That’s where data management platforms (DMPs) come in. DMPs allow marketers to understand customers and their purchasing behaviors better. This leads to more effective marketing campaigns that drive higher engagement and sales. With DMPs, marketers can glean insights into which campaigns drive the best results among target audiences.

This article looks at data management platforms in depth — what they are, why they are important, what they are used for and their future in a privacy-focused landscape. 

Table of contents

• What is a data management platform?
• DMPs vs. CDPs
• Data protection laws
• The future of data management platforms
• Additional resources

Estimated reading time: 5 minutes

What is a data management platform?

A data management platform is exactly what the name suggests. It is a digital platform that allows businesses to collect, store and organize data that is then used and analyzed to drive marketing and other business decisions. DMPs collect data related to:

• Customer demographics.
• Purchasing history.
• Website clicks.
• The online registration forms they fill out.
• And other sources.

This information is then segmented to provide businesses with actionable insights and a clear understanding of customers and their purchasing habits. 

While DMPs can use first- and second-party data, they heavily rely on third-party data from online sources. The differences between data sources are essential. 

• First-party data is information collected directly from your audience, like website clicks, social media follows, likes and comments, email addresses, etc. It’s considered extremely valuable because it’s collected first-hand, assuring greater accuracy and availability. 
• Second-party data is first-party data that someone else has collected and sold to you.
• Third-party data is gathered by entities that don’t have a direct relationship with the consumers whose data is being collected. 

Once data is collected, DMPs organize it into segments so marketers can build specific campaign audiences. These audiences can be people who fit into certain demographics or purchasing behaviors. Audience segments are built using any number of data points, like family size, household income and age ranges. 

Most DMPs have reporting features for analyzing audience data to discern patterns and understand customer behavior. Because large portions of the data DMPs collect are anonymous (via cookies and IP addresses, for example), marketers get the 10,000-foot view and create generalized audience profiles.    

DMPs vs. CDPs

DMPs aren’t the only avenue by which brands and businesses can harness the power of data. Customer data platforms (CDPs) are similar to DMPs in that they collect information, organize it and provide actionable insights. 

But there is one significant difference: CDPs generally only use first-party data and collect and store specific information about customers using personal identifiable information (PII). CDPs connect the data points gathered back to the individual user, providing even better knowledge about customers and their behaviors. 

For example, with DMPs, marketers might know that a user in a specific age group in a specific geographic area searched for women’s skincare products and is interested in workout gear and running shoes. 

A CDP could tell you that user’s name, specific age, address and other identifying information. Also, because CDPs don’t rely on third-party data (i.e., third-party cookies) to collect information (remember, first-party data is gathered with permission), privacy and consent issues are less of a concern than those currently associated with DMPs which gather and use third-party data. 

Data protection laws

Marketers should note that legislation, like the EU’s General Data Protection Regulation (GDPR) and, stateside, the California Consumer Privacy Act (CCPA), protects consumers as it relates to their personal data and defines guidelines for any businesses that use — or share — that data.

Consumers are more aware of online privacy issues now and expect transparency about how their data is used. Marketers must tread carefully and be prepared for how this continuing evolution will impact their strategies and tools, including DMPs.

The future of data management platforms 

Central to the privacy discussion — and the compliance issues introduced by GDPR/CCPA — is Google’s plan to phase out third-party cookies in the second half of 2024. Created by advertising companies, these cookies track website visitors across the web to gain information about where consumers go and, crucially, what they buy. 

Because DMPs have historically relied heavily on third-party data to fill their pipelines, a future without third-party cookies would mean platforms must gather customer information from different sources, such as point-of-sale and social media. 

In an online environment without third-party cookies, many believe that DMPs are becoming redundant — with marketers increasingly turning to CDPs. That said, it’s probably premature to say that the platforms will become extinct anytime soon. DMPs will likely evolve as the conversation on data privacy and third-party cookies plays out. 

