DMPs are Dead. Long Live DMPs.

 

King

Much like latter day King of Rock and Roll, Elvis Presley, today’s ubiquitous data management platforms will eventually die as an independent buying category but live on in the greater consciousness. And karate. 

Gartner’s Marty Kihn recently made an argument that ad tech and mar tech would not come together, contrary to what he had predicted a few years ago. When Marty speaks about ad tech, people listen.

 

Like many people, when I read the headline, I thought to myself, “That makes no sense!” But those who read the article more closely understand that the disciplines of ad tech and mar tech will certainly be bound closer together as systems align – but the business models are totally incompatible.

Advertising technology and the ecosystem that supports it, both from a commercial business model perspective (percentage of media spend billed in arrears) and the strong influence of agencies in the execution process, has meant that the alignment with software-as-a-service (SaaS) marketing technology is not just an engineering problem to solve.

Marketing leaders and brands need to change the way they do their P&L and budgeting and reevaluate business process flows both internally and with outside entities such as agencies to ensure that even if the technology may be right, the execution needs to be optimal to achieve the desired results.

There are also plenty of technical hurdles to overcome to truly integrate mar tech and ad tech – most notably, finding a way to let personally identifiable information and anonymous data flow from system to system securely. While those technical problems may be overcome through great software engineering, the business model challenge is a more significant hurdle.

I remember getting some advice from AdExchanger contributor Eric Picard when we worked together some years ago. I was working at a company that had a booming ad tech business with lots of customers and a great run rate, operating on the typical ad network/agency percentage-of-spend model.

At the time, we were facing competition from every angle and getting disrupted quickly. Eric’s suggestion was to transform the company to a platform business, license our technology for a fixed monthly fee and begin to build more predictable revenues and a dedicated customer base. That would have meant parting ways with our customers who would not want to pay us licensing fees and rebuilding the business from scratch.

Not an easy decision, but one we should have taken at the time. Eric was 100% right, but transforming a “run rate” revenue ad tech business into a SaaS business takes a lot of guts, and most investors and management didn’t sign up for that in the first place.

This is a long way of saying that Marty is right. There are tons of ad tech businesses that simply cannot transform themselves into marketing software stacks, simply because it requires complete change – from a structural financial perspective (different business model) and a people perspective (different sales skills required).

[This post appeared in AdExchanger on 5/9/2017]

What is the future of DMPs?

In the 1989 film “Back to the Future II,” Marty McFly traveled to Oct. 21, 2015, a future with flying cars, auto-drying clothes and shoes that lace automatically.

What is the future of data management platforms? This is a question I get asked a lot.

The short answer is that DMPs are now part of larger marketing stacks, and brands realize that harnessing their data is a top priority in order to deliver more efficient marketing.

This is a fast-moving trend in which companies are licensing large enterprise stacks and using systems integrators to manage all marketing—not just online advertising.

As detailed in Ad Age (Marketing clouds loom), the days of turning to an agency trade desk or demand side platform (DSP) to manage the “digital” portions of advertising are fading rapidly as marketers are intent on having technology that covers more than just advertising.

Building consumer data platforms

A few years ago, a good “stack” might have been a connected DMP, DSP and ad server. A really good stack would feature a viewability vendor and start a dynamic creative optimization (DCO). The focus then was on optimizing for the world of programmatic buying and getting the most out of digital advertising as consumers’ attention shifted online, to mobile and social, rather than television.

Fast forward a few years, and the conversations we are having with marketers are vastly different. As reported in AdExchanger, more than 40% of enterprise marketers license a DMP, and another 20% will do so within the next 12 months. DMP owners and those in the market for one are increasingly talking about more than just optimizing digital ads. They want to know how to put email marketing, customer service and commerce data inside their systems. They also want data to flow from their systems to their own data lakes.

Many are undertaking the process of building internal consumer data platforms (CDPs), which can house all of their first-party data assets—both known and pseudonymous user data.

We are moving beyond ad tech. Quickly.

Today, when those in the market are considering licensing a “DMP” they are often thinking about “data management” more broadly. Yes, they need a DMP for its identity infrastructure, ability to connect to dozens of different execution systems and its analytical capabilities. But they also need a DMP to align with the systems they use to manage their CRM data, email data, commerce systems, and marketing automation tools.

