- Move items around a stockroom (Amazon has a fleet of robots that have increased operations from 100 items per hour to 300 items per hour for workers)
- Accomplish missions in dangerous scenarios, such as dismantling land minds or bombs
- File, package, and dispense prescriptions
- Paralegal and doc-review-focused work (such as searching hundreds of documents for mentions of certain items or concepts).
Their bandwidth, power, and automated processes make sense in these roles, and can out-perform the manual efforts of humans.
We can draw a parallel here to the task of Account-Based Everything. There is no doubt that some things, such as building relationships with target buyers, injecting personality into our messages, and adding a personal touch to client engagements can never fully be replaced by automation.
But a critical component of account-based sales and marketing is identifying which accounts are at a greater propensity to buy.
We’ve discussed earlier the data and steps that need to be taken to build a target account list, and some organizations choose to execute this process manually using gut feel and basic scoring.
Others rely on predictive analytics.
Using Predictive Analytics to identify your target accounts
In the real world, many factors contribute to a successful sale – much of which is invisible to your teams. Predictive Analytics is, at its core, a way of processing far more information than humans can process. This can be used to build models that better predict the propensity to buy, out-performing manual selection and scoring.
“The majority of ABM programs have a list of targeted accounts in the 500 to 2,000 range, so that’s still a lot of activity to track manually. Predictive is one thing that enables companies to scale their ABM efforts, something which was not possible even a few years ago.” – Megan Heuer, SiriusDecisions
How to use predictive scoring in Account Based Everything
Just as Netflix predicts which movies you’ll like based on the ones you’ve already watched, Predictive Analytics chooses the companies most likely to buy by analyzing the ones who have already bought (or become opportunities).
Predictive Analytics takes data about accounts that have progressed to a certain stage of the buying process, and uses it to highlight other accounts in your market that most look like these.
Models will often include all the firmographic (company information), technographic (what technologies are used at that company), intent (meaningful behavioral data from that account) and engagement data (how engaged your company is with that account) that you might use in a manual scoring model.
What’s different in a predictive analytics model is your ability to include many more dimensions and data points – often in the hundreds or even thousands. In fact, a big part of the value of predictive vendors is they do the data collection and cleansing work for you.
“Yes, you need to look for intent signals. But I’d hate to try to build a predictive model on it exclusively – there’s just not enough of it in the market compared to companies that t and companies you’re already engaging with.” – J.J. Kardwell, EverString
Shifting the conversation from argument to data-driven decisions
In many cases, the most basic approach – working with sales reps and their gut intuition to create a target account list – is enough to see value from Account Based Everything. But it can lead to arguments about who is truly qualified.
Unlike these manual processes to build target account lists, predictive models don’t go in with any biases or hypotheses. They simply analyze the data, building the model around any characteristics that best correlate with eventual success.
“Predictive Analytics looks at indicators at a level that humans just can’t understand. It’s not realistic to expect that you will come up with your best target accounts simply by having sales, marketing and product sit in a room. This reduces so much friction and allows everyone to feel like you’re making data-based decisions instead of having people bickering around a table.” – J.J. Kardwell, EverString
Partial landscape of predictive analytics vendors
Though not an exhaustive list, consider partnering with organizations that provide predicative analytics solutions, including:
No matter if your process is manual or predictive, your target account selection will be the most important component of your account-based strategy. Be mindful, and get it right.
Have you been successful with predictive analytics in your account-based strategy? Do you have any tips to share with our readers?