One solution seems simple, pivot more wholly to first-party data. Some DMPs, like Lotame’s so-called next-gen Spherical platform, already primarily utilize first-party data, the benefits of which are already well documented.

Brands and marketers should continue to focus on building customer experiences and providing reasons for customers to engage. Ultimately, all this will help increase the volume and quality of data collected.

Dig deeper

Want to learn more?

• Discover 6 data collection tactics for marketing in the cookieless future.
• Learn the future of data management platforms in the era of CDPs on MarTech Editorial Director Kim Davis’ report.  
• Learn what the third-party cookie phaseout means for marketing campaigns in this MarTech story.
• Find out how to use AI and machine learning to boost marketing data management.

The post Why we care about data management platforms appeared first on MarTech.

Have DMPs been around so long we just take them for granted (like “big data”)? Will an increasing reliance on first-party data managed through CDPs, plus all the privacy issues surrounding third-party data, conspire to make DMPs extinct?

Data management solutions vendor Lotame is betting against that. But it’s also going out of its way to position itself as a partner for CDPs.

Past and future

Alex Theriault, general manager of Lotame’s latest solution suite Spherical, began with a look in the rear-view mirror. “Lotame has worn a few different hats over the years. We initially came out as an ad network selling data and audiences. That was back in 2008. We were one of the first DMPs coming to market in 2011.” Through an aquisition, they expanded into the cross-device and full identity resolution space, and they also offer one of the largest global data marketplaces, the Lotame Data Exchange.

But with the fast-paced adoption of CDPs, accelerated by customers moving more decisively into digital during the pandemic, Lotame faced a question about its future identity. That led, said Theriault, to a lot of research.

An identity crisis

The research focused on the evolving CDP space the use cases CDPs are best-suited to serve. “Do we become a CDP like so many other companies? Or is our technology still highly in demand and future-proofed so we can navigate third-party cookie restrictions and privacy regulation changes?” These were the kinds of questions to be faced, said Theriault.

The answer was that the demand for the kind of functionality that has historically lived within a DMP would persist: “Such as access to third-party data, built-in analytics, modeling capabilities, and really mature pipes into the adtech ecosystem,” Theriault explained.

The role of CDPs is critical when it comes to managing and activating data volunteered by known customers or known site users. That leaves a gap, said Theriault, when it comes to targeting people who make it to the site, perhaps put something in their cart, but never execute a one-time buy or sign up for a subscription.

What a DMP can do

Just because third-party cookies are one day going away, that doesn’t mean an end to third-party data.

“Third-party data and third-party cookies are often conflated with one another,” Theriault explained. “Any company that has an identity graph — and Lotame is one of those; there’s definitely a handful of strong players in the space — is able to collect data in environments where third-party cookies are not accessible, whether it’s attached to a first-party cookie, or other digital identifiers such as CTV IDs or customer IDs. It was historically a probabilistic graph, but we’ve now expanded it to being a hybrid; so we can ingest data tied to email,” in other words, first-party data. “So we’ll support both a declared match as well as a probabilistic match.”

Theriault suggests that tracking third-party data using Lotame’s Panorama ID can be more effective than relying on third-party cookies. “We’ve run case studies in environments like Safari that are already third-party cookie-restricted that have improved on results brands have seen running campaigns on third-party cookies.”

What a DMP and CDP can do together

The outstanding question is how DMPs and CDPs can work in harmony to support brand marketing strategies. One way is through simple integration. Some CDPs — for example Segment, Tealium and mParticle have on-page tags (or pixels) on brand websites. “With Lotame also having a tag on page,” said Theriault, “there’s really easy connectivity. We let the CDP do the majority of hard work to gather the fragmented, siloed first-party data from different sources and prepare it, segment it, [and] sanitize it within the CDP.”

The Lotame tag for the same brand can do a “quick look-up” that distinguishes known customers (with customer IDs) from unknown visitors where information is limited or absent.

