Nov 11, 2024
Nov 11, 2024
by
by
Aditya Khargonekar
Aditya Khargonekar
Today’s buyer expects more
Account Research
Account Research
In our previous post, with the help of GenAI, we covered the time-old process of generating value hypotheses with which to engage an account. Here, we share thoughts on how one of the most common approaches to research may be due for an update.
Sales reps often approach account research with a singular goal: looking for a pattern that matches a specific pain point to your product or service in order to engage potential customers. However, the selling landscape has changed, and what got you here won’t necessarily get you to your next goal. In this post, we examine some of the implicit trade-offs that come with traditional pattern matching and suggest a broader approach that today’s market demands.
Traditional pattern matching has limitations
The pattern matching approaches of the last decade or so reflect a paradigm of right-person / right-time / right-message, and the constant search for alignment across different combinations. Teams have invested heavily in operationalizing this concept, combining enrichment, intent signals, and playbook orchestration to templatize the selling process.
Of course, there’s a reason why so much investment has gone into this approach. It can be especially effective in a seller’s market flush with latent demand, primarily as a mechanism to land that first meeting. Dubbed signal-based selling of late, the automation of this strategy has been taken to an extreme with the introduction of AI SDRs.
However, it has some critical limitations:
It is solution-centric. Because it deviates from a customer-centric orientation, it’s less salient in navigating more complex enterprise deals. It isn’t designed to generate durable value and lasting customer relationships.
It’s single-threaded. The entire positioning can become framed or anchored around a specific pain-solution. Sometimes that can be enough, but it’s often only one of a universe of potential ways to engage and deliver value to a customer.
It’s mostly static. You may line up the “right-” everything, but that signal typically reflects just a moment in time. Change is constant. There are many dimensions of change that can affect your relationships and deals, and nearly all of them are not within your control.
Full context enables pattern matching that truly understands the buyer
88% of buyers buy only when they see a seller as a “trusted advisor,” someone who demonstrates a deep understanding of their problems, but these same buyers say 80% of meetings are a waste of time because reps are unprepared. With buyer behavior continually evolving amidst a market that is changing even faster, we believe we should collectively be moving toward a stronger, broader version of pattern matching that taps into the rich backdrop of contextual information. A full contextual understanding is the only way to have a shot at being prepared for your buyer throughout their journey.
Established sales orgs typically have a tremendous amount of known priors based on years of salescraft experience—knowing what goes into launching a new initiative or in how things settle in the aftermath of a layoff; the constraints of a public company vs a private one; what happens at EOY vs post-SKO.
All of these are also patterns, some of which we explicitly build process around to enable better account planning, but many of which we do not. Those often fall under the monikers of “experience” or “business acumen” or “tribal knowledge”, and it’s not a coincidence that sellers who wear those labels are often the most successful at their craft.
Let’s take some commonly targeted patterns we see with today’s signal based approach as examples:
A new leader joins with a track record or mandate matching your service
A company is hiring for a particular role you serve
They have a competitive or complementary tool in their tech stack
A former champion moved to a new company
Their 10-K highlights at a market risk you can help with
Their annual budgeting cycle is coming up soon
Taking each in isolation, any one of those patterns should elicit an immediate response of how to engage with the account. But then consider how you might change the message, or who you reach out to, or even the account strategy when you take into consideration multiple or all of those points. The value of each individual pattern is made richer because it is informed by the context of additional ones, ultimately elevating your account strategy.
Continuous discovery is the way
However, doing this isn’t as simple as smashing together multiple existing patterns or adding more fields into your logic and filter criteria. You rarely know all the patterns to care about, much less the answer to the status of each and how to connect those dots. Before going down that path, the through-line we first need to establish is the mindset of continuous discovery.
Continuous discovery isn’t a new concept—it has long found application in product discovery as in sales. Its underlying premise is the belief that there is deep value in having a true understanding of your customer. Achieving that requires constant discovery of what you don’t know as well as validation of what you think you know because, you know, change. And so we leverage relationships, we conduct research, we test hypotheses, we analyze data.
Using continuous discovery for contextual pattern matching forces you past the limitations of signal-based selling. It requires a more customer-centric and holistic understanding of the account. It allows you to adapt and explore any number of patterns, at any given time, to find paths to offering value. It enables you to constantly be weaving together multiple inter-connected threads of value to the relationship. It helps you become your buyers’ trusted advisor.
AI makes continuous discovery possible
This type of account research isn’t easy—which is why it is so rarely done. Keeping up with the ceaseless flow of new information is a tall order, let alone sifting through it all to identify what matters. But when done and done well, buyers notice. It becomes the reason you stand out.
Fortunately, there are a growing number of tools, like Endgame, that are working on solving that aspect of the challenge—aggregating data from multiple sources and making intelligent connections between them, thanks to AI. AI also allows us to identify the patterns and communicate them in a way that is truly human readable.
Having full context as a starting point is now more possible than ever before.
What remains is how you navigate that context, the specific choices you make, and the reasons why. That’s the secret sauce, the experience or acumen, that AI should be enhancing rather than trying to replace.
While we don’t have a crystal ball for that, in our next post we’ll touch on some practical ways to think about and internalize continuous discovery in your selling motion, so you’re better equipped to take advantage of having that full context.