Data-driven marketing no longer lives in isolation. After I acquire a “luxury sedan intender” online, I want to retarget her—but I also want to show her a red sedan on my website, e-mail her an offer to come to the dealership, serve her an SMS message when she gets within range of the dealership to give her a test drive incentive, and capture her e-mail address when she signs up to talk to a salesperson. All of that needs to work together.

Personalization demands adtech and martech come together

We live in a world that demands Netflix and Amazon-like instant gratification at all times. It’s nearly inconceivable to a Millennial or Generation Z if a brand somehow forgets that they are a loyal customer because they have so many choices and different brands that they can switch to when they have a bad experience.

This is a world that requires adtech and martech to come together to provide personalized experiences—not simply to create more advertising lift, but as the price of admission for customer loyalty.

So, when I am asked, what is the future of DMPs, I say that the idea of licensing something called a “DMP” will not exist in a few years.

DMPs will be completely integrated into larger stacks that offer a layer of data management (for both known and unknown data) for the “right person;” an orchestration layer of connected execution systems that seek to answer the “right message, right time” quandary; and an artificial intelligence layer, which is the brains of the operation trying to figure out how to stitch billions of individual data points together to put it all together in real time.

DMPs will never be the same, but only in the sense that they are so important that tomorrow’s enterprise marketing stacks cannot survive without integrating them completely, and deeply.

[This post was originally published 11 May, 2017 by Chris O’Hara in Econsultancy blog]

How AI will Change UX

user_experience_sarah_weise In 1960, the US Navy coined a design principle: Keep it simple, stupid.

When it comes to advertising and marketing technology, we haven’t enjoyed a lot of “simple” over the last dozen years or so. In an increasingly data-driven world where delivering a relevant customer experience makes all the difference, we have embraced complexity over simplicity, dealing in acronyms, algorithms and now machine learning and artificial intelligence (AI).

When the numbers are reconciled and the demand side pays the supply side, what we have been mostly doing is pushing a lot of data into digital advertising channels and munching around the edges of performance, trying to optimize sub-1% click-through rates.

That minimal uptick in performance has come at the price of some astounding complexity: ad exchanges, third-party data, second-price auctions and even the befuddling technology known as header bidding. Smart, technical people struggle with these concepts, but we have embraced them as the secret handshake in a club that pays it dues by promising to manage that complexity away.

Marketers, however, are not stupid. They have steadily been taking ownership of their first-party data and starting to build marketing tech stacks that attempt to add transparency and efficiency to their outbound marketing, while eliminating many of the opaque ad tech taxes levied by confusing and ever-growing layers of licensed technology. Data management platforms, at the heart of this effort to take back control, have seen increased penetration among large marketers – and this trend will not stop.

This is a great thing, but we should remember that we are in the third inning of a game that will certainly go into extra innings. I remember what it was like to save a document in WordPerfect, send an email using Lotus Notes and program my VCR. Before point-and-click interfaces, such tasks were needlessly complex. Ever try to program the hotel’s alarm clock just in case your iPhone battery runs out? In a world of delightful user experience and clean, simple graphical interfaces, such a task becomes complex to the point of failure.

Why Have We Designed Such Complexity Into Marketing Technology?

We are, in effect, giving users who want big buttons and levers the equivalent graphical user interface of an Airbus A380: tons of granular and specific controls that may take a minute to learn, but a lifetime to master.

How can we change this? The good news is that change has already arrived, in the form of machine learning and artificial intelligence. When you go on Amazon or Netflix, do you have to program any of your preferences before getting really amazing product and movie recommendations? Of course not. Such algorithmic work happens on the back end where historical purchases and search data are mapped against each other, yielding seemingly magical recommendations.

Yet, when airline marketers go into their ad tech platform, we somehow expect them to inform the system of myriad attributes which comprise someone with “vacation travel intent” and find those potential customers across multiple channels. Companies like Expedia tell us just what to pay for a hotel room with minimal input, but we expect marketers to have internal data science teams to build propensity models so that user scores can be matched to a real-time bidding strategy.

One of the biggest trends we will see over the next several years is what could be thought of as the democratization of data science. As data-driven marketing becomes the norm, the winners and losers will be sorted out by their ability to build robust first-party data assets and leverage data science to sift the proverbial wheat from the chaff.