“In the instance the brand doesn’t have a customer ID, then we fill that void; so we would be creating a profile within our platform and start the brand being better able to understand these cart abandoners and pushing that information back to the brand.”

This is all happening through the recently introduced Spherical solution, billed as a first-party data accelerator.

The workflow between Spherical and partner CDPs is (at least) bi-directional. CDPs collect first-party data across channels, from offline, email and mobile, to web visits and CTV. It cleans and segments the data and pushes it to Spherical for analysis, enrichment and modeling based on Lotame’s DMP resources. Spherical can push the result audiences to adtech solutions or to social media pipes. Conversely, Spherical can send campaign data like clicks and impressions to the CDP.

Another layer in the stack?

One might expect to see pushback against this proffer from customers that have invested time and money in a CDP and perhaps also use a DMP. Theriault acknowledges this. “We really wanted to appeal to brands and agencies, so we’ve actually introduced a variable model that supports things like seasonality and — for an agency — the ability to test and learn and iterate with different brands and not be locked into minimum monthly fees. “We can just plug in and fill the gaps because we’re not trying to sell them an end-to-end platform.”

The benefits of all this connectivity, Lotame would say, lies in bringing data on known and unknown customers, deterministic and probabilistic data, together. Whether this is the future direction for the DMP space or whether brands will increasingly turn their backs on third-party data and market to their known audiences, remains to be seen.

The post The future of data management platforms in the era of CDPs appeared first on MarTech.

“The issue with addressability is that once cookies go away, and with the loss of identifiers, about 80% of the addressable market will become unknown audiences which is why there is a need for privacy-centric consent and a better consent-value exchange,” said Jeffrey Bustos, VP, measurement, addressability and data at the IAB.

“Everyone’s talking about first-party data, and it is very valuable,” he explained, “but most publishers who don’t have sign-on, they have about 3 to 10% of their readership’s first-party data.” First-party data, from the perspective of advertisers who want to reach relevant and audiences, and publishers who want to offer valuable inventory, just isn’t enough.

Why we care. Two years ago, who was talking about data clean rooms? The surge of interest is recent and significant, according to the IAB. DCRs have the potential, at least, to keep brands in touch with their audiences on the open internet; to maintain viability for publishers’ inventories; and to provide sophisticated measurement capabilities.

How data clean rooms can help. DCRs are a type of privacy-enhancing technology that allows data owners (including brands and publishers) to share customer first-party data in a privacy-compliant way. Clean rooms are secure spaces where first-party data from a number of sources can be resolved to the same customer’s profile while that profile remains anonymized.

In other words, a DCR is a kind of Switzerland — a space where a truce is called on competition while first-party data is enriched without compromising privacy.

“The value of a data clean room is that a publisher is able to collaborate with a brand across both their data sources and the brand is able to understand audience behavior,” said Bestos. For example, a brand selling eye-glasses might know nothing about their customers except basic transactional data — and that they wear glasses. Matching profiles with a publisher’s behavioral data provides enrichment.

“If you’re able to understand behavioral context, you’re able to understand what your customers are reading, what they’re interested in, what their hobbies are,” said Bustos. Armed with those insights, a brand has a better idea of what kind of content they want to advertise against.

The publisher does need to have a certain level of first-party data for the matching to take place, even if it doesn’t have a universal requirement for sign-ins like The New York Times. A publisher may be able to match only a small percentage of the eye-glass vendor’s customers, but if they like reading the sports and arts sections, at least that gives some directional guidance as to what audience the vendor should target.

Dig deeper: Why we care about data clean rooms

What counts as good matching? In its “State of Data 2023” report, which focuses almost exclusively on data clean rooms, concern is expressed that DCR efficacy might be threatened by poor match rates. Average match rates hover around 50% (less for some types of DCR).

Bustos is keen to put this into context. “When you are matching data from a cookie perspective, match rates are usually about 70-ish percent,” he said, so 50% isn’t terrible, although there’s room for improvement.

