If you want to learn about data, Chris O’Hara is the right person to ask. O’Hara, who leads global product marketing for Salesforce Marketing Cloud’s suite of data and audience products, is a big believer in the data revolution—but first, marketers need to take stock of what data they actually have.
“Some marketers think they have way more data than they actually have, and others think they don’t have a lot of data but actually do,” O’Hara said.
Before joining Salesforce, O’Hara was at Krux, the data management platform that Salesforce acquired in 2016, working on data marketing. In October, O’Hara, along with Krux alums Tom Chavez and Vivek Vaidya, released a book, “Data Driven,” which dives into how marketers should think about using data to overhaul customer engagement and experience.
Before the book’s release, Adweek talked with O’Hara about the book and about how marketers can leverage the data they have while keeping data privacy and consumer trust in mind. A portion of that conversation, which has been edited and condensed for clarity, is below.
Adweek: A lot of marketers have talked about the importance of getting better at explaining to consumers what exactly is being collected and how exactly data is being used. Do you think it’s the responsibility of tech and advertising companies to explain that to the public?
Chris O’Hara: Marketing is better when you have the permission of consumers. Consumers are entitled to know exactly how their data is being used, and consumers are absolutely entitled to have control over their own data. As you talk about the opportunities to get more personalized with customers, you’re allowed to deliver great personalization if the customer has opted in for you to do that on their behalf. If you do that without their consent, it feels creepy and wrong, right? It’s common sense. We’re always going to lead with the idea that trust comes first and that marketing is better with consent. Period.
You write in your book that the biggest risks of harnessing data are centered around privacy, security and trust. As concerns about data privacy grow, and as data breaches continue to occur, how does the industry best rebuild trust with the public? Where does the industry start with reestablishing trust and maintaining trust with consumers?
It’s all based on permission and an opted-in consumer. I like getting advertising messages that are relevant. When I am shopping for a car and I give Cars.com permission to introduce me to new models and send me an email every week, I appreciate it because I’ve asked for it. When I engage with certain sites on the web, like The Wall Street Journal, where I pay for content, I trust them with a certain amount of my data so they can make my reading experience better. That’s the way it should have been, always. Unfortunately, there are some companies in the space that have taken advantage of little oversight to do otherwise. But what we’ve seen in the market is that companies that are not leading with trust are not being valued as highly or perceived as more valuable than companies that do put trust at the center of their relationship with customers.
What’s the biggest misconception marketers have with data?
Something we write about in the book is that some marketers think they have way more data than they actually have, and others think they don’t have a lot of data but actually do. One of Pandora’s svps, Dave Smith, came to us and said, ‘I have one of the biggest mobile data assets in the world. Everyone who uses Pandora is logged in, so we know so much about our customers: what kind of cellphone they have, what kind of music they like, perhaps the ages of the kids in their home, when they listen.’ That’s a lot of data. Pandora probably has one of the largest data assets in the entire world. But Pandora doesn’t know when people are going to buy a car or people’s incomes, necessarily. They don’t know when you’re planning on taking a family vacation. So they turned to second- and third-party data to enrich their understanding of consumers.
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.
(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.
We have all heard about the Democratic Party’s skill with data, and there is no doubt the Obama campaign’s masterful use of first-party registration data to drive online engagement, raise funds and influence political newbies helped put him over the line.
Four years later, the dynamics are mostly similar, but we have moved into a world where mobile is dominant, more young new voters are highly engaged and the standard segmentation – at least on the Republican side – might as well be thrown out the window.
In other words, everyone is getting influenced on their mobile phone, especially through news and social channels. There are a ton more mobile-first, new voters out there, and nobody is really sure which voters make up this weird new Trump segment.
To get a handle on this, political advertisers need to properly onboard and analyze their data to identify who they should target, where they live and what they like.
Understand Voter Identity
In politics, a strong “ground game” is key. That means real, old-school retail politics, such as knocking on doors and getting voters in specific precincts out on Election Day. All campaigns have the voter rolls and can do their fill of direct mail, robocalls and door knocking.
But how to influence voters well before Election Day who are tethered to their devices all day and night? It requires a digital strategy that can reach voters across the addressable channels they are on, including display, video, mobile and email. This strategy should leverage an identity graph to ensure the right messaging is hitting the same voter – at the right cadence.
Maybe “Joe the Firefighter,” a disaffected moderate Democrat who has had it with the Clintons, visited the Donald’s website and is ready to “Make America great again.” Before cross-device capabilities were strong, you could only retarget Joe the next time you saw his cookie online.
