Talking Integration at DMEXCO

COLOGNE – At Salesforce, the acquisitions keep on coming, most recently that of AI-powered marketing intelligence and analytics platform Datorama. The company’s ongoing mantra is “integration” and it seems to have no shortage of assets to leverage in that quest.

It all stems from what Chris O’Hara, VP, Product Marketing, calls the “fourth industrial revolution” led by things like data, AI and the internet of things.

“It’s harder for marketers to deliver personalization at scale to consumers and that’s the goal. So everything we’re doing at Salesforce is really about integration,” O’Hara says in this interview with Beet.TV at the recent DMEXCO conference.

By way of examples, he cites the acquisition of ExactTarget about four years ago with the intention of making email “a very sustainable part of marketing, such that it’s not just batch and blast email marketing but it’s also your single source of segmentation for the known consumer.” The end result was the ExactTarget Marketing Cloud Salesforce Integration.

In late 2016, Salesforce bought a company called Krux and within six months had morphed it into Salesforce DMP. It was a way to assist marketers in making sense of households “comprised of hundreds of cookies and dozens of different devices” and aggregate them to a single person or households “so can get to the person who makes the decision about who buys a car or what family vacation to take,” O’Hara says.

Salesforce DMP benefits from machine-learned segmentation, now known as Einstein Segmentation, to make sense out of the thousands of attributes that can be associated with any given individual and determine what makes them valuable. Developing segments by machine replaces “you as a marketer using your gut instinct to try to figure out who’s the perfect car buyer. Einstein can actually tell you that.”

In March of 2018, MuleSoft, one of the world’s leading platforms for building application networks, joined the Salesforce stable to power the new Salesforce Integration Cloud. It enables companies with “tons of legacy data sitting in all kinds of databases” to develop a suite of API’s to let developers look into that data and “make it useful and aggregate it and unify it so it can become a really cool, consumer-facing application, as an example.”

Datorama now represents what O’Hara describes as a “single source of truth for marketing data, a set of API’s that look into campaign performance and tie them together with real marketing KPI’s and use artificial intelligence to suggest optimization.”

In addition to driving continual integration, Salesforce sees itself as “democratizing” artificial intelligence, according to O’Hara. “There’s just too much data for humans to be able to make sense of on their own. You don’t have to be a data statistician to be able to use a platform like ours to get better at marketing.”

This interview is part of a series titled Advertising Reimagined: The View from DMEXCO 2018, presented by Criteo. Please find more videos from the series here.

BY STEVE ELLWANGER ON SEPTEMBER 18, 2018 @STEVEELLWANGER

ADVERTISING REIMAGINED: THE VIEW FROM DMEXCO 2018, PRESENTED BY CRITEOTAGGED CRITEO, KRUX, SALESFORCE, DMEXCO 2018, MULESOFT, EXACTTARGET, CHRIS O’HARA

Watch: Talking AI at Programmatic IO NY

 

Quick 20-minute jam session on the future of artificial intelligence and what it means for programmatic advertising.

 

The Technology Layer Cake

 

This cake doesn’t look all that appealing, but thinking of your marketing stack in such a way is helpful. Think of the cream filling as helpful client success personnel should you want to extend the metaphor.

I saw a great presentation at this year’s Industry Preview where Brian Anderson of LUMA Partners presented on the future of marketing clouds. His unifying marketechture drawings looked like an amalgamation of various whiteboarding sessions I have had recently with big enterprise marketers, many of whom are building the components of their marketing “stacks.” Marketers are feverishly licensing offerings from all kinds of big software companies and smaller adtech and martech players to build a vision that can be summed up like this:

 

The Data Management Layer

Today’s “stack” really consists of three individual layers when you break it down. The first layer, Data Management (DM), contains all of the “pipes” used to connect people identity together. Every cloud needs to take data in from all kinds of sources, such as internet cookies, mobile IDs, hashed e-mail identity keys, purchase data, and the like. Every signal we can collect results in a richer understanding of the customer, and the DM layer needs access to rich sets of first, second, and third-party data to paint the clearest picture.

