Connecticut College Magazine

Chris O’Hara ’90 of Salesforce is helping companies ethically navigate the evolving world of digital marketing and consumer data.

BY DOUG DANIELS

There’s an iconic scene in Steven Spielberg’s Minority Report (a sci-fi thriller) in which Tom Cruise walks through a shopping mall, a steady stream of jabbering digital billboards scanning his retinas and instantly plying him with personalized advertisements that call his character out by name and even reference past purchases.

That movie is set in the year 2054. But today, we already have much of the technical capability to create the type of intrusive consumer experience Spielberg’s film envisioned, generating unprecedented opportunities for businesses and organizations to reach their customers, clients and audiences with precision-targeted strategies. This can be a double-edged sword, offering consumers more-personalized marketing and advertising and introducing them to products they love while also presenting a potential slippery slope into privacy violations and misuse of online data.   

“The challenge is striking a balance,” says Chris O’Hara ’90, P’24, VP of global product marketing in the Data and Identity Group at Salesforce, a cloud-based software company that provides customer relationship management services. “People really want personalization, but they also want privacy. And now, either because of new state-level laws or because of voluntary company policies, organizations are looking at how to start collecting data from consumers in a more responsible way and still offer that personalization.”

Salesforce, with its headquarters in San Francisco, has grown exponentially since its founding in 1999, with 60,000 employees and more than $21 billion in revenue for the 2021 fiscal year. And as the world’s digital reliance continues to spread, Salesforce and its subsidiaries are expected to see steady revenue growth in the years to come.

Tech giants like Apple and Google are dramatically changing the way they use and share personal data, and many companies are now scrambling to adjust to the new challenges to marketing and advertising as their access to that data becomes increasingly limited.

But while the privacy landscape is certainly changing in the U.S., not everywhere in the world is following suit. While Europe led the way with its highly restrictive General Data Protection Regulation, or GDPR, Japan has taken a different approach. There, billboards have been equipped with facial recognition cameras so they can identify a person’s gender and age as they’re walking past and instantly tailor ads to them, not unlike the scene in Minority Report. The technology is intended to be anonymous, but it isn’t a giant leap to totally personalized advertising, and that raises enormous privacy concerns. Facial recognition technology is becoming so common that many of us unlock our phones with it now. Beyond the business and advertising implications, there are also questions about data obtained by government and law enforcement entities. The FBI alone has access to nearly half a billion images for facial recognition uses.

Traditionally, data from various websites is aggregated and profiles are built based on site visits. Lots of the browsing data on laptops, and desktops are captured via “cookies,” or little snippets of code that identify users and store their data. Mobile phones and other web-connected devices (even some refrigerators) also capture behavioral data. Widely available for purchase on open marketplaces, this consumer data can include credit scores, online behavior, a person’s specific interests, particular websites they’ve visited—all information collected and shared almost entirely without the knowledge of the precise individual. If you have ever looked at a pair of shoes online and then noticed ads for those exact same shoes that follow you to a series of other websites, such as news sites, that is part of what cookies do.

A big development in data regulation came in 2018, when California passed the California Consumer Privacy Act, requiring more transparency and limits relating to data use. That’s when you may have started noticing the ubiquitous pop-ups on websites asking you to opt in or out of a company’s data collection or cookies policy.

Google has said it plans to enforce new policies by next year, depriving marketers of the ability to purchase the type of data they’ve long treasured. Exactly how consumer relationships are built moving forward and how marketers and advertisers ply their online trade will most likely involve a blend of new tactics and strategies that will replace the use of third-party cookies, which have been a central component of online advertising for years.

“One idea is something called FLoC—Federated Learning of Cohorts,” O’Hara explains. “The way that would work is Google will identify somebody as belonging to one or multiple cohorts, which are basically groups based on people’s interests. So you might be in the fashionista cohort, or the traveler cohort, or the outdoors-enthusiast cohort, and you’ll be assigned this random number and Google will build these various FLoCs as long as there are enough people who meet the criteria.”

