Amnon Mishor from Leadspace throws light on the importance of to fix the broken pieces of your marketing strategy and the role DMPs plays in the same.
A new report from Demandbase shows increased investment in ABM for B2B marketers over the next few years. But while ABM is growing in popularity and may even be a new form of marketing automation, most B2B marketing teams are still side-stepping the massive data problem festering just below the surface — and it’s only going to get worse if we don’t address it.
Investment in digital marketing technology is expected to reach $122B by 2022, and with that investment will come more data than most organizations can even dream of managing.
This data problem has existed for as long as people have been collecting it. But it became front and center as companies began using CRMs — particularly Salesforce. In its early days, the CRM promised to hold all your data so it could become your single source of truth. Unfortunately, that didn’t always work out as planned.
The problem here exists on many levels. With large companies often having many instances of CRM, inputting all this data has created a mess of overlap and dupes because CRM platforms weren’t designed to function across multiple organizations. What’s more, sales and marketing teams (customer service, and others too) all have their own unique way of adding data into the system, which complicates things even further. Layer on top of that the fact that CRM is just one part of the data equation–so much data comes from different sources and tools–that we have a mess of data and channels that won’t connect with each other.
Companies have tried to fix this problem. Engagio tried and didn’t quite get there before being acquired, which is a good indication that you can’t put a dashboard on top of junk data. Even Salesforce has spent years trying to address this problem. The latest version of a solution is Customer 360, but with limited scope so far only time will tell if this solution actually works.
What we’re left with, then, is a big problem: how much can we really trust our data? And if Salesforce couldn’t fix this problem, who can?
I believe there are several things it will take to be successful. In fact, Gartner recently released a Market Guide on Customer Data Platforms, explaining four characteristics any data platform needs to meet in order to be effective. I think these are the principles that will help us solve the data management problem:
First, it must be data agnostic. This means it has to be able to consume all kinds of data at the account level and person level. Go-to-market teams need to know more than just a company’s revenue or industry, and a contacts email address. They need to know what technology is in use at that company, how an account is related to a global corporation or another business unit, and if they’re a buying center. They also need to have a view of the types of interactions that might have occured recently with other parts of the business, what content they may have downloaded, and which other stakeholders are involved.
If we can’t get that level of understanding, we won’t get anywhere with ABM; it will be little more than a novel way of doing brand awareness advertising. If an organization has only 100 accounts, an orchestrated experience might be manageable. But if an organization has 10,000 accounts that are represented by 100’s of thousands of different records in siloed systems, things start breaking down quickly.
Second, it must be channel agnostic. This means a successful data tool must be able to add the intelligence and insights to any system or platform where sales and marketing need it to deliver a connected experience.
The average enterprise organization utilizes 91 marketing point solutions. This explosion in technology and channels has led to an explosion in data tied to each platform and channel, which is incredibly difficult to manually process.
Any successful data management tool for B2B will have to be able to pull data from CRM and Marketing Automation to the content management system/website and automatically make sense of it.
Third, it should be driven by AI and not just rules-based.
Data tools need to be able to do more than respond to rules to make sense of all our data. AI can help make sense of historic data to predict the likelihood, for example, of an account converting or a deal closing.
It can also take a profile and compare it to a wider set of data to flag lookalikes, or search out topic or concept patterns to separate the signal from the noise.
Fourth, it must be persistent. This means data management tools need to keep a record so that you can build on top of it. Normally, this important task falls to an excel spreadsheet; then a data scientist, ops pro, or worse, a marketer, tries to wrangle all of the data, make sense of it, and put it into the right channel. This process is often riddled with errors and inaccuracies because it’s a manual process.
There are a few tools that are helping to address this problem. Historically we’ve focused on cleaning up individual systems with platform-specific data tools (e.g. Data.com for Salesforce). Master Data Management (MDM) platforms provide robust, albeit sometimes complicated, solutions for data governance and stewardship. Some larger, more well-resourced teams use Data Lake technology and build integrations and AI applications in-house. But Customer Data Platforms are becoming an increasingly popular new packaged solution that can automatically take care of many sales and marketing data processes for you.
Clearly the data problem isn’t going to be solved overnight. However, it is time for the industry to make data management — not a proliferation in marketing point solutions — the number one priority. As long as our data management systems are broken, so are most sales and marketing tools, and likely the customer experience.
There are a few tools that are helping to address this problem. Historically we’ve focused on cleaning up individual systems with platform-specific data tools (e.g. Data.com for Salesforce). Master Data Management (MDM) platforms provide robust, albeit sometimes complicated, solutions for data governance and stewardship. Some larger, more well-resourced teams use Data Lake technology and build integrations and AI applications in-house. But Customer Data Platforms are becoming an increasingly popular new packaged solution that can automatically take care of many sales and marketing data processes for you.
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ABOUT THE AUTHOR
Doug Bewsher
Doug Bewsher is CEO of Leadspace, the leading Customer Data Platform (CDP) for B2B, serving the leading brands such as Microsoft, American Express and HPE. He has also served as a member of the board of Limelight Networks (NASDAQ: LLNW) for the last 3 years.
Prior to Leadspace, Doug was CMO for Salesforce.com and led corporate marketing and demand generation activities globally for what Forbes has named the world’s most innovative company four times in a row. He also served as CMO of Skype through its sale to Microsoft, ran the San Francisco office of Digitas, the leading Digital Advertising agency, and co-led McKinsey’s CRM practice based in San Francisco and London.
Doug has an MBA with distinction from INSEAD, and a MA in Physics from Oxford University. He is married to an Italian and lives in San Francisco, with his 15 year-old Chelsea turned Warriors fan.