Data models and buyer centricity have taken center stage in modern B2B, with many organizations falling back on their databases to provide insights into key accounts for effective strategy building. Organizations are also relying on signal data and account intelligence insights to build engaging campaigns that address buyers’ pain points.
2021’s Buyer’s Insights & Intelligence Series, hosted by Demand Gen Report, explored B2B’s renewed focus on buyer-focused strategies, and how organizations are using intent signals and account insights to deliver relevant messaging to the right accounts. This year’s event also examined the changing landscape of ABM, as organizations have centered their strategies around new datasets and models to improve account engagement and reporting.
Driving Engaging Buyer-Centric Marketing Strategies With Relevance
As today’s buyer demands more personalized experiences in an increasingly quickening purchasing journey, it seems that intent and automation need to work together to provide marketing teams with the information they need at the exact time they need it. From those insights, it’s up to marketing teams to tailor content as necessary.
“You might be killing it only using one of these P’s, but if the rest aren’t in place, you won’t drive the engagement you should be,” explained Amanda DePaul, Sr. Director of Demand Generation for Conversica.
Organizations should use those P’s as the baseline of their customer journey, which is increasingly speeding up. As evidenced in a session hosted by Demandbase, the timeline buyers follow for purchasing decisions is continuing to increase. As such, it’s important that marketing teams double-down on intent data analysis to identify the next best step to take.
“Supporting customers throughout their process allows you to dynamically adapt to them, it’s intricate but not daunting,” said Tracy Kraft, VP of Revenue Marketing for Demandbase. “One way we’re doing this is by mapping our advertising to those buyer stages so that we truly are identifying the right account with the right content at the right, which helps us meet buyers where they are.”
With an emphasis on the early stages of the journey, experts from Outreach shined a light on the importance with engaging customers from the on-set of communication during their fireside chat session. To build trust, organizations need to ensure prospects feel valued and heard. It’s from the establishment of that initial relationship that a stronger relationship will form and increase trust throughout the rest of the journey. Of course, that trust-building comes from the analysis of various datasets.
However, it’s easy to become overwhelmed with the litany of buying signals and data points needed for tailored outreach. In ON24’s session, Cheri Keith explained that the first step is that the data being pulled should align with marketing goals and, to help identify if the goals are being met, organizations can poll current clients to get a feel for their temperature.
Implementing More Datasets To Create Relevant Engagement
To maintain a buyer-centric strategy, B2B organizations need timely, clean data to personalize their buyer engagement and address buyers’ specific pain points. To reach that level of buyer personalization, organizations have started to lean on first-party and third-party data and intent signals to help build out their digital marketing initiatives.
During Folloze’s session, Randy Brasche and FireEye’s Marlowe Fenne explored the importance of first- and third-party data, as they can help you gain a holistic view of a buyer and can allow for more organizational agility. These data insights can inform an organization’s strategies and help them pivot their content and messaging for each individual buyer for increased engagement and conversion rates.
“First- and third-party data insights are what light the fuse under the buyer’s journey,” Fenne explained. “It frames how you started that journey and can really help you accelerate customers through their journeys in new ways that are engaging and relevant. Make agility one of your advantages as you look at your data, and you will find more success with engaging your customers.”
In TechTarget’s session, Andrew Briney pointed to content interactions as one of the most accurate sources of intent signals, as it helps organizations identify where an account’s interests are for more accurate personalization. Organizations can use this intent data to develop messaging that speaks directly to their buyers’ needs more accurately and efficiently.
“Direct interactions help you stop guessing and start knowing who to reach out to,” said Briney. “This is a precise way to gauge what your buyers want because it’s based-on interactions directly from individual users at these accounts. This allows you to create a model that pinpoints who the exact buyers are and what they are interacting with for more relevant engagement.”
Leveraging Predictive Models To Automate Account List Building
With most marketers doubling down on ABM to engage key accounts, they are turning to predictive models to measure how they provide value for those accounts and determine how they can improve their engagement for greater revenue generation.
In a session from RollWorks, Allison Dyer explained that B2B marketers need to leverage their ICPs and existing accounts to create a framework that vets new intent signals and helps build new target account lists for engagement. This framework feeds predictive models with new account data and provides marketers with various account insights to uncover new ways to engage them in future campaigns.
“We’re using our machine learning and predictive models to do this at scale,” Dyer continued. “We divide our models into tiers so that when the outputs come out, everyone on the marketing and sales team understands high-value engagement within those tiers. By matching account data with the target account list that is coming out of sales, marketers can gain key insights that they never thought about.”
During this webcast, Andrew Mahr, Chief Customer Officer at Triblio, also spoke to the relevance of predictive models in ABM, exploring how marketers can use these models to orchestrate multi-stage, multichannel programs automatically for optimal account reporting.
Mahr explained that predictive models allow B2B organizations to build personalized 1:1 engagement programs at scale, but can also help predict which types of programs will be successful based on prior account interactions. Organizations can implement account data into its predictive models to automatically identify intent for more accurate account reporting, informing SDRs about which accounts to interact with and at what times more consistently.
“I would call this ‘tomorrow’s ABM,’ where everything is happening in an automated process that is more synchronized and dynamic,” said Mahr. “Data picks the accounts we should be engaging with and drops them all into the right campaign at the right time. We can then design these programs for consistent output to sales reps and build predictability into the forecasting and pipeline development process.”
For additional insights and access to the full sessions, check out the full series on-demand.