Join me, co-author Martin Kihn and our friend Art Sebastian, head of digital for Casey’s discuss CDPs and their impact on personalization.
CDP and the “Five Vs”
I jumped into my friend’s amazing blog to write about the “five Vs” that every CDP needs to be successful. Read the full article on Vala’s ZDNet blog!
The rise of the customer data platform has been interesting to watch. CDPs are an exciting new software category, and most progressive organizations are looking at them as a way of solving some fundamental business challenges — how do you get a “single source of truth” for customer data, when customers create so much of them? Data, that is.
Endless advertising and martech software acquisitions, patched together through brittle “data extensions” and manual integrations lead to many differing views of customers, mostly centered on what channel they are engaging on. Companies tend to have a “marketing” customer they can understand through interactions in e-mail, an “advertising” customer they know through pseudonymous online interactions, and “sales” customers they understand by their profile in a CRM system. Connecting those identities into a rich profile can unlock a lot of value.
Imagine if the call center employee, for example, could have access to a rich profile of every customer that included her recent purchases, loyalty status and points, marketing interactions, and lifetime value score? You might be able to have a real, personalized interaction rather than reading from a canned call script. Imagine further, if the system was smart enough to assign an inbound call priority based on those data attributes, such that a “Platinum” loyalty member got routed to the local call center, rather than the overseas location? Better, more personalized, service. Less customer churn. The possibilities are endless!
The good news is that this is happening today. Large enterprises with sophisticated IT departments, in-house developers, and large software budgets are connecting these systems together to create such results. The bad news is that it’s very expensive, requires constant vigilance and development to keep it working, and its dependent upon licensing solutions from dozens of software vendors for data ingestion to data activation, and everything in between.
The other problem is that this innovation seems to be aimed solely at marketing use cases today. Despite the fact that 80% of companies we surveyed in our State of Marketing Report say they have already begun to connect their marketing and service systems, today’s CDPs seem to be narrowly focused on marketing, advertising, and personalization use cases. But why stop data management there?
If you are embarking on a true data management journey and want some guideposts for building a system that can truly connect your entire enterprise at the data platform layer (where it counts), there are five critical things to think about:
Your systems need to manage a high volume of data, coming in at various speeds. Some data, such as CRM and legacy enterprise system data is slower moving, and generally comes in via batch mode, in the form of tables. These are things like customer records, purchase history data, and the like. But there’s a lot of data that needs to some into the system in real time. A online customer looking for a local store where they can apply an offer they received is information happening in real time that can be applied to real world use cases. Unless you can read and react to that signal quickly, they are likely find the nearest competitor. So having a system that can handle data at many different speeds is a requirement, especially as more and more signals are created from real time and real life interactions.
You then need to map first-party data into a single information model. Data silos, as discussed above, are just the tip of the iceberg. It’s obvious that connecting marketing, advertising, and CRM systems can create new use cases that drive business value, but the true underlying issue isn’t the systems themselves, but how they store data. One system labels a first name as “First_Name” and another as “FirstName.” It seems trivial, but every system has a slightly different main identifier or “source of truth,” and the goal is to have one. This starts with being able to provision a universal information model, or schema, which can organize all of the differently labeled data into a
common taxonomy. Companies are starting to organize around a Common Information Model (The “Cloud” Information Model for companies like Google, Amazon, and Salesforce) as a way of creating a Rosetta Stone for data.
Companies must ensure they can provision a single, persistent profile for every customer or account. Social media systems think of your social “handle” as your primary identifier. E-mail systems use the e-mail address. DMPs usually see people as cookies. Every system is somewhat different. How do you get a “single source of truth” for people data? All of these identity types need to roll up to a rich profile or universal ID. This gets resolved in “known” PII data by making sure one person is the same among many different e-mail and postal addresses, as an example. In the digital world, where people tend to have dozens of cookies and device IDs, these identifiers also need to be mapped to the universal ID. It’s a hard problem to solve, but a system that has a strong identity spine is the only way to get there.