This capability will go hand-in-hand with an ability to map all kinds of distinct signals – mobile phones, tablets, browsers, connected devices and beacons – to an actual person. This is important for marketers because browsers and devices never buy anything, but customers do. Leading-edge companies will depend on data science to learn more about increasingly hard-to-find customers, understand their habits, gain unique insights about what prompts them to buy and leverage those insights to find them in the very moment they are going to buy.

In today’s world, that starts with data management and ends with finding people on connected devices. The problem is that executing is quite difficult to automate and scale. Systems still require experts that understand data strategy, specific use cases and the value of an organization’s siloed data when stitched together. Plus, you need great internal resources and a smart agency capable of execution once that strategy is actually in place.

However, the basic data problems we face today are not actually that complicated. Thomas Bayes worked them out more than 300 years ago with a series of probabilistic equations we still depend on today. The real trick involves packaging that Bayesian magic in such a way that the everyday marketer can go into a system containing “Hawaiian vacation travel intenders” for a winter travel campaign and push a button that says, “Find me more of these – now!”

Today’s problem is that we depend on either a small amount of “power users” – or the companies themselves – to put all of this amazing technology to work, rather than simply serving up the answers and offering a big red button to push.

A Simpler Future For Marketers?

Instead of building high-propensity segments and waiting for users to target them, tomorrow’s platforms will offer preselected lists of segments to target. Instead of having an agency’s media guru perform a marketing-mix model to determine channel mix, mar tech stacks will simply automatically allocate expenditures across channels based on the people data available. Instead of setting complex bid parameters by segment, artificial intelligence layers will automatically control pricing based on bid density, frequency of exposure and propensity to buy – while automatically suppressing users who have converted from receiving that damn shoe ad again.

This is all happening today, and it is happening right on time. In a world with only tens of thousands of data scientists and enough jobs for millions of them, history will be written by the companies clever enough to hide the math on the server side and give users the elegance of a simple interface where higher-level business decisions will be made.

We are entering into a unique epoch in our industry, one in which the math still rules, but the ability of designers to make it accessible to the English majors who run media will rule supreme.

It’s a great time to be a data-driven marketer! Happy New Year.

Follow Chris O’Hara (@chrisohara) and AdExchanger (@adexchanger) on Twitter. 

(Interview) On Beacons and DMPs

how-beacons-might-alter-the-data-balance-between-manufacturers-and-retailersHow Beacons Might Alter The Data Balance Between Manufacturers And Retailers

As Salesforce integrates DMP Krux, Chris O’Hara considers how proximity-based personalization will complement access to first-party data. For one thing, imagine how coffeemakers could form the basis of the greatest OOH ad network.

How CRM and a DMP can combine to give a 360-degree view of the customer

360-degree-gif-01For years, marketers have been talking about building a bridge between their existing customers, and the potential or yet-to-be-known customer.

Until recently, the two have rarely been connected. Agencies have separate marketing technology, data and analytics groups. Marketers themselves are often separated organizationally between “CRM” and “media” teams – sometimes even by a separate P&L.

Of course, there is a clearer dividing line between marketing tech and ad tech: personally identifiable information, or PII. Marketers today have two different types of data, from different places, with different rules dictating how it can be used.

In some ways, it has been natural for these two marketing disciplines to be separated, and some vendors have made a solid business from the work necessary to bridge PII data with web identifiers so people can be “onboarded” into cookies.

After all, marketers are interested in people, from the very top of the funnel when they visit a website as an anonymous visitor, all the way down the bottom of the funnel, after they are registered as a customer and we want to make them a brand advocate.

It would be great — magic even — if we could accurately understand our customers all the way through their various journeys (the fabled “360-degree view” of the customer) and give them the right message, at the right place and time. The combination of a strong CRM system and an enterprise data management platform (DMP) brings these two worlds together.

Much of this work is happening today, but it’s challenging with lots of ID matching, onboarding, and trying to connect systems that don’t ordinarily talk to one another. However, when CRM and DMP truly come together, it works.

What are some use cases?

Targeting people who haven’t opened an email

You might be one of those people who don’t open or engage with every promotional email in your inbox, or uses a smart filter to capture all of the marketing messages you receive every month.

