AI strategies for B2B marketing leaders: How to win the buyer journey
How can B2B organizations differentiate themselves in the current market? Learn how AI is helping leaders solve common B2B challenges that have plagued organizations for decades.
By Josh Buchholtz, David Frigeri, and Laura Zalles
B2B companies have always faced complex and unique challenges across the marketing and sales journey, resulting from large, investment-heavy purchases engineered largely by people, not devices. While organizations have tried to combat these challenges with technology and data solutions, buying processes, team dynamics, and customer experiences still often fall short.
AI provides an opportunity to solve these problems.
While every organization faces its own set of challenges, from working with hundreds of B2B clients across industries, we’ve identified four common problems that AI can help solve which plague the majority of B2B businesses and their customers:
- Marketing communication is not always relevant to specific needs of prospects.
- Product information available to prospects is unorganized and overwhelming, leading to decision fatigue.
- Ineffective lead prioritization and support results in sales burnout, swivel chairing, and unoptimized buying experiences.
- Complex and cumbersome buying processes create long cycle times and stagnate new purchases.
These challenges result in elongated lead times, wasted marketing dollars, failed conversions, and poor experiences. A recent Gartner study found that 77% of B2B customers rated their own buying journeys as extremely complex or difficult.
As GenAI continues to overhaul the way businesses work, B2Bs have a unique opportunity to capitalize on AI/ML technologies to solve core problems that in the past have seemed unattainable. These technologies are empowering B2B leaders to tackle deep-rooted challenges in never-before-seen ways.
We’re already seeing B2B front-runners capture this AI opportunity. A 2023 global survey of over 2,000 businesses by the International Data Corporation found that early AI adopters have seen a 32% increase in customer retention and a 31% increase in business agility directly attributable to AI investments. By transforming the customer journey with AI/ML, B2Bs are doing more than optimizing processes or making marginal enhancements — they’re transforming expectations about what a B2B customer experience can be, immediately differentiating themselves from competition.
Let’s dive into how AI/ML can address key challenges in the B2B sales and marketing journey.
Problem 1: Broad-brush marketing communication
Marketing communication isn’t always relevant to prospects’ needs, creating inefficiencies and conversion risk in the key awareness stage of the buyer journey.
The challenge
One of the major challenges in the awareness stage of the B2B customer journey is ensuring that marketing communications and messaging are relevant and effective for each specific prospect. With buyer ecosystems that are more diverse than ever and often vary dramatically across regions, many B2Bs struggle to properly segment leads. This can easily result in broad-brush marketing communication rather than tailored, needs-based engagement.
By failing to understand what makes each prospect unique, which information prospects are interested in, and how to best communicate, B2B marketing effectiveness can come up short. This often results in some combination of confusion and frustration, and often leads to prospects simply considering other options.
Beyond challenges with segmenting and understanding leads, B2B marketing organizations are often plagued with content production and execution inefficiencies. In fact, 82% of sales leaders said that sales enablement content or delivery — most often owned by marketing teams — must significantly change to meet revenue goals in five years. This puts added pressure on B2B marketers to more deeply understand their audiences.
How can AI help?
Historically, B2B marketers have wrestled with the tension between being truly customer-centric via personalization at scale, and the large investment required to do so. AI now provides B2B CMOs a solution to this problem with two correlated AI/ML solutions:
- AI/ML models give businesses the ability to trawl through customer databases and all records of interaction to find correlations and clusters between customers and prospects. This greatly enhances B2Bs’ abilities to segment customers and prospects and removes much of the manual effort and complexity-related risk that are ubiquitous to existing segmentation processes.
- AI/ML models can identify the types of messaging and communications that have been most effective in the past based on both company and individual-level data. By leveraging large language models (LLMs), B2Bs can develop individualized engagement plays at scale, providing each customer with the level of personalized engagement that best fits them (but without the manual effort).
By utilizing these solutions in tandem, B2B marketing teams can segment customers in real time and deliver personalized messaging that addresses the right needs in the right way at the right point in the journey.
Problem 2: Information overload
Overwhelming and unorganized sales and product information across channels creates confusion and decision fatigue for prospects, driving lead abandonment.
The challenge
Most B2B products — whether a piece of machinery, a software system, or a financial product — are both technical and complex. Specifications and system integrations do not lend themselves to brevity. This can quickly lead to overly complex information being cascaded to prospects across a variety of mediums — company websites, sell sheets, pamphlets, digital selling tools, and more.
This oversaturation of information clouds value propositions and creates confusing digital journeys, requiring prospects to dig through large amounts of information to find the answers they need. Confusion leads to frustration, which can quickly result in lead abandonment. Even if a prospect moves forward, they may be left with a sour taste in their mouth.
For years, the power of the website and information self-service has been clear for B2B marketing organizations (approximately 66% of B2B buyers in the US discover products from internet search results, and 70% of the buying process is completed before any initial sales interaction,) yet many websites and digital experiences continue to fall flat.
It’s clear that having information available to potential customers is crucial for maintaining a diverse and healthy sales funnel. Some B2B organizations are deploying full self-service mechanisms for select products, with research showing that 75% of B2B buyers prioritize a rep-free sales experience. The more that B2Bs can enable the customer to explore and narrow the decision funnel on their own, the better.
How can AI help?
