Customer Experience Tools
Next-best-action models recommend in real-time actions that a sales or service person should take with a customer, such as an offer or a troubleshooting solution, based on the customer’s profile, previous actions and needs. The models are powered by artificial intelligence and deployed to acquire and retain customers at each customer episode.
Next-best-action tools use predictive analytics that enable companies to track a dynamic view of their customers and use this information to better understand context and anticipate customer needs. This results in relevant and personalized interactions with customers across all channels, leading to better experiences, improved employee productivity and higher customer lifetime value.
While analytics will not replace human judgment, it has become reliable enough that commercial leaders should start incorporating analytics into prescriptive playbooks. Organizations that refine their processes to take advantage of these approaches will raise the return on their sales and marketing activities and reduce service costs.
How companies use next-best-action models
- Deliver specific messages. A model can prescribe content and messaging relevant to the customer’s segment, buyer persona, industry and stage in the sales process. Content that has already been customized to communicate a particular value proposition can be triggered automatically. Some companies also use multivariate testing to optimize language in call scripts so that reps say precisely the right thing at the right time. Most marketing plans and dynamic content serving should be constantly tested against predictive analytics.
- Invest in the right sales opportunities. Analytics-based pipeline scoring applications can independently assess the probability that a deal will close, based on modeling the attributes of comparable past deals won or lost. That reduces the time sales reps spend chasing low-probability deals.
- Focus on specific offerings. A recommendation engine determines the appropriate offer based on the characteristics of the specific customer and opportunity. This is particularly helpful for organizations with large product sets or complex offerings that require configuration or customization.
- Take specific sales actions. Insights derived from analyzing successful pursuits or sales reps help define the appropriate sales cadence through the right channels. A company can prescribe cadences to its reps and maintain them through periodic reminders.
- Take preventive actions to minimize churn. Dynamic customer context allows employees to act immediately and with relevance. Personalized interactions to provide the right offer or content in the right channel at the right time, or even predict an issue before it occurs, reduces churn and avoidable service interactions.
- Design for impact. Pick the micro-battles, where you can create the greatest effect, such as a particular stage of the sales process, or certain types of troubleshooting interactions. Base your decisions on current customer pain points or drop-offs, or where automation can provide the most efficiencies and improve economics.
- Build a comprehensive view of your customer. All information gathered on your customers will help your model become more accurate. Information includes such things as purchasing behavior, preferences, dislikes and previous offers.
- Test recommendations. Try testing approaches such as A/B and multivariate to prioritize recommendations and feed the model accordingly. Communicate regularly with users about how the model improves outcomes for them, and solicit their feedback.
- Integrate into technology framework. The accuracy of a model depends on the completeness of its data sources. Artificial intelligence and machine learning can help with accuracy. Integrating this tool with others, such as customer relationship management and social-media sentiment tools, allows you to collect more comprehensive data and deliver recommendations to users at the right time in the right technology medium.