How an Enterprise IT Company Accelerated Growth with ABM

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A Fortune 500 IT services company providing network solutions came to Dun & Bradstreet for the affinity and intent data, AI-driven predictive modeling, and personalized experiences to implement and measure a successful account-based marketing program. 

Need: Better Targeting, Increased Personalization

A nearly $1 billion Fortune 500 enterprise IT solutions company needed to evolve their marketing strategy and approach to reach their goal of becoming No. 1 in their space. In their two decades in business, inbound marketing had been their core engine for driving buyers to their sales pipeline, but they recognized it was time to diversify their approach to ignite growth.

They wanted to add account-based marketing to their toolset so that they could be more strategic about identifying and targeting the right buyers at the right time in their buying cycle.

To do this successfully, they had some basic needs to meet first:

  • Understand their customer journey to better target their marketing
  • Identify upsell and cross-sell opportunities
  • Attract new accounts via digital channels
  • Overcome an existing lack of personalization based on affinity and intent data
  • A way to measure success of their account-based marketing program

They needed a partner that could offer them a range of ABM solutions to help them set up a new program of targeted and personalized experiences, backed by AI-driven data and insights. Dun & Bradstreet was that partner.

Solution: ID Ideal Buyers Through Data and Drive Contextual Engagement

The company identified that Dun & Bradstreet had the right datasets and platform to accomplish its goals, but also could layer on other solutions to help build its account-based marketing program. We worked together to:

  • Use AI and data to build predictive models to identify which accounts they could best target with upsell and cross-sell opportunities

Our predictive model uses buyer profiles to help identify when buyer might upgrade, or what their next actions might be using affinity and intent data. The company was finding that a lot of their customers were researching similar products because buyers just weren’t aware of what the company had to offer. Armed with data and predictive modeling, the company could better target buyers they knew were interested in products like theirs – or who were in the market for upgrades – and their sales teams could have conversations about cross-sell bundles or upselling.

  • Use digital channels to extend awareness and attract new demand

The company wanted to generate incremental demand by attracting new contacts within accounts with a laser-focused ABM strategy. That meant, using data, drilling down with dimensions, and strong messaging to demonstrate the company’s value prop. It also meant understanding the maturity of different markets around the globe and how to adjust their messaging and tactics by market.

Our data and insights provided the company with the foundation to understand intent and other behavioral signals to then build very targeted segments to capture the right audiences. Then, they activated those insights and used multichannel tactics that included programmatic, paid search, and paid social ads. Once they drove demand with advertising, they also had an email nurture strategy using Marketo. This all combined into the ABM strategy the company believed would provide the results it sought – but with Dun & Bradstreet’s data at its core.

  • Show buyers the right message at the right time to get them into the pipeline and convert and create personalized web experiences with relevant offerings and customized CTAs

We did this by building best fit profiles on a foundation of intent data that then allowed for customized messaging based on buyers’ interest in solution areas and their stage in the buyer journey. Lattice showed the company who was searching and who was active, and the predictive models based on affinity and intent data could help the company see potential cross-sell opportunities.

Based on this, the company created a “frequently bought together” path based on products customers bundled the most to drive them toward packages of products. They also developed cross-sell messaging based on most likely product customer would buy next.

The company is now layering in usage data to understand whether customers are in a cycle that indicates they’re ready for another purchase, whether that’s more of the same product or an additional product. They’re created targeted messaging for these buyers as well.

  • Empower sellers with insights so they can prioritize the accounts to go after and have more contextual and engaging conversations

The heart of account-based marketing is the integration and collaboration between marketing and sales teams. This company is using the data and insights from Dun & Bradstreet to give its sales teams a global view of its buyers and their buyer journeys. The goal is to gain a true understanding of not just where the buyer is in their journey, but why. This way, sales teams can have meaningful conversations that lead to conversions – and that’s all driven by the customer intelligence Dun & Bradstreet provides.

Result: Higher Engagement, More Conversions

In an accelerated world, it’s crucial to have sales conversations when buyers start their journeys to find solutions to their problems. This company has received very positive feedback on conversations between its sales teams and customers since implementing its new account-based marketing programs. They’re finding that customers are gaining information and awareness of the company’s products at every stage of their journey – before sales conversations happen. 

The company also is measuring results and reported some key findings:

  • 25% higher conversions on up-sell/cross-sell offers
  • 20% increase in customers signing up for trial offers 
  • 94% increase in engagement and 5X pipeline through digital channels

These numbers can be attributed to the more targeted, data-driven marketing the company is now doing.

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