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]

(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.
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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.