Tech Companies Are Repackaging CDPs for B2B Sales Customers - Here’s Why That’s a Problem
Many of business-to-business (B2B)’s best sales and marketing technology innovations started in the business-to-consumer (B2C) world. Especially when it comes to anything involving customer data. One such innovation is a platform that can capture and unify all customer data. In B2C, this is called the Customer Data Platform (CDP).??
Forrester defines a CDP as a tool that “centralizes customer data from multiple sources and makes it available to systems of insight and engagement.” On the surface, this sounds a lot like what a few large tech companies are promising their AI data platforms can do. And at a surface-level, they’re telling the truth. Those companies are taking existing CDP products and repackaging them as B2B AI data platforms.?
Sounds great, right? I’m here to tell you why it’s actually not so great. It can be incredibly tempting to rinse and repeat B2C strategies and technologies. But using a bad fit B2C technology strategy for a B2B audience can have a range of repercussions. These can range from wasting budget to losing customer trust because the technology doesn’t create the white-glove, personalized experiences that B2B customers expect. The reality is that B2B sales organizations need an AI data platform that is customized for B2B customers and their unique requirements.?
CDPs: The one-to-many solution for B2C
The promise of a single view of the customer has been around almost as long as e-commerce itself, but attaining it has been a nearly impossible task. CDPs collect customer touchpoints from online and in-person interactions with a company and aggregate them into a single data layer that can be accessed by other intelligence tools to provide insights. They are designed to provide high level insights about consumers so companies can aggregate those insights into persona profiles and market to those personas.?
This is a one-to-many approach that is highly effective in B2C sales and marketing. B2B customers expect more white glove interactions, requiring more granular customer insights. CDPs simply weren’t designed to provide this level of insight. Technologies built for B2B are fine tuned for quality - even if they were inspired by tools originally designed for B2C companies. Marketing automation tools are a great example of this evolution.?
How CDPs fall short for B2B sales organizations
I already mentioned that there are a few large tech companies that are taking their existing CDP products and repackaging? them for B2B sales AI customers. They are essentially taking a CDP data model that was created to manage large scale B2C customer datasets and using it as the foundational data model for their B2B sales AI products. They did not start from scratch and develop a data model and product purpose-built for B2B companies (the purpose-built approach is what we did at People.ai ). To understand why this is not an ideal solution, it’s helpful to see the types of high-level insights CDPs provide next to the level of granular insights a sales organization needs to know about its customers (using a customer success software company as an example).?
A typical insight that a B2C CDP can provide might look like this:?
B2B sales organizations need relationship-focused insights that look like this:?
I’ve worked on many different CDPs during my career and they simply weren’t built to collect the level of detail B2B sales organizations need to know about their customers. Here are a few under the hood reasons why:?
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Here is why these things matter in B2B sales.?
B2B sales are relationship-driven which means every detail about a prospect account must be correct: names, titles, company information, competitive information, history of interactions, etc. When you ask your AI tool to write you a meeting follow-up email to a prospect, you expect it to include contextual information about what was discussed, who was in attendance, and specific next steps. CDPs are incapable of providing this level of detail.?
CDPs can collect all of the relationship notes and interactions between a company and its sales prospect. But the next step is taking all that raw data and matching it to the correct contacts, opportunities, and accounts in your CRM. CDPs don’t do this well. Have you ever gotten an email from a B2C company that got your name wrong? This happens because CDPs over-match. They’re trying to develop segmentations of as many of their target persona as possible - all with the ultimate goal of getting at least some of those email recipients to purchase. Data quality is less of a priority because it’s a volume game. They know that you’re not going to stop buying from them because they got your name wrong. If you want the shoes, you’re still going to buy the shoes.
CDPs designed for B2C weren’t intended to deal with the complexities of B2B CRM environments. They lack the ability to match customer and activity data to accounts and opportunities. When data is matched incorrectly - or not matched at all - you get poor AI insights. A CDP-driven AI tool might call someone by a different name or a different title - big no-no’s in B2B sales. Or it will yield completely generic outputs like “A meeting took place on March 26 between the AE and Linda at the prospect company.” This is not as helpful as a platform (like People.ai ) that can surface risks, suggest new contacts to connect with, and provide an instant summary of everything happening across every account in a sales organization.?
Read more about why matching is the behind the scenes hero of a great AI sales platform.?
The GTM AI data platform breakdown: What to look for and why
It’s impossible to know whether a product is powered by a CDP or a different data platform model at first glance. AI sales companies might have a great UX and demo to “prove” the value of their product. Here are three things to ask any company that you’re vetting to understand if their data platform is appropriate for your B2B business.?
#1: How did you build your data platform? Did you repurpose an existing product or build something from the ground up??
What to be listening for: Just ask the obvious question first. A repurposed data platform is a major red flag.?
#2: What data sources does your AI sales platform use to generate insights? Do you have access to deal data, account data, relationship insights, and engagement data?
What to be listening for: The AI solution is only going to be as useful as the data it has access to. Too much data isn't the answer either. AI needs to have access to relevant data in order to produce high-quality outputs. It should be trained on the most important events of a deal to actually give you accurate next best actions.
#3: Is your AI tool able to differentiate between similar CRM records when finding a match?
What to be listening for: When there's an activity that might match to multiple CRM records (e.g. multiple opportunities in an account), can the system use all of the signals to decide between them to find the best one? A rules-based matching system helps the tool select the most specific account or opportunity match based on multiple categories of signals (ideally 10+). This advanced method allows the tool to make accurate matches even when the available data is less than ideal.
BTW, we wrote a comprehensive guide on how to select a high quality AI sales vendor, including an RFP download. It can give you more guidance on the right questions to ask about data models, high-quality matching. AI outputs, data privacy and security, and overall usability.?
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2 个月Great article Linda - I think some people assume B2B is simpler as there are fewer customers, but underplay the complexity of the account-to-account relationships and the nuances around different roles that contacts can play.