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.”

The post Adobe’s roadmap for B2B, CDP and product analytics appeared first on MarTech.

In a recent survey, Pew Research Center found that “81% of the public say that the potential risks they face because of data collection by companies outweigh the benefits.” And “79% of consumers are concerned about how companies use the data collected.”

It’s clear – businesses need to prioritize customer trust and data privacy.

The e-book, In Data We Trust, shows you how customer data platforms (CDP) help obtain customer trust through privacy-driven personalization.

Check out the e-book to discover how a CDP can benefit your brand and keep reading to discover the five key ways a CDP establishes trust.

5 ways a CDP establishes trust in data with customers

1. Reduce risk from siloed data

Data silos result in costly processes and increased risk in multiple areas, such as inaccurate customer insights, including privacy preferences. With a CDP, first-party data is collected, meaning it’s consented and directly from the source.

2. Propagate privacy preferences

If your consent data is not real-time, you could breach privacy regulations while consent preferences are waiting to be updated. For example, if someone requests their data to be deleted and it takes your organization longer than is legally mandated to fulfill that request, your brand can face significant penalties for non-compliance. A CDP propagates privacy preferences throughout the entire customer journey across all channels and maintains them through the lens of the customer.

3. Enable operational efficiency and business agility

To be a privacy-driven organization, businesses must break down communication and data silos to understand what data is being processed and why. Some CDPs enforce a common nomenclature for data, allowing business and IT units to speak one common language. This shared language prevents departments from falling behind whenever privacy requirements change or new technology investments call for new integrations.

4. Give customers transparency and control over their data

Customers are empowered by global privacy regulations to manage when their data is collected, stored, and utilized. A CDP becomes a trusted repository of customer data and the governed supply chain that connects customer devices to the platforms that deliver value. As a trusted steward of their data, your customers will have access to the most accurate set of their personal data when they request it.

5. The ultimate customer experience

A CDP helps brands better understand customer behavior and preferences through a single customer view. Customers want to be known and understood regardless of where and how they’re engaging with your brand. That said, customer data can reside in different systems with different privacy settings. CDPs enable you to collect trusted customer data from all touchpoints to produce a 360 view of your customers, enabling real-time engagement on any channel based on customer preferences. And this level of personalization can be done at scale through a vendor-neutral, real-time CDP like Tealium, which you can learn more about here.

The post In data we trust: How to establish customer trust through data privacy appeared first on MarTech.

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.

• Have achieved a meaningful level of brand awareness in the market,
• Deliver messages that resonate with prospects and demonstrate intent,
• Close the deal by addressing pain points/solving problems.

Leading with awareness

Brand awareness is a requirement for successful ABM campaigns.

“You really need to have a baseline level of awareness in the marketplace,” said Megan Creighton, head of digital strategy for The Ricciardi Group, a boutique B2B marketing agency, who spoke at The MarTech Conference. “People really need to know who you are and you need to be part of the conversation in order for any of your downstream programs to really thrive.”

When you engage your ideal customer, they should know about you.

“In order for your message to be well received, I think that the person receiving it has to really feel that you’re a credible person to deliver that message in order for them to buy,” said Creighton. “So, being able to bolster your position in the marketplace is what is going to make your thought leadership resonate and what’s going to make that person on the receiving end feel more comfortable about interacting with your brand and wanting to go on that buying journey process with your brand.”

Top-of-funnel awareness messaging should prepare the way for and be consistent with messaging delivered further down the funnel, e.g. engagement, nurturing audiences and, ultimately, sales presentations.

“Once we raise the profile of the brand and have that baseline awareness, then we are able to get hyper-targeted from there, which is the fun part,” Creighton said.

Messaging has to resonate

To get the most out of any engagement with a prospect, the message has to resonate. So, marketers should use the data they have to create messages that do just that.

Dig deeper: How to use AI and machine learning to personalize and optimize campaigns

“Accounts are made up of people who are making the decisions and they have preferences on how they want to be [reached],” said Creighton.

Content should demonstrate awareness of the market in order to create a personal connection. Website content, social media messages, email, and other communications must deliver consistent messages in order to achieve maximum success.

Researching the market can also help identify key prospects. B2B brands should consider content syndication programs and surveys with customized questions that yield insights when prospects engage with this content.

