In 2017, B2B marketers were fully focused on account-based marketing (ABM), and that’s a good thing. ABM helps marketers get an accurate picture of active demand units and their needs, allowing for better targeting with the right content. ABM also helps align marketing and sales, shifting the goal across both departments to deepening engagements with accounts identified as key targets. Marketing and sales messages and tactics focus on addressing the specific needs of decision makers within each account, moving accounts through the sales funnel. With all this emphasis on ABM, marketers need to make sure they’re building a truly integrated strategy.
The 101 of ABM
With ABM, sales and marketing don’t have to waste resources on leads that don’t turn into sales. By engaging with active demand units, ROI improves. Marketers have long struggled to determine which accounts have a propensity to buy and identifying active demand units, but now with predictive marketing technology, marketers have a more sophisticated and robust understanding of accounts and demand units. Whether determined by historical data and CRM inputs alone, or ascertained through predictive intelligence stemming from multiple data sources, ABM allows marketers to hone in on the most important accounts.
Driving Results with ABM
ABM offers the ability to deliver more customized content, services and interactions based on an account’s stage in the buyer journey. This results in more meaningful discussions, deeper engagement and increased conversion rates. By eliminating unqualified buyers early, ABM leads to shorter sales cycles focused on accounts most likely to buy. The concentration on high-yield prospects improves the impact of marketing and drives more revenue, faster.
ABM success centers on meaningful, timely engagement, but delivering relevant and personalized content across multiple tactics and accounts can prove a challenge. That’s where predictive analytics and machine learning can have a highly valuable impact. Predictive analytics reveal who is active and what content they consume, allowing for more personalized content. Machine learning provides and ingests feedback on previous touches so the right mix of tactics is selected for the next outreach.
Strategies Based on Predictive Intelligence
Developing go-to-market strategy based on themes marketers want to communicate, rather than on the needs of the individual target account, used to be standard practice. Instead of starting from the marketing department, ABM needs to be informed by the account, letting the needs of the target accounts drive the strategy. Predictive analytics make this process so much easier, identifying which accounts show high buying propensity and using that information to create profiles of ideal customers, open up white space and ensure sales and marketing align on the best prospects.
From there, content and tactics must evolve to match prospects’ journey through the sales funnel. As they move through their buyer journey, the ways marketing and sales engage with them must also mature. The best method is through real-time engagement data from machine learning. As customers engage with marketing tactics, that data should feed back into an algorithm. The output is an even more detailed picture of target accounts.
Unfortunately, many marketers miss this step or do not fully capitalize on the power of real-time engagement data. While they may have engagement data from display advertisements that helps them better understand how accounts respond to that content, display ads are only one channel in an integrated strategy. Knowing how the target market responds to direct mail, email campaigns, inside sales conversations and other marketing and sales tactics adds a wealth of knowledge to the understanding of an account and its stage in the buyer journey, fueling more effective tactics.
Success Through Integration
Instead of just building a better mousetrap, spending all resources to make one tactic better and better, marketers need to realize the value of an integrated strategy that incorporates a variety of tactics and has the ability to natively learn from how customers respond to those tactics. For instance, direct mail often yields a surprisingly high response rate in a world of flooded email inboxes. Combining direct mail with email, digital and other tactics grants marketers the opportunity to see what works. Engagement data from a variety of tactics can provide important insight about your target market and can fine tune your ABM approach. Continually integrating new data sources into an algorithm-based approach drives ever-better engagement.
James Regan
Latest posts by James Regan (see all)
- How to bring a predictive element into a data-first B2B sales and marketing strategy - December 7, 2018
- Mind the gap: Building a bridge between marketing and sales with predictive analytics - September 24, 2018
- The Future of ABM - May 26, 2018
