Harnessing the sales potential of a B2B decision engine

Across the sales funnel, business-to-business (B2B) sellers increasingly use artificial intelligence (AI)-powered decision engines to deliver personalized customer experiences. This emerging technology allows companies to link sales and marketing campaigns to reach decision-makers with tailored content and offers that anticipate their needs and guide them through the buying process—from awareness to a purchase, service and support. While early-stage results reflect decision engines’ ability to lift sales and elevate customer engagement, foundational issues often limit their potential.

Decision engines are advanced software tools that use AI and machine learning algorithms. They analyze large volumes of data and make next-best action recommendations based on variables such as a decision-maker’s industry, role, relationship and interactions with the seller and stage in the buyer journey. Predictions address a wide range of situations, from delivering personalized content through the ideal channel to determining optimal pricing and product or service features to gain competitive advantage.

In a typical B2B purchase scenario, decision engines help sellers create personalized experiences not just for a single buyer but also for multiple stakeholders on a buying committee. Beyond the software itself, organizations must have people and processes in place to produce modular content, manage a customer strategy across the funnel, train sales reps and use customer analytics to understand what drives engagement.

To unleash the full value of a decision engine, the organization’s customer strategy, content, operations and other technology components likely need to be retooled. When organizations are missing one or more of these, we see varying decision engine performance across similar companies and markets.

AI-based decision engines increasingly play a decisive role in B2B deals as organizations work to keep pace with industry trends and outpace competitors. To effectively stand up this new capability, organizations must have the core business processes, resources and technology infrastructure in place:

• Clean, high-quality data, including decision-makers’ activities, preferences and purchase history
• A firm understanding of the sales funnel
• A clear vision for optimizing omnichannel customer experiences at key stages of the buyer journey
• A robust library of content to tailor the experience

Based on an individual’s online behavior and role in the buying process, companies are going further in the development of modular content. The advent of generative AI enables B2B companies to mix and match content fragments to deliver an integrated message tailored to the customer’s content and tone preferences.

From there, business leaders can assess a decision engine on outcomes, in the context of new analytics, go-to-market strategies and content. Linking the improved functionality with customer use cases helps justify the expense, quantify the revenue potential and qualify improvements to the decision-maker experience. It also assists with prioritizing the timeline for when the capabilities should be implemented, consistent with their projected return on investment.

While the desire to assemble a decision engine internally is understandable, companies may not fully appreciate the expense and risk associated with starting this process from scratch. Not every organization has the financial, technical and data science resources available in-house to write custom algorithms that can perform at scale. Nor are they all equipped to build, test, train and maintain the engine itself and then sustain the engine with an ongoing product roadmap and governance. For companies that want to jump start their program, working with an outside consultant or vendor with a build-operate-transfer (BOT) license may be the way to accelerate time to market.

B2B suppliers who launch a decision engine through the BOT model can get up and running with off-the-shelf components and then integrate custom algorithms once they’re ready to launch. This best-of-both-worlds approach allows companies to begin generating returns on their initial investment while leveraging their internal expertise and proprietary algorithms for competitive advantage.

In competitive markets, the purchase process can be a key differentiator for decision-makers choosing between companies with similar products and services at comparable pricing. Equipped with a cloud-based decision engine and guided by next-best action recommendations, B2B sellers can consistently anticipate questions and concerns. They’re able to respond with personally relevant content based on an account’s prior interactions and the decision-maker’s specific role. When customers feel heard and cared for, their engagement, loyalty and revenue potential follow.

Within the next several years, we see decision engines taking on additional assignments. AI technology can help speed up internal operational efficiency, support expanded geographies and improve supply chain operations. This next set of opportunities—and others we can unlock as decision engines become more accurate, flexible and powerful—signals the blue-sky potential B2B sellers can harness to transform their business and speed innovation.

Implementing a B2B decision engine to deliver personalized sales and marketing content positions sellers to transform their customer engagement and business outcomes. Not only will this prepare organizations to create and deliver targeted content, such as personalized product recommendations, offers and marketing messages, but it also will equip their sales teams with valuable insights to identify the most relevant products or services for each customer and optimize sales pitches to drive conversions.