“Identify…who was really showing that they were actively researching and showing a high intent for a solution that the brand is offering,” Creighton said.

Message your ability to address challenges

Once a prospect is aware of your solution, has engaged and demonstrated intent, addressing pain points and articulating the benefits of your solution become the mission.

“Now that we’ve identified that [accounts] are also expressing a level of intent… they may be more likely to give us more information,” said Creighton.

Deeper market analysis can also be prioritized. For instance, sales for a product or service related to your business’s offerings could be on the rise. Knowing who among your prospects is showing intent for those related products will help you prioritize the prospects your business should target.

Data from customer interactions and surveys will help identify topics and content for nurturing campaigns and for audience segmentation.

Register for The MarTech Conference here.

The post 3 best practices for B2B ABM marketers appeared first on MarTech.

If you’re considering a CDP, there are a lot to choose from, and they come in several different flavors. The unique quirks of any given CDP are usually determined by its origin story. Most CDPs started as something else and tacked on additional services to become full-fledged CDPs. The one that started as an email service provider (ESP) will be a different animal than the one that started as a recommendation engine. They also differ in whether they focus more on B2B, B2C, retail, publishing, etc. 

One way to cut through the fog is to distinguish these services by their back-end and front-end components. 

Back-end vs. front-end

Back-end components include the technical infrastructure and processes that are used to collect, store, harmonize, and manage customer data. This category typically includes data integration, warehousing, governance, and security features. The back-end component is responsible for ensuring that customer data is accurate, complete, and accessible, with the goal of merging disparate records from multiple sources to create a single customer view. 

The front-end component of a CDP can be divided into marketer-facing and customer-facing features. The marketer-facing side would include data visualization and reporting, while the customer-facing side might include recommendation engines, paywall management, and custom content displays. 

Some CDPs are almost exclusively back-end, with almost no customer-facing front-end features. Other CDPs include lots of front-end “activations.” To make it more complicated, all of these functions are available from stand-alone, dedicated services. 

The trick to evaluating a CDP is to figure out which components are necessary for your use cases, and which need to be part of the CDP itself. 

For example, a CDP might have a built-in ESP. That may or may not be a good thing for you. If one of your use cases requires you to send an email the moment a user takes an action on your website, you’ll either need the CDP to be able to send the email, or you’ll need a real-time connection to an external ESP. 

It’s helpful to think of a CDP the way you might think of a vacation resort. The resort owner wants to be able to say that the resort has some activity, like a water slide, so they build a token water slide on the property. It’s not going to be as good as the dedicated water slide down the road, but it’s also not down the road. It’s right there on the resort. 

In the same way, the ESP that’s built into a CDP is probably not going to have as many features as a dedicated ESP, but that doesn’t matter. What matters is which solution fulfills the requirements of your use cases. 

To make it even more complicated, there are a lot of “CDP-like” services that do some of the work of a CDP. 

To navigate this confusing mess, consider a few use cases and see how the back-end vs. front-end metric can help. 

Recommendation engines for content

Adding customized recommendations to an article on your website can enhance a visitor’s experience with your brand and increase page views. 

The functionality required by that use case depends on what data the recommendation engine will use. 

If you want to recommend articles based (at least in part) on which e-newsletters the customer receives, or which products the customer subscribes to, you’ll need a back-end connection with the ESP and/or the fulfillment system, and you’ll need the ability to merge the user’s online profile with that data. But if you only want to make recommendations based on the user’s web behavior, you don’t need that back-end function, and you might not even need a CDP. Many stand-alone recommendation engines can handle that. 

Questions to ask: 

• Does this use case require back-end data management? 
• Is the CDP’s front-end function good enough, or do I need a dedicated service? 
• Does the CDP integrate with that dedicated service? 

“Customers who bought this…”

In the retail space, vendors want to provide product recommendations, which can increase the value of each order. 

If the recommendations are based (at least in part) on the customer’s order history, the recommendation engine needs that back-end data. If the recommendations are simply based on averages across all customers, specific information about the customer’s purchase history is irrelevant. 

Managing a paywall

Publishers who don’t wish to rely exclusively on ad revenue to fund the creation of their content may offer access to premium content for a fee. This requires the creation and maintenance of accounts to manage access to this content. 