To an email marketer, these people represent a big chunk of their database. Email is without a doubt the one of the most effective digital marketing channels, even though as few as 5% of people who engage are active buyers. It’s also relatively fairly straightforward way to predict return on advertising spend, based on historical open and conversion rates.

The connection between CRM and DMP enables the marketer to reach the 95% of their database everywhere else on the web, by connecting that (anonymized) email ID to the larger digital ecosystem: places like Facebook, Google, Twitter, advertising exchanges, and even premium publishers.

Understanding where the non-engaged email users are spending their time on the web, what they like, their behavior, income and buying habits is all now possible. The marketer has the “known” view of this customer from their CRM, but can also utilise vast sets of data to enrich their profile, and better engage them across the web.

Combining commerce and service data for journeys and sequencing

When we think of the customer journey, it gets complicated quickly. A typical ad campaign may feature thousands of websites, multiple creatives, different channels, a variety of different ad sizes and placements, delivery at different times of day and more.

When you map these variables against a few dozen audience segments, the combinatorial values get into numbers with a lot of zeros on the end. In other words, the typical campaign may have hundreds of millions of activities — and tens of millions of different ways a customer goes from an initial brand exposure all the way through to a purchase and the becoming a brand advocate.

How can you automatically discover the top 10 performing journeys?

Understanding which channels go together, and which sequences work best, can add up to tremendous lift for marketers.

For example, a media and entertainment company promoting a new show recently discovered that doing display advertising all week and then targeting the same people with a mobile “watch it tonight” message on the night of it aired produced a 20% lift in tune-in compared to display alone. Channel mix and sequencing work.

And that’s just the tip of the iceberg — we are only talking about web data.

What if you could look at a customer journey and find out that the call-to-action message resonated 20% higher one week after a purchase?

A pizza chain that tracks orders in its CRM system can start to understand the cadence of delivery (e.g. Thursday night is “pizza night” for the Johnson family) and map its display efforts to the right delivery frequency, ensuring the Johnsons receive targeted ads during the week, and a mobile coupon offer on Thursday afternoon, when it’s time to order.

How about a customer that has called and complained about a missed delivery, or a bad product experience? It’s probably a terrible idea to try and deliver a new product message when they have an outstanding customer ticket open. Those people can be suppressed from active campaigns, freeing up funds for attracting net new customers.

There are a lot of obvious use cases that come to mind when CRM data and web behavioral data is aligned at the people level. It’s simple stuff, but it works.

As marketers, we find ourselves seeking more and more precise targeting but, half the time, knowing when not to send a message is the more effective action.

As we start to see more seamless connections between CRM (existing customers) and DMPs (potential new customers), we imagine a world in which artificial intelligence can manage the cadence and sequence of messages based on all of the data — not just a subset of cookies, or email open rate.

As the organizational and technological barriers between CRM and DMP break down, we are seeing the next phase of what Gartner says is the “marketing hub” of interconnected systems or “stacks” where all of the different signals from current and potential customers come together to provide that 360-degree customer view.

It’s a great time to be a data-driven marketer!

Chris O’Hara is the head of global marketing for Krux, the Salesforce data management platform.

(Coverage) Salesforce Bolsters Einstein AI With Heavy-Duty Data Management

Through its acquisition of Krux, Salesforce is combining its artificial intelligence (AI) layer with deeper data management in Salesforce Marketing Cloud.
pbc

Customer Relationship Management and Data Management come together in a delicious way.

Today at its Salesforce World Tour stop in New York, the company began to roll back the curtain on how its AI and data layers will work together. Salesforce announced new AI, audience segmentation, and targeting features for Marketing Cloud based on its recent acquisition of data management platform Krux. The company’s new Marketing Cloud features, available today, add more data-driven advertising tools and an Einstein Journey Insights dashboard for monitoring end-to-end customer engagement in everything from e-commerce to email marketing.

Salesforce unveiled its Einstein AI platform this year, baking predictive algorithms, machine and deep learning, as well as other data analysis features throughout its Software-as-a-Service (SaaS) cloud. Einstein is essentially an AI layer between the data infrastructure underneath and the Salesforce apps and services on top. The CRM giant is no stranger to big money acquisitions, most recently scooping up Demandware for $2.8 billion and making a play for LinkedIn before Microsoft acquired it. The Krux acquisition gives Salesforce a new, data-driven customer engagement vector.

