It’s as clear as you want your data to be – this is the time to have a 360-view of your customer. You want to improve their journey and experience and protect their privacy. So where do we go from here?

When identifying a solution, marketers must first identify their pain points, develop a cohesive data strategy and then decide on the right technology.

To learn how some of the most successful marketers vetted and invested in the right technology, register and attend “Data Down the Drain? CDPs Bring Value to an Underutilized Asset,” presented by BlueConic.

Click here to view more MarTech webinars.

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“If you are in a decision-making role you are probably tasked with at least evaluating whether this is a technology that you need to pay attention to,” said Milicevic. “And if you’re a practitioner, you very likely have to come up to speed on how to use it and on whether it’s relevant to your company.”

What is a DCR?

“It’s a technology that creates a secure, collaborative environment where two or more parties can use data for specific, mutually agreed upon purposes while eliminating exposure of that data to other parties,” said Milicevic, citing the IAB.

Why use a DCR?

“The key innovation here is how potentially sensitive customer data sets are handled,” Milicevic explained. “[Marketers] simply need a better, more secure environment to collaborate with potentially sensitive data sets — first-party data sets in particular.”

First-party data is becoming increasingly scarce with the introduction of privacy regulations like GDPR and CCPA, as well as the phasing out of third party cookies by Google and other privacy actions by major tech companies along the lines of Apple’s Mail Privacy Protection (MPP) program.

Dig deeper: How companies are leveraging data clean rooms as cookies vanish

Who uses DCRs?

DCRs can be used by brands, agencies and publishers. The catch is that these organizations should already have a high level of data maturity — they’ve made prior investments in data technology and have substantial teams to work with the technology. This means that right now the technology favors larger companies.

“Process cost and maturity are two significant gating factors that currently put data clean rooms as a super-premium or ‘luxury’ solution,” Milicevic said.

How much does it cost to use a DCR?

Two-thirds of DCR users have spent a minimum of $200,000 on the technology, and a quarter of those surveyed by the IAB have spent over $500,000, according to Milicevic.

The annual cost can go up over $2 million annually when adding in privacy protection tools and other technology that makes the DCR usable.

What are current and emerging use cases for DCRs?

Current uses for DCRs include

• Data privacy compliance;
• Data anonymization;
• Data cleansing and normalization and
• Data transformation and enrichment.

Emerging use cases include:

• Attribution;
• ROI measurement and modeling;
• Mixed media modeling and
• Predictive analytics.

“In addition to privacy safety and the ability to combine first-party data sets is…being able to do very advanced analytics in a much easier way,” said Milicevic. “If you are a data scientist or have data scientists on your team, you’ve probably heard quite a few complaints about how long it takes to get data into a shape where it can be analyzed. Data clean rooms will reduce this complexity significantly for a lot of advanced analytics.”

Where does a DCR fit in your stack?

Generally, the DCR fits between the organization’s data layer and activation layer.

Here is a basic map that is by no means exhaustive:

At the bottom of the stack is the data infrastructure layer that might include a data warehouse, data lake or similar container. Data governance and identity tools also live in this layer.

Sitting above that is what Milicevic calls the “trust layer,” and that’s where the DCR is. Also in the trust layer are decisioning tools that use data to inform activation found in the layer above it. The activation layer includes all advertising activations and other tools like CDPs that can have activation capabilities.

“What’s particularly attractive about data clean rooms is that they pull out the business logic that used to previously live either in the data infrastructure or activation layers…and now it’s centralizing it,” said Milicevic.

Register and watch The MarTech Conference here.

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Customer Data Platforms (CDPs) are here to stay and have become a must-have element of the martech stack.

The latest MarTech Intelligence Report, Customer Data Platforms: A Marketer’s Guide, found that interest in CDPs increased 32% last year. Many respondents listed CDPs as a high-priority technology investment. However, according to Forrester, nearly 90% of marketers say their CDP doesn’t meet their needs.

So where do we go from here?

Register and attend “Data Down the Drain? CDPs Bring Value to an Underutilized Asset,” presented by BlueConic.

Click here to view more MarTech webinars.

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On May 2, designated as International Martech Day, Scott Brinker unveiled the latest edition of his marketing technology landscape. From 9,932 last year, the total number of solutions in the landscape has grown to 11,038. This represents something like a 7,000% growth in the space over the last 12 years.

