#BII21 Highlights a Renewing Focus on Buyer-Centric Strategies, the Importance of Intent Data in Account Experiences, and Personalization

Demand Gen Report | July 30, 2021

In current B2B, data models and buyer centricity have taken centre stage, with many firms relying on their databases to deliver insights into critical accounts for effective strategy development. Companies are also depending on signal data and account intelligence insights to create compelling advertisements that address the pain points of purchasers.

The Buyer's Data & Intelligence Series for 2021, hosted by Demand Gen Report, looked at B2B's increasing emphasis on buyer-focused tactics, as well as how firms are employing intent signals and account insights to send relevant messaging to the correct accounts. This year's event also looked at how the ABM landscape is changing, with firms focusing their strategy on new datasets and models to increase account engagement and reporting.

Using Relevance to Drive Engaging Buyer-Centric Marketing Strategies:

As today's buyer expects more tailored experiences in an increasingly hurried purchasing journey, it appears that intent and automation must collaborate to supply marketing teams with the information they require at the precise time they require it. It is up to marketing professionals to tailor content based on these data.

According to Conversica thinking leaders, the “four P's” of client engagement and sales effectiveness are persistence, promptness, customization, and performance. Conversational interaction, according to the experts, can assist streamline communications and give a consistent, timely, and relevant line of outreach.

“You may be killing it with just one of these Ps, but if the others aren't in place, you won't be driving the engagement you should be,” noted Amanda DePaul, Sr. Director of Demand Generation at Conversica.

Organizations should use those Ps as the foundation of their customer journey, which is becoming increasingly accelerated. The timescale buyers use to make purchasing decisions is getting longer, as revealed by a Demandbase session. As a result, marketing teams must focus even more on intent data analysis in order to determine the appropriate next move to take.

“Supporting consumers throughout their process allows you to dynamically adjust to them; it's intricate but not intimidating,” said Tracy Kraft, Demandbase's VP of Revenue Marketing. “One way we achieve this is by mapping our advertising to those buyer stages, so that we genuinely identify the correct account with the right material at the right time, which allows us to reach customers where they are.”

During their fireside chat event, specialists from Outreach shed light on the necessity of engaging customers from the start of communication, with an emphasis on the early stages of the journey. Organizations must ensure that prospects feel appreciated and heard in order to establish trust. It is the establishment of that initial relationship that will lead to the formation of a stronger relationship and an increase in trust over the rest of the journey. Of course, the study of diverse datasets contributes to the development of trust.

However, the plethora of purchase signals and data pieces required for personalised outreach can quickly become overwhelming. Cheri Keith highlighted in ON24's presentation that the first stage is that the data being pulled should coincide with marketing goals, and that firms can poll current clients to get a sense of their temperature to assist identify if the goals are being met.

Using More Datasets to Create More Relevant Engagement:

B2B firms want fast, clear data to personalise buyer engagement and solve buyers' individual pain points in order to sustain a buyer-centric strategy. To achieve that level of buyer personalisation, businesses have begun to rely on first- and third-party data, as well as intent signals, to assist build up their digital marketing activities.

Randy Brasche and Marlowe Fenne of FireEye discussed the value of first- and third-party data at Folloze's session, as they may help you acquire a holistic view of a buyer and allow for more organisational agility. These data insights can help organisations inform their plans and pivot their content and messaging for each individual buyer in order to enhance engagement and conversion rates.

“Insights from first- and third-party data are what light the fuse under the buyer's journey,” Fenne explained. “It frames how you began that trip and may actually help you accelerate clients through their journeys in new engaging and relevant ways. When you look at your data, make agility one of your advantages, and you will have better success engaging your customers.”

Using Predictive Models to Automate Account List Creation:

With most marketers double down on ABM to engage important customers, they are looking to predictive models to identify how they deliver value to those accounts and how they may improve engagement for greater revenue generation.

Allison Dyer stated in a RollWorks session that B2B marketers must utilise their ICPs and existing accounts to construct a platform that validates new intent signals and helps build new target account lists for engagement. This system feeds new account data into prediction models and gives marketers with numerous account insights to discover new ways to engage them in future campaigns.

“We're doing this at scale with machine learning and predictive models,” Dyer added. “We break our models into tiers so that when the results are released, everyone on the marketing and sales team understands the high-value engagement within those tiers. Marketers might acquire critical insights that they never considered by matching account data with the target account list that comes out of sales.”

During this webcast, Andrew Mahr, Chief Customer Officer of Triblio, discussed the importance of predictive models in ABM, specifically how marketers can utilise these models to automatically orchestrate multi-stage, multichannel programmes for optimal account reporting. According to Mahr, predictive models not only enable B2B firms to design customised 1:1 engagement programmes at scale, but they can also assist forecast which types of programmes would be successful based on prior account interactions. Account data can be incorporated into predictive models to automatically determine intent for more accurate account reporting, telling SDRs about which accounts to interact with and when to connect with them more consistently.

“This is what I would call ‘tomorrow's ABM,' where everything happens in an automated process that is more synchronised and dynamic,” Mahr added. “Data selects the accounts with which we should engage and places them all in the right campaign at the right moment. We can then construct these systems to provide consistent output to sales representatives while also incorporating predictability into the forecasting and pipeline creation processes.”


Food for thought - 8 segmentation challenges in B2B marketing including: complex decision making exactly who are you segmentating.

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