“We’re working to apply AI to all our applications,” said Eric Stahl, Senior Vice President of Marketing Cloud. “In Marketing Cloud, Krux now gives us the ability to do things like predictive journeys to help the marketer figure out which products to recommend. We can do complex segmentation, inject audiences into various ad networks, and do large-scale advertising informed by Sales Cloud and Service Cloud data.”

As Salesforce and Krux representatives demonstrated Krux and how it fits into the Marketing Cloud, the data management platform acted more like a business intelligence (BI) or data visualization tool than a CRM or marketing platform. Chris O’Hara, head of Global Data Strategy at Krux, talked about the massive quantities of data the platform manages, including an on-demand analytics environment of 20 petabytes (PB)—the entire internet archive is only 15 PB.

526951-krux-data-pattern-analysis“This is our idea of democratizing data for business users who don’t have a PhD in data science,” said O’Hara. You can use Krux machine-learned segments to find out something you don’t know about your audience, or do a pattern analysis [screenshot above] to understand the attributes of those users that correlate greatly. We’re hoping to use those kinds of signals to power Einstein and do things like user scoring and propensity modeling.

The Einstein Journey Insights feature is designed to analyze “hundreds of millions of data points” to identify an optimal customer conversion path. In addition to its Krux-powered Marketing Cloud features, Salesforce also announced a new conversational messaging service called LiveMessage this week for its Salesforce Service Cloud. LiveMessage integrates SMS text and Facebook Messenger with the Service Cloud console for interactions between customers and a company’s helpdesk bots.

The more intriguing implications here are what Salesforce might do with massively scaled data infrastructure like Krux beyond the initial integration. According to O’Hara, in addition to its analytics environment, Krux also processes more than more than 5 billion monthly CRM records and 4.5 million data capture events every minute, and maintains a native device graph of more than 3.5 billion active devices and browsers per month. Without getting into specifics, Salesforce’s Stahl said there will be far more cross-over between Krux data management and Einstein AI to come. In the data plus AI equation, the potential here is exponential scale.

 

Match Game 2015

Match gameIf you work in digital marketing for a brand or an agency, and you are in the market for a data management platform, you have probably asked a vendor about match rates. But, unless you are really ahead of the curve, there is a good chance you don’t really understand what you are asking for. This is nothing to be ashamed of – some of the smartest folks in the industry struggle here. With a few exceptions, like this recent post, there is simply not a lot of plainspoken dialogue in the market about the topic.

Match rates are a key factor in deciding how well your vendor can provide cross-device identity mapping in a world where your consumer has many, many devices. Marketers are starting to request “match rate” numbers as a method of validation and comparison among ad tech platforms in the same way they wanted “click-through rates” from ad networks a few years ago. Why?

As a consumer, I probably carry about twelve different user IDs: A few Chrome cookies, a few Mozilla cookies, several IDFAs for my Apple phone and tablets, a Roku ID, an Experian ID, and also a few hashed e-mail IDs. Marketers looking to achieve true 1:1 marketing have to reconcile all of those child identities to a single universal consumer ID (UID) to make sure I am the “one” they want to market to. It seems pretty obvious when you think about it, but the first problem to solve before any “matching” tales place whatsoever is a vendor’s ability to match people to the devices and browser attached to them. That’s the first, most important match!

So, let’s move on and pretend the vendor nailed the cross-device problem—a fairly tricky proposition for even the most scaled platforms that aren’t Facebook and Google. They now have to match that UID against the places where the consumer can be found. The ability to do that is generally understood as a vendor’s “match rate.”

So, what’s the number? Herein lies the problem. Match rates are really, really hard to determine, and they change all the time. Plus, lots of vendors find it easier to say, “Our match rate with TubeMogul is 92%” and just leave it at that—even though it’s highly unlikely to be the truth. So, how do you separate the real story from the hype and discover what a vendor’s real ability to match user identity is? Here are two great questions you should ask:

What am I matching?