Once you manage to resolve and identify data from many different sources and systems, you end up with…a lot of data. It has been theorized that, in 2020, 1.7MB of data was created every second for every person on earth. That’s hard to fathom, but it’s a problem that is not going away in a world that increasingly values every click, call, and video view. If you want to use those interactions to form the basis of your digital engagement strategy, you have to store them somewhere. That necessitates a system that can handle billions of data attributes, millions of rows, and thousands of interconnected tables. Machine learning works best when pointed at petabytes of analytic data. Your system needs to be built for a world in which more data is created every day, and there are more systems that require them to work well.
The real question is, how do you make data actionable in every channel — marketing, sales, service, commerce, and analytics — and get tangible value from them? Once you have a clean, unified set of scaled data there are many ways to derive value from it. Segmentation tools can pull data from any source and stitch it into scaled groups of addressable customers. Analytics tools get more powerful when analyzing a robust and comprehensive dataset whether for BI or for media analytics. The best part? AI systems get more powerful. Success in machine learning is not about the algorithms — it’s about giving them the ability to run across a highly scaled, true, set of data that creates results.
If you are thinking about starting your company’s digital transformation journey with a CDP or an enterprise data management system, the five Vs are a great framework for success.
I joined hosts Martin Kihn and Tina Rozul to discuss all things CDP (Customer Data Platform) on the popular Marketing Cloud podcast.
Talk Data to Me!
The authors of Customer Data Platforms join my friend Russ Artzt, co-founder of Computer Associates, to talk about CDPs and the new book on the Datavana podcast, hosted by Jaime Muirhead
DisrupTV on CDP!
Great conversation about CDPs with Vala Afshar and Ray Wang, hosts of DisrupTV on CDPs with my co-author Martin Kihn (starts at 22:33).
Customer Data Platforms: The Book!
I’m very excited to announce a new book I have written with Martin Kihn, which is the first book on the customer data platform (CDP) category, a very hot topic in advertising and marketing technology right now! Look for it in November 2020, from Wiley. Pre-order the hardcopy here.
Marketers are faced with a stark and challenging dilemma: customers demand deep personalization, but they are increasingly leery of offering the type of personal data required to make it happen. As a solution to this problem, Customer Data Platforms have come to the fore, offering companies way to capture, unify, activate, and analyze customer data. CDPs are the hottest marketing technology around today, but are they worthy of the hype? Customer Data Platforms takes a deep dive into everything CDP so you can learn how to steer your firm toward the future of personalization.
Over the years, many of us have built byzantine “stacks” of various marketing and advertising technology in an attempt to deliver the fabled “right person, right message, right time” experience. This can lead to siloed systems, disconnected processes, and legacy technical debt. CDPs offer a way to clear out the cobwebs and easily solve for a balanced and engaging customer experience. Customer Data Platforms breaks down the fundamentals, including how to:
Understand the problems of managing customer data
Understand what CDPs are and what they do (and don’t do)
Organize and harmonize customer data for use in marketing
Build a safe, compliant first-party data asset that your brand can use as fuel
Create a data-driven culture that puts customers at the center of everything you do
Understand how to leverage AI and machine learning to drive the future of personalization
Orchestrate modern customer journeys that react to customers in real-time
Power analytics with customer data to get closer to true attribution
In this book, you’ll discover how to build 1:1 engagement that scales at the speed of today’s customers.
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.
Q&A: Salesforce’s Chris O’Hara Wants Marketers to Capitalize on the Data Revolution
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.
Measuring Digital Marketing Success
According to Blue Kai, I’m a tech-savvy, social media-using bookworm in the New York DMA, currently in the market for “entertainment.” At least that’s what my cookie says about me. Simply by going to the Blue Kai data exchange’s registry page, you can find out what data companies and resellers know about you, and your online behavior and intent.
In this brave new world of data-supported audience buying, every individual with an addressable electronic device has been stripped down to an anonymous cookie and is for sale. My cookie, when bounced off various data providers, also reveals that I’m male (Acxiom), have a competitive income (IXI), three children in my family (V12), a propensity for buying online (Targusinfo) and am in mid-management of a small business (Bizo). I’m also in-market for a car (Exelate) and considered to be a “Country Squire,” according to Nieslen’s Prizm, which is essentially a boring white guy from the suburbs who “enjoys country sports like golf and tennis.” Well, I’m horrible at tennis, but everything else seems to be accurate.