Today, Joe can get an equity message reinforced on display (“Make America great again!”), a mobile “nudge” to take action when we see Joe on his tablet at night (“Donate now!”) and follow up with an email a few days before the big rally (“Come see the Donald at the Civic Center!”).
Beyond this capability is the incredibly important task of laddering up individual identity into householding, so we can understand the composition of Joe’s family, since households often vote together and contain more than one registered voter.
Nail Geographic Targeting by County and District
Since “all politics is local,” it follows that all digital advertising should be locally targeted. This is table stakes for digital providers that work with campaigns, and targeting down to the ZIP+4 level has brought a level of precision to district-level outreach that approaches direct mail.
But direct mail (household targeting) is the crown jewel and digital is still trying to cross that divide, but is held back by a fragmented ecosystem of identity and, more importantly, privacy considerations.
This has always been a key consideration, given the fact that a small percentage of key districts can flip the presidency to one party or another.
Affiliation Modeling Through Behavior
Sometimes getting an understanding of someone’s party affiliation is super obvious, such as “liking” a specific candidate on social media. But, sometimes, a user’s affinity has to be derived through attributes derived through his or her behavior and the context of content consumed over time.
Data management platforms are bringing more precision to this type of modeling. Functionality, such as algorithmic segmentation, is helping digital analysts go beyond the basics. It’s fairly easy to correlate two or three attributes, such as income and gender, to estimate party affiliation. In this cycle, for example, we have seen a strong bias toward Trump from lower-income males with less than a college degree.
However, it’s hard for humans to correlate eight or more distinct attributes. Maybe those lower-education, low-income, rural males who love NASCAR actually lean toward Bernie Sanders in certain districts. Letting the machines crunch the numbers can give digital campaign managers an unseen advantage, and that capability has just now become available at scale.
“In 2016, relying on TV advertising to sway voters is no longer a solid campaign tactic,” JC Medici, Rocket Fuel’s national director of politics and advocacy, told me via email. “To secure the White House in November, candidates must now add a strong digital media strategy by utilizing best-in-class AI, correlated with strong voter and propensity data assets to ensure they are delivering ads to the right voter, on the right screen, at the right time.”
One of the hot new areas for political campaign targeting is social affinity, the idea that there is a mutual affinity that can be measured between interests.
Yes, when someone “likes” Hillary, you have an obvious target. But, how about those folks who haven’t stated an obvious choice? Maybe 80% of Hillary fans also liked cat shelters, yellow dresses and Chris Rock.
When strong correlations between deterministic social behavior are shown, it becomes fairly easy to leverage that data for targeting – and make informed choices regarding media. People who liked Hillary also like certain TV shows, actors, causes and websites. Campaign managers can leverage data from Affinity Answers, Affinio and other companies to understand these relationships and exploit them to build support for candidates, while leveraging the ability to geotarget at very granular levels on Facebook.
The Free State Project, an organization committed to getting 20,000 “liberty-loving” people to move to New Hampshire and work toward limited government, just reached its goal – talk about a tough conversion. President Carla Gericke credits the use of data-driven targeting on Facebook for the achievement.
Speaking of social, it is also highly important to get the context right.
“Programmatic has introduced two new challenges: bots (who don’t vote) and brand safety,” Trust Metrics CRO Marc Goldberg told me. “In the age of immediate and shocking news, it has become more important that a political ad does not end up next to porn, hate or issues that are contradictory to the politician’s beliefs. One screen shot and bam, you are on Twitter.”
Onboarding And Offboarding
Perhaps the most critical functionality for digital political campaigns continues to be the ability to “onboard” offline data, such as phone numbers, email addresses and party affiliation, and match it to an online ID for targeting purposes. This is essentially table stakes, considering the years of political investment in collecting offline records for phone banks and direct mail campaigns.
Previously, the onboarding of such data was limited to associating it with an active cookie for retargeting use. But with the emergence of real cross-channel device graphs, this data can now be tied to a universal consumer ID that is persistent and collects attributes over time.
Simply put, that onboarded email – now a UID – can be mapped to a number of identities, including Apple and Android mobile identifiers, third-party IDs from Experian and the like and device IDs from Roku and other OTT devices. In other words, the device graph enables that email to be associated with the voter’s omnichannel footprint, giving campaigns the ability to sequentially target messages, map creative to execution channels and truly understand attribution.
