The Role of Intent Data in Abm

Sales and marketing leaders’ obsession with “intent” is undermining their Account-Based GTM strategy. Here are the 3 biggest mistakes GTM teams are making on intent (and how to use intent effectively): 1. Intent is not magic Unfortunately, intent has been marketed as if it’s magic. As if it can 100% accurately identify ALL companies that have a qualified opportunity. It cannot. Intent is simply an indication of interest and engagement on a *topic* related to the product you sell. At its best, vendors should utilize good sources and strong algorithms so that the level of confidence in the signal is clear. But often, in the interest of showing huge volumes of intent, vendors end up stretching the signal to cast as wide a net as possible and generate a large amount of false positives. 2. Intent is not your Ideal Customer Profile (ICP) I see this every day. Sales and Marketing teams get a list of high intent accounts and then “go after them.” This is counterproductive and wasteful because not all high intent accounts are in your ICP. The whole purpose of an account-based GTM is to align Sales and Marketing resources to accounts that have the highest LTV and thus generate the greatest enterprise value. This means being ultra clear on your ICP and avoiding the “intent temptation” of going after accounts that are interested in your solutions but are not in your ICP. Just because someone WANTS something doesn't mean they can or should buy it. 3. Intent should not be used in isolation from other data sets Intent only becomes powerful when it’s focused on your ICP and combined with other important data sets. Used in isolation, without other signals, you will never maximize your investment in intent. If tech companies want to increase the power and benefit of intent, they first need to combine intent with technographic data. Overlay the list of high-intent accounts with a list of companies that have the technologies your customers need to have and your hit rate on demand gen will improve significantly. The more robust solution to integrating intent into your broader GTM is to model it, with all other relevant data (firmographics, technographics, website engagement, Sales and Marketing engagement, etc) against closed won opportunities over the last 2 years. This will give a relative weighting for each data feature and intent keyword such that intent can be integrated into a more accurate score to represent propensity to buy soon. TAKEAWAY: Addressing the above issues are intended to arm you against what we’ve all heard many times, “This intent is BS, I called an account and they’re not ready to buy!” Don’t expect magic. Intent can't make a bad account great. But if you understand how intent relates to your ICP, and then use it in conjunction with other data sets, it becomes a powerful part of your account-based go-to-market strategy.

55% of sales leaders witnessed increased lead conversions with intent data, a stat that marks a new era in the art of sales and marketing. 🔍 A Personal Tale: From Data Jungle to Targeted Strategy 🔍 I once partnered with a client who was overwhelmed by a deluge of intent data from Bombora. Picture navigating a dense jungle without a map. The data was vast but unstructured, not effectively mapped to accounts. I was reminded of Craig Rosenberg's words - "The key on intent is fit comes first." 💡 Turning Complexity into Clarity: The Role of Context Our quest was clear: to cut through this jungle and find a path. We initiated a meticulous cleanup, aligning intent data with specific accounts. Then, we took a pivotal step further by focusing on contextual intent data. 🧭 Unlocking the ‘Why’ Behind the Data Contextual intent data is like a compass in uncharted territory. It goes beyond identifying interested accounts; it's about grasping the reasons behind their interest. This deeper understanding enabled us to tailor our approach, addressing the specific needs and challenges of each account. 🌈 The Outcome: Precision-Driven Sales and Marketing Success The transformation was remarkable. Sales dialogues became more focused and resonant. Marketing campaigns struck a chord, addressing the unique context of each account's journey. 🛤️ A 5-Step Blueprint to Mastering Contextual Intent Data Data Harvesting: Collect intent data with an eye for the underlying context of each interaction. Intelligent Mapping: Align this data with specific accounts, illuminating your path through the data forest. Tailored Tactics: Customize your outreach based on the nuanced context of each segment. Adaptive Campaigns: Launch dynamic, context-sensitive campaigns that connect deeply with each account's narrative. Strategic Refinement: Continuously evolve your strategies, responding to the ever-shifting landscape of intent signals and contexts. 📈 Beyond Just Data Points: Contextual intent data isn't merely a collection of information; it's a storytelling tool. It's about transforming raw data into compelling narratives that not only reveal who is ready to buy but also why they are on this journey, creating more meaningful and effective sales and marketing engagements. Step into the world of contextual intent data and watch your sales and marketing narratives change from abstract data points to stories that connect and convert. #ContextualIntentData #SalesInnovation #MarketingTransformation #DataDrivenDecisions #BusinessGrowth #B2Bmarketing #ABM #accountbasedmarketing #METABRAND #IndustryAtom