As a marketer, you now have an interesting choice. Instead of finding “Country Squire” or “Suburban Pioneer” on content-specific sites they’re known to visit, now I can simply buy several million of these people, and find them wherever they may be lurking on the Web. This explains why you suddenly see ads for BMWs above your Hotmail messages right after you looked at that nice diesel station wagon on the VW.com Web site.
Today’s real-time marketing ecosystem works fast and works smart. But, how do you decide whether to buy the cookie or the site?
Most marketers insist that audience buying is meant for performance campaigns. This is largely a pricing consideration. Obviously, if I want to sell sneakers to young men, it makes sense to buy data and find 18-35-year old males who are “sneaker intenders” based on their online behavior and profile, and reach them at scale across the ad exchanges. Combined data and media will likely be under a $4 CPM, and probably less since both the data and media can be bid upon in real time. For most campaigns with a CPA south of $20, you need to buy “cheap and deep” to optimize into that type of performance. It sounds pretty good on paper. There are a few problems with this, however:
What are they doing when you find them?
OK, so you found one of your carefully selected audience members and you know he’s been shopping for shoes. Maybe you even retargeted him after he abandoned his shopping cart at footlocker.com and dynamically presented him with an ad featuring the very sneakers he wanted to buy, and you did it all for a fraction of a cent. The problem is that you reached him on Hotmail and he’s engaged in composing an e-mail. What are the chances that he’s going to break task and get back into the mind-set of purchasing a pair of sneakers? Also, what kind of e-mail is he composing? Work? A consolation note to a friend who has lost a loved one? Obviously, you don’t know.
Maybe you reached that user on a less than savory site, or perhaps on a social media site where he’s engaged in a live chat session with a friend. In any case, you have targeted that user perfectly—and at just the wrong time. This type of “interruption” marketing is exactly what digital has promised us it wouldn’t be.
Perhaps a better conversion rate can be found on ESPN.com, or a content page about basketball, where that user is engaged in content appropriate to the brand.
How do you know where the conversion came from?
Depending on your level of sophistication and your digital analytics tool set, you may not be in the best position to understand exactly where your online sales are coming from. If you’re depending on click-based metrics, that is even more true. As a recent comScore article points out, the click is a somewhat misleading metric. Put simply, clicks on display ads don’t take branding or other Web behavior into account when measuring success.
Personally, I haven’t clicked on a display ad in years, but seeing them still drives me to act. Comparing offline sales life over a four-week period, comScore reports that pure display advertising provides average lift of 16 percent and pure SEM provides lift of 82 percent—but search and display combined provide sales lift of 119 percent. So you simply can’t look at display alone when judging performance—and you have to question whether you’re seeing performance lift because you’re targeting, or because your buyer has been exposed to a display ad multiple times. If it’s the latter, you may be inclined to save the cost of data and go even more “cheap and deep” to get reach and frequency.
How do you value an impression?
Obviously, the metric we all use is CPM, but sometimes the $30 CPM impression on ESPN.com is less expensive than the $2 RTB impression from AdX. Naturally, your analytics tools will tell you which ad and publisher produced the most conversions. Additionally, deep conversion path analysis can also tell you that “last impression” conversion made at Hotmail might have started on ESPN.com, so you know where to assign value.
But, in the absence of meaningful data, how do we really know how effective our campaign has been? I believe that display creates performance by driving brand value higher, and some good ways to measure that can now be found using rich media. When consumers engage within a creative unit, or spend time watching video content about your brand, they’re making a personal choice to spend time with your message. There’s nothing more powerful than that, and that activity not only drives sales, but helps create lifetime customers.
For today’s digital marketer, great campaigns happen when you understand your customer, find them both across the Web and on the sites for which they have an affinity—and find them when they are engaged in content that’s complementary to your brand message. Hmmm . . . that kind of sounds like what we used to do with print advertising and direct mail. And maybe it really is that simple after all.
Chris O’Hara is svp of sales and marketing for Traffiq. He may be reached through his blog at Chrisohara.com.
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.