One new feature launched alongside the landscape is the Marketing Technology Capability Heatmap. It reveals the most searched-for capabilities, with personalization at number one, and identifies the categories that offer them.

Presenting at Best of Breed Marketing Summit alongside Marketing Tribe founder Frans Riemersma, Brinker, VP platform eco-system at HubSpot, said: “On average, across companies of all sizes, you still have around 291 SaaS subscriptions. Even while there is consolidation of the industry in the economic environment of today, a motivation to rationalize stacks, we still see people using this very wide variety of tools.”

Dig deeper: Why marketers are replacing foundational martech

Other key trends. Among the trends identified within the new landscape, Brinker and Riemersma highlighted the following:

• Two-way data flows between data warehouses and front-line solutions like CDPs.
• Composability emerging in a number of categories including DXPs, commerce and CDPs.
• “AI as your co-pilot is real,” said Brinker; and it’s going to change the relationship between buyers and sellers because buyers will have AI too.
• Generative AI will extend the no-code revolution beyond marketing ops and power users to any marketing or business user.

Why we care. Everyone who knows marketing technology knows the marketing technology landscape. It’s an evolving but iconic image. It’s such a crowded graphic, however, that it simply became hard to read. This is why it’s so valuable that it has become, essentially, interactive. Registered users can explore the actual content of the landscape rather than just gaze on it in wonder.

The landscape is available at Chief Martec and can be sorted, filtered and queried at martechmap.com.

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Inevitably, this begs the question: should you employ your existing DWH as a customer data platform (CDP)? After all, when you re-use an existing component in your stack, you can save resources and avoid new risks.

But the story isn’t so simple, and multiple potential design patterns await. Ultimately, there’s a case for and against using your DWH as a CDP. Let’s dig deeper.

DWH as a CDP may not be right for you

There are several inherent problems with using a DWH as a CDP. The first is obvious: not all organizations have a DWH in place. Sometimes, an enterprise DWH team does not have the time or resources to support customer-centered use cases. Other enterprises effectively deploy a CDP as a quasi-data warehouse. (Not all CDPs can do this, but you get the point.)

Let’s say you have most or all your customer data in a DWH. The problem for many, if not most, enterprises is that the data isn’t accessible in a marketer-friendly way. Typically, an enterprise DWH is constructed to support analytics use cases, not activation use cases. This affects how the data is labeled, managed, related and governed internally. 

Recall that a DWH is essentially for storage and computing, which means data is stored in database tables with column names as attributes. You then write complex SQL statements to access that data. It’s unrealistic for your marketers to remember table and column names before they can create segments for activation. Or in other words, DWHs typically don’t support marketer self-service as most CDPs do. 

This also touches on a broader structural issue. DWHs aren’t typically designed to support real-time marketing use cases that many CDPs target. It can perform quick calculations, and you can schedule ingestion and processing to transpire at frequent intervals, but it is still not real-time. Similarly, with some exceptions, a DWH doesn’t want to act off raw data, whereas marketers often want to employ raw data (typically events) to trigger certain activations.

Finally, remember that data and the ability to access it don’t maketh a CDP. Most CDPs offer some subset of additional capabilities you won’t find in a DWH, such as:

• Event subsystem with triggering.
• Anonymous identity resolution.
• Marketer-friendly interface for segmentation.
• Segment activation profiles with connectors.
• Potentially testing, personalization and recommendation services.

A DWH alone will not provide these capabilities, so you will need to source these elsewhere. Of course, DWH vendors have sizable partner marketplaces. You can find many alternatives, but they’re not native and will require integration and support effort. 

Not surprisingly, then, there’s a lot of chatter about “composable CDPs” and the potential role of a DWH in that context. I’ve argued previously that composability is a spectrum, and you start losing benefits beyond a certain point. 

Having issued all these caveats, a DWH can play a role as part of a customer data stack, including:

• Doing away with a CDP by activating directly from the DWH. 
• Using the DWH as a quasi-CDP with a reverse ETL platform.
• Coexisting with a CDP.

Let’s look at these three design patterns.

1. Connecting marketing platforms directly to your DWH

This is perhaps the most extreme case I critiqued above, but some enterprises have made this work, especially in the pre-CDP era and platforms (like Snowflake with its broad ecosystem) are looking to try to solve this.