In our previous post, with the help of GenAI, we covered the time-old process of generating value hypotheses with which to engage an account. Here, we share thoughts on how one of the most common approaches to research may be due for an update.
Sales reps often approach account research with a singular goal: looking for a pattern that matches a specific pain point to your product or service in order to engage potential customers. However, the selling landscape has changed, and what got you here won’t necessarily get you to your next goal. In this post, we examine some of the implicit trade-offs that come with traditional pattern matching and suggest a broader approach that today’s market demands.
Traditional pattern matching has limitations
The pattern matching approaches of the last decade or so reflect a paradigm of right-person / right-time / right-message, and the constant search for alignment across different combinations. Teams have invested heavily in operationalizing this concept, combining enrichment, intent signals, and playbook orchestration to templatize the selling process.
Of course, there’s a reason why so much investment has gone into this approach. It can be especially effective in a seller’s market flush with latent demand, primarily as a mechanism to land that first meeting. Dubbed signal-based selling of late, the automation of this strategy has been taken to an extreme with the introduction of AI SDRs.
However, it has some critical limitations:
It is solution-centric. Because it deviates from a customer-centric orientation, it’s less salient in navigating more complex enterprise deals. It isn’t designed to generate durable value and lasting customer relationships.
It’s single-threaded. The entire positioning can become framed or anchored around a specific pain-solution. Sometimes that can be enough, but it’s often only one of a universe of potential ways to engage and deliver value to a customer.
It’s mostly static. You may line up the “right-” everything, but that signal typically reflects just a moment in time. Change is constant. There are many dimensions of change that can affect your relationships and deals, and nearly all of them are not within your control.
Full context enables pattern matching that truly understands the buyer
88% of buyers buy only when they see a seller as a “trusted advisor,” someone who demonstrates a deep understanding of their problems, but these same buyers say 80% of meetings are a waste of time because reps are unprepared. With buyer behavior continually evolving amidst a market that is changing even faster, we believe we should collectively be moving toward a stronger, broader version of pattern matching that taps into the rich backdrop of contextual information. A full contextual understanding is the only way to have a shot at being prepared for your buyer throughout their journey.
Established sales orgs typically have a tremendous amount of known priors based on years of salescraft experience—knowing what goes into launching a new initiative or in how things settle in the aftermath of a layoff; the constraints of a public company vs a private one; what happens at EOY vs post-SKO.
All of these are also patterns, some of which we explicitly build process around to enable better account planning, but many of which we do not. Those often fall under the monikers of “experience” or “business acumen” or “tribal knowledge”, and it’s not a coincidence that sellers who wear those labels are often the most successful at their craft.
Let’s take some commonly targeted patterns we see with today’s signal based approach as examples:
A new leader joins with a track record or mandate matching your service
A company is hiring for a particular role you serve
They have a competitive or complementary tool in their tech stack
A former champion moved to a new company
Their 10-K highlights at a market risk you can help with
Their annual budgeting cycle is coming up soon
Taking each in isolation, any one of those patterns should elicit an immediate response of how to engage with the account. But then consider how you might change the message, or who you reach out to, or even the account strategy when you take into consideration multiple or all of those points. The value of each individual pattern is made richer because it is informed by the context of additional ones, ultimately elevating your account strategy.
Continuous discovery is the way
However, doing this isn’t as simple as smashing together multiple existing patterns or adding more fields into your logic and filter criteria. You rarely know all the patterns to care about, much less the answer to the status of each and how to connect those dots. Before going down that path, the through-line we first need to establish is the mindset of continuous discovery.
Continuous discovery isn’t a new concept—it has long found application in product discovery as in sales. Its underlying premise is the belief that there is deep value in having a true understanding of your customer. Achieving that requires constant discovery of what you don’t know as well as validation of what you think you know because, you know, change. And so we leverage relationships, we conduct research, we test hypotheses, we analyze data.
Using continuous discovery for contextual pattern matching forces you past the limitations of signal-based selling. It requires a more customer-centric and holistic understanding of the account. It allows you to adapt and explore any number of patterns, at any given time, to find paths to offering value. It enables you to constantly be weaving together multiple inter-connected threads of value to the relationship. It helps you become your buyers’ trusted advisor.
AI makes continuous discovery possible
This type of account research isn’t easy—which is why it is so rarely done. Keeping up with the ceaseless flow of new information is a tall order, let alone sifting through it all to identify what matters. But when done and done well, buyers notice. It becomes the reason you stand out.
Fortunately, there are a growing number of tools, like Endgame, that are working on solving that aspect of the challenge—aggregating data from multiple sources and making intelligent connections between them, thanks to AI. AI also allows us to identify the patterns and communicate them in a way that is truly human readable.
Having full context as a starting point is now more possible than ever before.
What remains is how you navigate that context, the specific choices you make, and the reasons why. That’s the secret sauce, the experience or acumen, that AI should be enhancing rather than trying to replace.
While we don’t have a crystal ball for that, in our next post we’ll touch on some practical ways to think about and internalize continuous discovery in your selling motion, so you’re better equipped to take advantage of having that full context.