In the same way that AI/ML models can identify and segment customers in real time, models can assess interaction data — think, time spent on specific web pages or past search queries — and proactively identify the information a customer or prospect is seeking. Search algorithms have been using these models for years, and as AI/ML technology advances, B2Bs have a unique opportunity to bring in similar tools as “wayfinders” to help the customer navigate resources. These tools can accelerate a prospect’s ability to understand a B2B’s unique value proposition and which products or services best meet their needs. AI/ML tools can further be utilized to improve the search-and-find experience and help customers intuit what they need via conversational interactions with AI assistants.
Doing so helps narrow the decision window for product purchase. B2Bs that we work with are experimenting with LLM-integrated chat features and AI/ML-driven product recommendation tools that take millions of rows of customer, industry, and product data points to enrich buying experiences and help prospects make decisions easier. Leaders are also using this data to create more tailored customer content to accompany each sale.
With the use of AI/ML, information finding can become a casual and even enjoyable experience for prospective customers, rather than a long and arduous process that may still result in a call to customer support or sales. In addition, utilizing these models empowers sales teams with more data than ever about prospective customers. By creating richer digital experiences and feeding the data that comes from those interactions to sales teams, B2Bs can continue to iterate on their journeys and optimize based on real customer data.
Problem 3: Ineffective lead prioritization
Ineffective lead prioritization and support results in sales burnout, swivel chairing, and inefficient buying experiences.
The challenge
The B2B sales cycle is involved, with rounds of negotiation and communication between sales teams and leads to work toward conversions — each of which can have significant strategic importance. With limited resources, B2Bs must prioritize which leads to invest in and tailor that investment in the most effective way possible.
During the conversion stage of the buying journey, the opportunity cost of investing in a weak lead — or not investing in a potentially lucrative lead — can be catastrophic. However, the complexity of B2B product ecosystems can make it difficult to effectively prioritize across business lines, move resources toward the leads that matter, and maximize the ability of those resources to successfully convert the lead.
How can AI help?
AI/ML models are being trained on historical sales data and business objectives to sort and prioritize leads as they come in, automatically generating the next action (e.g., sales meeting) and proactively providing teams with the necessary context and strategy. These models empower sales teams with the information they need to work with each lead and automate tasks for meeting preparation. This means less time spent trawling through data and more time in direct, meaningful interaction with leads.
GenAI tools are also working from segmentation and lead data to create talking points and pitch decks tailor-made to each prospect’s needs. Similar to effective marketing communication, AI/ML models enable 1:1 sales targeting at scale instantaneously. This not only enables sales teams to support conversions with confidence but also off-loads manual work and lets them spend more time interacting with customers.
We’re working with selling organizations to develop AI/ML RFP assistant tools, which provides sales with insights generated from past wins to significantly reduce the time to respond to RFPs and ensure the quality of those responses.
Problem 4: Long cycle times
Complex and cumbersome buying processes create long cycle times and stagnate new purchases.
The challenge
As B2B customers attempt to negotiate the best option and complete a purchase, they often fall witness to the complexity of the B2B ecosystem and must trudge through complex and cumbersome sales processes. This complexity is created from the number of participants needed across the sales journey and the ability for a sales member to align these various buyers to a singular goal. As teams attempt to coordinate across this complexity, the result is often long lead times and reduced sales team efficacy.
Buyers are, of course, heavily invested in each purchase — and the stakes are high to make sure it’s the right one. With so much on the line, potential customers can be highly risk-averse and, without proper support, may pick a worse (but cheaper) option or even back away from the opportunity altogether. At the same time, we know that buyers are willing to invest in more expensive options, but only if the value is made clear to them.
How can AI help?
AI/ML solutions provide personalized purchasing support at scale, helping customers reduce risk and feel confident that the decision they’re making is the right one. While AI/ML models segment customers and identify their needs as they go through the purchasing process, GenAI can create impactful content and comparisons to help customers conceptualize the impact of each product. Instead of looking at product comparison tables or reading paragraphs of descriptions, customers can receive dynamic support that applies product outcomes to their unique needs and use cases. These models can even provide customers with business cases to take back to their teams as they work to provision the right product for their company.
Even with robust AI/ML tools enhancing the self-service experience, some situations — and customers — will still require additional customer service to complete the buying journey. B2Bs can further empower their customer service representatives with AI/ML tools and even utilize generative AI models to interact directly with customers.
We are working with several B2B clients to deliver AI-powered customer service across the sales journey, both in the form of digital humans and in the form of augmenting service representatives with the most opportune data. Our clients are able to enhance their call center experiences for both customers and employees by optimizing workflows and automatically probing for relevant information to increase the accuracy of responses. This serves to decrease wait times and increase overall customer satisfaction — both with the customer service experience itself and with the guidance they received.
We’ve also helped B2Bs bridge the gap between personalized support and self-service with AI service desks. Like a chatbot on steroids, the copilot tools in these service desks act as “digital humans” that guide the customer through product options and help them identify what will best suit their needs. They can also help current customers troubleshoot issues, make modifications, and even reorder or renew their purchases.
Conclusion
Even as adoption steadily increases across industries, AI/ML is still scary to many B2Bs, especially middle-market organizations that operate with fewer resources than their larger competitors. By infusing AI/ML into sales and marketing strategies, B2B CMOs and CROs are empowered to unlock the challenges that have long plagued the buying journey.
How are B2B clients we’re working with solving this challenge? And what is the actual ROI AI is delivering for marketing leaders? Stay tuned for Part 2 to learn more about real-world AI ROI analysis and dive into best practices for B2B organizations.
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