In a previous post, I gave a few examples of how to optimize the use of MQL’s as a starting point for qualifying opportunities. Now, for those of you with a taste of adventure, we move beyond the MQL and look at other ways to determine how an account’s behavior can be the equivalent or better of what an MQL represents.
Based on the actual buyer journey of thousands of accounts, across dozens of product/solution types, we will explore what signals to look for by combining behavior from external data sources and as well as your own.
Know Who’s Looking to Buy
One of the more fundamental roles (and there are quite a few roles) Intent data plays is to help you see what accounts are researching when they are not on your site. Since this external data represents the vast majority of what and how an account is researching, it will help us be more definitive when answering many questions we should have when it comes to what accounts we should be prioritizing.
Using Intent data to further qualify a content syndication lead is a good example we can use. If a content syndication lead is the sole signal coming from an account, and we do not see other signals that indicate that a group of people are engaged in research activity, then can we define that one action as a qualified lead? The answer is, probably not. That is not to say the person who downloaded that piece of content isn’t someone we want to know about. But, if you are looking for a genuibe, in-market account going through a typical research journey, then you may want to classify that content syndication ‘lead’ as more like contact extension (someone to add to our database).
On the other hand, if we can see that the individual is part of a larger, wider group that is consuming content in sync together, then that one action becomes a part of a series of actions used to determine whether that account is behaving as the equivalent of an MQL.
After studying and deconstructing thousands of buyer journeys, we can say that there will be a significant amount of behaviors shared by many accounts. Generally, we like to see at least 20% of ALL accounts showing similar behaviors to nominate that specific moment in time as a potential automated trigger. Getting back to the content syndication example: if the action of the one person can be linked back to other research cues, then if the sum of ALL actions constitute a trending Intent score, we can start believing there is an internal conversation going on. If there is persistence over time of this conversation, then we can also start using these signals to represent an approximation of an MQL.
Serious product/solution research is a persistent pattern of ‘hunt and collate’. An account will ‘hunt’ for that categorical content that will form the basis of the sum total of sourced knowledge. Then, there will be a series of ‘collation’ periods when the group organizes and distills. This is a pretty common path taken across many B2B product/solution decisions. In the following very typical example, we can see this hunt and collate behavior being exhibited by a group coalescing around the content that represents a product/solution:
The Start of The Buyer Journey
One tell tale clue that a potential buyer journey is beginning is when after months of no above normal activity (remember, we are only tracking collective behaviors above the norm at an account), an account’s Intent score goes from 0 to say, 30, 40 or 50 in less than two weeks. You can see that happening in the above example, where there is a sudden move to above 30 in about one week’s time. Also notice that there are 9 Unique individuals representing this out-of-the-blue trend. The Intent score is made of quite a few variables over time (which we will get into in another installment). But at the very beginning, we are looking at how quickly the account develops a solid Intent score by the number of Unique individuals, the total number of pageviews consumed in the relevant categories AND, potentially, whether these folks are in a ‘content conversation’.
Content Conversations and Link Sharing
Another telltale sign of a group embarking on a buyer journey is when there is explicit content sharing. Link sharing is something we all do. We find something we know if of interest to a group of people, we cut and paste and share using one form of communication or another. The more compressed the time between when the link is shared and how quickly it is read , helps us determine the quality of the conversation. This can happen across the entire length of the decision making process.
The Calm Before the Storm
Another common shared behavior is that a buyer journey has its peaks and valleys. It also has its acceleration points. When a project, initiative or RFP is kicked off, you will generally see a good amount of research happening over a few weeks time which is then followed by a lull. The pause or cessation of activity can last for weeks and sometimes even months. Then, a second wave or another burst of concerted activity occurs. This can happen multiple times, again, depending on the natural time to sale of your product/solution. Regardless, there is definitely a compression of time as a group makes its way to a decision. In the below example, you will see three distinct waves of research, with the time between these lessening. As time progresses, the period between each wave will shrink by 20 – 40%.
You will also see that there is an increase in the number of individuals participating in the conversation leading up to the moment in time the account becomes a qualified opportunity (as represented by the black dot). More content, more collating, more discussing, compressed time – we see this pattern across everything we have looked at – regardless of the product/solution being sold. Is this the way EVERY account behaves? Absolutely not. But when we see enough of them do, we form the basis of automated triggers and marketing/sales plays.
By embracing this commonality of behavior as an MQL alternative, we can follow and monitor many more accounts that are in market for your products/solutions. All based on the behavior of known accounts that have actually become qualified opportunities in your CRM.
Why wait for a fraction of accounts that become an MQL? We need to stop trying to get people to do what we want them to do (filling out forms) and start trying to understand what they are endeavoring to do through this collective behavior.
