By Vijeta Verma, Director of Data Integration, MeritB2B
Many B2B marketers don’t have enough data to implement effective campaigns built around the well-known B2C mantra of “right time, right person, right message.” Personalization and “right-timing” – knowing when to show the right message – requires a depth of insight and a level of scale that typical B2B data collection practices can’t support.
B2B brands are not selling to companies, they’re selling to people. And the reality is that people have come to expect targeted, personalized messaging that’s relevant to their needs in their own lives. The B2B marketers that understand this, and lean into a more personalized strategy, will have a competitive advantage. Such an approach requires two elements, deeper data collection and the use of AI to help build the right segments, run predictive analytics and apply insights to broader audiences.
Build Up First and Zero- Party Data
In B2C marketing, many companies are hard at work collecting data towards a goal of the 360-degree view of the customer. This view includes everything from demographics to purchase history, interests and even preferences across channels and content types. With third-party data being limited by the major platforms (Google and Apple) and by regulations like CCPA and the GDPR, many are focusing on first and “zero-party” data. First-party data is anything that marketers can glean from consumer behavior on their properties from site searches to newsletter opens. Zero-party data, a term coined by Forrester Research, refers to data that’s explicitly asked for – perhaps through a survey or purchase process.
Many B2B companies I work with tend to be stuck on demographic data. The limited amount they know about their audience is not enough to personalize messaging at the individual level. There are a variety of different insights that can build out the typical B2B audience profile; household data, brand loyalties, browsing behaviors, and prior purchases help easily segment customers.
While volumes are lower with B2B marketers, data collection techniques are the same. B2B marketers should work with their website, app and newsletter teams to track and store any information that indicates intent, interest, budget, etc. From what white papers are downloaded to what keywords are searched for, these individual insights don’t just drive specific marketing campaigns, they can combine to create a more rich customer profile.
On top of that, B2B marketers can use zero-party data collection techniques like surveys, quizzes and download forms to fill the picture out further. For example, rather than using progressive profiling to ask for typical missing contact info, marketers can prioritize the data collection by providing a quick content drop down so they can share their interests and you gain more meaningful intelligence.
Add to this second and third-party data about intent, company details about teams, budgets and locations, and suddenly B2B marketers have a much more robust profile to work with.
Take Off With AI
With enough data in the picture, B2B marketers can start to take advantage of AI for a few important parts of their marketing process. First, AI can help build the right segments, analyzing complex data profiles to cluster different groups based on the most critical differentiators. AI can help ascertain that someone’s title matters more than their budget, or that team size is a better indicator of which product category they will care about. All of this is too complicated for many simple models to determine, and AI can help.
AI can also help B2B marketers understand what elements matter most for message and channel targeting, bringing campaigns closer to those “right time, right place” elements. AI can combine behaviors across thousands of customer touch-points to understand what makes the biggest difference and how prospects react.
Cart abandonment is a perfect example of a customer or prospect touch-point that can become an opportunity for personalization and well-timed messaging from the use of AI. If marketers can crack the reason behind the cart abandonment, they can create personalized campaigns that can reduce cart abandonment in the first place and deliver relevant messaging after the fact. AI can help by running risk modeling, time series regression and anomaly detection against the historical data to gain insights into typical customer and prospect behavior.