This article will explore ABM’s historical development, look at its current state as highlighted by the October 30, 2023 release of the Gartner® Magic Quadrant for Account-Based Marketing Platforms (in which Demandbase was once again named a Leader), and offer insights into the category’s future direction with artificial intelligence.

A history of ABM

ABM’s roots are traced back to the customer-centric philosophy propounded by Don Peppers and Martha Rogers in their work, “The One to One Future.” In this groundbreaking book, Peppers and Rogers envisioned a future where businesses would move away from mass marketing and instead engage with customers on an individual basis, fostering one-to-one relationships that were both personalized and enduring. This laid the groundwork for what would become account-based marketing (ABM). The term was coined in 2004 by the ITSMA, which originally emphasized the notion of treating each account as its own distinct market — a concept that delivers significant ROI at accounts worth millions of dollars a year.

The success of early 1:1 approaches to ABM caused marketers to seek ways to scale the impact, and practitioners developed additional approaches for 1:Few and 1:Many ABM. These were fueled by technology. In particular, Demandbase was an early pioneer of ABM technology, enabling businesses to identify otherwise unknown accounts on a webpage and use that to focus advertising investments and deliver personalized web experiences.

ABM began to really take off in 2015 with the entrance of new players, including Engagio and Terminus, and has continued to grow in popularity and maturity since then. A key milestone in the maturation of ABM was the strategic merger of Demandbase and Engagio in 2020, combining their strengths to forge the most comprehensive ABM platform to date.

Beginning in 2022, a new term emerged: account-based experience (ABX). By its very nature, traditional ABM focused on identifying valuable accounts and attempting to engage them, regardless of whether the time was right or if they were interested in hearing from you at all. And that’s exactly the kind of customer experience buyers hate. In contrast, ABX is all about engaging business buyers with relevant messages delivered in a trusted way on the buyer’s terms. At its core, ABX is about using data-driven insights to know where each account is in its buying journey and matching your go-to-market (GTM) accordingly. And, as a side benefit, since it takes the word “marketing” out of the name, ABX is a more inclusive term that represents a complete account-based strategy that drives sales and marketing alignment. 

2022 also saw another trend emerge: the promotion of data and account intelligence from a supportive role to become the bedrock upon which ABM platforms are evaluated and chosen. And this is only being amplified by the rise of artificial intelligence (AI), with its voracious hunger for and profound capability to analyze vast data sets. 

The impact of ABM

With all this change, ABM has gone from being an unknown acronym to arguably one of the most successful GTM strategies in history for B2B firms. The most recent annual ABM Benchmark Study from Momentum ITSMA and the ABM Leadership Alliance found that ABM remains the leading priority for B2B marketers and that firms are increasingly allocating budget to ABM initiatives. This investment is yielding tangible business outcomes, driving an 84% growth in pipelines and a 77% increase in revenue, outperforming other marketing strategies. Moreover, 72% of businesses report that ABM delivers a higher ROI than other marketing tactics, and two-thirds acknowledge its pivotal role in enhancing alignment between marketing and sales teams, thereby streamlining their joint efforts towards shared goals.

Today: The maturity of the ABM market

Today’s ABM market is characterized by a level of maturity that reflects both consolidation and standardization. The lines that once distinguished one ABM platform from another have blurred as companies have evolved and core functionalities have become standard. 

The 2023 Gartner® Magic Quadrant for Account-Based Marketing Platforms defines account-based marketing (ABM) platforms as software that enables B2B marketing and sales teams to run ABM programs at scale, including account selection, planning, engagement and reporting. Platforms enable the creation of target account lists by unifying first- and third-party data. In addition, platforms may engage audiences by activating channels such as display advertising, social advertising, email and sales engagement, using a mix of native capabilities and integrations. 

According to the report, the core capabilities of an ABM platform today include:

Must-Have

• Account-level intent data (proprietary and/or licensed)
• Campaign orchestration and activation across channels
• Account measurement and analytics

Standard

• Target account list creation and management
• Native user experience for ad campaign orchestration
• Sales alerts and insights based on engagement
• Integrations with CRM, B2B marketing automation, and sales systems

Optional

• Predictive analytics (customer profile fit, propensity to buy)
• Attribution modeling
• Customer data and account insights (firmographics, technographics, psychographics)

The future of ABM and AI

As we look to the future, we believe ABM platforms will evolve into full AI-powered go-to-market (GTM) platforms that will not only harmonize the lead-based and account-based approaches but will also centralize the concept of buying groups in their operational framework.