The idea here is that your engagement platform directly connects to push-pull data with a DWH. Many mature email and marketing automation platforms are natively wired to do this, albeit typically via batch push. Your marketers then use the messaging platform to create segments and send messages to those segments in the case of outbound marketing.

Imagine you had another marketing or engagement platform, a personalized website or ecommerce platform. Again you draw data from DWH, then employ the web application platform to create another set of segments for more targeted engagement.

Do you see the problem yet? There are two sets of segmentation interfaces already. What happens if you had 10 marketing platforms? 20? You will keep creating segments everywhere, so your omnichannel promise disappears. 

Finally, what if you had to add another marketing platform that did not support direct ingestion from a DWH?

2. Employ DWH with reverse-ETL tools

This approach solves several problems with the first pattern above. Notably, it allows (in theory) a non-DWH specialist to create universal segments virtually atop the DWH and activate multiple platforms. With transformation and a better connector framework, you can apply different label mappings and marketer-friendly data structures to different endpoints.

Here’s how it works. Reverse ETL platforms pull data from the DWH and send it to marketing platforms after any transformation. You can perform multiple transformations and send that data to several destinations simultaneously. You can even automate it and have exports run regularly at a predefined schedule.

But a copy of that data (or a subset of it) is actually copied over to target platforms, so you really don’t have just a single copy of data. Since the reverse-ETL platform does not have a copy of data, your required segments or audiences are always generated at query time (typically in batches). Then you export them over to destinations.

This is not a suitable approach if you want to have real-time triggers or always-on campaigns based on events. Sure, you can automate your exports at high frequency, but that’s not real-time. As you increase your export frequency, your costs will exponentially increase.

Also, while reverse-ETL tools provide a segmentation interface, they tend to be more technical and DataOps-focused rather than MOps-focused. Before declaring this a “business-friendly” solution suitable for marketer self-service, you must test it carefully.

3. DWH co-exists with CDP

Your enterprise DWH serves as a customer data infrastructure layer that supplies data to your CDP (among other endpoints). Many, if not most, CDPs now offer some capabilities to sync from DWH platforms, notably Snowflake.

There are variations in how these CDPs can co-exist with DWH. Most CDPs sync and duplicate data into their repository, whereas others (including reverse-ETL vendors) don’t make a copy. However, there could be trade-offs you need to consider before finalizing what works for you.

In general, we tend to see larger enterprises preferring this design pattern, albeit with wide variance around where such critical services as customer identity resolution ultimately reside. 

Dig deeper: Where should a CDP fit in your martech stack?

Wrap-up

DWH platforms play increasingly essential roles in martech stacks. However, you continue to have multiple architectural choices about which services you render within your data ecosystem.

I think it’s premature to rule out CDPs in your future. Each pattern has its trade-offs to keep in mind while evaluating your options. 

The post Should you use your data warehouse as your CDP? appeared first on MarTech.

Marketo Engage and the B2B customer journey

Despite relatively little discussion of Marketo Engage during the main-stage keynotes, a look under the hood revealed a lot of activity surrounding Adobe’s offerings for B2B marketers. We asked Brian Glover to share some highlights. Glover is a senior director of product marketing at Adobe with special responsibility for Marketo, the B2B instance of Adobe Real-Time CDP and the B2B attribution solution Bizible.

Marketo Engage and Real-Time CDP working together

Adobe Real-Time CDP and Marketo Engage, working together, forms the foundation for B2B workflows in Experience Cloud, Glover said. “One of the announcements we made is that, within the Real-Time CDP you can create a list of accounts; you can send the list to multiple destinations, and one of those is Marketo Engage.” The advantage of bringing those accounts to Marketo is the ability to begin to engage with people in those accounts that are already known. “As you run paid media you start to bring in more of the other roles from a buying committee.”

Dynamic chat for B2B

Glover also highlighted Adobe’s evolving dynamic chat offering including new generative AI capabilities. “We announced that we are offering a full suite of conversational marketing capabilities, so for automating conversations on your website, and to pass a live conversation through to a sales rep, to be able to continue that conversation,” he explained.

