Account-based marketing (ABM) is neither a product nor a point solution. Rather, it’s a strategy, a mindset, and ultimately a cultural movement. Done correctly, this culture shift leads to better sales and marketing partnership, and eventually the ability to maximize revenue potential together with sales. On the marketing side, ABM forces them to think like salespeople, which means going from a lead-based to an account-based mentality. Instead of reacting to leads that are interested in products, marketing needs to catch up and align with sales by proactively selling into accounts that are a great fit for the brand. Additionally, they are catering messaging and personalization towards buying groups and personas, instead of individuals. Sales must also undergo a mentality shift because ABM forces them to trust technology and software to help scale their traditional strategic selling efforts. It requires them to trust marketing and really work together across the aisle.
While the idea of strategic selling has been around for a while, the technology has not. As the world of marketing and selling gets more complicated, ABM becomes more of a need than a want. Indeed, Sirius Decisions finds that 93% of B2B companies consider ABM to be extremely important for their success in driving more revenue.
For the past few years, I’ve been reminded by customers and prospects about how difficult ABM execution is, especially getting started and obtaining early buy-in from sales. On average, it takes six to nine months to get ABM up and running, a lengthy process in part because of the challenge of aligning mentalities to create a shared strategy between marketing and sales. Alignment is required throughout the entire workflow in order to maximize the potential of ABM and maximize revenue together for the brand. Without this step, it’s impossible to define target account lists and prioritize accounts.
Another roadblock is building the right target account list to support the strategy. This is difficult because sales and marketing are forced to work with limited account level data, and don’t have the manpower or tech to scale their processes, especially the brands that have thousands of accounts in their database already. Often, the best fit accounts your looking for are buried deep in the databases or across different marketing and sales tools and data sources. As a result, sales and marketing are forced to spend months building target account lists that nobody agrees on and are often based on opinion, intuition, and gut feel instead of data.
The combination of clean data and artificial intelligence helps solve the challenges above. Successful ABM’ers use the combination of both to remove the guesswork out of creating a shared strategy and building the right predictive target account list. Data refers to clean first-party customer data that contains firmographic, technographic, and behavioral activity data. AI refers to algorithms and machine learning to create an ideal customer profile (ICP) based on your first party customer data and then using the ICP to quickly scan and predict which accounts in your database should be considered target accounts. The AI prediction and recommendation is based on the first party data that you trained the ICP model with. As a result, successful ABM’ers can ensure they have a list justified by data, instead of opinions. Remember, your AI is only as accurate as the first-party data you train it with.
Kickstarter Strategies & Predictive Target Account Lists
1. Land & Expand: Predictive Up-sell/Cross-sell List
The first key strategy is getting more from your existing customers. This is about landing and expanding across your customer base, selling them additional products, or up-selling the current products you have. For this scenario, you would train your AI-based ICP model with customers that just recently purchases up-sell/cross-sell products. You would then tell the model to scan your existing customer base that has not yet purchased certain up-sell/cross-sell products. The result would be considered a predictive up-sell/cross-sell list.
2. More New Business: Predictive Best-fit List
The second strategy is to win more new business. This is about net new business coming into your ABM funnel and increasing the number of quality opportunities. This scenario REALLY requires collaboration and coordination with the sales team. No longer can you have a lead hand-off with clear marketing and sales demarcations. It’s about having one team executing together in a coordinated fashion. In this case, you would train your AI-based ICP model on your recent closed-won customers in the past 3-6 months. You would then tell the model to scan your known account database to see which accounts are best-fit accounts. The result would be considered a predictive best-fit list.
3. Get Back on Track: Predictive Quick-win List
The third strategy requires focusing in on what accounts you can close fastest. This is a great strategy to have when you feel you are not on track to hit your quarterly ABM goals. This involves looking at your average sales cycle and velocity for previous closed-won deals. In this case, you would train your AI-based ICP model on your previous high-velocity closed-won opportunities. You would then tell the model to scan your know account database to see which accounts are more likely to close the fastest. This is a great tactic for marketing if they need to get back on track to hit their quarterly goals.
For more on how to kick start your ABM efforts, check out our webinar on the same topic.
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