By Gigarev, based on research from Dentsu’s 2022-2024 B2B Customer Journey Index
In today's rapidly evolving B2B landscape, customer satisfaction is no longer a “nice-to-have”—it’s a critical differentiator. However, as recent research from Dentsu highlights, satisfaction levels across the B2B customer journey have stagnated since their decline in 2023, revealing an urgent need for companies to rethink and optimize their buyer experiences.
At Gigarev, we believe that this underperformance presents an immense opportunity for B2B organizations to leverage advanced technologies like Large Language Models (LLMs) and Large Action Models (LAMs) to streamline and personalize every stage of the B2B journey. These technologies can help transform a traditionally cumbersome and fragmented process into a seamless, friction-free experience that fosters stronger customer relationships and drives long-term success.
Key Findings from Dentsu’s Research
Based on the research by Dentsu (2022-2024 B2B Customer Journey Index), satisfaction levels remain worryingly low across several key stages of the B2B buying process, with minimal improvement seen in the last two years. Below is a summary of the insights:
Stage of the Customer Journey | SP2 (2022) | SP3 (2023) | SP4 (2024) | Commentary |
Initial Research | 38% | 36% | 38% | Small fluctuation, no significant improvement. Initial research remains a sticking point. |
Initial Interaction with Supplier | 41% | 39% | 42% | Slight improvements over time; dissatisfaction continues at this stage. |
Diagnosing Requirements | 43% | 42% | 43% | Minimal progress, signifying persistent difficulties. |
Quality of Offer Received | 48% | 46% | 45% | Steady decline in satisfaction over time. |
Negotiation/Contracting | 43% | 41% | 41% | Marginal decline, with ongoing challenges. |
On-Boarding | N/A | N/A | 41% | Shows that this final step in the journey is also under-optimized. |
These insights show that despite the digitalization and data-driven capabilities available, many B2B organizations are still struggling to deliver positive customer experiences across the entire journey—from initial research to onboarding.
Key Challenges in the B2B Customer Journey
Initial Research & Interaction: B2B buyers often struggle to find relevant information quickly, leading to frustration during the early stages of their journey. This negatively affects their perception of the company and reduces trust, hindering progression through the sales funnel.
Diagnosing Requirements: Accurately diagnosing buyer needs remains a challenge, often leading to mismatched solutions. This weakens the value proposition and makes the buyer hesitant about the offer.
Quality of the Offer: The perceived value of offers has declined over time, signaling a disconnect between what buyers need and what companies are providing.
Negotiation & Contracting: The process of negotiating and finalizing deals remains tedious and time-consuming, creating a bottleneck in the journey.
Onboarding: This stage, crucial for setting the tone of long-term relationships, is currently under-optimized and fails to create a smooth transition from purchase to adoption.
The Role of LLMs and LAMs in Optimizing the B2B Journey
At Gigarev, we see the integration of Large Language Models (LLMs) and Large Action Models (LAMs) as a game-changer for addressing the gaps in the B2B journey. These technologies offer the potential to create highly personalized, seamless experiences that not only satisfy customers but also make the entire buying process more efficient.
1. Enhancing Initial Research & Supplier Interaction
With LLMs, B2B organizations can offer real-time, personalized responses to buyers' queries. Instead of providing generic information, AI models can deliver contextually relevant data, enhancing the initial research phase. Buyers get what they need faster, reducing friction and boosting trust early in the process.
Example: LLMs can analyze the user’s research history and purchasing behavior to deliver tailored product recommendations, relevant case studies, and clear next steps, resulting in more meaningful early interactions.
2. Improving Diagnosis of Buyer Requirements
LAMs can transform the diagnosing requirements stage by automatically assessing a buyer's needs based on prior interactions, industry data, and past performance. This technology can recommend more precise solutions, ensuring that offers are aligned with the buyer’s specific pain points.
Example: By incorporating machine learning models, a B2B company could detect patterns in a buyer’s business challenges and propose the most relevant service or product package. This eliminates the guesswork and makes the buying decision easier.
