This article is a preview of our new guide, Will You Be My Customer? Buying Signals in the Era of Predictive Intelligence. Download the full guide here.
If your company is like most, your sales and marketing teams are constantly wondering: Will this customer buy from us? If so, when will they buy, and which products?
It used to be that identifying and interpreting buying signals was more art than science. Salespeople were trained to look and listen for verbal and nonverbal cues. Is the prospect nodding, smiling and asking questions? Or is he silent, frowning and distracted? The salesperson could then tailor a response that would (hopefully) keep the opportunity moving forward.
Then conversations between B2B buyers and sellers moved online, and everything shifted. Interactions with prospects today are far less linear and controlled. We now see “buying signals” in digital terms, and we leverage many different technology tools and processes to generate leads and support sales.
But this isn’t where the story ends. In the era of big data, buying signals exist in digital multitudes and are more nuanced and siloed than ever. Today, powerful predictive technologies are picking up where the older generation of marketing solutions left off.
The Limitations of Current Solutions
Take solutions that provide sales and marketing teams with alerts about “trigger events.” Trigger events are defined as any new circumstances or changes (such as personnel changes, mergers, industry events, regulatory changes) in a prospect’s business or industry. In theory, a salesperson who receives an “alert” about a trigger event will be in a better position to approach prospects with a relevant message, at a time when that prospect is likely to be receptive to a sales conversation.
But are those prospects really “in market” to make a purchase? Trigger events offer only insights into what is happening in the prospect’s company or industry; but they don’t tell you if that prospect actually has a current need for your products. If your company sells cleaning supplies, for example, and your sales team gets an alert that a major company just moved offices, your salespeople might treat that event as a promising opportunity. But the fact that a company moved offices doesn’t mean they’re ready to invest in a closet full of new bathroom cleansers. It might be true, but it could just as well be a wild goose chase for your sales team.
Marketing automation also has limitations that prevent you from engaging with buyers who never “raise a hand” (for example, by registering to download a content asset on your website). What about visitors who come to your website and choose to remain anonymous? According to LinkedIn, that’s about 90% of website visitors. What about buyers that never make it to your website at all?
Buying Signals in a Predictive Age
The key differentiator with predictive intelligence is that it looks for patterns in online data over time. This data exists both on your website (including data generated by your anonymous visitors) as well as off your website (i.e., data generated by the activities of B2B buyers across the web). This keeps you ahead of the buying curve, unlike alerts or automation-based buying signals, which generally reflect a single and potentially stale data point. Many salespeople have seized on an alert generated by sales-intelligence platforms and called a prospect only to be told, “We started looking at solutions six months ago and just made a purchase.” Predictive intelligence makes sure you don’t arrive too late to secure your chance to win the deal.
While predictive-intelligence solutions hold transformative power for marketers, it’s important to note that not all predictive solutions are created equal. Some predictive vendors use a mix of limited data and criteria that can result in “false positives,” which would keep your sales team chasing unpromising leads. Alternately, some vendors supply “false negatives” which would cause your company to overlook or disregard buyers who might be looking for a solution like yours.
A Tale of Two Predictions
Let’s say your company, Fantastic Inc., sells widgets, and you want to use a predictive solution to find prospects and accurately gauge those prospects’ likelihood to buy your widgets. The chart below outlines the way 6sense uses predictive buying signals of Acme Inc., a company looking to buy widgets, compared to that of other predictive vendors.
The other predictive vendor is able to pick up the job title and recent engagement activity of a senior decision maker (identified as “John Duncan”). At face value, John Duncan’s activities seem to indicate that he’s a promising lead. Thus, the other vendor scores the lead highly and sends it to a sales team. The sales team then emails John Duncan repeatedly. Pretty soon, Duncan opts out of emails. And that’s that.
This wouldn’t happen with a predictive intelligence platform like 6sense, which could gauge the depth, breadth and quality of data to predict the account and buying contact’s buying stage. The 6sense predictive intelligence platform would help you know exponentially more. Specifically, you know that this account is in market to buy widgets. And 6sense would tell who the critical buying committee members are. Imagine how easy it would be for marketing and sales to take this insight and work together to win this account!
What Makes a Great Predictive-Intelligence Solution?
As you can see, the capabilities of one predictive vendor versus another can mean the difference between a lot of wasted time and a sale. The very best and most accurate predictive intelligence platform connects data from both first- and third-party data sources – both known and unknown – using buying intent and buyer activity data. Once the data is connected at scale, you will not only get the best possible predictions, you’ll also avoid false positives and false negatives. The best solution simply helps you be right more often.
This is revolutionary for many marketers, who are still stuck tracking activity that only gets recorded when buyers enter their websites and identify themselves (by filling out forms). Obviously, that means these marketers are missing out on a huge amount of activity that happens before buyers identify themselves, as well as any activity generated by buyers who never identify themselves at all.
These marketers are also missing out on buyers who never visit their website, period. Maybe those buyers don’t know their solution exists or assume they’re not the right fit for them. Predictive intelligence can find those leads and predict how likely they are to buy.
We’ve come a long way from the days when buying signals meant smiles and nods from prospective buyers. The challenge for the future won’t be finding the right leads. It will be about knowing how to approach those leads effectively given the wealth of insight you already have about them. (Not a bad challenge to have!)
If you’d like to learn more about leveraging buying signals in today’s environment, check out our new guide Will You Be My Customer? Buying Signals in the Era of Predictive Intelligence.