This is the first and most obvious question: Just what are you asking a vendor to match? There are actually two types of matches to consider: A vendor’s ability to match a bunch of offline data to cookies (called “onboarding”), and a vendor’s ability to match a set of cookie IDs to another set of cookie IDs.

First, let’s talk about the former. In onboarding—or matching offline personally identifiable information (PII) identities such as an e-mail with a cookie—it’s pretty widely accepted that you’ll manage to find about 40% of those users in the online space. That seems pretty low, but cookies are a highly volatile form of identity, prone to frequent deletion, and dependent upon a broad network of third parties to fire “match pixels” on behalf of the onboarder to constantly identify users. Over time, a strong correlation between the consumer’s offline ID and their website visitation habits—plus rigor around the collection and normalization of identity data—can yield much higher offline-to-online match results, but it takes effort. Beware the vendor who claims they can match more than 40% of your e-mails to an active cookie ID from the get-go. Matching your users is a process, and nobody has the magic solution.

As far as cookie-to-cookie user mapping, the ability to match users across platforms has more to do with how frequently the your vendors fire match pixels. This happens when one platform (a DMP) calls the other platform (the DSP) and asks, “Hey, dude, do you know this user?” That action is a one-way match. It’s even better when the latter platform fires a match pixel back—“Yes, dude, but do you know this guy?”—creating a two-way identity match. Large data platforms will ask their partners to fire multiple match pixels to make sure they are keeping up with all of the IDs in their ecosystem. As an example, this would consist of a DMP with a big publisher client who sees most of the US population firing a match pixel for a bunch of DSPs like DataXu, TubeMogul, and the Trade Desk at the same time. Therefore, every user visiting a big publisher site would get that publisher’s DMP master ID matched with the three separate DSP IDs. That’s the way it works.

Given the scenario I just described, and even accounting for a high degree of frequency over time, match rates in the high 70 percentile are still considered excellent. So consider all of the work that needs to go into matching before you simply buy a vendor’s claim to have “90%” match rates in the cookie space. Again, this type of matching is also a process—and one involving many parties and counterparties—and not just something that happens overnight by flipping a switch, so beware of the “no problem” vendor answers.

What number are you asking to match?

Let’s say you are a marketer and you’ve gathered a mess of cookie IDs through your first-party web visitors. Now, you want to match those cookies against a bunch of cookie IDs in a popular DSP. Most vendors will come right out and tell you that they have a 90%+ match rate in such situations. That may be a huge sign of danger. Let’s think about the reality of the situation. First of all, many of those online IDs are not cookies at all, but Safari IDs that cannot be matched. So eliminate a good 20% of matches right off the bat. Next, we have to assume that a bunch of those cookies are expired, and no longer matchable, which adds another 20% to the equation. I could go on and on but, as you can see, I’ve just made a pretty realistic case for eliminating about 40% of possible matches right off the bat. That means a 60% match rate is pretty damn good.

Lots of vendors are actually talking about their matchable population of users, or the cookies you give them that they can actually map to their users. In the case of a DMP that is firing match pixels all day long, several times a day with a favored DSP, the match rate at any one time with that vendor may indeed be 90-100%–but only of the matchable population. So always ask what the numerator and denominator represent in a match question.

You might ask whether or not this means the popular DMP/DSP ”combo” platforms come with higher match rates, or so-called “lossless integration” since both the DMP and DSP carry an single architecture an, therefore, a unified identity. The answer is, yes, but that offers little differentiation when two separate DMP/DSP platforms are closely synched and user matching.

In conclusion

Marketers are obsessing over match rates right now, and they should be. There is an awful lot of “FUD” (fear, uncertainty, and doubt) being thrown around by vendors around match rates—and also a lot of BS being tossed around in terms of numbers. The best advice when doing an evaluation?

  • Ask what kind of cross-device graph your vendor supports. Without the fundamental ability to match people to devices, the “match rate” number you get is largely irrelevant.
  • Ask what numbers your vendor is matching. Are we talking about onboarding (matching offline IDs to cookies) or are we talking about cookie matching (mapping different cookie IDs in a match table)?
  • Ask how they are matching (what is the numerator and what is the denominator?)
  • Never trust a number without an explanation. If your vendor tells you “94.5%” be paranoid!
  • And, ask for a match test. The proof is on the pudding!