Chat is also being embedded in things like lead forms. “In a lead form you can initiate a conversation or offer up the ability to book a sales meeting. We’re elevating the value that marketing can provide to sales by giving them more touchpoints for having a conversation rather than just capturing details and the prospect waiting for a sales person to reach out to them.”

A pause of hours or even days between an indication of interest and a follow-up by the vendor is a customer experience gap, Glover said. “By bringing dynamic chat into marketing auto-workflows we’re closing that gap.”

In the background to chatbot conversations lies the development of countless answers to frequently-posed questions. Generative AI will be used to help scale the drafting of responses — but all to be reviewed by human eyes before they go live.

Integrations between Marketo Engage and Workfront

Glover describes Adobe Workfront as “the command-and-control center of the content supply chain.” But many customers also use Workfront to manage the entire campaign development process, from strategy to assets, to reviews and approvals. This makes integration with Marketo Engage valuable.

“As the campaign is built out in Marketo, it sends the status back to Workfront; in Workfront it gives customers a single view of the status of a campaign so everyone has visibility. Teams can move faster, get more campaigns to market faster, which is important right now because a lot of teams are not growing as fast as they would like and many have budget and resource constraints.”

That B2B buyer journey

Glover agrees that the B2B buying experience is changing, becoming more digital, more self-serve and more omnichannel. And he has statistics that support this:

Digital-native millennials and zoomers now make up 65% of B2B buyer group members, and so the bar for what defines an acceptable experience continues to be raised. And now, 55% of B2B executives say their buying cycle time has increased over the previous year — anything that creates delays, confusion or uncertainty in the already complex B2B buying journey simply adds cost and risk to deals.

Adobe announces new innovations to drive B2B experience-led growth

Dynamic chat plays a role here too. “B2B buyers are absolutely looking to have more self-serve experiences,” he said. “It’s a digitally native population now that is participating in — and often leading — these buying committees. Automating conversations at scale, and making it easier to self-serve in terms of doing your own research, is one of the investments that we’re making.”

Bringing robust external data to Adobe Real-Time CDP

Adobe’s CDP offering remains one composable element of the overall Adobe Experience Platform suite. “In 2022 we updated our go-to-market to say everybody gets Experience Platform foundational capabilities, but they’re going to transact with Adobe on the applications,” explained Ryan Fleisch, head of product marketing for Real-Time CDP and Audience Manager.

So, Real-Time CDP is one of those applications. It was built natively on Experience Platform,” he continued, “as was Customer Journey Analytics, Journey Optimizer and some of the newer applications you’re seeing launched during Summit. The advantage is that, if I buy Real-Time CDP, I’m getting access to things like real-time profiles, a governance framework, AI models and many other services.”

In broad numbers, hundreds of brands around the world are using Adobe’s CDP for a variety of use cases. “We’re seeing a lot of brands that will start with Real-Time CDP and grow into other Adobe Experience applications. We’re also seeing a number of them adopt some of those simultaneously because they understand the natively connected benefits they get,” said Fleisch.

Data for specific use cases

One phenomenon that can be observed across a number of these enterprise-level CDPs that form part of larger marketing suites (Oracle’s Unity, for example) is that customers import data to support specific use cases. They don’t necessarily view the CDP as a repository for all customer data.

“If you’ve already done all the work to put your data in a warehouse or cloud storage system, we don’t want to make you duplicate those efforts,” Fleisch said. “You probably don’t need all that data readily available at your fingertips in a matter of milliseconds for the use cases you’d be powering here. So our approach is to think of a foundational layer of technology like a cloud data warehouse and think of Experience Platform and Real-Time CDP as an experience layer that sits on top of that.”

It’s also not necessary to copy all the use case data into the CDP. Adobe has the muscles to drill down into enterprise data storage locations and federate data from those systems.

Third-party data that isn’t from cookies

Despite widespread adoption of CDPs, many businesses are not yet ready to give up on DMPs, the solutions that deliver large quantities of third-party data and thus support new customer acquisition.

“If you trace the origins of CDPs they go back to about 2013 and they started out with just known customer data,” Fleisch reflected. “That was the primary use case.” But it’s not a use case that can make up for the loss of third-party cookies.