This type of group-level web tracking would represent a significant shift for how advertisers identify new potential customers. Instead of companies targeting the browsing history and hyperspecific consumer data about individuals, they’ll now be forced to settle for information about various cohorts as a whole.

What will these new privacy rules mean for big publishers and big marketers, and how will they collect and use data responsibly?

CHRIS O’HARA ’90

Say, for example, you are identified as a European-travel intender and antique-thimble collector. Instead of having your personal information shared, you would simply be assigned anonymously to a larger group that could include thousands or millions of antique thimble collectors. No individual-level data that could violate privacy would be included.

“The advertising industry is pushing back on this idea of cohorts, because obviously they’re used to doing things a certain way, and it has sparked a really big, raging debate about what the future is going to look like and how it’s going to work,” O’Hara says. 

“What will these new privacy rules mean for big publishers and big marketers, and how will they collect and use data responsibly? Advertisers need to learn to use Google, Facebook and Amazon in new, effective ways that are still responsible and ethical.”

One important ongoing debate, O’Hara says, revolves around whether companies such as Facebook, Twitter and Google are objective “platforms” for content or they should be considered “publishers,” since they do exercise some editorial control and create their own algorithms and other structural elements that can control who sees what information.

O’Hara has been working in data-driven marketing and advertising from the earliest days, when the concept seemed more science fiction than concrete business necessity. An English major at Conn, his passions resided in creative writing, philosophy and drama. But a post-college job as a writer and editor for a cigar magazine introduced him to emerging marketing and advertising strategies just when the internet was beginning to broaden its reach, in the 1990s.

He quickly discovered he had a knack for sales and advertising and began learning about data-driven tools that would later prove revolutionary. He joined Salesforce four years ago, when it acquired Krux, the startup where O’Hara led data strategy, for $800 million. Krux, a data management firm, helped clients in the marketing industry better understand, analyze and target the customers who visited their websites and apps so that they could improve engagement and enhance customer relations through the use of far more precise ad targeting. 

He and the co-founders of Krux wrote Data Driven, a book that examines the ways in which new data is transforming marketing. His latest book is titled Customer Data Platforms: Use People Data to Transform the Future of Marketing Engagement. The extensive line of marketing products Salesforce offers includes software for marketing emails, mobile ad campaigns, social media advertising and analytics, making it a one-stop shop for digital marketing. O’Hara’s role involves marketing and strategy for the products related to data. 

“My job is to meet with clients and discuss data strategy with them and determine how to better understand people through their data,” he says. “This way, I can help them build better experiences across different touchpoints for their customers.”

A big part of what O’Hara does for clients at Salesforce is coordinate customer data in ways that improve customer service and incorporate more efficiency. For example, few people have been spared the misery of speaking to a call center representative who has a boilerplate script yet no knowledge of your history with the company or how to resolve your individual issue. Large companies often have disparate databases where customer info is stored. Salesforce helps consolidate data and use it more effectively so that when somebody contacts a call center, the person on the other end of the phone instantly has access to the caller’s order history, their interests and the marketing campaigns they’ve already been exposed to. The customer doesn’t have to start from scratch with each new person they speak to, repeatedly providing order numbers and other details.

“It may sound simple, but that’s really what creates value,” O’Hara says. “That’s what makes people more likely to stay as a customer, and more likely to spend more money, and it makes them happier and more satisfied. And that’s really, from a broad perspective, the type of stuff we’re working on.”

O’Hara contends that the Covid-19 pandemic has accelerated and forced changes in data collection. While some companies may have been reluctant to embrace a “digital first” model, the pandemic has left them with little choice. The challenge remains to find balance when it comes to privacy and managing data. Prior to Covid, people at least had some control over their marketing experience, in the sense that they could choose to log on to Facebook or Instagram, or to use a phone app to make purchases. But during the pandemic, to help ensure social distancing, many daily tasks—like getting coffee—have transitioned into the digital-first approach O’Hara mentions.

“I love that I can choose to place a coffee order with Dunkin’ Donuts through their iPhone app, go pick it up at the drive-through, and not have to exchange any money to get my order,” O’Hara says.

