When big data first became a trendy talking point among sales and marketing teams, it felt like the answer to marketers’ problems. But now, the reality is that marketers do have access to seemingly limitless amounts of data, big data no longer feels like the easy solution. In fact, many marketers struggle with execution. They have access to tons of meaningful data, but they don’t know what to do with it. The ability to utilize that data to orchestrate and operationalize marketing tactics represents a real challenge for many marketers. Using predictive analytics gives marketers a huge leg up to create and execute comprehensive, multi-channel account-based marketing programs rooted in data.
Making ABM Predictive
When executing an account-based marketing program, marketers need to make account-by-account decisions on which content aligns with an account’s stage in the buyer journey. Often, it’s impossible to know exactly an account’s pain points or interest level, so marketers rely on their instinct and experience to make assumptions about what messages and tactics will best resonate. In some cases, marketers simply apply a one-size-fits-all approach to all accounts.
Using predictive intelligence to direct ABM orchestration means marketers don’t need to lean as much on their assumptions because they have a much stronger idea of what messaging will be relevant to a particular account. Predictive orchestration minimizes much of the guesswork in marketing programs.
Vendors who engage with prospects before their competitors do are more likely to win the business. According to Forrester, the first viable vendor to reach a decision maker and set the buying vision has a 74 percent average close ratio. Adding predictive technology to a marketing stack allows companies to better time their outreach in order to engage with prospects at the right time, with the right message, through the right channel.
The Future of ABM
Predictive analytics already drive efficiencies for account-based marketers, and the future of ABM technology looks even brighter. For instance, artificial intelligence and machine learning allow marketers to accomplish effective ABM at a much larger scale. Whether executing a marketing program to one account or one dozen, marketers can make account-by-account decisions grounded in intelligence on what to send and when to send it. AI makes these decisions for marketers, and in much larger numbers.
With the rise of AI and machine learning, much of a marketer’s role will focus on designing a data plan and uncovering inputs that feed a predictive ABM model. The machine will make most account-level decisions on behalf of marketers, such as which tactic should deliver which message and at what time. As AI and machine learning help marketers make far better use of the massive amounts of data currently available to them, marketing and sales departments will be hungry for even more data, and they’ll look for new sources of data that may inform customer decisions.
Technology helps marketers make smarter decisions. With predictive analytics, prospective accounts receive relevant messages when they will be the most effective. In turn, salespeople have better conversations with prospects and, most importantly, the end result is more closed deals and more revenue. Incorporating predictive analytics into a marketing technology stack means that all marketing efforts happen on a larger scale and with more accuracy. Marketing dollars go further and deliver greater return on investment with predictive analytics as part of the equation. And as AI and machine learning become increasingly available to marketers, ABM’s impact will only increase.
Big data alone might not be the panacea for all of the marketers’ pain points, but with predictive analytics and machine learning, marketers can put that data to use to orchestrate, execute and drive results.
Also Read: Scale, Drive Revenue, and Win with ABM