What’s even more exciting is the idea of offboarding some digital data back into the CRM. How valuable would it be to know that a potential voter watched an entire YouTube video on a candidate after being reached by the phone bank? Certain types of behavioral data, depending on compliance with privacy policies, can be brought back into the CRM to impact the effectiveness of offline voter outreach.
It is fair to say that 2016 is the most exciting campaign season we’ve had in a generation – and it’s only the primary season. As data-driven marketers, we will see campaigns push the limit in applying big marketing dollars to digital channels, trying to unlock new, mobile-first millennial voters, while persuading independents through more addressable advertising then ever.
Today data is like water: free-flowing, highly available, and pervasive. As the cost of storing and collecting data decreases, more of it becomes available to marketers looking to optimize the way they acquire new customers and activate existing ones. In the right hands, data can be the key to understanding audiences, developing the right marketing messages, optimizing campaigns, and creating long-term customers. In the wrong hands, data can contribute to distraction, poor decision-making, and customer alienation. Over the past several weeks, I asked over thirty of the world’s leading digital data practitioners what marketers should be thinking about when it comes to developing a data management strategy. The result was the newly available Best Practices in Data Management report. A few big themes emerged from my research, which I thought I would share:
Welcome to the First Party
Digital marketing evolves quickly but, for those of us working as digital marketers or publishers for the past 10 years, we have seen distinct waves of transformation impact the way we use data for audience targeting. Early on, audience data was owned by publishers, who leveraged that data to control pricing for premium audiences. The Network Era quickly supplanted this paradigm by leveraging tag data to understand publishers’ audiences better than the sites themselves. Buying targeted remnant inventory at scale created new efficiencies and easy paychecks for publishers, who found themselves completely disintermediated. The DSP Era (which we are still in) continued that trend, by completely separating audiences from media, and giving even more control to the demand side. Today, the “DMP Era” promises a new world where publishers and advertisers can activate their first party data, and use it for remarketing, lookalike modeling, and analytics.
The ubiquity of third party data (available to all, and often applied to the same exact inventory) makes activating first party data more valuable than ever. Doing so effectively means regaining a level of control over audience targeting for publishers, and being able to leverage CRM data for retargeting and lookalike modeling for the demand side, as well as a deeper level of analytics for both sides. If there has been one huge takeaway from my conversations with all of the stakeholders in the data-driven marketing game, it is that getting control and flexibility around the use of your own first-party data is the key to success. As a marketer, if you are buying more segments than you are creating, you are losing.
The New Computing Paradigm
In order to successfully activate all of the data your company can leverage for success takes a lot of work, and a lot of advanced technology. Whether you are a publisher trying to score audiences in milliseconds in order to increase advertising yield, or an advertiser attempting to deliver a customized banner ad to a prospect in real-time, you need to store an incredible amount of data and (more importantly) be able to access it at blazing speeds. In the past, having that capability meant building your own enormous technology “stack” and maintaining it. Today, the availability of cloud-based computing and distributed computing solutions like Hadoop has created a brand new paradigm or what former Microsoft executive and current RareCrowds CEO Eric Picard likes to call the “4th Wave.”
“Being a Wave 4 company implicitly means that you are able to leverage the existing sunk cost of these companies’ investment,” says Picard. That means building apps on top of AppNexus’ extensible platform, leveraging Hadoop to process 10 billion daily transactions without owning a server (as Bizo does), or simply hosting portions of your data in Amazon’s cloud to gain speed and efficiency. As digital marketing becomes more data intensive, knowing how to leverage existing systems to get to scale will become a necessity. If you are not taking advantage of this new technology paradigm, it means you are using resources for IT rather than IP. These days, winning means applying your intellectual property to available technology—not who has the biggest internal stack.
Social Data is Ascendant
One of the most interesting aspects of data management is how it is impacting traditional notions of CRM. In the past, digital marketing seemed to end below the funnel. Once the customer was driven through the marketing funnel and purchased, she went into the CRM database, to be targeted later by more traditional marketing channels (e-mail, direct mail). Now, the emergence of data-rich social platforms had actually created a dynamic in which the funnel continues.
Once in the customer database (CRM), the post-purchase journey starts with a commitment beyond the sale, when a consumer joins an e-mail list, “friends” a company’s page, follows a company’s Twitter account, or signs up for special offers on the company’s site. The next step is an expression of social interest, when the consumer agrees to make public his “like” for a company or brand by “friending” a company’s page, following a company’s Twitter account. Beyond the “like” is true social activation, wherein the consumer actively (not passively) recommends the product or service, through commenting, sharing, or other active social behaviors. The final step is having the consumer sell on your behalf (directly via affiliate programs or, in the softer sense, as a “brand ambassador”). This dynamic is why Salesforce has acquired Radian6 and Buddy Media.