“Historically brands have used DMPs to just buy broad swaths of audiences and go target those. But we’ve seen an evolution in the CDP space and also in expectations for the space. We now have a privacy-safe way to bring in durable third-party data from partners like Epsilon, Merkle — and really any others of your choosing; this is an open framework. These companies have a long heritage in customer data from a variety of sources with consent attached to it. Being able to bring that into the CDP is fulfilling this request and vision of a single data management system that can take me from acquisition all the way through loyalty and everything in between.

The launch of use case playbooks

“In the past few years, CDPs have been the talk of the town,” said Fleisch, “but a lot of brands that adopted them are left wondering: What are the use cases I should be doing?” With many data sources feeding a CDP, brands can struggle to know what data to leverage for a particular use case.

“We want to make this easier for brands,” said Fleisch. “We’re launching use cases playbooks to actually give you guided workflows: Here’s how we would recommend populating audience segments, setting up journeys, campaigns, etc. You’re making time-to-value that much faster rather than having a blank canvas in front of you.”

Analytics for product use and engagement

Another announcement that almost got lost in the rush: The launch of Adobe Product Analytics as a complement to Adobe Customer Journey Analytics.

“With Customer Journey Analytics, as a brand you’re looking at customers and the different touchpoints they have — notification, mobile, web and any other ways you interact with them,” explained Haresh Kumar, head of strategy and product marketing for AEM. “With Product Analytics you are looking at product usage, the user journey. As a product manager, building products, you want to better understand where the value is, where users are spending more time.”

The persona Adobe is targeting with these new capabilities, said Kumar, is the product manager; the outcome the application is aimed at supporting is “product-led growth.”

It’s important to understand that, in a sense, Product Analytics is dealing only with digital products — only processing data generated by the software associated with the products themselves. The main-stage demonstration featured user engagement with a GM automobile — but it was specific to engagement with a software-driven dashboard component of the automobile.

But in a way, that’s the point. Many products these days, from cars to refrigerators to smart homes, have software components. Also, what’s provided is not just a retrospective look at how users are interacting with existing features, but guidance for future product development.

A new Adobe Express for enterprise

Finally, Kumar also highlighted the launch of Adobe Express for enterprise. Adobe Express allows the creation of a wide range of brand content without the need for training in design. The enterprise version integrates with the Adobe Experience Manager DAM and will also give access to the Firefly generative AI solution with safety guardrails for commercial use.

“That’s one big announcement,” said Kumar. “When you bring Adobe Express and Adobe Experience Manager assets together, you get not only the shared library of your assets but also genAI creation of more variations of content.”

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A bit of history: The enterprise ‘portlets’ race

This encounter reminded me of the “enterprise portal” era. Please indulge me while I look back to the late 2000s and early 2010s — a time most customers and vendors would care to forget but that still carries lessons today. 

Enterprise portals were supposed to provide a single, convenient interface into a potentially broad array of enterprise applications, displayed as separate blocks on a screen in a dashboard motif. The technology underpinning those individual blocks went by many names, but for now, let’s call them “portlets.” 

It quickly became clear that portal programs were fundamentally highly complex integration projects, so enterprises naturally sought to leverage pre-fab connector code. Vendors responded with portlet catalogs, and an arms race ensued. “We have 250 portlets,” a vendor would brag.

These portlets would vary dramatically in provenance, support, usability, performance, security and (crucially) technical underpinnings. A “portlet” was typically a reference instance of some Java or C# code somebody wrote for a single client implementation. More often than not, the code needed to be overhauled, sometimes from scratch. 

Vendors retorted — not unfairly — that problems often originated in how remote systems were configured rather than with the portal platform itself. Maybe so, but enterprises eventually got jaded about portlets. Amid other technology and business changes in the digital world, portal platform technology gradually fell out of fashion.

The new CDP connector race

Fast forward to today, and the world is coming to understand CDPs as integration environments (among other things). Every CDP selection team we work with strives to find vendors with pre-built connectors to match up against their incumbent platforms. Yet, nearly every CDP implementation finds expensive developers significantly modifying or rewriting those connectors.

CDP vendors are seemingly succumbing to the pressures their portal brethren endured. If customers value a diverse catalog of connectors, then as a CDP vendor, you must display many of them, ready or not. In CDP demos, connectors appear on the screen as neat blocks (with the connected platform logo appearing prominently) that you can drag around — almost like portlets! 