This evolution stems from the realization that B2B buying decisions are seldom made by lone individuals or entire accounts. Instead, they are the result of a consensus among a buying group comprising diverse roles — decision-makers, influencers, gatekeepers, and end-users — each with a unique contribution to the final decision. This will lead to a reorientation from targeting individual leads (MQLs) or accounts (MQAs) to B2B buying groups (qualified buying groups, QBGs). Just as in the classic story of Goldilocks and the Three Bears, leads are too narrow, accounts are too broad, but buying groups are “just right.” But unlike today, where managing buying groups is riddled with guesswork and manual effort, future AI-driven GTM platforms will operationalize the process by sifting through extensive data points to generate dynamic B2B buying groups, pinpoint their members, assign roles and personas, understand how they influence one another, and suggest new contacts.

A GTM platform will also incorporate increasingly sophisticated AI to pave the way for “self-driving go-to-market” strategies. In this advanced setup, the user’s role evolves to defining the objectives and constraints, much like setting the destination and speed on an autopilot system. The AI then takes over, using its learning algorithms to identify the optimal customer segments, tailor the messaging, and select the most effective channels for interaction. It not only executes these actions but also monitors the outcomes, analyzes the performance data, and iteratively refines its approach. This continuous loop allows for a dynamic and self-learning customer journey, with human oversight ensuring alignment with overarching business goals and maintaining the quality of customer engagement.

In short, the future of ABM is intricately linked to the rise of B2B buying groups and the integration of AI, transforming ABM from a marketing strategy to a cornerstone of sophisticated, data-driven platforms that span all aspects of go-to-market, from marketing to sales and beyond. These platforms will offer a holistic approach to engaging with buying groups, streamlining the process of identifying and closing opportunities, and ensuring that every interaction is informed, relevant, and impactful.

Conclusion

As we take stock of ABM’s past and peer into its future, we are reminded of its profound influence on B2B marketing. The methodology has transitioned from a niche discipline to a cornerstone of B2B go-to-market, offering granular insights, precise targeting, and enhanced customer experiences. Its trajectory points toward deeper integration with all aspects of go-to-market and increased predictive capabilities — elements poised to elevate B2B interactions to unprecedented levels.

Demandbase is proud of our central role in ABM’s evolution and our continuous commitment to shaping its future, and we’re proud to be recognized as a Leader in the Gartner® Magic Quadrant, as well as being named a Gartner Peer Insights Customers’ Choice for ABM earlier this year. To learn more about why Demandbase is recognized as a Leader and get complimentary access to read the full Gartner® Magic Quadrant for ABM Platforms report, click here.

Gartner Notices & Disclaimers

Gartner, Magic Quadrant for Account-Based Marketing Platforms, Ray Pun, Christy Ferguson, Jeff Goldberg, Julian Poulter, Jenifer Silverstein, 30 October 2023

Gartner, Voice of the Customer for Account-Based Marketing Platforms, Peer Contributors, 28 June 2023

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, MAGIC QUADRANT and PEER INSIGHTS are registered trademarks of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.

The post The Account-Based Revolution: From Origins to AI-Driven Futures appeared first on Demandbase.

Recent studies are starting to provide us a nuanced answer: it’s both. While AI was proven to significantly elevate performance across various tasks, there’s a caveat. The technology excels in some areas but falls short in others, particularly when it comes to accuracy and creativity. 

Let’s explore these studies and what they say about maximizing the benefits of AI in our B2B go-to-market (GTM) strategies.

AI for the win

A recent study by Harvard Business School found that Boston Consulting Group (BCG) consultants using ChatGPT-4 significantly outperformed those who did not across 18 real-world tasks. These included creative tasks (“Propose at least 10 ideas for a new shoe targeting an underserved market or sport.”), analytical tasks (“Segment the footwear industry market based on users.”), writing and marketing tasks (“Draft a press release marketing copy for your product.”), and persuasiveness tasks (“Pen an inspirational memo to employees detailing why your product would outshine competitors.”).

The study found that the BCG consultants using AI completed 12.2% more tasks while doing it 25.1% faster. They also produced over 40% higher quality results compared to those not using AI. 

That’s not just a marginal improvement; it’s a significant leap in productivity and quality that can translate into real competitive advantages for companies. Projects move more quickly from planning to execution, and higher quality work leads to better customer satisfaction, fewer revisions, and a more robust bottom line. These improvements suggest AI, when used properly, will be truly transformative for go-to-market.

Notably, the study also uncovered that AI acts as a “skill leveler.” The consultants who initially scored the lowest saw the biggest increases in performance when they teamed up with AI. While top performers also improved, the boost was less dramatic. This has deep implications for performance management across functions and disciplines. But, as we’ll see, AI isn’t always the right answer.

What about creative thinking?

Another study in Nature investigated the creative abilities of humans and AI chatbots. Participants were tasked with thinking of unique uses for common items. On average, the AI chatbots performed better and came up with more creative ideas than humans (as measured by an objective calculation of “semantic distance” and subjective ratings by human judges). The chatbots were also more consistent and showed less variability than the humans.