For digital marketers, going beyond the funnel and activating consumers through social platforms means understanding their stated preferences, affinities, and that of their social graph. Most companies already do this with existing platforms. They real key is tying this data back into your other data inputs to create a 360 degree user view. That’s where data science and management platforms come in. If you are not ingesting rich social data and using it to continually segment, target, expand, and understand your customers, you are behind the curve.
Swat ’em away, but they’ll still keep coming — those ‘pigeons’ of corporations that can’t stop flocking to consultants’ birdseed
Remember that television commercial featuring the two consultants talking to a corporate guy? It went something this like this:
Consultants: First you need to optimize your sales force using a state-of-the-art CRM tool, align your marketing message across multiple media to drive your quarterly goals, and implement a company-wide monitoring system to insure message optimization across multiple business units, resulting in huge gains across multiple metrics. This plan is sure to turn your business around.
Corporate Guy: Great! When can you start doing it?
Consultants: [Break down in gales of laughter]. We don’t actually do anything… we just tell you how to do it! [They dissolve in paroxysms of malevolent laughter].
Anyway, you get the drift. Despite the almost universal reckoning that corporate consultants do little more than sell glorified PowerPoint presentations full of the latest business jargon, companies such as my beloved Big Media Company continue to employ them. Let me introduce you to the very best consulting scam ever invented, one that Big Media Company fell for hook, line and sinker.
Scarily enough, it’s called “SPIN Selling.” “SPIN,” of course, is an acronym. Let me save you $500,000 and give you the S.P.I.N. Selling overview in a nutshell: First, find out what people want before you try and sell them something. Then, tailor your sales pitch to address their needs. Sounds simple, right?
Instead of barging into some agency, breaking out your media kit, and telling your customer your circulation, readership, and what special issues you have coming up, why not sit down over a cup of coffee and ask him a bunch of questions. Like: How is your business? (a Situation question); Is the price of paper leading to an increase in your costs? (Problem); Why is it important to solve this problem (Implication); and, If I lowered your rate, would this help you reach more potential customers? (Need/ payoff).
So, you SPIN a customer, slowly walking him through his situation, how it affects his business, and how you — his savior — may solve his problems using whatever it is you happen to be selling. It’s how probably 90 percent of all salesmen and 100 percent of successful ones approach their business. It’s called consultative sales or, put more simply, selling something that people need. What the company that sells the SPIN program offers, however, is more ingenious than anything that’s gone along with products I’ve ever hocked. They take what is a very straightforward and simple sales process (ask questions, provide answers) and pile a bunch of meaningless process and acronyms on top of it, creating a sales pseudoscience that, like Boggle, is “easy to learn, impossible to master.”
Let me tell you how it works (applicable not just to SPIN, but all bullshit media sales consultants and sales consulting in general): The Consultant comes into Big Media Company (the Pigeon) with a long list of corporate stooges who have used their product (IBM, Honeywell, or any Fortune 500 client whose size exceeds that of the Pigeon, and whose CEO is likely to be impressed by). The Consultant says they can increase sales by 20 percent a year using their new patented sale methodology. The Pigeon’s CEO cuts that estimate in half and still figures he’s up a few million net, even after paying the Consultant a healthy $500,000 fee. Soon enough, the Pigeon signs up, and mandates sales training for everyone on staff.
Naturally, since the test is based on the yet-untaught sales principles offered in the coursework, the results are terrible. Pigeon’s people are way behind the curve!
The Consultant comes in for about a month, and trains everyone, 20 at a time, using the same off-the-shelf Powerpoint presentation, with Pigeon’s name sprinkled throughout for that customized look. People are asked to take a test before the training to establish a “baseline” of sales effectiveness. Naturally, since the test is based on the yet-untaught sales principles offered in the coursework, the results are terrible. Pigeon’s people are way behind the curve! Compared to (insert Fortune 500 company’s results here), Big Media Company is a non-player in the 12th percentile!
The training commences, filled with obscure terminology and acronyms designed to turn what is essentially an easy-to-understand concept into something on which you can slap a patent. After the trainings are complete, another test is administered to make sure Pigeon’s salespeople have absorbed the expensive, mandated training. Lo and behold, the results come in, and Consultant has really made an impact! Compared to the initial baseline results, the latest monitoring shows that Pigeon’s staff is really embracing this new sales dynamic! Sadly, however, there is still work to be done. We show that IBM’s salespeople achieved a 15 percent higher result on their post-training assessment, so we recommend a further dose of advanced training (at a discounted rate of $250,000).