“But that’s my choice, and I know [Dunkin’ will] be using that data to suggest new items for me to try, for example. Where things cross the line is if I’m just walking by a Dunkin’ Donuts, having never opted in to anything, and my mobile phone lights up within 50 feet and says, ‘Hey, Chris, take 20 percent off a large coffee and a donut.’

“That’s an invasion of privacy, and at that point, you’re just like Tom Cruise walking through that mall.”

{This originally appeared in Connecticut College’s CC Magazine, Summer 2021 issue)

Paleo Adtech Podcast

Chris O’Hara is V.P. of Global Product Marketing at Salesforce, focusing on the data and identity suite of products including Audience Studio (a DMP) and the Salesforce CDP. A well-known speaker, pundit and author, Chris has written eight titles including six on culinary pursuits (listen to the episode for more on this fascinating jaunt in his personal journey), “Data Driven” with Krux co-founders Tom Chavez and Vivek Vaidya and “Customer Data Platforms: Use People Data to Transform the Future of Marketing Engagement,” co-written with Paleo Ad Tech co-host Martin Kihn. The latter is the #1 book on the hottest category in marketing technology today. It’s also one of the only books on the category, but let’s not quibble.

After a smoke-filled start as cigar review editor at Smoke magazine, Chris held various sales roles at publishers including MediaBistro, at the time a thriving content and job search site for media mavens, before finding his way to ad tech via start-ups such as Traffiq and nPario. The latter was an early DMP/CDP that provided the data spine for WPP agencies and Xaxis. It was launched by ex-Yahoo and SAS execs in 2010.

An encounter with Krux co-founder Tom Chavez while writing a position paper on DMPs for eConsultancy led to a position as head of DMP marketer sales for that pioneering platform. Meanwhile, Chris was a prolific writer for industry publications such as AdExchanger and his own blog, The Devil’s Work, a reference to “idle hands” (we think). Krux was acquired by Salesforce in 2016, bringing Chris to his current bivouac.

In this frolicsome episode, Marty and Jill follow Chris up the dot-com boom and back down again, as his family grows and he’s out there “hustling” for ad sales, perfecting his writing and pitching voice, and earning his ad tech pedigree. He shares what it’s like to work in a decommissioned building, how long it takes an ex-Russian Army officer to eat to a large steak, and why it’s time to break up with the third-party cookie. Don’t miss it.

Data Driven Wins the Axiom Awards!

Proud to announce that my book, Data Driven, has won the 2019 Silver medal for best Business Technology book!

In August of 2007, Jenkins Group launched the Axiom Awards, “recognizing and promoting the year’s best business books.” Now, 12 years later, they have announced the winners of the 2019 Axiom Business Book Awards, honoring this year’s best business books, their authors, and publishers.

The Axiom Business Book Awards are intended to bring increased recognition to exemplary business books and their creators, with the understanding that business people are an information-hungry segment of the population, eager to learn about great new books that will inspire them and help them improve their careers and businesses.

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

Whiteboarding in Canada!

Good time with the Canadian Marketing Association digging into data management!

The Identity Based Data Platforms of the Future

Today’s disparate traditional databases and connected devices make “people-based” marketing as difficult and awkward as this interaction. 

Currently, marketers don’t have a single source of truth about their consumers. Tomorrow, there must be a single place to build consumer profiles with rich attribute data, and provisioned to the systems of engagement where that consumer spends their time.

At a recent industry event, we heard a lot about the upcoming year in marketing, and how data and identity will play a key role in driving marketing success.

As a means to master identity, some companies have heralded the idea of the customer data platform (CDP), but the category is still largely undefined. For example, many Salesforce customers believe that they already have a CDP. The reason? They have several different ways of segmenting known and unknown audiences between a data management platform (DMP) and CRM platform.

In an article I wrote here last year, I introduced a simple “layer cake” marchitecture, describing the three core competencies for effective modern marketing. In such a fast moving and evolving industry, I have since refined it to the core pillars of identity, orchestration and intelligence:

With this new marchitecture, brands have the ability to know consumers, engage with them through each touchpoint and use artificial intelligence to personalize each experience.

