B2B companies are getting their magic back through the technology and data revolution created by account-based marketing (ABM). Using data, B2B marketers can orchestrate engagement tactics for demand generation and revamp their ABM approach. ABM-focused demand generation relies on attracting specific personas from targeted accounts and serving prospects with personalized landing pages throughout their journey.
According to a report from Demand Metric, 45 percent of B2B marketers are testing or already using ABM, and 26 percent are interested in adopting this approach. While in the past, the audience development team decided on which vendors to use to manage databases, today the C-suite is getting involved in the data-management decision since technology is at the core to marketing and business building. Savvy B2B companies are using data, lead scoring and predictive analytics to modernize their account-based marketing strategies.
Since account-based marketing focuses marketing efforts on a specified group of target accounts, it is critical to determine which accounts are worth targeting and begin there. After all, all of your efforts will be for naught if you are targeting the wrong accounts.
ABM works both for acquisition of new clients, as well as for marketing to existing customers. Look-alike modeling, in which companies target companies that resemble their existing clients, can help companies expand their roster.
To do so, the company must identify the attributes and behaviors that their current clients exhibit such as company size, industry vertical, number of employees and location. By choosing targets based on profiles that have these shared attributes, they are more likely to target accounts based on an ideal customer profile.
Predictive modeling drives ABM strategies and ensures that the right message is in front of the right accounts, based on a number of demographic and behavioral triggers. Once a marketer has identified the account, it is key to accurately manage the data and organize it by accounts instead of by contacts. This approach is not always easy to do, but it helps form a holistic picture of the accounts. By associating leads to accounts and passing account details to unconverted prospects, then segmenting account details like owner, active opportunity, customer, products purchased, sales stage, target account or strategic account, a marketer can put data to use no matter where a customer is in the purchase cycle.
Once these targets are aligned, marketers can benefit from adding predictive lead scoring to their ABM strategy. Using firmographic, demographic and behavioral data, B2B marketers can identify account-level characteristics and create scores for which leads are the most likely to convert.
Buyers are already 57 percent along their journey before contacting a sales rep, according to Harvard Business Review (HBR). Predictive marketing analytics can reveal intent early on and allows marketers to identify pain points of their target audiences, while understanding the unique needs of the individual decision makers within the targeted account. Predictive account scoring can even help identify which person within an organization would be most likely to advocate for a brand, enabling the marketers to put the right content in front of the right person. From there, B2B marketers should target the content to each decision maker, based on their own specific perspective.
There is plenty of magic left in B2B, the box of tricks just requires a little polishing.