You get the gist. By the time Big Media Company — or any other Pigeon — realizes that their sales are about the same as last year, and that Consultant’s package is perhaps better suited to selling something like consulting services, rather than classified advertising, it’s too late.
Moral of story: Never buy something from a salesperson who is full of more shit than you.
[This post originally appeared in MediaBistro, 8/30/2006]
Swapping an editorial gig for ad sales in order to write? Doesn’t make sense to Anonymous either, but he’s living it
“Shut the F@#$ up, Trudy!”
More training today. this time on CRM. That’s “customer relationship management,” by the way. The way it works is, Big Media Company spends a few hundred thousand dollars on a piece of software that tells you when to call your customers. You put in names, addresses, your client’s daughter’s name and age, underwear size, etc. Then, when you give him a ring about the August issue, you can bullshit a little and pretend you care about his family, all the while looking up his sales history, sock color preference, and any other thing they can load into the program through SAP or whatever general ledger software Big Media Company happens to be running.
During the class—mandatory for anyone earning over $50,000 at the Company, incidentally—some lady named Trudy* actually starts bitching about it. Things haven’t been right since we rolled up the new CRM application, she says. There’s no help desk. I couldn’t believe what I was hearing. Does this ridiculous, menial, little peon—from accounts receivable, of all departments—really think she is “speaking truth to power” here?
The CRM consultant—who happens to be a Big Media Company Player through and through—issues Trudy the old “let’s talk out your work-related issue, even though you and I both know nothing will change the software rollout” invitation, and asks her for more information. It’s a damn shame he can’t say what his eyes are telling me he wants to say.
His exact words are, “That’s an interesting observation, Trudy. I’ll bet, when the rollout is complete, we can find you a CRM software person to sit down with your team and get everybody up to speed. Let’s discuss this in more detail offline, and we’ll get to the bottom of this training issue. Anyone else want to share a similar experience?”
What he wants to say goes a little like: “Shut the fuck up and read the manual like everyone else.” But he doesn’t cave to the urge. Oh, well. Can’t wait for the “Violence in the Workplace” mini-session next week!
The 2.5 percent Solution
A merrier Christmas, Bob, my good fellow, than I have given you for many a year! I’ll raise your salary, and endeavour to assist your struggling family, and we will discuss your affairs this very afternoon, over a Christmas bowl of smoking bishop, Bob! Make up the fires, and buy another coal-scuttle before you dot another “i,” Bob Cratchit.
I love Big Media Company. After capping annual raises at 2.5 percent a year sometime back in 1973, we have editors at our company who literally bring a can of Friskies to work for lunch. With gas at $8 a gallon, the price of cigarettes going through the roof, and the general expense living in New York creates, the 2.5 percent raise policy means that, with inflation, the Big Media employee effectively gets a pay cut each year. Half of our guys live in Brooklyn—and not the nice part either (unless you know something about the J train that I don’t).
Not everybody’s hurting, though. The sales guy who’s consistently bringing in the cash isn’t complaining—and when he is, the boss usually busts his ass to find that extra $10 grand to placate him so he doesn’t have to go through the hell of hiring and training somebody else.
Editors? Slap them in front of a Mac and a telephone, and throw them a decent pizza party every once in a while, and you’re good to go. There are a billion budding Noam Chomskys ready to “cut their teeth” with some “good writing experience” at Big Media Company. It makes me sick.
That’s when I knew publishing was a big racket. It was also when I knocked on the publisher’s door to switch into a job selling ad space.
I used to be an editor. I remember the day I switched to sales. It was when I recommended a guy I knew as a salesman for a job at my magazine. He came in knowing fuck-all about the Industry, and started off making about $40,000 more than me right off the bat—all before he had even sold his first ad. That’s when I knew publishing was a big racket. Not coincidentally, it was also when I knocked on the publisher’s door to switch into a job selling ad space.
Although I still regret the day I left editorial, it’s pretty much been steady roast beef on a roll with extra lettuce and tomato every since, and that Friskies can hang out in the cabinet until I get a cat.
I guess my English teacher knew what he was talking about. He told us to get a job as a garbage man (or anything providing a steady income), so we could afford to write. If you want to be a writer, why not be an ad salesman to pay the bills?
[This post originally appeared in MediaBistro, 6/28/2006]