Ask any rep what they think of “intent” from ABM platforms, and you’ll more than likely get a look of disgust. 3 reasons why: 🪫 Intent level is low What’s being passed off as buying intent is pretty loose. That’s because much of the volume of account-based intent comes from bidstream data—i.e., partnerships with media sites like Forbes, Business Insider, TechTarget, et al. What’s happening is someone lands on a publisher’s post around a topic (think “digital transformation”), that visit is matched to the IP of a business, and it’s then surfaced as keyword intent. --- 🏢 It’s account-level data When intent comes from any account of meaningful size, good luck finding the actual person behind the action. Was that visit to an article about “cybersecurity” coming from an intern, a hacker, or a CISO? Your guess is as good as mine. --- 💯 Lack of context Most of these platforms and scoring vendors will provide a predictive account score or a “hot account” tag. But what’s missing is the context as to what’s behind that score. Was it a web visit? Was it a lot of keyword intent? Was it from checking out a competitor review? Without that context (and the person behind it), it’s not clear what to touch on and personalize to get a response. --- In short, account-based intent is a guessing game. It’s like trying to find Waldo (and guess the journey that Waldo took) at every account showing intent. === “So, Kevin, what’s a better alternative?” Glad you asked 🙂 We’re rethinking intent and building a better approach with Common Room. Here’s how it’s different… Person-level signals: We give you buying signals—job changes, web visits, product activity, social interactions, and more—from real people. Actionable account-level signals: We go a level of intent deeper than bidstream data for account signals (e.g., hiring trends, 10-k summarizations, tech stack changes, etc.). Waterfall enrichment: Not only do we tell you the person behind the signal, we also fill in critical attributes like phone number, role, company size, and hundreds of other data points automatically out of the box. ← is critical for prioritization. Unification of activity: Person-level activity across signal surface areas (think web visit to social channel to job change) is all rolled up and unified into a single user profile. In turn, you get visibility into the entire user journey. Contextual scoring: Not only do we make it possible to score for signal and fit at both the person and account level, we also show context of what’s powering the score so reps can personalize outbound accordingly and meet prospects where they are.

I generally try to keep my discourse free of invective. The discourse around #intent data on this platform is nudging me toward it, however. There's a lot of uninformed nonsensical and misinforming voices on this topic. These voices tend to sound more sure of themselves than others, as people who don't know what they're talking about so often do. Here's the deal, and you can find me saying precisely these things since about 2015 when I was an analyst at SiriusDecisions and wrote the first "Intent Data Framework" with the late great Matt Senatore in 2016ish. Intent data should never have been called that. The category should have been 'interest' signals. We used that term in our Buyer Signals Framework in 2020 at Forrester (w Jessie Johnson). What intent data providers and your digital properties are capturing are signals of interest. These signals are created as individuals look for, find, and consume content. There are signals you receive on your digital properties, and there are those you buy from others. If you sell anything that costs more than about 35-50k a year, then a signal from just one individual inside a company is almost certainly a red herring. ➡️ As the number of people from an organization who are emitting the same signal of interest increases (as you get person 2,3,4... showing the same interest), the likelihood that the interest represents something the company is interested in and not just individual people increases. ⬅️ That's the whole key right there. ***More individuals doing the same thing increases the odds that you're looking at a buying group/account signal.*** That's the stuff you should care about. Out of all the signals on your own digital properties, just a tiny fraction will be from people who fill out your forms. The anonymous signals are no less valuable, but you have to spend money to de-anonymize them back to their accounts. Some third parties have actual people names - TechTarget being the most prominent example, along with G2, TrustRadius. Just about all other 'intent' signals are anonymous to the person, but identify interest from accounts. It's generally a bad idea to send sellers after intent signals, because they don't have names attached. They require from-scratch prospecting. Does that make them worthless. No. 1️⃣ if there are two accounts a rep could prospect into, and there's intent data activity from one but not the other, the choice of where to spend time should be clear. But, 2️⃣ these signals are best used to direct marketing spend. Just about 30% of any audience is in market at any given time, and only 2-5% will be buying this quarter. So... Directing your precious demand/ ABM budget to 30% of the audience and not 100% all the time is how you create an advantage for yourself. Will it be perfect? No. Find me something that is. Opt out at your peril. John A. Steinert Sydney Sloan 6sense Jason Telmos

Intent ≠ ICP. With all the intent data over-promises from ABM Platforms, it's easy to get these things mixed up. I've watched too many B2B teams burn six figures chasing "predictive" intent signals, only to have sales waste time on accounts that will never close. RevOps leaders know this WTF-Intent-QL pain. The problem isn't intent data itself. The issue comes from treating intent as a replacement for fit or skipping the ICP exercise. Intent tells you someone *might* be in market. Fit tells you if they're worth your time when they are. A classic 2x2 can help. * High Intent + High Fit = Right-Now Revenue (send your SDRs and AEs here today). * Low Intent + High Fit = Future Customers (build awareness and engagement for future quarters). * High Intent + Low Fit = Time Wasters (the intent-QL trap). * Low Intent + Low Fit = Never Neverland (ignore completely) The best RevOps leaders use this framework to stop wasting spend in never-neverland and focus sales & marketing where it matters. Full breakdown here: https://lnkd.in/gid3dmaH