One obstacle is a persistent lack of interoperability between identity solutions — although it does exist; LiveRamp’s RampID is interoperable, for example, with The Trade Desk’s UID2.

Nevertheless, said Bustos, “it’s incredibly difficult for publishers. They have a bunch of identity pixels firing for all these different things. You don’t know which identity provider to use. Definitely a long road ahead to make sure there’s interoperability.”

Maintaining an open internet. If DCRs can contribute to solving the addressability problem they will also contribute to the challenge of keeping the internet open. Walled gardens like Facebook do have rich troves of first-party and behavioral data; brands can access those audiences, but with very limited visibility into them.

“The reason CTV is a really valuable proposition for advertisers is that you are able to identify the user 1:1 which is really powerful,” Bustos said. “Your standard news or editorial publisher doesn’t have that. I mean, the New York Times has moved to that and it’s been incredibly successful for them.” In order to compete with the walled gardens and streaming services, publishers need to offer some degree of addressability — and without relying on cookies.

But DCRs are a heavy lift. Data maturity is an important qualification for getting the most out of a DCR. The IAB report shows that, of the brands evaluating or using DCRs, over 70% have other data-related technologies like CDPs and DMPs.

“If you want a data clean room,” Bustos explained, “there are a lot of other technological solutions you have to have in place before. You need to make sure you have strong data assets.” He also recommends starting out by asking what you want to achieve, not what technology would be nice to have. “The first question is, what do you want to accomplish? You may not need a DCR. ‘I want to do this,’ then see what tools would get you to that.”

Understand also that implementation is going to require talent. “It is a demanding project in terms of the set-up,” said Bustos, “and there’s been significant growth in consulting companies and agencies helping set up these data clean rooms. You do need a lot of people, so it’s more efficient to hire outside help for the set up, and then just have a maintenance crew in-house.”

Underuse of measurement capabilities. One key finding in the IAB’s research is that DCR users are exploiting the audience matching capabilities much more than realizing the potential for measurement and attribution. “You need very strong data scientists and engineers to build advanced models,” Bustos said.

“A lot of brands that look into this say, ‘I want to be able to do a predictive analysis of my high lifetime value customers that are going to buy in the next 90 days.’ Or ‘I want to be able to measure which channels are driving the most incremental lift.’ It’s very complex analyses they want to do; but they don’t really have a reason as to why. What is the point? Understand your outcome and develop a sequential data strategy.”

Trying to understand incremental lift from your marketing can take a long time, he warned. “But you can easily do a reach and frequency and overlap analysis.” That will identify wasted investment in channels and as a by-product suggest where incremental lift is occurring. “There’s a need for companies to know what they want, identify what the outcome is, and then there are steps that are going to get you there. That’s also going to help to prove out ROI.”

Dig deeper: Failure to get the most out of data clean rooms is costing marketers money

The post How data clean rooms might help keep the internet open appeared first on MarTech.

Last year companies used an average of 10 different sources. That increased to 15 this year and is projected to hit 18 by the end of 2023, according to Salesforce’s eighth annual “State of Marketing” report. 

This comes at a time when multiple studies show marketers losing faith in their data.

It should be no surprise that the sources most used by marketers are also the most reliable. Transactional data and known digital identities are used by 83% of marketing organizations, with declared interests/preferences nearly tied with them at 82%. 

Dig deeper: How companies are leveraging clean rooms and first-party data as cookies vanish

The least-used sources were non-transactional data (58%) and offline identities (69%), followed by third-party data and anonymized digital identities at 75% each.

Dealing with privacy changes. Despite Google pushing back the deadline for phasing out third-party cookies, new regulations mean marketers must adapt new ways to get consumer data now.  

Providing customers with incentives to share information is the most popular method, being used by 56% of marketers. Other actions being taken to address privacy laws:

• Creating a first-party data strategy 54%
• Creating second-party data-sharing agreements 52%
• Investing in new technologies (e.g., a customer data platform) 51%
• Reducing internal data silos 49%

There may be some privacy protection fatigue setting in. The number of marketers saying they go beyond regulations and industry standards to protect customer privacy dropped from 61% last year to 51% in 2022.