However, the most creative ideas from humans were on par with or better than those from the chatbots. The study concluded that in instances of high creativity and divergent thinking, the best humans still outshine AI, underlining the unique aspects of human creativity that AI has yet to replicate or surpass​.

Falling asleep at the wheel

While Generative AI is immensely powerful in some tasks, it fails completely or subtly in others. It’s great at turning CMO challenges into a lyrical poem, but it’s terrible at math and I’ve never been able to get it to return something that fits a specific word count. 

There’s a boundary that separates tasks where AI does well from tasks where AI does poorly, but unless you use AI frequently, it’s hard to know where that boundary is. The HBS study calls that unclear line “The Jagged Frontier”.

Ethan Mollick, one of the HBS study’s authors, explains it like this in his excellent post:

“Some tasks that might logically seem [to be]…equally difficult – say, writing a sonnet and an exactly 50-word poem – are actually on different sides of the wall. The AI is great at the sonnet, but, because of how it conceptualizes the world in tokens, rather than words, it consistently produces poems of more or less than 50 words.  Similarly, some unexpected tasks (like idea generation) are easy for AIs while other tasks that seem to be easy for machines to do (like basic math) are challenges for LLMs.”

To examine this, the HBS study included a task that would exploit the blind spots of AI to make it give a wrong, but convincing, answer to a problem that humans could easily solve. Sure enough, human consultants got the problem right 84% of the time without AI help, but when they used the AI, they did worse, only getting it right 60-70% of the time.

That’s why Mollick warns against “falling asleep at the wheel.” Over-reliance on AI can lead to mistakes, especially when humans let AI take over tasks it’s not equipped to handle. In another HBS study from Fabrizio Dell’Acqua, recruiters who used advanced AI found themselves becoming careless and less discerning in their judgments. They overlooked highly qualified applicants and ultimately made poorer decisions than those who either used less sophisticated AI or no AI at all. When AI performs extremely well, there’s a tendency for humans to disengage, allowing the machine to take full control rather than using it as an augmentative tool. 

Centaurs and cyborgs: two approaches to AI

OK, we’ve learned that:

• Consultants using AI outperformed those who did not in terms of speed and quality, with the biggest gains from the lowest performers.
• The best humans still outperform artificial intelligence in creative divergent thinking. 
• There’s a risk that over-reliance on AI can cause knowledge workers to disengage and let the machine take over, leading to errors and lower performance. 

So how should we use all these insights to navigate the path of when and where to use artificial intelligence?

The HBS/BCG study identifies two approaches to navigate this jagged landscape. Workers using the “Centaur” approach clearly divide up the work between humans and machines, strategically allocating tasks based on each entity’s strengths (e.g. the human guides the strategy, and the AI does the brute force work). On the flip side, workers using the “Cyborg” approach integrate humans and machines deeply, working in tandem on almost every step (e.g. such as initiating a sentence for the AI to complete).

There’s no one right strategy to use. For example, I used both approaches in helping to write this post, sometimes using ChatGPT-4 to summarize the original research and other times having it draft or finish specific sentences and paragraphs. And no matter what, I reviewed the results and made sure it worked with my voice. The key takeaway is that the strongest approach combines the strengths of humans with the strengths of AI. 

Implications of AI on B2B go-to-market strategies

From account-based marketing (ABM) to branding content to customer success, these results suggest nuanced ways in which humans and AI can work together to maximize efficiency and effectiveness in your go-to-market.

• Account-Based Marketing (ABM): AI is essential for scoring accounts and identifying buying groups more efficiently than manual methods. Machine learning algorithms can analyze market trends and customer behavior to not only pinpoint high-value accounts but also forecast their account journey. But humans should own the final account selection and it’s important to keep a human touch in crafting personalized messages and experiences.
• Content Marketing: AI can dramatically speed up content production, but human involvement is still crucial for ensuring quality and generating truly unique and compelling stories.
• Branding: AI can assist in data analytics, extracting sentiment and trends from millions of data points. This information can guide branding strategies, but the human element is still required to craft the narrative and emotional connection that defines strong brands.
• Demand Generation: Automated systems can optimize ad placements, perform A/B testing, and personalize interactions at a scale impossible for humans, thereby increasing the efficiency of demand generation efforts. But the data also suggests that AI can sometimes get it wrong, meaning ongoing human oversight is essential for quality control.
• Sales Development and Sales: AI can automate repetitive tasks such as drafting outreach emails and logging activities, allowing sales teams to focus on more complex tasks. However, given that AI is still not perfect at understanding nuance, human involvement remains vital for relationship-building and closing deals.
• Customer Success: AI chatbots and automated support systems can handle a large volume of routine queries, freeing up human customer success managers to deal with more complex issues. The Centaur approach would work well here: let the AI handle the straightforward queries while human teams manage more complex customer needs.

