Rishi Dave is both an experienced Account Based Marketing (ABM) practitioner – from his time at Dell and now at D&B – and, now, an important vendor in the ABM space. So we asked him to put on different hats for this Q&A.
As an ABM practitioner
How are you applying Account Based Marketing to your own sales and marketing efforts at D&B?
ABM is central to our strategy for driving pipeline. We use data, analytics and collaboration with sales to prioritize opportunities. Once we’ve done this, we architect personalized experiences on- and offline at various levels of scale depending on the opportunity within the account. Then, we measure it closely, learn, and optimize.
From your experience at Dell and now D&B, what would you say is the key to success in ABM?
Excellence in operational execution and measurement. You have to allocate your resources well and execute tactics flawlessly and at a high quality to make an impact. Through measurement, you learn what works and what to optimize.
Does this require a lot of culture change? What does the transition look like?
It does. We actually focused on culture first before scaling ABM. No matter how good your ABM or digital machine is, if you don’t have something differentiated to say and a culture that supports it, you will not break through the clutter. We started first by developing purpose and values and then a creative expression. We then developed a messaging system around personas that linked this to the pain points we solve for customers. This all laid the foundation for our ABM strategy.
As an ABM vendor
Why is data so important in Account Based Marketing?
Data and analytics help you prioritize accounts and measure success. Modern marketing is dependent on data and analytics for targeting, nurturing and closing opportunities. Data also gives you the complete picture of the account that lets you execute the right tactic at the right time.
In selecting accounts for an ABM program, what kind of data is the most important?
It all depends on the company and what data and analytics models have demonstrated predictive value in the past.
Companies often start with integrating the data across the enterprise and its silos and then leveraging it to build models that estimate demand and opportunity within accounts. Typically, people will start with firmographic data and then move to demand estimation models.
We’re fortunate at Dun & Bradstreet to have access to the same data and analytics capabilities we develop for our customers. We use propensity and attrition analytics models to tell us who is most likely to grow their relationship with us and who poses a potential risk of not renewing. Demand estimation models based partly on look-alike modeling tell us where we have opportunities in different lines of business and with which accounts.
How important is it to integrate your data sources with sales and marketing platforms like CRM and marketing automation?
It is critical. We embed our data into world-class cloud-based platforms so customers can use Dun & Bradstreet data turnkey in the apps and processes that matter most to the way they work. They can supplement and clean their data to develop more personalized experiences for their customers and prevent dirty and duplicate data that leads to lost opportunities and bad customer experiences.