The State of Marketing 2022 research is based on a survey of 6,000 marketing leaders across 35 countries, including marketing managers, directors, VPs and CMOs.

Why we care.  The acronym GIGO isn’t used much these days, but the concept will always be true. Garbage in, garbage out is a fact when it comes to data and analytics. Bad information makes for bad strategies. Hopefully, more data sources means more ways to cross check it.

The post Marketers using more data sources in search of better data quality appeared first on MarTech.

To gain better campaign performance with less readily available data, here’s how marketers are getting their first-party data in order and partnering with clean rooms.

Getting first-party data in order

“You have to be able to work with third-party providers or make sure that you have a robust first-party internal identity resolution strategy to be able to confidently resolve and standardize your zero- and first-party data,” said Kelly Leger, managing director for Deloitte Digital at The MarTech Conference.

Due to the depreciation of third-party cookies and new regulations that govern how companies obtain permission to use customer data, many of the usual streams of data are being interrupted, causing signal loss.

“Because there will be disparate customer data sets out there and signal loss will be occurring, you have to make sure that your customer data is shored up,” Leger said.

The first-party and zero-party data that your company has needs to be standardized and cleansed. Marketers need easy access to this existing data. 

To make sure future campaigns are set up for success, marketers also need to optimize the data coming in from paid media campaigns, as well as the data obtained through owned and operated channels. To remain competitive and relevant to customers, orgs need to gain all the insights they can from their “campaign exhaust” — the learnings that marketers use from campaigns.

Lastly, it’s important for business to make sure that their handling of customer data, and especially personal identifiable information (PII), is done consensually and according to that latest applicable laws.

“This first-party data, and your zero-party data, are going to become more important than ever ,” said Leger. “[Customer data] is the most valuable asset that you have.”

Dig deeper: 6 data collection tactics for marketing in the cookieless future

Partnering with advertising clean rooms

“What we’re seeing is that the stack is really centering around first-party data, and bringing in third-party data to augment and understand more about the consumers,” Leger explained.

Hopefully your brand has many customers who raise their hands and get proactive on your owned channels. They might sign up for newsletters, take surveys or engage in other ways that make themselves known to your brand.

But, there are also many passive users. To help communicate with more of them, brands can partner with a third party that has data of their own to resolve identities without disclosing that data to you and violating the user’s privacy. The use of these advertising clean rooms enables brands to enhance the data they have to make campaigns more targeted and more effective.

“Ads clean rooms are clean rooms that sit inside the digital giants and enable the ability to use your first-party or third-party known customer data in conjunction with your insights and learnings from your campaign data,” said Leger.

She added, “The campaign and insights from that walled garden or that digital giant can’t leave that clean room, but can definitely help you optimize, create better targeting capabilities and enable the ability to better refine all of your advertising within that platform.”

Using enterprise clean rooms

In addition to using an ads clean room for a campaign within a walled garden or digital giant, brands can also partner with an enterprise clean room outside of these walled gardens on the open web.

Using an enterprise clean room can help drive and optimize ad campaigns on multiple channels, allowing your business to find relevant customers on those channels.

“These enterprise clean rooms act in a similar way where you can connect your zero- and first-party data to third-party data or second- party data relationships across your enterprise and enterprise partners for deeper insights and expanded activation capabilities,” said Leger.

She added, “Again, sitting outside of those digital giants’ walled gardens, you have a little bit more capability with this data to be able to take it back into your first-party ecosystem…to better inform your audience and your segmentation.”

All of this data should be consented. Also, it’s predominantly anonymized, because to optimize these campaigns, marketers are using anonymized data attributes, as opposed to PII.

By getting their own first-party data in order, and using these ad clean rooms and enterprise clean rooms as partners, businesses can confidently improve their campaign strategies, even as the data landscape continues to change.

