segments across accounts configurable via the user interface.
9.4.3
The systems will provide a role based sharing of all consumer data.
9.4.4
The systems will support monetization of sharing of all consumer data across accounts.
9.5
Security practices
Support for industry standard best practices in data security. (links to single sign on,
Do you have any internal groups and/or processes dedicated to consumer-privacy and security-related compliance?
9.6
Consumer privacy functionality
Support for consumer opt out by brand.
Does your system offer any functionality to allow consumers to control their data and privacy settings? 23

24. 10. Raw data access and portability
How does the vendor manage client data access and portability?
10.1
Raw data availability
Support for of all raw data for given client for a period of 14 months.
What level of access do clients have to raw data? What is the method and timeframe associated with extraction? In what range of formats and configurations can the system export the data? Is there an associated cost?
10.2
Client data portability
What is the portability of client data should the system relationship be terminated? 24

25. 11. Reporting and analytics
What reporting capabilities does the vendor offer?
11.1
Reports
The system will provide reports on consumer segments.
What reports are available as a standard part of using the system? What is the refresh rate and lookback window on each?
11.1.1
The system will return any report requested via the UI in <3 seconds.
11.1.2
Counts of each segment (Segment reach)
11.1.3
Counts of each individual data point (data point reach)
11.1.4
Forecast of unduplicated reach of segments across N activation partners selected via the user interface. (Segment unduplicated reach)
11.1.5
Forecast of unduplicated reach of data points across N activation partners selected via the user interface. (Data point unduplicated reach)
11.1.6
Forecast of audience duplication of segments across N activation partners selected via the user interface. (Segment duplication matrix)
11.1.7
Forecast of duplication of data points across N activation partners selected via the user interface (Data Point duplication matrix)
11.1.8
Campaign performance by segment
11.1.9
Camapaign performance by data point
Campaign performance by activation partner
Campaign performance by channel (medium)
Campaign performance by device
Campaign performance by sub-device (e.g. Android vs iPhone)
Campaign performance by content category
Campaign performance by outcome
Campaign performance by geography
Non-targeted segment performance
Non-targeted data point performance
Campaign performance by creative
Correlation analysis
11.2
Report flexibility
Define later.
Does your system provide clients with the ability to create customized reports? Is there a cost for customized reports? 25

26. 12. User interface/user experience (UI/UX)
User interface (UI)
What self-serve capabilities are available via the vendor's UI?
12.1
Availability of self-serve
The system will provide self-service of all critical functionalty via the user interface.
In which, if any, of the following areas does your system provide self-serve capabilities? Tag management, segment building and management, reporting, audience data syndication, other? Where yes, please describe for each.
12.2
Customization
Support for customization of the color scheme of the user interface
To what level can the interface be customized? How does this customization occur? File? Drop down selection?
Support for customization of branding to client brand by account. 26

27. 13. Support 13 Client service What client service is available? 13.1
What client service is available?
13.1
Implementation and consultative services
What implementation services are provided? What project-based and managed services do you provide (e.g., strategic services, development and customization, and advanced troubleshooting)?
13.2
Training
What training programs are offered for initial and, if applicable, advanced training? What ongoing training resources are provided to users?
13.3
Technical support
What is your technical support availability (24x7x365)? Does it vary by region? 27