Well, not so fast. Like portlets, CDP vendor connectors may result simply from the output of a single implementation. More importantly, in some cases, a single connector cannot possibly address the complexity of the martech platform on the other end. 

Consider Salesforce Sales Cloud, mentioned above. The platform suffers from an problematic object model that most licensees contort or heavily extend. It can be like connecting to a very angry octopus. And Salesforce is by no means alone here. In such situations, a CDP vendor’s connector can only provide the basic scaffolding and leave the rest up to a developer. 

Is the enemy us?

Portals died out for another reason. If eyes are windows to the soul, portals were windows into enterprise intestines. A portal was only as useful as the underlying applications. Often, those applications were messy, lacked common content and metadata models, employed diverse access control regimes, exhibited different UX models and sometimes exposed low-quality data. 

At my company, we see a similar phenomenon with CDPs. Depending on how you scope a CDP effort (and different patterns are emerging here), the CDP may expose the immaturity of your broader customer data management regime — all the more reason to match any prospective CDP to your broader data architecture. 

Dig deeper: How to ID and organize data with a new CDP

Watch out for CDP vendors claiming to have diverse connector catalogs

As always, forewarned is forearmed. First, reconsider overweighting a vendor who claims to have connector catalogs that match up well against your stack. Among other reasons, simply moving CSV files around can solve many (non-real-time) use cases. When you need packaged connectors, specific integration experience becomes useful but doesn’t inherently hedge against substantial development in your future. The key is to find out how much development.

Hopefully, you’re following an agile CDP selection process that concludes with a competitive bake-off and a more technical proof of concept (PoC) with one or two finalists. A PoC is a great environment to test a few essential connectors. You’ll then come to understand the level of effort to overhaul where necessary — and that could be often.

Like their portal vendor predecessors, CDP vendors will promise “quick start” packages to accelerate an initial implementation. Don’t believe it. Once again, some delays may stem from the time you’ll need to get your own data house in order, but also, I can guarantee you that someone will be doing connector development, and this work gets measured in quarters, not months. Budget your resources accordingly.

The post The CDP connector myth 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.

When creating a ‘golden record’ ruins the customer experience 

Sometimes, stitching together all of a customer’s information into a single profile can accidentally interfere with your use cases. Let me illustrate.

While planning a family trip, I added my daughter Anne as a guest on the reservation through her personal email address. But she received a reminder about the trip from the travel company in her work email, which made her nervous. Why did they send the reminder to her work email? Who told them about her work email?  

Near as I can tell, this is what happened. Anne had created an account with the travel company using her work email for a work-related event. Somewhere along the way, the travel company added her personal email to that profile. When I added her personal email to the guest list, the travel company attached that action to her profile, so when it came time to send out a reminder, it used the default email in that profile, which was her work address. In other words, in an over-zealous attempt to create a “golden record” for Anne, they forgot the purpose of the use case: to send a reminder to the guest email that’s entered for the event. 

Take another example. Joe is a good customer. He’s an office manager, and he buys things from your store for his company. But he has a side hustle and buys many of the same things for personal use. He keeps those accounts separate, using separate email addresses. Merging those records doesn’t help your relationship with Joe. It annoys the heck out of him and gets him in trouble with accounting. 

Dig deeper: The myth of the single customer record

And then there’s my experience. I work as a consultant for many different companies. Sometimes I need accounts on the same service for different clients on different email addresses. Some services won’t allow a customer to have multiple accounts with the same phone number for two-factor authentication. So I must find a workaround that doesn’t help the service provider or me. 

These are all examples where merging records around a person can ruin a customer experience. On the other hand, there are instances where you better merge the records. For instance, if you’re a restaurant delivering food and know that Sam has an allergy, you must ensure that information migrates across all Sam’s accounts. 

Merging customer records: When to do it and how

The bottom line is clear: use cases are more important than identity. But how do you know when and what to merge? I’ve created two frameworks to work through these issues: the device framework and the person framework. By thinking through each use case with both frameworks, you can develop the correct procedures for merging records. 

Device framework 

The device framework is how most people work through customer data issues. 

Device profile: A device makes a request to your site. Your CDP creates a profile for that device and collects information about it. At this level, you can segment on things like operating system, geography, screen size, etc. 

