This article is Part III of a 4-part series on the shifting landscape of B2B buying and selling, how revenue teams have adapted, and where we think the market is headed.
In the previous two parts, we talked about the problems facing revenue teams today and how we got there. Account-based marketing is a step in the right direction by adapting how companies engage with buyers depending on the stage of their buyer's journey. Right message, right person, right time, through the right channel.
However, having implemented ABM solutions ourselves and talking with many current or former users of ABM, there are hard setup and maintenance costs, as well as difficulties in running such a complex operation effectively - particularly speed, coverage, and consistency.
But first, a word about demand capture and demand creation.
Understanding Demand: Creation vs. Selling
Demand creation focuses further up the funnel at the awareness and consideration stages of the buyer's journey. It's about finding ways to get our brand in front of the 97 to 99 percent of our total addressable market who may not be actively in the market but have the potential to become prospects in the future. This involves utilizing channels like LinkedIn, Reddit, ads, webinars, blog posts and influencers to educate and engage with our target audience.
Demand capture is about capturing the buyer in the decision and purchase stage. Again, only 1 to 3 percent of people are currently in the market and showing purchase intent. This is where all of our sales team's efforts should go. There's typically a big cost for sales spending time on accounts that are not in-market.
One common mistake companies make is the tendency to allocate most of their budget towards demand capture, even though 70% of the buying journey has already occurred by the time a seller is involved. By then, the buyer already has a top 5 list of vendors they are looking to evaluate.
By neglecting demand creation, we risk commoditizing ourselves among competitors and miss the opportunity to distinguish ourselves as a leader.
If we are truly solving a problem, our buyer will be in the market for our solution one day. And if we did demand creation right, they'll be googling for our solution via branded search terms rather than typing our category name, where we may not even rank in the first four search results.
The Importance of Speed in Selling
When a target company exhibits buying intent, the window of opportunity that the buyer is thinking about you could be seconds. If a buyer visits the site and has a question about the product but is unable to meet with a rep until a day later, that may be too late if the budget discussion is tomorrow. By the time a sales rep reaches out, the moment may have passed, and the buyer has gone to a competitor.
Here's an example of how orchestration could solve this.
The VP of Marketing directs a B2B marketing manager at SaaS Co. to research a B2B intent solution to get more in-market leads. The budget meeting is tomorrow, so the marketing manager is pulling a list of potential vendors to evaluate. SaaS Co's marketing manager asks the Pavilion Go-to-market community to ask for alternatives to 6sense because 6sense is so expensive. Someone mentions Warmly. The marketing manager visits Warmly's homepage, the case studies page, and pricing page. Upon reading the pricing page, someone reaches out via live chat. It's an AE at Warmly asking if the marketing manager has any questions. The marketing manager asks how pricing works. The AE responds. The marketing manager doesn't realize he is speaking to an AI trained on data across Warmly's internal data. By this point, the actual AE was notified and jumped in to take over the conversation. The AE asks if the marketing manager would like to jump on a video call, at which point the two have a discovery call.
They both schedule the next steps to catch up again after SaaS Co's budget meeting at SaaS Co. tomorrow. The marketing manager hangs up the video call, notes the solution in his deck and ends his day.
However, the orchestration doesn't end there.
Immediately after the Warmly AE and SaaS Co. marketing manager hang up on video chat, the CFO of SaaS Co. receives a LinkedIn connection request from the AE at Warmly. The AE addresses the problems that SaaS Co. is facing financially. It's a tough economy; SaaS Co. is trying to reduce the risk that the money they're spending on sales and marketing will be squandered. The AE details exactly how Warmly integrates into SaaS Co's technology stack and maximizes the ROI of existing marketing spend in the follow-up message. The CFO ignores the message but keeps Warmly in mind.
The rest of the buying committee, the VP of Marketing, CRO, VP of Sales, Head of Sales Development, and the marketing manager who was just on the call all receive custom emails from the Warmly AE addressing each of the risks that Warmly would help eliminate.
The CRO at SaaS Co. opens her email and finds a customized message about how Warmly "helps to eliminate revenue leaks in the funnel," a topic the CRO had been reading up about in the past few weeks. The CRO clicks on a link in the email, which directs her to the Warmly homepage, which includes case studies of how Warmly solved revenue leaks with SaaS Co's competitors.
