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How to embrace AI to unlock B2B personalization

Cameron Turner
Matrix of purple and blue data

Harnessing the power of next-best action models in B2B sales and marketing

Person to person. Sales rep to customer. This has been the traditional B2B business development approach. Over the last few decades, technology investment in sales and marketing has focused on automating processes, providing appropriate self-service, and enabling a consultative sales approach. These investments have often been examined through a lens that assumes that more sales reps, improved vendor relationships, and other non-technical initiatives will result in higher ROI. However, the rise of AI and its role in personalization is changing this picture.

Personalization (and its AI/ML foundations) is at the heart of the next wave of technical advancement that will transform B2B business development. It’s a leap forward in innovation that leaders can’t ignore as it forces its way to the top of the ROI ladder for many large organizations. In this complex, human-centric world, organizations are just scratching the surface of what personalization can enable. We believe that leading organizations will choose next-best action models to harness AI and amplify conversion, loyalty, and advocacy.

Account-based marketing (ABM) enables companies to construct a dynamic brand personalized to each customer and finely tuned to the interests of the viewer. Personalized sales enablement follows a similar course but goes deeper into the more connected world of customer sales, creating relationships built on a foundation of data.

What is personalization?

Personalization goes beyond addressing a client by their name or recommending relevant products. It involves understanding the unique preferences, pain points, and goals of each business, and then systematically customizing the experience your company provides across the entire customer engagement cycle.

The solution you can’t afford to ignore

Next-best action (NBA) models use machine learning algorithms to recommend personalized strategies and actions based on a deep understanding of each client. This serves as a guiding force for sales, marketing, and service personnel, directing their efforts toward the most effective strategies for each client.

These models help organizations stand out in competitive markets, with benefits including:
Matrix of purple and blue data

Creating a next-best action model

There are seven key and interlinked steps in developing and managing this type of model:

  1. Develop individual models
    The longest step is the first An NBA model is a connected and enhanced combination of many individual AI models. Because of the time and effort needed to build and operationalize multiple models, each must be independently valuable to ensure the organization builds momentum on the journey!

    Examples of foundational AI models include but are not limited to:
    • Product recommendations
    • Dynamic pricing
    • Loyalty program
    • Customer segmentation
    • Customer lifetime value

  2. Integrate models and data
    If not already connected, bring together the data and algorithms from the individual models. It is crucial to have these models and their corresponding data in an environment that allows for interoperability.

  3. Develop and tune NBA model
    Once the information is together, it’s time for the main event: developing and tuning a decision framework model, focusing on the next-best action to take to manage the customer. Personalization doesn’t stand still. Client needs, market dynamics, and business landscapes are constantly evolving, so the next-best action model should too.

  4. Score alternatives
    There are countless ways to interact with a customer, and both the content and method of communication should be considered. By analyzing previous successes and failures in the market, each approach can be evaluated and ranked. By combining these learnings with customer insights, an organization can rank all options based on their probability of success. However, the final call should fall to a human, based on their wider knowledge which may not be represented in the data that a model was trained on.
     
  5. Create user interface
    To make a next-best action model practical and accessible, a lightweight and user-friendly interface for sales and marketing teams is essential. This interface should provide actionable insights, recommendations, and a clear rationale behind each suggested action.
     
  6. Train users and manage adoption
    All of the above should be built with the input of a group of sellers and marketers to ensure relevance and value. Once all MVP (minimum viable product) components are in place, it’s time to train the remaining users and measure and manage adoption via a dedicated adoption acceleration and/or change management program.

  7. Iterate
    Actions based on model outputs will generate successes and failures. Logging these events and recycling them back into training data for future model training increases accuracy over time, even as new options and creative approaches are explored beyond the limitations of the model’s recommendations.

The power of personalization is accelerating and is now a vital part of the B2B business model. By combining the strengths of single AI models and integrating them into a next-best action system, enterprises can significantly enhance the success of their sales and marketing strategies.

Being able to understand, predict, and respond to client needs on a personalized level not only strengthens client relationships but also positions businesses as industry changemakers. B2B leaders must embrace the transformative potential of personalized, data-driven approaches and invest in the development and implementation of personalization models with the next-best action model as their North Star.

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