Activity: If that device makes multiple requests, you can enrich the profile with other information, such as the kind of content accessed. With this activity information, you can segment on things like “likes videos” or “accesses tax content.” 

Identifiers: Some of the device’s activities help to narrow down who is behind that device. For example, a device might make a request to your site after clicking on one of your emails. That helps to create narrower segments and, in some cases, helps you to identify the person. Identifiers can be used to create powerful segments, like “everyone who is registered for our e-newsletter.” 

Person: Some identifiers make a strong connection to a person, while others only hint at the person’s identity. As you collect identifiers, you can sometimes resolve the profile down to a particular person with more or less certainty, depending on the nature of the identifiers you have collected. Once you have a profile identified with a person, you can develop use cases such as presenting renewal offers near expiration. 

People

The person framework helps you avoid the abovementioned problems, like Anne’s concern about misusing her work email or my troubles with two-factor authentication. This framework requires us to step out of a data-centered world and think about the real lives of actual people. 

Person: Rather than starting with a device profile and trying to resolve it down to a person, we start with a person and imagine how that person behaves in the real world. Let’s return to Joe, your good customer who purchases office equipment for work and his home business. 

Devices: Joe has two phones: one from the office and one for personal use. He’s careful to do office work on one and home/personal work on the other. Joe also has an office PC but a Mac at home. Again, he uses one for office work and the other for his own ventures. 

Identifiers: Joe is cautious about keeping things separate. His office email is for office work, his personal email is for friends and family and he has another email address for his side hustle. Joe does not want these merged or confused. 

Personas: Rather than thinking of Joe as a single person, you have to think of Joe’s three distinct personas: Office Joe, Personal Joe and Side Hustle Joe. 

Dig deeper: 19 CDP use cases that can annoy or engage your customers

Using these frameworks for your use cases 

Now that we have the basics let’s take one use case and work it through both frameworks. The use case is: “Send relevant job listings to all mechanical engineers who opt into our job postings email.” 

Device framework 

The top of the device funnel doesn’t help much with this use case because we can’t identify mechanical engineers by what kind of device they use. Once we get to the activity level, we can find profiles that frequent content relevant to mechanical engineers.

We can use on-site quizzes or simple questionnaires to gather identifiers. In this case, job title. Once we have the job title, we can create a segment of mechanical engineers and promote the “sign up for job listings for mechanical engineers” email list. 

That’s good enough for this use case. We don’t need to resolve identity down to the person, although a name might be suitable for personalizing the emails. Resolving things down to the person might create a problem, as we’ll see. 

Person framework 

Julia, our mechanical engineer, works for a company that doesn’t take kindly to people looking around for new gigs. The IT department monitors all the emails that come to office addresses. Because of this, Julia is careful to keep her work and personal lives separate. 

While she only has one laptop, she does all her office work in Chrome and all her personal browsing (from home) in Firefox. She signs up for the job posts email on her personal account, but not on her work account. 

If an overzealous data scientist merged these two accounts into one profile for Julia and started sending the job-post emails to Julia’s work address, Julia would not be happy. 

Rethink profile merging with these frameworks

People are more complicated than your data structure recognizes, so it’s essential to view your use cases from two perspectives: 

• From the data side (the device framework).
• By imagining the life experiences and concerns of real people who often act online through different personas. 

Make sure you structure your data, merge rules and use cases to allow people to act within whatever personas suit them. Don’t try too hard to create a single profile for each person in every case. 

The post Why and how you should rethink profile merging appeared first on MarTech.

Many studies prove CDPs drive business value. They do this by:

• Building an omni-present single customer view.
• Creating consistent experiences across channels.
• Informing the delivery of personalized content.
• Providing real-time access to customer profiles. 
• Eliminating redundancies through technology platform consolidation.
• Creating efficiencies through automation and time to activation.

However, they do this in conjunction with other systems, not on their own. This makes it difficult to understand the value contribution and prove ROI. But the following framework will help you assess its value.

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

The CDP adoption framework

Driving greater CDP adoption guarantees additional business benefit. Adoption is straightforward to understand and measure, provided you use a comprehensive framework which looks at: 

• Platform utilization. 
• Organizational adoption.
• ROI tied to CDP-powered activations.

This framework will provide quantitative and qualitative data to inform your understanding of:

• How far your organization has come.
• How far it needs to go to reach your ideal maturity level.
• What you need to do to get there. 