Mastering each layer of complexity is difficult, requiring an investment in time, technology and people. Lets focus on perhaps the most important – the data management layer where the new CDP category is trying to take hold.

The next wave of data management

By now, it’s safe to say marketers have mastered managing known data. A few years ago, when I was working for a software company that also managed postal mailing lists, I was astonished at the rich and granular data attached to mailing lists. There is a reason direct mail companies can justify $300 CPMs – it works, because direct marketers truly know their customers.

After joining Salesforce, I was similarly awed by the power to carefully segment CRM data, and provision journeys for known customers spanning email, mobile, Google and Facebook, customer service interactions and even community websites.

How can we get to this level of precision in the world of unknown (anonymous) consumer data?

As marketing technology and advertising technology converge, so must the identity infrastructure that underlies both. Put more simply, tomorrow’s systems need a single, federated ID that is trust-based. Companies must have a single source of truth for each person, the ability to attach various keys and IDs to that unified identity, as well as have a reliable and verifiable way to opt people out of targeting.

Let’s take a look at what that might look like:

This oversimplification looks at the various identity keys used for each system and the channels they operate in. Today, the CRM is the system of record for engaging consumers directly in channels like direct mail, email campaigns and service call centers. The DMP, on the other hand, is the system of record for more passive, anonymous engagement in channels like display, video and mobile.

When consumers make themselves known, they “pull” engagement from their favorite brands by requesting more information and opting into messaging. At the top of the funnel, we “push” engagements to them via display ads and social channels.

As a marketer, if you have the right technologies in place, you can seamlessly connect the two worlds of data for more precise consumer engagement. The good news is that, martech and adtech have already converged. Recent research from Salesforce shows that 94% of marketers use CRM data to better engage with consumers through digital advertising, and over 91% either already own or plan to adopt DMP over the next year.

So, if mastering consumer identity is the most important element in building tomorrow’s data platform then what, exactly, are the capabilities that need to be addressed? There are three:

1. A single data segmentation engine

Currently, marketers don’t have a single source of truth about their consumers.

Here’s why: Brands build direct mail lists and email lists in their CRM. Separately, they build digital lists of consumers in a DMP tool. Then, they have lists of social handles for followers in various platforms like Facebook and Twitter. Consumer behaviors like browsing and buying that happen on the ecommerce platforms are often not integrated into a master data record. And distributed marketing presents a challenge because a big mobile company or auto manufacturer may have thousands of franchised locations with their own, individual databases.

Segmentation is all over the place. Tomorrow, there must be a single place to build consumer profiles with rich attribute data, and provisioned to the systems of engagement where that consumer spends their time.

2. Data pipelining and governance capabilities

This identity layer must also have the ability to provision data, based on privacy and usage restrictions, to systems of engagement.

For example, when a consumer buys shoes, they should be suppressed from promotions for that product across all channels. When a consumer logs a complaint on a social channel, a ticket needs to be opened in the call center’s system for better customer service. When a person opts out and chooses to be “forgotten,” the system needs to have the ability to delete not only email addresses, but hundreds of cookies, platform IDs and other addressable IDs in order to meet compliance standards with increasingly restrictive privacy laws and, more importantly, giving consumers control over their own data.

Finally, marketers need the ability to ingest valuable DMP data back into their own data environments to enrich user profiles, perform user scoring, as well as build propensity models and lifetime value scores. This requires granular data storage, fast processing speeds and smart pipelines to provision that data.

3. Leaping from DMPs to holistic data management

Ad technology folks are guilty of thinking of cross-device identity (CDIM) as the definition of identity management. Both deterministic and predictive cross-device approaches are more important than ever, but in a world where martech and adtech are operating on the same budgets and platform, today’s practitioner must think more broadly.

Marketers can no longer depend solely on another party’s match table to bridge the divide between CRM and DMP data. A more durable, and privacy-led connector between known and unknown ID types is required. Moreover, when they can, marketers need the ability to enrich email lists with anonymous DMP attributes to drive more performance in known channels—now only possible when a single party manages the relationship.