In many cases, those accounts will need to be coordinated with other accounts, such as a magazine subscription. For example, a magazine subscriber might get through the paywall for free, or at a discounted rate. In that case, the paywall management system will have to integrate with back-end data from the magazine fulfillment system. 

Landing page optimization

A/B or multivariate landing page tests can dramatically increase the success of an online store, online forms, and e-newsletter sign-up pages. Services that facilitate the creation and deployment of such tests usually do not distinguish between customers and non-customers, and that seems to work for most situations. In those cases, you don’t need a CDP. 

However, if you have reason to believe that your customers are significantly different than the average web visitor, you might need your landing page optimization calculation to show different stats for different groups. 

For example, a website with medical content might have a split audience that includes medical professionals and ordinary citizens. You wouldn’t want the results of an A/B test on a landing page for a report written for doctors to include stats on how everyone else responded. In this case, back-end information on the audience might be crucial. 

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

Surveys

Surveys can help you understand your customers, which can help you provide better service. Many CDPs can manage surveys, but very few CDPs can compete with the functionality of a dedicated survey platform. How does this affect your evaluation of potential CDP vendors? 

Questions to ask: 

• Will my surveys be enhanced by incorporating back-end customer data? (E.g., not asking things you already know, or asking different questions to different audiences.) 
• Is it important to be able to extend the survey process over time through progressive profiling? 

Building a worksheet

I hope these examples have prompted you to imagine a worksheet somewhat like this. 

This is an overly simple example, but you can use this general idea to customize a worksheet for your specific requirements. 

The key is to start with use cases and think them through in terms of front-end and back-end functions, also considering 3rd-party alternatives. The more your use cases require back-end functions, the more you’re likely to need a CDP. And once you’ve created this document, it will make the RFP/discovery process much easier. 

The post How to create a CDP worksheet from your use cases appeared first on MarTech.

Automation and optimization for personalized messages

“[Automation and optimization] are two broad areas that marketers leverage machine learning for,” said Alex Holub, head of machine learning at customer data platform (CDP) company mParticle, at The MarTech Conference. Holub’s AI startup Vidora was acquired by mParticle in 2022.

First, marketers can use this technology to automate a process like the generation of emails or the scheduling of when those emails go out to customers,” Holub said.

Secondly, AI and machine learning can be used to determine the best time to send the message or the best message that can be sent. This kind of optimization draws on large customer datasets automatically, instead of having data teams sift through the data and ask questions themselves.

From heuristics to optimization

Holub described a fast fashion company he worked with that replaced an older heuristic method in their email campaigns with a new machine learning optimization strategy that generated 90% more revenue.

The brand was sending weekly emails to millions of engaged customers, so they wanted to be able to pick the best product to promote to each user. The solution used personalization and automation to deliver these messages at scale.

“Prior to leveraging machine learning, they were leveraging heuristics — so they had analysts go in, look at their data and try to determine for different segments of users who should receive which promotion for which product,” Holub explained.

Using the heuristic approach, data scientists looked at past purchases to determine what messages to send. The machine learning approach could not only analyze more data quicker, but it could determine the right data to look at. 

“The great thing about machine learning is that it will figure out what behaviors are the best behaviors to use in order to determine who should receive which email,” he said.

Centralizing and activating data

In order to implement AI and machine learning into a company’s messaging strategy, marketers must first make sure their customer data is centralized.

“Getting all their data in one location in a high-quality manner, that’s typically a huge challenge for businesses,” said Holub. This is why CDP technology like mParticle, Oracle and others goes hand-in-hand with AI.

Dig deeper: Oracle launches industry-specific AI models for its Unity CDP

When customer data is centralized within an organization, the next biggest challenge is for the business to be able to activate that data through the right channels and messages to customers.

“The second challenge is activating the outputs of machine learning,” said Holub. “So, if you build a machine learning model and you’re saying, ‘Hey, I should engage these particular folks with this message,’ but you’re not able to activate that machine learning model, you’re not able to activate those particular messages.”

He added, “So, typically, it’s almost always the input and the output of the machine learning that are the biggest challenges.”

Register for The MarTech Conference here.

Our new report, “Customer Data Platforms: A Marketer’s Guide” is now available for free download.

The post How to use AI and machine learning to personalize and optimize campaigns appeared first on MarTech.