CDP

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.

CDP

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.

Big Data · CDP · CRM · Data Management Platform · Demand Side Platform (DSP) · Digital Display · Digital Media Ecosystem · DMP

Q&A: Salesforce’s Chris O’Hara Wants Marketers to Capitalize on the Data Revolution

datadrivenIf 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.