These three tenets of identity are the starting point for building the data platform of the future. The interest and excitement around CDPs is well placed, and a positive sign that we are evolving our understanding of identity as the driving force behind the changes in marketing.

[This article originally appeared in Econsultancy’s blog on 2/1/2018]

4 Better Ways to Sell Technology

A lot of you guys make your living selling technology in the advertising and marketing technology space. It’s a great and noble occupation, but not for everyone. Our industry moves very fast, and software is always a stutter step behind. We are trying to solve problems for big brands and media companies, and a lot of what we sell sounds pretty much the same as the competition. Even if you truly have the best product, it’s really hard to get people’s attention. When you finally get it, it’s very hard to truly differentiate yourself and your products. In first meetings and big pitches, you have to leave the meeting accomplishing three basics: your potential customer should like you enough to work with you, trust you to do the work, and believe that your company can solve their problem.

In first meetings and big pitches, you have to leave the meeting accomplishing three basics: your potential customer should like you enough to work with you, trust you to do the work, and believe that your company can solve their problem. Like, trust and belief are pretty simple asks—but very hard to establish in meetings.

Does your typical one-hour meeting look like this?

• Get the monitor set up and internet access established (10 minutes)
• Go around the room with introductions (5 minutes)
• Salesperson introduces the meeting and explains why you are there (10 minutes)
• Salesperson gives the standard “about the company” pitch (15 minutes)
• Subject matter expert talks about some use cases and benefits (20 minutes)
• Demo (0 minutes. Oops. No time left for demo).

I have been in many of these meetings as a potential buyer, and I have also presided over quite a few of these meetings. Some are better than others, but for the most part, they are pretty terrible. Here are four things you can change up for your next meeting.

Stop the Slides

Here’s what happens when you deliver a slide presentation. If you show a slide with text on it, your audience will start reading it. In fact, they will finish reading it way before you stop delivering the content, and then they start thinking about what they are going to do for lunch. Maybe you think you’ve built the most perfect slide ever…full of compelling content and gleaming with ideas? Well, perhaps you have but you’ve alienated half of the room; the slide is the perfect level for the folks who already get it, and way too technical for the newbies (or vice versa). The approach here is to use a good headline and a gigantic picture of something interesting. Show a hammer, elephant, or a guy jumping out of a plane. The internet is full of great options. “Why is there a picture of a guy jumping out of a plane?” your prospect wonders. Your potential client will listen to you until he figures it out.

Grab a Marker

In the technology space, we sell a lot of complicated stuff, and we have a lot of ‘splaining to do in meetings, to borrow the popular Desi Arnaz phrase. Many of our potential customers don’t really know how the Internet works, and that’s okay. A 23-year old media planner at an agency isn’t immediately required to grok the differences between data integration types, but they still have influence over considerable budget dollars. What they need is some education, and that’s where your friend the whiteboard comes in. Why do mediocre actors salvage their careers on the stage? Because it’s harder. You have to know your material, deliver your lines, and there’s nowhere to hide. People respect that, and they will respect you when you close your laptop, pick up a dry erase marker and start explaining what your technology does, why it’s different, and how it will solve a problem. Plus, the element of theater is fun. People know exactly what you are going to say when you deliver a slide, so you will likely be judged on your delivery and the cut of your suit. Pick up a marker, and you will be judged by the size of your brain.

Show, Don’t Tell

Similar to the educational nature of whiteboarding, there is magic in a good software demo. After explaining all of the wonderful problems you are going to solve over 40 minutes, you will likely have a highly skeptical audience. Every other vendor has rolled in and also promised to solve the age-old “right person, right message, right time” conundrum, and you are just the latest in the pack. Whenever there is an opportunity to go into the software and demonstrate exactly what you are talking about, you should take it. “Did you ask about my integration with Amazon? Great, let me pull that up in our UI and show you exactly what to do.” As an industry, we also seem to suffer from using solutions engineers as a crutch. Guess what? If you need a highly technical person to walk through a few screens, then your client just found out that you have a product that only his most technical people can use. That’s a gigantic loser. If you sell software, you should be capable of giving a basic UI demo.