3. Tailoring Offers for Maximum Impact
LLMs and LAMs enable dynamic, data-driven offer generation. Rather than offering a static proposal, companies can present highly customized solutions that resonate deeply with the buyer's unique challenges and objectives.
Example: AI can help sales teams create personalized proposals, adjusting the offer based on buyer feedback and predicting potential objections. This adaptability results in a higher perceived value and a greater likelihood of deal closure.
4. Streamlining Negotiation and Contracting
Contracting in the B2B world can be complex and time-consuming. LLMs can help automate much of this process by providing contract templates, drafting custom terms, and offering real-time updates on negotiation status. This reduces delays and minimizes the risk of miscommunication.
Example: AI-powered contract automation tools can draft agreements that reflect the specific requirements of both parties, offering flexibility and reducing back-and-forth negotiations.
5. Creating a Smooth Onboarding Experience
LAMs can orchestrate the onboarding process by automating task assignments, ensuring that each new client receives a tailored, step-by-step onboarding plan. By integrating with customer management systems, LAMs can ensure that every client’s journey from purchase to adoption is streamlined and optimized.
Example: Automatically assigning key onboarding tasks to both the internal team and the client ensures all onboarding steps are completed efficiently and correctly, reducing time to value and improving client satisfaction.
The Future of AI in the B2B Journey: AI Agents for Both Buyers and Sellers
Looking ahead, the future of B2B buying will likely involve AI agents on both the buyer’s and seller’s side to fast-track the process and further improve the buying experience. Imagine intelligent agents negotiating, answering questions, diagnosing needs, and even customizing offers in real-time, all without the need for human intervention at every step.
These AI agents would be able to:
Instantly retrieve and compare offerings based on the buyer’s specific needs and context.
Negotiate contracts dynamically based on preset preferences and constraints.
Adapt recommendations on the fly to match buyer feedback in real-time.
Provide ongoing support post-purchase, ensuring that the transition from buying to adoption is frictionless.
By deploying AI agents, B2B companies can significantly reduce the time to close deals, increase the relevance of their offerings, and improve the overall buyer experience. This is not just about automation but about creating a smarter, more responsive, and tailored experience that both buyers and sellers can benefit from.
Why This Shift is a Must-Do for B2B Companies
The B2B journey is long, complex, and involves multiple stakeholders. However, buyers are increasingly demanding frictionless, consumer-grade experiences. With decision-makers more digitally savvy and expecting more from their vendors, it is crucial that companies optimize every touchpoint, from research to adoption.
Here’s why B2B companies cannot afford to ignore this shift:
Complex Decision-Making: The typical B2B buying cycle involves multiple stakeholders and touchpoints. LLMs and LAMs simplify this process, offering clarity, speed, and precision at every stage.
Improved Retention: Satisfied customers are more likely to return and recommend services to others. This not only increases customer lifetime value but also strengthens a company’s reputation.
Data-Driven Insights: LLMs and LAMs allow companies to capture vast amounts of data, providing actionable insights that improve customer interactions and decision-making.
Cost Efficiency: Automating key stages of the journey saves time, reduces errors, and enhances customer satisfaction, ultimately reducing acquisition and operational costs.
Conclusion: The Future of B2B Customer Experience
The stagnation in B2B satisfaction levels highlighted by Dentsu’s research should act as a wake-up call for companies to embrace advanced technologies like LLMs and LAMs. At Gigarev, we believe that optimizing the customer journey is no longer an option—it’s a necessity.
Companies that invest in transforming their customer experience with AI-driven solutions will benefit from greater efficiency, more personalized interactions, and higher levels of customer satisfaction. By making this investment today, B2B companies can position themselves as industry leaders, offering their customers the frictionless, high-quality experiences they expect and deserve.
The future holds even more exciting possibilities, with AI agents working on both sides of the B2B equation to fast-track negotiations, improve relevance, and deliver better results, faster.
Research Source: Dentsu’s B2B Customer Journey Index 2022-2024
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