Dig deeper: Clean rooms expand for advertisers

The post How companies are leveraging clean rooms and first-party data as cookies vanish appeared first on MarTech.

“This is basically turning AI and ML into a useful tool for marketing itself,” said Theresa Kushner, head of North American Innovation Center, NTT DATA Services, at The MarTech Conference.

In this way, businesses can better understand all the data streaming in that relates to what’s being done in markets, including who is buying products and other important buying trends.

“AI and ML can help you sort through, organize that information and present it to you in a way that makes it more digestible within your marketing program,” Kushner said.

Here are three main steps for how to get AI and ML to work in your market data management.

(Among the many ways of collecting market data, one is web scraping, discussed in depth here.)

Connecting data across teams

Data is growing exponentially. And it doesn’t just sit idly in your company’s databases and data management platforms. It gets piped in in streams, Kushner said.

“And oftentimes that data is just as important to marketing as it is to the product divisions that use it,” she added. “So using AI and ML can help you sort through where the data goes for marketing, where the data goes for product design, where the data is most important for finance, etc.”

Therefore, AI and ML can help with creating rules for which data goes where. And it helps if this constantly updated data is visible on a dynamic dashboard, as opposed to clunky spreadsheets.

But in order to get started with making all of this market data more manageable, marketers who own the data need to connect with the other departments that will benefit from it. Marketers also need to be in close contact with data engineers.

“[Data engineers] understand where the data is coming from and how it may be transformed from one system to another, where data is being archived or where it’s not being archived,” Kushner explained.

Because they know about all the sources of the data, data engineers are also the first people to check with about any data quality issues.

Dig deeper: Are you applying the right models for AI and ML?

Evaluate where AI and ML can solve problems

With all of this market data being piped in from different sources, it’s a constant challenge for marketers to connect the dots. Frequently, data engineers are the ones going in manually and making sure that important financial and product data are being compared on an even basis.

Therefore, these labor-intensive functions can be identified as areas where AI and ML tools can help make market data management more efficient.

“AI and ML can detect those patterns of defects, so to speak, and correct them for you,” said Kushner.

Dig deeper: Why we care about AI in marketing

Implement key programs supported by reports to show progress

Once these areas are identified, put a program in place where AI and ML can be used, so that data people don’t have to go inspect every data point themselves by hand.

A simple example would be where service information is stored in multiple places within the organization. In some places, the data could be tagged as services, but maybe elsewhere this data is kept as product data. Using an algorithm to identify and bring together these seemingly different data sets can be a very important business problem that AI can solve.

For this case, or for any other market data management program using AI, make sure that the issue is included in a report. This way, leadership will be able to understand, from the report, the problem that existed and how AI and ML are being used to solve it.

“You need reports to make sure that you’ve pinpointed the most important issue to the business…so that the business understands that this is very valuable to them,” Kushner said.

The post How to use AI and machine learning to boost marketing data management appeared first on MarTech.

Dig deeper: Marketers should care about consumer privacy

First-party vs. third-party data

The first thing to know is that first-party cookies, placed in a limited number of digital touchpoints, can be an important source of data and address privacy concerns.

“Currently third-party cookies are blocked by most major browsers, and they’re actually coming to the end of their lifespan by the end of 2022, moving into 2023, as Google has just recently announced that it will no longer be providing third-party cookie support for their Chrome web browsers,” said Jim Thao, marketing automation manager for Lively Inc., at The MarTech Conference.

“Third-party cookie data is really easy to collect, but the [negative] to this is that it is really easy to share across domains, which brings forth a lot of privacy concerns regarding where that data is being shared,” Thao said.

“First-party cookies are currently supported by all browsers,” he said. “And they’re future-proof in the sense that they’re executed and implemented directly by the publishers onto the organization’s domain.”

An example of a first-party cookie is Marketo Munchkin, which Thao describes as “a script that’s provided to the customer and consumer to place directly on their web in order to track activity and engagement.”

