How to find the next best action in a sales process?

Sales professionals often face the challenge of choosing the best action to keep a deal moving forward, whether it’s scheduling a follow-up call, addressing specific concerns, or offering tailored solutions. Making these decisions is challenging, as sales teams often operate with only partial information while trying to navigate shifting buyer preferences, market conditions, and competitive landscapes.

This is where AI comes in. By using data and predictions, AI can help sales teams determine the most effective next step at any stage of the sales process. In this article, we will explore how AI can assist in finding the "next best action" and how this approach can lead to better sales outcomes.

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What is the "Next Best Action" concept?

The "next best action" refers to the most effective step a sales rep can take to progress a deal. It could be anything from sending a personalised email, scheduling a follow-up call, or offering a special incentive. Identifying this action is crucial for maintaining momentum in the sales process and ensuring that prospects stay engaged.

However, choosing the right action is not always straightforward. Sales conversations are complex, with multiple variables to consider, including the customer’s pain points, their stage in the buying journey, and external factors like competitor activity. Knowing when to push forward and when to take a step back requires both insight and timing, and this is where AI's potential lies. By analysing patterns in historical sales data and ongoing customer interactions, AI helps pinpoint the most impactful action to take next, ensuring sales reps are always prepared to move deals forward effectively.

The key challenges in identifying the next best action

Identifying the next best action in a sales process comes with its own set of challenges. Here are some of the most common difficulties sales teams face:

• Complexity of sales deals: Modern sales are rarely linear. Prospects have varying needs, priorities, and decision-making processes. A single buyer might have multiple stakeholders, each with different interests. Understanding how to address these complexities and progress the conversation toward a sale requires clear insight into the context of each interaction.
• Limited information and uncertainty: Sales reps often have only a snapshot of a prospect’s needs or intentions. It can be difficult to make the right decision when much of the context, such as the buyer’s internal discussions or changing priorities, is unknown. This lack of information can lead to hesitation or missed opportunities to take timely action.
• Balancing multiple priorities: Sales professionals must juggle various priorities, including prospect follow-ups, proposal drafting, and handling objections, all while trying to advance deals. Determining which action will have the most significant impact requires not only intuition but also an ability to process multiple factors simultaneously.
• Evolving market dynamics: The sales landscape changes quickly, with new competitors entering the market, product features being updated, and customer expectations shifting. Keeping up with these changes and knowing how to adapt conversations accordingly is crucial for ensuring that the next action is relevant and timely.

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How AI can identify the Next Best Action

AI technology offers a way to navigate these complexities by providing data-driven guidance on the next best action in a sales process. 

Analysing historical data for pattern recognition

AI systems can analyse vast amounts of historical sales data to identify patterns in successful deals. By understanding which actions were effective in similar situations, AI can recommend steps that are most likely to move a prospect closer to a sale. This allows sales reps to leverage proven strategies tailored to each opportunity.

Real-time contextual recommendations

 AI systems provide recommendations based on real-time insights gathered from ongoing interactions. For example, if a sales rep is on a call and a prospect mentions concerns about pricing, the AI system might suggest highlighting a specific cost-benefit analysis or offering a payment plan to address the objection immediately. This ability to respond to shifting conversations helps sales reps stay agile and confident.

Personalisation at scale

One of AI’s biggest advantages is its ability to tailor actions to individual prospects and for each rep. Based on buyer personas, behaviour, and engagement history, AI can suggest the best way to reach out or follow up, ensuring every action aligns with the prospect's specific needs and context. This level of personalization increases the chances of maintaining engagement and advancing the sale.

Predictive modelling for deal progression

AI uses predictive modelling to assess the likelihood of various actions leading to a successful outcome. For instance, based on similar deals, AI might predict that offering a product demonstration is the most effective step to take after a certain type of conversation. This predictive approach ensures that every action taken is not just a guess but a calculated move toward closing the deal.

Measuring the effectiveness of AI-Driven Next Best Action

The effectiveness of AI-driven next best action strategies can be assessed from two perspectives: the sales representative's performance and the technical evaluation of the actions themselves.

Evaluating sales performance through KPIs

For sales teams, the impact of AI-driven recommendations is often measured through metrics like deal velocity, win rate, and customer responsiveness offer a clear view of how well AI suggestions translate into real-world outcomes. Tracking how quickly deals progress after taking recommended actions helps to identify which strategies are most effective at each stage of the sales funnel. For example, a rise in win rates after implementing AI-based proposals for handling objections indicates that these actions are effectively contributing to closing more deals.

A continuous feedback loop is crucial for refining these insights. Sales reps should regularly provide feedback on how well AI recommendations align with real-world interactions. This input helps further train and adjust the AI's predictive models, ensuring that the suggestions provided are not just theoretically sound but practically effective. Over time, this feedback-driven process enhances the quality of AI recommendations, helping to align strategies closely with the evolving dynamics of sales conversations.

Additionally, AI’s role extends to sales training. By tracking reps’ performance over time and identifying patterns in skill gaps, AI can recommend specific training sessions or coaching on certain techniques, making learning more personalised. This personalised development ensures that sales reps are better equipped to address objections, understand buyer needs, and confidently navigate complex deals.

Technical evaluation of action impact

On the technical side, AI doesn't just track results after the fact; it predicts the impact of each potential action in advance. By analysing historical data, customer behaviour, and contextual cues from ongoing conversations, AI can calculate the likely outcomes of various next steps. This predictive analysis allows AI to rank actions based on their potential effectiveness in achieving desired outcomes, such as advancing a deal or closing a contract.

This continuous evaluation of action impact makes the AI highly adaptable. As new data is introduced, whether from market shifts, competitor activity, or changing customer preferences, the AI recalibrates its models to predict more accurately which next actions will have the greatest influence on deal progression. This adaptability is key to maintaining an edge in dynamic sales environments, as it allows the system to remain proactive and ahead of emerging trends.

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Finding the next best action in a sales process is critical for success. While traditional approaches rely on intuition and experience, AI has transformed this decision-making process into a data-driven strategy. By analysing past interactions, predicting outcomes, and offering real-time recommendations, AI enables sales reps to take the most impactful steps at the right time.

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