The DM layer also needs to tie every single ID and attribute collected to an individual, so all the signals collected can be leveraged to understand their wants and desires. This identity infrastructure is critical for the enterprise; knowing that you are the same guy who saw the display ad for the family minivan, and visited the “March Madness Deals” page on the mobile app goes a long way to attribution. But the DM layer cannot be constrained by anonymous data. Today’s marketing stacks must leverage DMPs to understand pseudonymous identity, but must find trusted ways to mix PII-based data from e-mail and CRM systems. This latter notion has created a new category—the “Customer Data Platform” (CDP), and also resulted in the rush to build data lakes as a method of collecting a variety of differentiated data for analytics purposes.

Finally, the DM layer must be able to seamlessly connect the data out to all kinds of activation channels, whether they are e-mail, programmatic, social, mobile, OTT, or IOT-based. Just as people have many different ID keys, people have different IDs inside of Google, Facebook, Pinterest, and the Wall Street Journal. Connecting those partner IDs to an enterprises’ universal ID solves problems with frequency management, attribution, and offers the ability to sequence messages across various addressable channels.

You can’t have a marketing cloud without data management. This layer is the “who” of the marketing cloud—who are these people and what are they like?

The Orchestration Layer

The next thing marketers need to have (and they often build it first, in pieces) is an orchestration layer. This is the “When, Where, and How” of the stack. E-mail systems can determine when to send that critical e-mail; marketing automation software can decide whether to put someone in a “nurture” campaign, or have a salesperson call them right away; DSPs decide when to bid on a likely internet surfer, and social management platforms can tell us when to Tweet or Snap. Content management systems and site-side personalization vendors orchestrate the perfect content experience on a web page, and dynamic creative optimization systems have gotten pretty good at guessing which ad will perform better for certain segments (show the women the high-heeled shoe ad, please).

The “when” layer is critical for building smart customer journeys. If you get enough systems connected, you start to realize the potential for executing on the “right person, right message, right time” dynamic that has been promised for many years, but never quite delivered at scale. Adtech has been busy nailing the orchestration of display and mobile messages, and the big social platforms have been leveraging their rich people data to deliver relevant messages. However, with lots of marketing money and attention still focused on e-mail and broadcast, there is plenty of work to be done before marketers can build journeys that feature every touchpoint their customers are exposed to.

Marketers today are busy building connectors to their various systems and getting them to talk to each other to figure out the “when, where, and how” of marketing.

The Artificial Intelligence Layer

When every single marketer and big media company owns a DMP,and has figured out how to string their various orchestration platforms together, it is clear that the key point of differentiation will reside in the AI layer. Artificial intelligence represents the “why” problem in marketing—why am I e-mailing this person instead of calling her? Should I be targeting this segment at all? Why does this guy score highly for a new car purchase, and this other guy who looks similar doesn’t? What is the lifetime value of this new business traveler I just acquired?

While the stacks have tons of identity data, advertising data, and sales data, they need a brain to analyze all of that data and decide how to use it most effectively. As marketing systems become more real-time and more connected to on-the-go customers than ever before, artificial intelligence must drive millions of decisions quickly, gleaned from billions of individual data points. How does the soda company know when to deliver an ad for water instead of diet soda? It requires understanding location, the weather, the person, and what they are doing in the moment. AI systems are rapidly building their machine learning capabilities and connecting into orchestration systems to help with decisioning.

All Together Now

The layer cake is a convenient way to look at what is happening today. The vision for tomorrow is to squish the layer cake together in such a way that enterprises get all of that functionality in a single cake. In four or five years, every marketing orchestration system will have some kind of built-in DMP—or seamless connections to any number of them. We see this today with large DSPs; they all need an internal data management system for segmentation. Tomorrow’s orchestration systems will all have built-in artificial intelligence as a means for differentiation. Look at e-mail orchestration today. It is not sold on its ability to deliver messages to inboxes, but rather on its ability to provide that service in a smarter package to increase open rates and provide richer analytics.

It will be fun to watch as these individual components come together to form the marketing clouds of the future. It’s a great time to be a data-driven marketer!

[This post was originally published April 4, 2017 on Econsultancy blog

How AI will Change UX

 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. 

Talking Salesforce at Industry Preview

(Watch) I joined Ryan Joe, AdExchanger Senior Editor, to talk about Salesforce’s acquisition of Krux and how Salesforce is thinking about artificial intelligence, the role of data management, and applications for better marketing.