Each CDP has its distinct collection of capabilities. That said, several categories of utilization can be analyzed for any platform as part of a universal adoption framework.

Here’s how to assess each of those seven categories.

Data availability

Your CDP is only as good as the data residing within it. The following chart shows how to assess your data.

Integrations

Your CDP fuels the experiences you create with customers through inbound or outbound channels. To create coordinated experiences consistently, it must collaborate with all key platforms, including:

• Platforms that decide what is most relevant.
• Platforms that deliver those prescribed experiences. 

Your CDP must continually augment profiles with signals captured from inbound and outbound interactions. 

A well-integrated CDP connects with platforms that support relevant-time decisions without information gaps.

A CDP that isn’t designed with interoperability will not provide the level of maturity required to achieve what most organizations desire — real-time optimization at the moment of interaction.

Dig deeper: What is identity resolution and how are platforms adapting to privacy changes?

Platform features ​

The features available in any platform typically fall into two categories:

• Features that were priorities in your buying evaluation.
• Those that were not. 

Too often, we find that those ancillary features are forgotten and under-leveraged. 

For instance, just because you have a more advanced site personalization platform doesn’t mean you can’t find opportunities to leverage out-of-the-box site personalization capabilities. They are usually fast to implement as the integration is pre-built.

User community access

While marketers are usually the driving force behind adoption, CDPs aren’t just for them. It is essential to drive use of the CDP by people outside of the marketing department. This requires education and strategic partnerships.

The fact is that CDP intelligence can have more impact on sales or customer service programs than on marketing which is accustomed to using rich first-party data.

The responsibility for successful CDP adoption doesn’t fall only on marketing and IT stakeholders. A team focused on CDP success must include marketing, IT, marketing analytics, sales, agencies, product, service, creative and even legal teams to establish and refine new processes for providing customer experiences.

Audience management

This can be evaluated by looking at the following:

• Access How broadly accessible are audiences across touchpoints, and how much are they being used in the platforms that are creating experiences?
• Automation Leveraging more advanced techniques (i.e., creating event-driven audiences for use within journeys or automated delivery of audiences to activation platforms) allows for more time to support common urgent needs that arise within an organization.
• Time to campaign How long does moving from ideation to campaign design to implementation take? A CDP should accelerate the process. But the more manual data and platform work required, the less efficient the process will be.
• Use of machine learning (ML) When injected into audience management methodology, predictive modeling will increase the sophistication most marketers aspire to achieve in their personalization goals.

Activations

Simply leveraging a CDP within customer experience programs doesn’t fully indicate how well an organization has adopted a platform. What you need to do is measure the ROI from use cases enabled directly by the CDP. This is achievable with some discipline.

Whenever possible, leverage existing measurement methodologies and infrastructure to compare results from activations before and after using the CDP. Create a plan that clearly captures the KPIs, audience, creative and test group sizing before execution. Ensure all platforms and integrations are configured appropriately to support the execution and data capture required for the test.

Identity resolution​

Every CDP promises a single customer view (SCV). SCV can’t be accomplished without identity resolution, no matter the nuances in your data or mix of offline and online identifiers. 

Ensure you’ve established comprehensive rules for stitching together all identifiers across all data sources. More importantly, all identifying events occurring throughout any part of the customer experience must be adequately handled by the platforms delivering those experiences. 

Those platforms must capture all identifiers and their associations and provide that information to the CDP’s identity resolution processes.

Scoring your CDP

Quantitative output

In looking across the above categories in the framework, record your current and future state maturity on a scale of 1-5.

It’s important to understand that it’s unrealistic for every (any!) organization to score a 5 within all categories. This scoring should not be arbitrary. 

At Actable, we have established clear definitions of maturity across multiple subcategories within each category we use in the scoring rubric. Define these guidelines before scoring to ensure you are objectively scoring individually or by committee.

Qualitative output

As you look at the gap between your current state and target state maturities, what areas do you need to focus on closing this gap? 

Perhaps data quality is holding you back. Or you need to prioritize building that missing integration that will enable a better understanding of customers.

Or it’s time to implement a test of that capability or channel that you always considered a nice-to-have.

The post Measuring CDP adoption: A comprehensive framework appeared first on MarTech.