The reality is that these experiences were automatically generated and sent via the orchestration system right after the AE and the marketing manager hung up their call.
AI carefully selected the message, buying committee members, and channels based on all the surrounding context on the account and patterned historical data.
Whereas before, such an analysis and outreach would have taken hours to pull together, it was sent minutes after the AE and marketing manager hung up. The call recording was synced to the CRM, transcribed, and processed alongside all other relevant data collected on the account.
The buying committee of SaaS Co. meets the next day to discuss the budget and solutions. Warmly is top of mind, and the marketing manager reaches out to the AE to schedule another call with the VP of Marketing, CRO, and Sales.
The AI also advises the AE to ask to include the CFO on the call because the CFO is deemed a vital decision-maker for similar accounts in the past.
Maximizing Engagement: The Power of Coverage
A similar story is played out a hundred more times the working day as companies visit the site, are qualified in or out, and delivered the right experience by the orchestration platform. A single rep can only handle one account at a time, but an orchestration platform can simultaneously service every single account at every stage of the buyer journey.
The previous example discussed a possible experience delivered to the account if they were in-market.
What about those that aren't in-market?
They receive demand-creation experiences, like display ads or personalized emails that route to educational blog pages.
When the target accounts finally enter the "buying window", their perspectives have already been shaped by Warmly's content. The account is primed, and we move to demand capture, involving the sales team.
The target accounts arrive on Warmly's landing page, the AI qualifies them in, notifies the rep when a human needs to be in the loop, and the cycle repeats.
Achieving Uniformity with Consitency
Based on observing the data coming into the orchestration platform, the leadership team finds that they are losing deals based on price to competitors, specifically to companies in B2B SaaS at the Series A stage. The team tweaks the orchestration platform to show 20% discounts to in-market B2B SaaS accounts at the Series A stage. The AI also adapts this promotion into their messaging, while keeping the price the same for all other prospects.
Normally, this type of change would take multiple ongoing training enablement sessions as SDRs and reps leave and are onboarded or returning from vacation. In the past, reps may test messaging and pricing on their own. Sometimes they'll include certain titles in the deal while leaving important ones out. Now, everything is standardized. This change is implemented immediately and fed through the platform.
Adaptive Systems and their Multiplier Effect
When the whole go-to-market functions of demand creation (marketing) and demand capture (sales) are playing together harmoniously and the experience is delivered correctly, buyers are happy, because they feel like it's being done for them, not to them.
When data no longer lives in siloes and is combined to create derived insights that feed back into the platform, the system continuously learns better experiences to deliver for each type of account.
Advancements in AI, like vector embeddings, can extend LLMs to have long-term memory on the surrounding historical context and experiences delivered to not just one account but every account that is being tracked in the CRM. This allows the system to create highly customized experiences that extend across the life-cycle of the buyer's journey. Like Amazon and Netflix, millions of buyers aren't receiving templated emails but carefully selected personalized experiences.
The Non-linear Nature of B2B Purchasing
B2B buying doesn’t play out in any kind of predictable, linear order. Instead, buyers engage in what one might call “looping” across a typical B2B purchase, revisiting each of say six buying jobs at least once.
There's a multiplier effect when all the pieces are working in accordance and adapting in real-time to the ever-evolving ecosystem that is B2B buying.
Redefining the Role of Human Interaction
Go-to-market teams have already been downsizing and learning to be just as effective with fewer headcounts.
It's gotten so difficult to get someone on the phone that when someone finally does pick up, after 100 dials, we end up word vomiting just to have them hang up again. Horrible experience for the buyer and for the seller.
In the very near future, sellers will move further away from these manual, repetitive tasks because of the increased sophistication, efficiency and effectiveness of these new adaptive systems. SDRs and AEs can get back to focusing on solving complex customer problems and building long-term relationships. And marketers can spend more time building empathy for the people they are seeking to serve.
And because AI has become quite good at synthesizing data into something humans can understand, we can drill down into the system and surface important answers to questions like who is our ICP, where are the bottlenecks, why did we deliver certain experiences, what's been working or not working, and why.
That's the magic of AI powered revenue orchestration. It gives power back to us so that we can be more creative, strategic, and do what we do best. Leave the rest to automation.
Read on to for Part IV on Warmly: The Autonomous Revenue Orchestration Platform.