The data resulting from first-party cookies, although not as easy to collect, can enhance personalization and streamline web experiences for customers.

3 first-party data tactics

Audience segmentation. First-party cookies can collect data when a customer engages with a brand’s digital properties. For instance, they can collect location data directly from leads either on a website or by manual inputs into the company’s database.

This data can be used to execute lead routing based on location, via auto-assignment rules on the back end. One example: An apparel company can use the data to automatically promote sweatshirts and knit caps in colder climates and tank tops in warmer ones.

Email personalization. First-party data can be collected through forms customers fill in, by emails they send to the company or via other manual inputs.

Dig deeper: Why we care about email marketing

Gated content. Content marketers get first-party data from forms filled out by customers to access gated materials. Customers are more likely to share data when they feel they are getting something valuable in exchange. But the exchange shouldn’t end there.

Marketers should use that data to recognize these customers and take down the gates to access more content. That way, the customers won’t be turned off by filling out the same forms over and over.

3 tactics for zero-party data

Zero-party data is similar to first-party data in that it is highly accurate and reliable. Collecting zero-party is a more involved process, but it’s worth the effort.

“The largest difference between zero-party data and first-party data is that this data is willfully and freely provided by the customer,” said Thao. “And in my experience with using zero-party data strategies, it really requires a communication loop which consists of a question, an answer and, a lot of times, responses back and forth.”

Quiz or game. Use a quiz, game or interactive questionnaire where users provide information. Offering a reward like a coupon code or other kind of discount makes this a clear value exchange.

Customer satisfaction surveys. Many customers respond well when they feel like they have a role in contributing to their experience with your company. That’s why customer satisfaction surveys are such a good source of zero-party data. And, even though surveys are a concept with a long history in marketing, they can be administered at a specific time that makes sense during the customer interaction.

Dig deeper: Brands need to leverage customer content now

Product feedback. Similarly, customers may be willing to provide data when ask for product feedback after a purchase. Inviting them to write a review or fill in a survey can empower the customer and makes them feel like part of a community, especially if they can share the review.

“What we can do here as marketers is offer a platform and a process that allows users to provide feedback,” said Thao. “It provides that feedback loop regarding products and services and also allows them to engage with the community and get support that way.”

When customers are more involved in the process, your marketing automation program will benefit from better data.

The post 6 data collection tactics for marketing in the cookieless future appeared first on MarTech.

The move gives Zeta access to more than 300,000 active AWS customers who will be able to deploy ZMP as part of their Enterprise Discount Program.

Planning rapid growth. Zeta has set itself the target of overtaking other major marketing cloud platforms such as Adobe, Oracle and Salesforce by 2025. “As we continue to build our path to Zeta 2025, our long-term goal to elevate to the largest marketing cloud in the industry, becoming the first marketing cloud natively available to AWS customers will be an accelerant through the co-selling relationship,” said Steve Gerber, president and Chief Operating Officer at Zeta Global in a release.

This follows a number of acquisitions made by Zeta over the last three years including surveying tool Apptness, AI and content classification company Temnos, DMP and DSP Sizmek, and location data company PlaceIQ.

Dig deeper: More about Zeta Marketing Platform in our guide to email marketing platforms

Why we care. It’s good to have ambitions. Right now, Zeta is staffed on a scale comparable with competitor Acoustic and remains dwarfed by Adobe, Oracle and Salesforce — although Oracle in particular offers way more than a marketing platform.

Nevertheless, this seems like a good step to take on the path to growth. We wonder whether other marketing suites will follow the same route.

The post Zeta becomes first marketing platform to join the AWS marketplace appeared first on MarTech.

Beyond that: only 26% say their CDP meets most of their current needs, 35% say it’s meeting some of their current needs and 28% say it doesn’t meet any of their current needs. And perhaps most damning, 45% report it has underperformed against business expectations.

