In today's B2B environment, delivering personalised customer experiences is a key component of successful engagement strategies. Companies are increasingly turning to data-driven approaches such as Next Best Offer (NBO) and Next Best Action (NBA) to predict and deliver the most relevant actions or offers to their customers. By integrating machine learning (ML) and real-time analytics, B2B companies can create personalised, contextual offers based on historical customer data and preferences. This not only increases interactions, but also conversion rates.
What is Next Best Offer/Next Best Action?
Next Best Offer (NBO) recommends the optimal product or service based on previous interactions, purchase history and real-time behaviour. Next Best Action (NBA) extends this approach by suggesting the next logical step in the customer journey - be it a targeted offer, a follow-up email or specific content that matches the customer's interests.
These approaches are designed to drive relevant and timely interactions that help organisations nurture leads, increase customer loyalty and ultimately improve their bottom line.
The role of machine learning and real-time analytics
The key to successful NBO/NBA implementations is machine learning and real-time data processing. ML algorithms analyse large amounts of data, learn from it and predict which actions or offers will be best received by each individual customer.
This is how it works in practice:
Collect data: First, customer data is aggregated from various sources - emails, website visits, transactions and other digital footprints.
Machine learning models: These models analyse historical data to identify patterns and predict future behaviour. If a customer shows an interest in a particular service, ML highlights this preference and suggests a suitable offer.
Real-time analytics: Companies can act on customer data in real time. This level ensures that offers and actions are based on the most recent interactions. For example, if a customer searches for a specific product on a B2B website, the system can immediately suggest a relevant offer at the moment of greatest interest.
Personalised recommendations: The result is personalised, contextual offers or actions that are perfectly tailored to the customer's needs, significantly increasing interaction rates and conversion.
Integration with marketing and sales processes
When implemented effectively, NBO and NBA can be seamlessly integrated into B2B marketing and sales processes:
Marketing Automation: NBO/NBA can be embedded into email campaigns, social media advertising and in-store recommendations to ensure customers receive the right message at the right time. Platforms such as Braze and Snowflake make it possible to automate and personalise this process at scale.
Sales enablement: Sales teams can use real-time insights to personalise their approach and follow-up. For example, if a customer shows interest in a particular service, the next logical action could be to offer a personalised demo or schedule a meeting to accelerate the sales cycle.
Customer segmentation: Account-based marketing (ABM) allows you to segment customers based on their value and engagement, so that the most valuable customers receive the most relevant and personalised interactions.
B2B example: NBO/NBA with Braze, Snowflake and Emporix
A fictional example shows how Snowflake, Braze and Emporix can work together to implement a Next Best Offer and Next Best Action strategy:
A B2B software company that sells digital tools to large enterprises has a top customer who is browsing the platform's advanced analytics capabilities but has not yet made a purchase.
Data Aggregation (Snowflake): Snowflake acts as a central data platform that combines all customer data - such as past purchases, website visits and ongoing interactions - into a single profile. By eliminating data silos, the company gains real-time access to this data and can build comprehensive customer profiles.
Predictive modelling (machine learning): Using ML algorithms, the company develops a model that flags the customer as a potential buyer of a premium analytics package. The prediction is based on past browsing behaviour and use of existing products.
Real-time engagement (Braze): Braze enables real-time customer interaction by delivering a next best offer via in-app notifications or email campaigns. As the customer continues to explore the analytics features, Braze can trigger a personalised message offering, for example, a discount on the premium package.
Transaction and fulfilment (Emporix): Once the customer has accepted the offer, Emporix ensures that the transaction runs smoothly. The offer is seamlessly integrated into the e-commerce workflow.
Increase customer loyalty and conversion rates
By using NBO/NBA strategies, companies can significantly increase customer engagement. Timely and relevant offers based on real-time data make customers feel understood, resulting in
Increased conversion rates,
contributes to higher customer retention and loyalty, and
Helps improve sales efficiency.
Conclusion
Integrating Next Best Offer and Next Best Action strategies into B2B marketing and sales processes is a powerful way to drive personalisation at scale. By leveraging machine learning and real-time analytics, companies can deliver more relevant and contextual offers that meet customer needs and improve business outcomes.
The combination of Snowflake, Braze and Emporix demonstrates how thoughtful technology integration can create seamless, personalised experiences that guide customers to the next logical action - increasing both customer satisfaction and business success.
I will show you how to apply NBO/NBA strategies.