In a digital-first B2B marketing world, generating high-quality leads that align with internal ICPs is more important than ever. With the pandemic causing a spike in web traffic and an abundance of unqualified leads, lead scoring has reemerged as a key practice among savvy B2B marketers.
According to research from Demand Gen Report, 92% of successful B2B marketers are prioritizing the quality of their leads over the quantity in their databases. They are also leveraging data to score leads, improve scoring models and produce higher-quality leads, which helps them find leads that best match their ICP and reach maximum conversion rates.
“There was a reemergence of the importance of lead scoring that had companies stop and start talking about how lead scoring was affecting their businesses,” said Dave Dabbah, CMO of CleverTap, in an interview with Demand Gen Report. “And with the pandemic, you really had to adjust your lead scoring system so that you were accelerating potential leads and getting those leads into the sales organization quicker.”
B2B organizations in 2021 are taking a second look at their lead scoring models to improve their lead quality. Organizations are turning to first- and third-party data to drive higher quality leads to sales teams, but scoring efforts are still hindered by static leads and inaccurate or outdated data.
“Marketing and sales teams are always hoping that the perfect lead is going to be served to them on a silver platter, and that it's just there and easy to close,” Dabbah explained. “It’s difficult getting sales organizations to buy into the fact that the perfect lead really doesn't exist, it's a rarity. Leads actually need to be worked on in an appropriate way.”
However, the prospect of improving lead scoring models by implementing new types of buyer data is promising, as 68% of marketers that use intent and behavior data to score leads that align with their ICPs have seen higher account win rates. By improving their datasets and database quality, marketers can score leads more accurately and see higher conversion rates.
In this article, we will uncover how B2B marketers are leveraging intent and behavior data to improve their lead scoring accuracy. We will also explore how organizations are building stronger lead scoring models using their databases.
Leveraging Behavior & Intent Data Improves Lead Scoring Accuracy
Act-On leverages behavior data in its multichannel databases to help clients identify specific patterns of buying intent that align with the company’s ICPs. Act-On also uses pre-determined action triggers to score leads from multiple channels in real time, which allows for efficient lead nurturing.
“Act-On is very mid-market and above focused, so almost all of our clients have unique things happening in their business,” said Greenberg. “This whole concept of custom behaviors is a very important component, at least in our mix and with our customers. They can use that behavior data to drive triggers and score leads.”
Intent data is also very prominent in modern lead scoring, as it allows marketers to identify specific accounts by key signals such as revenue, company size and more. Heinz Marketing, for example, pulls intent signals from various third-party sources to inform the lead scoring process, which helps the team create a lead scoring model from multiple perspectives and engage the highest-scoring leads from those specific areas.
“We are pulling in intent signals from a variety of sources,” said Matt Heinz, CEO of Heinz Marketing. “We're pulling in data and insights from sales platforms, like LinkedIn Sales Navigator, and feeding that into our processes for daily and weekly alerts, follow-ups and communication cycles with high-scoring prospects and customers.”
Clean, Powerful Databases Build Strong Scoring Models
With lead scoring relying heavily on accurate data, especially intent and behavior data, it’s important for marketers to ensure the databases they are drawing from are enriched and up to date.
SugarCRM leverages historical customer interaction and conversion data in its lead scoring model and uses AI to autonomously score leads based on the data. This allows SugarCRM’s marketers and sales reps to not only prioritize leads with higher scores, but also more accurately engage accounts for higher conversion rates.
“Data enrichment is key,” said David Campbell, VP of Product Marketing at SugarCRM. “If marketers can supplement their internal data with data from outside their company, they can increase the quality and the scope of their lead scoring. And that makes their lead scoring models more accurate and effective.”
CleverTap has also built a “master database program” to completely overhaul its lead generation process, which leverages website traffic, intent signals and existing brand interaction data from its regional databases to organize its lead scoring data and improve targeting and engagement.
“The database marketing piece is by far one of the most important pieces of a progressive, fast-moving company,” said Dabbah. “When we sit back, we look at the data in those regions and build a big database of records of people we want to target. By the time we know the lead’s score, it really doesn't take long to go through the process and pass it over to the SDR for lead nurturing.”
As B2B remains present in the virtual world, the need for a lead scoring model is more apparent than ever. Marketers who use various forms of data and draw from their own internal databases to inform their lead scoring models will not only be more accurate in their scoring, but also improve conversions and ROI.
“I think there’s an understanding that marketers need to be more data-driven,” said Greenberg. “There's patterning and behavior out there that really leads to the next level of effective marketing, which really helps improve your lead scoring, the customer experience and the user engagement side of things.”