What CDPs do. The aim of CDPs is to help marketers manage the huge amount of customer data generated by many organizations. In addition to centralizing the data, they are supposed to enhance it by identifying customers and assembling customer profiles. They are a relatively recent addition to martech stacks: 85% of CDP implementations are three years old at most, and 43% are within the last year.

Dig deeper: What is a CDP and how does it give marketers the coveted ‘single view’ of their customers?

What’s not working. Close to half of survey respondents expect CDPs to handle personalization, campaign execution across channels, and data activation. Unfortunately, these are the very things they’re not doing well. Here’s how many owners say they’re “mostly satisfied” with some core functions: 

• Customer segmentation: 29% 
• Customer profile assembly: 25%
• Integration with campaign endpoints: 24% 
• Personalization: 22%

Additionally, 46% report their systems having challenges with data analysis, reporting (45%), and data centralization (34%). 

Vendors aren’t helping. As bad as all that is, the dissatisfaction with vendors is even worse. More than half (52%) are unhappy with the technical support they receive, while 31% say the same about the customer support overall.

Why we care. There is clearly an opportunity for vendors here. First one to provide a CDP that lives up to its promise wins. That is a technical challenge which will clearly take a lot of time, expertise and ingenuity to meet. Customer and technical support, on the other hand, can be worked on now. One place to start may be on the sales side, where there can be a big gap between what’s in the pitch and what’s in the product. You’ll stand out from the competition if they’re all overpromising and underperforming.

Ever wonder how frequently marketing software is replaced?
Here’s the answer.

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Of the 100,000 sites examined, email addresses were collected from 1,844 websites in the EU and 2,950 sites in the U.S., according to “Leaky Forms: A Study of Email and Password Exfiltration Before Form Submission.”

Ever wonder how frequently marketing software is replaced?
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U.S. vs. EU results. “Comparing results from the EU and the U.S. vantage points, we found that 60% more websites leaked users’ emails to trackers, when visited from the U.S. Measuring the effect of consent choices on the exfiltration, we found their effect to be minimal. Based on our findings, users should assume that the personal information they enter into web forms may be collected by trackers — even if the form is never submitted,” write researchers Asuman Senol (imex-COSIC, KU Leuven), Gunes Acar (Radboud University), Mathias Humbert (University of Lausanne and Frederik Zuiderveen Borgesius (Radboud University).

Among the third-party collectors of email addresses are martech firms such as Adobe (Bizible), Criteo, Facebook, LiveRamp, Neustar, Oracle Netsuite (Bronco Marketing Platform), Salesforce Pardot and Taboola. Among the top websites where emails were collected before form submission were USA TODAY, Trello and The Independent in Europe; Business Insider, Issuu and Time in the U.S.

Dig deeper: Why data compliance is more than consent management

The paper, to be presented at USENIX Security’22 in August, reported, “Taboola said in certain cases they collect users’ email hashes before form submission for ad and content personalization; they keep email hashes for at most 13 months; and they do not share them with other third parties. Taboola also said they only collect email hashes after getting user consent; however, our findings and subsequent manual verification showed that was not always the case.”

While this activity is legal at a federal level in the U.S., it is banned in the EU under GDPR.

The worst offending categories include: Fashion/Beauty (11.1% EU; 19% U.S.) Online Shopping (9.4% EU; 15.1% U.S.); and General News (6.6% EU; 10.2% U.S.). The least problematic: “Despite filling email fields on hundreds of websites categorized as Pornography, we have not [found] a single email leak.”

Why we care. With the end of cookies, it is inevitable that marketers will look for new sources of consumer data. Few are as useful as email addresses which are unique and persistent and can be tracked across the web and in the real world via things like loyalty programs. However, taking them without consent is a blatant violation of law in the EU and privacy expectations in the U.S. Also, the researchers found passwords being taken by what we in the martech field call “session replay scripts.” These are in practice indistinguishable from what the rest of the world calls keylogger malware.

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