 Tell Stories

People are people, and they communicate best with storytelling. You don’t need to be a latter-day Walt Disney at your next meeting, but you do have to be able to tell a story similar to this: “Ron from Big Company has the same exact problem you guys are having. We worked with Ron and his team for 18 months and figured out exactly how to solve it. Ron is now an SVP. Hey, we should get you out to lunch with Ron, and he can tell you all about it.”

An old boss used to tell me that a sale needs to get your client “paid or made” We can certainly help people get paid by saving the money through efficiency, and “make” their careers with a successful implementation. People love to hear that similar people are having the same issues, and they don’t want to feel left behind. By golly, if this was good enough for Ron at Big Company it’s good enough for me. A good story should be realistic, inspire, differentiate your technology—but also be referenceable.

Because they will call Ron.

Why Most of Today’s DMPs are Lousy

How Granular Data Collection and a Robust Second-Party Data Strategy Changes the Game

The world’s largest marketers and media companies have strongly embraced data management technology to provide personalization for customers that demand Amazon-like experiences. As a single, smart hub for all of their owned data (CRM, email, etc)—and acquired data, such as 3rd party demographic data —DMPs go a long way towards building a sustainable, modern marketing strategy that accounts for massively fragmented digital audiences.

The good news is most enterprises have taken a technological leap of faith, and embraced a data strategy to help them navigate our digital future. The bad news is, the systems they are using today are deeply flawed and do not produce optimal audience segmentation.  

A Little DMP History

Ten years ago, a great thing called the data management platform (DMP) started to power big publishers. These companies wanted to shift power away from ad networks (upon whom the publishers relied to monetize their sites) and give publishers the power to create relevant audiences directly for advertisers. By simply placing a bit of javascript in the header of their websites, DMPs could build audience segments using web cookies, turning the average $2 CPM news reader into a $15 CPM “auto-intender.” By understanding what people read, and the content of those pages,  DMPs could sort people in large audience “segments” and make them available for targeting. Now, instead of handing over 50% of their revenue to ad networks, publishers could pay a monthly licensing fee to a DMP and retain the lion’s share of their digital advertising dollars by creating their own segmented audiences to sell directly to advertisers.

Marketers were slower to embrace DMP technology, and they quickly grasped the opportunity too. Now, instead of depending on ad networks to aggregate reach for them, they started to assemble their own first-party data asset—overlapping their known users with publishers’ segments, and buying access to those more relevant audiences. The more cookies, mobile IDs, and other addressable keys they could collect, the bigger their potential reach. Since most marketers had relatively small amounts of their own data, they supplemented with 3rd-party data—segments of “intenders” from providers like Datalogix, Nielsen, and Acxiom.

The two primary use cases for DMPs have not changed all that much over the years: both sides want to leverage technology to understand their users (analytics) and grow their base of addressable IDs (reach). Put simply, “who are these people interacting with my brand, and how can I find more of them?” DMPs seem really efficient at tackling those basic use cases, until you find out that they were doing it the wrong way the whole time.

What’s the Problem?

To dig a bit deeper, the way first-generation DMPs go about analyzing and expanding audiences is through mapping cookies to a predetermined taxonomy, based on user behavior and context. For example, if my 17-year-old son is browsing an article on the cool new Ferrari online, he would be identified as an “auto intender” and placed in a bucket of other auto intenders. The system would not store any of the data associated with that browsing session, or additional context. It is enough that the online behavior met a predetermined set of rules for “auto-intender” to place that cookie among several hundred thousand other “auto- intenders.”

The problem with a fixed, taxonomy-based collection methodology is just that—it is fixed, and based on a rigid set of rules for data collection. Taxonomy results are stored (“cookie 123 equals auto-intender”)—not the underlying data itself. That is called “schema-on-write,” an approach that writes taxonomy results to an existing table when the data is collected. That was fine for the days when data collection was desktop-based and the costs of data storage were sky-high, but it fails in a mobile world where artificial intelligence systems crave truly granular, attribute-level data collected from all consumer interactions to power machine learning.