Final Thoughts

AI offers significant advantages in automating and optimizing various aspects of a B2B go-to-market strategy. However, it’s crucial to remember that the technology is not a silver bullet. A hybrid approach, blending AI’s speed and data-crunching abilities with human creativity and nuance, appears to be the most effective strategy to optimize B2B go-to-market for the foreseeable future.

The post Can AI Really Make Go-to-Market More Productive? appeared first on Demandbase.

Be it software solutions, artificial intelligence, or go-to-market (GTM) strategies — the common thread behind the value is quality data. In GTM, data helps businesses find the right people and companies to sell to and make sure they’re reaching them in the right way. The right data underpins everything from building target account and contact lists to fueling sales prospecting and cleaning existing databases. And with buying centers more fragmented and buyer journeys more complex than ever, B2B data must go beyond mere lists of contacts and companies to include things like buying intent and existing tech stacks. 

This is why I was pleased to see Demandbase’s inclusion in Forrester’s recent landscape report on B2B marketing and sales data providers. At a time when data quality and privacy compliance are top of mind for B2B leaders, and generative AI is transforming the way companies use data, the report offers practical guidance on the changing dynamics in the B2B data provider space and how to evaluate potential partners.

Key Capabilities to Look for

Forrester defines B2B marketing and sales data providers as: Solutions that offer comprehensive data, insights, and data management services to optimize marketing and sales efficiency and effectiveness.

Every business has unique goals and challenges, and the report suggests you begin by understanding the key capabilities required for your highest priority scenarios.

There are two categories of capabilities to evaluate. First, the accuracy and completeness of the data across all key data types, and second, the core capabilities of the provider. 

The core B2B data types include:

• Account Identification: Match unidentified signals to accounts and interpret patterns of engagement
• Company data: Pinpoint the companies that matter most with information like industry, size, location, and news.
• Contact data: Connect with decision-makers using data such as name, title, email, and phone, plus social profiles and connections.
• Technographic data: Know what technologies a company already uses and what they’ll buy next, so you can identify accounts that are the perfect fit for your business.
• Intent data: Determine which accounts are in-market for the products you sell.

But how do you assess whether a provider can deliver accurate and complete data at scale? Don’t take the provider’s word for it. Don’t just think about the volume of their data — think about the quality as well. In order to select a vendor of choice, you need to get hands-on with sample data sets. Conduct apples-to-apples comparisons with the accounts and buying groups you know best. And ask questions about compliance and privacy practices, especially around contacts.

Forrester recommends evaluating other capabilities as well, including real-time search and import of data, matching capabilities, and native / API integrations. The key is to match the capabilities you need to the use cases you have. 

For example, if sales prospecting is critical, prioritize providers with seamless CRM integration and tools that put AI-driven insights directly into seller workflows. If you’re focused on ICP profiling or territory planning, look for robust firmographic and technographic data you can slice to uncover market potential. And if data unification and quality are top of mind, you’ll focus on API-level integration with CDPs, data lakes, and data warehouses as well as data hygiene capabilities and value-added services.

View Your Data Provider as an Ongoing Partner 

B2B data partnerships can’t be one-and-done purchases. The technology and depth of data you need will change over time. Expect to collaborate closely with providers on privacy standards, product innovation and more value from your data. At Demandbase, we make large investments in these collaborations because they directly strengthen our customer relationships and ability to drive growth. We also don’t try to trick our customers into automatically renewing with complex agreements and overly-aggressive opt-out periods.

In today’s landscape, data providers must be strategic advisors that actively guide customers into the future. Forrester’s report emphasizes the increasing importance of this consultative approach.

Key Takeaways for Selecting Your Next Data Partner

As you dive into vendor evaluations, keep these recommendations from Forrester’s report in mind:

• Thoroughly vet potential new primary providers, as switching later causes major disruptions.
• Conduct extensive side-by-side comparisons using sample data sets.
• Align capabilities to your core business scenarios and requirements.
• Ask detailed questions about privacy practices and opt-in data collection.
• Expect significant integration and training investments when changing providers.
• Seek long-term, consultative partnerships that enhance data value over time.

With the right data partner, your marketing and sales teams will be equipped to identify your total addressable market, refine target account selection, engage elusive prospects, and exceed revenue goals. The data landscape is more complex than ever, but by approaching partnerships strategically, you can thrive in today’s B2B marketplace.

To gain access to the full “The Marketing And Sales Data Providers For B2B Landscape, Q4 2023” report (available to Forrester subscribers or for purchase), visit here. 

The post The Marketing And Sales Data Providers B2B Landscape, Q4 2023: How to Select the Right Provider for Your Needs appeared first on Demandbase.