28. Few stand alone independents remain
Millions of dollars
Funding1
Purchased1
Revenue2
Employee base size3
Glassdoor Review4
Tagline5
Ranking6
Stand Alone DMPs
“Build audience profiles that you can use anywhere”
$ 58
$ X
Mid 4
$ 8
“A Holistic System for Consumer Engagement”
n/a
$ 19
Mid
4.3
$ 50
“Accurate data. Personalized dialogue.”
$ 119
$ X
Small
3.2
$ 63.7
“Data at the Center of Great Marketing.”
$ 400
$ 39.2
Mid
4.3
$ 50
“Data. It's Changing Everything”
n/a
$ 18
Mid
3.5
$61.7
“We are the global leader in knowledge-based marketing solutions”
$ 100
$ 25
Small
2.5
$ X
“Transforming How Marketers Connect with Consumers”
$ 200
$ 36
Mid
3.9
$ 3.2
Revenue
Media buying
$ 28
$ 230
$ 341
Small
3.8
“Stop Hoarding Your Data, Start Experiencing the Results of a DMP”
$ 100
n/a
$ 325
Big
3.7
“Outcomes. Transparency. Control.”
$ 163.5
n/a
$ 264
Mid
3.4
“Customers evolve. Now your brand can evolve with them.”
$ 82.5
n/a
$ 237
Mid 5
“True buying power.”
$ 57
n/a
$ 18
Mid
2.6
“The World's Leading SaaS-Based Advertising Automation Suite.”
Sources: 1 Crunchbase.com (values from Adobe refer to Demdex; from Neustar to Aggregate Knowledge; and from KBM to i-Behavior); 2 owler.com (for the DMPs it refers to the revenue of the division, and for the Media Buying to the Vendor’s) ; 3 Crunchbase.com, linkedin.com and self-reported (Mindshare’s DMP RFI report scorecard v2), where Small is <100 employees, Mid is and Big is >501 (for MediaMath, Turn, TTD, and AudiScience the value was estimated for the Vendor instead of the division/product) ; 4 glassdoor.com ; 5 Vendor’s corporate websites. ; 6 Ranking is an average of Mindshare’s, Mediacom’s, and Maxus’ scorecards, and Forrester Report.

29. range of clients                           
Offered 
Offered but not its strength
range of clients 
Not offered
Saas1
Management Service1
Publisher centric1
Advertiser centric1
Key Clients2
Stand Alone DMPs                            
Media buying                    
Sources:; 1 Econsultancy: Data Management Platforms Buyers Guide” (Oct-2013); “The Forrester Wave Report: Data Management Platforms, Q4 2015” (Nov-2015); “Forrester Report: Measurement is a digital media buyer’s best friend” (Jan-2015); and Vendors’ corporate sites; 2 Self reported (Mindshare’s DMP RFI Report Scorecard v2) and Vendors’ corporate websites.

30. Little Differentiation
Offered 
Offered but not its strength
Little Differentiation 
Not offered
Attribution
Online - Offline
Data Sale1
Look alike
Cross Device2
Strengths
Weaknesses
Stand Alone DMPs  
1 & 3  
Technical Competence
Integration  
1 & 3  
Customization
Inventory discovery and forecasting  
1 & 3  
Data manipulation & interpretation
Data ingestion & Syndication  
1, 2 & 3  
Identification; segmentation; syndication; and data analysis
Cross-channel attribution measurement  
1 & 3  
Flexibility
Ingestion  
1, 2 & 3  
Device identification criteria
Ingestion and syndication;
Customization  
1, 2 & 3  
Source of 3rd party data;
Strong analytical tools
Integration
Media buying  
1 & 3  
Technology / Engineers
Rocket Fuel’s reputation  
1 & 3  
Flexibility
Not user friendly (too technical)  
1 & 3  
Flexibility
Not user friendly (too technical); Integration  
1 & 3  
Ease of use, and data activation
Not industrial strength DMP; and analytics  
1 & 3   -
Outdated
Sources: “Econsultancy: Data Management Platforms Buyers Guide” (Oct-2013); “The Forrester Wave Report: Data Management Platforms, Q4 2015” (Nov-2015); “Forrester Report: Measurement is a digital media buyer’s best friend” (Jan-2015); and Vendors’ corporate sites.
Notes: 1 Refers to the type of Data (1 = 1st Party; 2 = data owned by the vendor; 3 = 3rd Party); 2 Mix between proprietary technology and 3rd party outsourced (indifferent for clients).