There is another way to do this. It’s called “schema-on-read,” which is the opposite of schema-on-write. In these types of systems, all of the underlying data is collected, and the taxonomy result is created upon reading all of the raw data. In this instance, say I collected everything that happened on a popular auto site like Cars.com? I would collect how many pages were viewed, dwell times on ads, all of the clickstream collected in the “build your own” car module, and the data from event pixels that collected how many pictures a user viewed of a particular car model. I would store all of this data so I could look it up later.

Then, if my really smart data science team told me that users who viewed 15 of the 20 car pictures in the photo carousel in one viewing session were 50% more likely to buy a car in the next 30 days than the average user, I would build a segment of such users by “reading” the attribute data I had stored.  This notion—total data storage at the attribute (or “trait”) level, independent of a fixed taxonomy—is called completeness of data. Most DMPs don’t have it.

Why Completeness Matters

Isn’t one auto-intender as good as another, despite how those data were collected? No. Think about the other main uses of DMPs: overlap reporting and indexing. Overlap reporting seeks to overlay an enterprise’s first party data asset with another. This is like taking all the visitors to Ford’s website, and comparing that audience to every user on a non-endemic site, like the Wall Street Journal. Every auto marketer would love to understand which high-income WSJ readers were interested in their latest model. But, how can they understand the real intent of users if they are just tagged as “auto intenders?” How did the publisher come to that conclusion? What signals contributed to having that those users qualify as “intenders” in the first place? How long ago did they engage with an auto article? Was it a story about a horrific traffic crash, or an article on the hottest new model? Without completeness, these “auto intenders” become very vague. Without all of the attributes stored, Ford cannot put their data science team to work to better understand their true intent.

Indexing, the other prominent use case, scores user IDs based on their similarity to a baseline population. For example, a popular women’s publisher like Meredith might have an index score of 150 against a segment of “active moms.” Another way of saying this is that indexing helps understand the “momness” of those women, based on similarity to the  overall population. Index scoring is the way marketers have been buying audience data for the last 20 years. If I can get good reach with an index score above 100 at a good price, then I’m buying those segments all day long. Most of this index-based buying happens with 3rd-party data providers who have been collecting the data in the same flawed way for years. What’s the ultimate source of truth for such indexing? What data underlies the scoring in the first place? The fact is, it is impossible to validate these relevancy scores with the granular, attribute-level data being available to analyze.

Therefore, it is entirely fair to say that most DMPs have excellent intentions, but lack the infrastructure to perform 100% of the most important things DMPs are meant to do: understand IDs, and grow them through overlap analysis and indexing. If the underlying data has been improperly collected (or not there at all), then any type of audience profiling by any means is fundamentally flawed.

Whoops.

What to do?

To be fair, most DMPs were architected during a time when it was unnecessary to collect data through a schema-on-read methodology—and extremely costly. Today’s unrelenting shift to AI-driven marketing necessitates this approach to data collection and storage, and older systems are tooling up to compete. If you want to create a consumer data platform (“CDP”), the hottest new buzzword in marketing, you need to collect data in this way. So, the industry is moving there quickly. That said, many marketers are still stuck in the 1990s. Older DMPs are somewhat like the technology mullet of marketing—businesslike in the front, with something awkward and hideous hidden behind.

Beyond licensing a modern, schema-on-read system for data management so marketers can collect their own data in a granular way, there is another way to do things like indexing and overlap analysis well: license data from other data owners who have collected their data in such a way. This means going well beyond leveraging commoditized third-party data, and looking at the world of second-party data. Done correctly, real audience planning starts with collecting your own data effectively and extends to leveraging similarly collected data from others—second party data that is transparent, exclusive, and unique.

Five Principles of Modern Marketing

Every marketer and media company these days is trying to unlock the secret to personalization. Everyone wants to be the next Amazon, anticipating customer wants and desires and delivering real-time customization.

Actually, everyone might need to be an Amazon going forward; Harris Interactive and others tell us that getting customer experience wrong means up to 80% of customers will leave your brand and try another – and it takes seven times more money to reacquire that customer than it did initially.

