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:

Velocity
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.

Variety
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.

Veracity
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.

Volume
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.

Value
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.

Data Driven Wins the Axiom Awards!

DATA_DRIVEN_AXIOMProud 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.

The Identity Based Data Platforms of the Future

21_WhoAreYou_nowplay_small_1

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:

marchitecture cake

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:

federated ID

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]

What is the future of DMPs?

In the 1989 film “Back to the Future II,” Marty McFly traveled to Oct. 21, 2015, a future with flying cars, auto-drying clothes, and shoes that lace automatically. Sadly, none of these things happened. 

What is the future of data management platforms? This is a question I get asked a lot.

The short answer is that DMPs are now part of larger marketing stacks, and brands realize that harnessing their data is a top priority in order to deliver more efficient marketing.

This is a fast-moving trend in which companies are licensing large enterprise stacks and using systems integrators to manage all marketing—not just online advertising.

As detailed in Ad Age (Marketing clouds loom), the days of turning to an agency trade desk or demand side platform (DSP) to manage the “digital” portions of advertising are fading rapidly as marketers are intent on having technology that covers more than just advertising.

Building consumer data platforms

A few years ago, a good “stack” might have been a connected DMP, DSP and ad server. A really good stack would feature a viewability vendor and start a dynamic creative optimization (DCO). The focus then was on optimizing for the world of programmatic buying and getting the most out of digital advertising as consumers’ attention shifted online, to mobile and social, rather than television.

Fast forward a few years, and the conversations we are having with marketers are vastly different. As reported in AdExchanger, more than 40% of enterprise marketers license a DMP, and another 20% will do so within the next 12 months. DMP owners and those in the market for one are increasingly talking about more than just optimizing digital ads. They want to know how to put email marketing, customer service and commerce data inside their systems. They also want data to flow from their systems to their own data lakes.

Many are undertaking the process of building internal consumer data platforms (CDPs), which can house all of their first-party data assets—both known and pseudonymous user data.

We are moving beyond ad tech. Quickly.

Today, when those in the market are considering licensing a “DMP” they are often thinking about “data management” more broadly. Yes, they need a DMP for its identity infrastructure, ability to connect to dozens of different execution systems and its analytical capabilities. But they also need a DMP to align with the systems they use to manage their CRM data, email data, commerce systems, and marketing automation tools.

Data-driven marketing no longer lives in isolation. After I acquire a “luxury sedan intender” online, I want to retarget her—but I also want to show her a red sedan on my website, e-mail her an offer to come to the dealership, serve her an SMS message when she gets within range of the dealership to give her a test drive incentive, and capture her e-mail address when she signs up to talk to a salesperson. All of that needs to work together.

Personalization demands adtech and martech come together

We live in a world that demands Netflix and Amazon-like instant gratification at all times. It’s nearly inconceivable to a Millennial or Generation Z if a brand somehow forgets that they are a loyal customer because they have so many choices and different brands that they can switch to when they have a bad experience.

This is a world that requires adtech and martech to come together to provide personalized experiences—not simply to create more advertising lift, but as the price of admission for customer loyalty.

So, when I am asked, what is the future of DMPs, I say that the idea of licensing something called a “DMP” will not exist in a few years.

DMPs will be completely integrated into larger stacks that offer a layer of data management (for both known and unknown data) for the “right person;” an orchestration layer of connected execution systems that seek to answer the “right message, right time” quandary; and an artificial intelligence layer, which is the brains of the operation trying to figure out how to stitch billions of individual data points together to put it all together in real time.

DMPs will never be the same, but only in the sense that they are so important that tomorrow’s enterprise marketing stacks cannot survive without integrating them completely, and deeply.

[This post was originally published 11 May, 2017 by Chris O’Hara in Econsultancy blog]

The Technology Layer Cake

 

spumoni-layer-cake

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

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

 

The Data Management Layer

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

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

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

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

The Orchestration Layer

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

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

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

The Artificial Intelligence Layer

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

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

All Together Now

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

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

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