Account-based marketing (ABM) has proven its mettle over the past several years, and is now well on the way to becoming an industry standard. By now, it’s hard to miss the reports, statistics and success stories of companies increasing pipeline, engagement and conversion rates using ABM-led strategies.
But, like any marketing or sales strategy, ABM also comes with challenges of its own. Some of these challenges are strategic or organizational in nature — like aligning Sales and Marketing, or formulating appropriate content marketing strategies. Many of these challenges were highlighted in our recent The State of Account-Based Marketing Report with Marketo as the key obstacles preventing many organizations from effectively implementing ABM.
What the report doesn’t cover, however, are the numerous tactical and operational issues many companies face with ABM. One such challenge we’ve seen time and time again with many of our own customers is the very basic — and frankly, extremely boring — task of building target account lists.
Finding your “named accounts” or target accounts is of course the foundation of any ABM campaign. But how can you find the right ones? Where do you start? Traditional account and lead list vendors are often used as a starting point for Marketing and Sales to identify their named accounts, and then populate them with the right leads for account penetration, respectively. But these vendors are also very blunt objects, and generate a huge amount of extra work for Marketing in particular.
Typically, a significant portion of the data you buy will be inaccurate or out of date. Then there’s all the duplicate leads/accounts that you already have in your CRM and/or Marketing Automation Platform. After hours of data cleansing work weeding out those useless leads and accounts, there’s the arduous task of matching the remaining leads to their respective accounts. A good lead-to-account matching platform can solve this specific challenge — but that’s assuming your leads and accounts actually match up, and inevitably a portion won’t.
The Solution: Automating the Target Account List-Building Process
For the Marketing team at Tipalti, this challenge was particularly acute. A significant portion of their target market exists in niche industries like adtech and ecommerce; targeting those industries with traditional data vendors just won’t work.
Instead, Tipalti used look-alike modeling to automate the entire target account list-building process for them.
Look-alike modeling uses Artificial Intelligence to identify net-new accounts which closely resemble your best existing customers. By automating their target account list building in this way, Tipalti freed up their Marketing team to focus on the creative aspects of their job — like creating original campaigns and powerful, engaging content. More importantly, their AI predictive model was able to scour the endless sea of B2B “big data” for qualified accounts far more effectively.
The results speak for themselves: For example, using look-alike modeling, Tipalti increased their conversion rates by 20%. On top of that, they were able to grow their target market reach by 13%.
You can read more about Tipalti’s ABM success, and how they did it, in the free case study below:
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