How important is personalization? In a recent study, 75% marketers of marketers said that there’s no such thing as too much personalization for different audiences, and 94% know that delivering personalized content is important to reaching their audiences.

People want and expect personalization and convenience today, and brands and publishers that cannot deliver it will suffer similar fates. However, beyond advanced technology, what do you need to believe to make this transformation happen? What are the core principles a company needs to adhere to, in order to have a shot at transforming themselves into customer-centric enterprises?

Here are five:

Put People First

It’s a rusty old saw but, like any cliché, it’s fundamentally true. For years, we have taken a very channel-specific view of engagement, thinking in terms of mobile, display, social and video. But those are channels, apps and browsers. Browsers don’t buy anything; people do.

A people-centric viewpoint is critical to being a modern marketer. True people-based marketing needs to extend beyond advertising and start to include things like sales, service and ecommerce interactions – every touchpoint people have with brands.

People – customers and consumers – must reside at the center of everything, and the systems of engagement we use to touch them must be tertiary. This makes the challenges of identity resolution the new basis of competition going forward.

Collect Everything, Measure Everything

A true commitment to personalized marketing means that you have to understand people. For many years, we have assigned outsized importance to small scraps of digital exhaust such as clicks, views and likes as signals of brand engagement and intent. Mostly, they’ve lived in isolation, never informing a holistic view of people and their wants and desires.

Now we can collect more of this data and do so in real time. Modern enterprises need to become more obsessive about valuing data. Every scrap of data becomes a small stitch in a rich tapestry that forms a view of the customer.

We laughed at the “data is the new oil” hyperbole a few years back – simply because nobody had a way to store and extract real value from the sea of digital ephemera. Today is vastly different because we have both the technology and processes to ingest signals at scale – and use artificial intelligence to refine them into gold. Businesses that let valuable data fall to the floor without measuring them might already be dead, but they just don’t know it yet.

Be A Retailer

A lot of brands aren’t as lucky as popular hotel booking sites. To book a room, you need to sign up with your email. Once you become a user, the company collects data on where you like to go, how often you travel, how much you pay for a room and even what kind of mattress you prefer. Any brand would kill for that kind of one-to-one relationship with a customer.

Global CPG brands touch billions of lives every day, yet often have to pay other companies to learn how their marketing spend affected sales efforts. Brands must start to own customer relationships and create one-to-one experiences with buyers. We are seeing the first step with things like Dash buttons and voice ordering, though still through a partner, but we will see this extend even further as brands change their entire business models to start to own the retail relationship with people. The key pivot point will come when brands actually value people data as an asset on their balance sheets.

See The World Dynamically

The ubiquity of data has led to an explosion of microsegmentation. I know marketers and publishers that can define a potential customer to 20 individual attributes. But people can go from a “Long Island soccer mom” on Monday to an “EDM music lover” on Friday night. Today’s segmentation is very much static – and very ineffective for a dynamic world where things change all the time.

To get the “right message, right place, right time” dynamic right, we need to understand things like location, weather, time of day and context – and make those dynamic signals part of how we segment audiences. To be successful, marketers and media companies must commit to thinking of customers as the dynamic and vibrant people they are and enable the ability to collect and activate real-time data into their segmentation models.

Think Like A Technologist

Finally, to create the change described above requires a commitment to understanding technology. You can’t do “people data” without truly understanding data management technology. You can’t measure everything without technology that can parse every signal. To be a retailer, you have to give customers a reason to buy directly from you. Thinking about customers dynamically requires real-time systems of collection and activation.

But technology and the people to run it are expensive investments, often taking months and years to show ROI, and the technology changes at the velocity of Moore’s Law. It’s a big commitment to change from diaper manufacturer to marketing technologist, but we are starting to understand that it is the change required to survive an era where people are in control.

Some say that it wasn’t streaming media technology that killed Blockbuster, but the fact that people hated their onerous late fees. It was probably both of those things. Tomorrow’s Blockbusters will be the companies that cannot apply these principles of modern, personalized marketing – or do not want to make the large investments to do so.

[This article originally appeared in AdExchanger on 8/7/2017.]

DMPs are Dead. Long Live DMPs.

 

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]