From insights to strategy: Next best action AI and its business impact

A memorable gift often results from someone considering all they know about the recipient’s tastes and preferences. This instinct to use available information to make better decisions is ingrained in human nature. Likewise, in a competitive marketplace where attention spans are shorter than ever, businesses must strive to create a memorable customer experience by responding to individual preferences and behaviors. In a world of abundant data and countless marketing channels, capturing and maintaining customer attention by leveraging available data to provide tailored experiences is difficult yet imperative. As Deloitte has noted, consumers no longer want to be categorized into broad segments. Instead, they expect individualized interactions and recommendations.

Meeting these demands requires a new approach to customer engagement. Traditional marketing techniques and classic statistical models struggle to cope with today’s data landscape’s sheer volume and complexity. Here, Artificial Intelligence (AI) comes to the forefront. It has the potential to transform customer experiences by enabling personalization at scale. AI can consolidate various data sources into a single customer profile and use advanced analytics to determine the optimal response to individual behaviors, often referred to as the Next Best Action (NBA). This approach applies across industries, from financial services to life sciences, where companies are reimagining customer engagement methodologies to align with the shift in consumer mindset.

Yet, amid these compelling arguments, one might still question why AI is necessary for this level of personalization. Why are traditional statistical models insufficient for these requirements?

In this article, we will answer these questions by delving deeper into the intricacies of Next Best Action AI, offering insights into its mechanisms and the key technologies that underpin it. Furthermore, we will explore its practical use cases across various industries, highlighting the transformative impact that AI can have on customer engagement strategies. As we navigate through the complexities of personalization and the role of AI in enabling it, we aim to provide a comprehensive understanding of the opportunities and challenges that lie ahead in the quest for a truly individualized customer experience.

• What is Next Best Action AI?
• Why is Next Best Action AI important?
• AI Next Best Action models
• How does Next Best Action AI work?
• Benefits of Next Best Action AI
  • Improved acquisition
  • Enhanced customer relationships
  • Boosting retention
• Next Best Action AI case studies
  • Case study 1: Improving the insurance quote process with Next Best Action AI
  • Case study 2: Next Best Action AI in retail
• Use cases of Next Best Action AI
  • Customer service
  • Marketing and sales
  • Financial services
  • Healthcare
  • E-commerce

What is Next Best Action AI?

Next Best Action is an approach powered by machine-learning algorithms that assist organizations in understanding and influencing customer behavior. It does so by identifying patterns in how customers interact with various touchpoints and predicting the actions most likely to lead to a successful outcome, such as a conversion. Unlike generic personalization strategies that rely solely on demographics or broad audience segments, NBA is grounded in data-driven insights that adapt to changing customer behaviors in real time.

The process begins with collecting customer behavioral data as they interact with different touchpoints across channels. This data informs the machine-learning algorithm, enabling it to recalculate the likelihood of conversion for various action options continually. It’s a dynamic approach that leverages real-time data to optimize customer interactions.

NBA decisioning involves choosing the most suitable channel to deliver the most appropriate message at the optimal moment. The goal is to tailor communications and offers to a targeted customer profile to maximize the desired outcome, often a purchase.

Creating a successful NBA model involves:

• Developing a comprehensive view of customer interactions across multiple channels.
• Identifying customer segments.
• Understanding the specific tactics that resonate best with each group.

This tailored approach can help create a differentiated customer experience that feels more personalized and relevant to the individual customer.

Moreover, NBA transcends the boundaries of individual departments, utilizing data-driven insights and analytics from marketing, sales, customer service, and more. It integrates data from all customer interactions across departments and leverages machine learning and AI to predict what a consumer may want or need next, be it content, a message, or an offer. By adopting this technique, companies can achieve true one-to-one personalization, taking their customer engagement strategies to the next level.

Why is Next Best Action AI important?

Next Best Action AI is a real-time decision-making process used by marketers to create tailored experiences for customers and prospects. Unlike pre-planned marketing efforts, such as seasonal direct mail campaigns, NBA operates in near-real time, responding to specific events or interactions occurring at a given moment. This real-time aspect is what sets NBA apart from other marketing approaches.

The primary objective of NBA is to create an optimal customer experience, which is essential for building customer loyalty and enhancing brand reputation. Take a retailer, for example, whose goal is to offer the best possible shopping experience across all platforms, whether in-store or online. As a customer interacts with the brand, the retailer needs the ability to respond to those interactions in ways that align with the customer’s preferences and needs.

However, personalizing the customer experience in this way becomes challenging at scale. It is one thing to tailor experiences for a handful of customers, but quite another to do so for thousands or millions. This is where Next Best Action AI comes into play, making it possible to understand context, analyze behavior, and generate decisions dynamically and in near-real time.

Next Best Action AI goes beyond the capabilities of rule-based algorithms, which are foundational to A/B and multivariate testing. While rule-based algorithms can inform basic decision-making, they struggle to handle the complexities of identifying behavioral cues at scale, especially when multiple actions are available. Machine learning, a subset of AI, excels in this regard by learning from vast datasets to pick up on patterns and nuances in customer behavior, enabling the generation of more precise and contextually relevant actions.

Additionally, Next Best Action AI improves customer engagement by offering a more agile, responsive, and personalized approach to marketing. By leveraging AI technologies, marketers can dynamically cater to each customer’s unique preferences and needs, enhancing the overall customer experience and ultimately driving customer satisfaction and loyalty.

Marketing cloud platforms such as Adobe and Salesforce have integrated AI and machine learning technologies that cater to diverse business areas, from sales and service to marketing. These advanced technologies facilitate personalization and optimization across customer interactions and marketing efforts.

• Adobe Sensei’s One-Click Personalization: Adobe Sensei’s Auto Target feature, also known as “One Click Personalization,” uses machine-learning models to learn which content layouts resonate most with consumers. This empowers marketers to tailor the customer experience based on insights into what is most likely to resonate with individual customers. In addition, algorithmically guided tools assist sellers in finding the optimal cadence for contacting prospects and recommend messaging strategies with the highest probability of closing deals.
• Salesforce Einstein’s Next Best Action: Salesforce Einstein employs Next Best Action technology to assist sellers in making informed decisions about product recommendations, the right time to make those recommendations, and the most effective channels to use. This is made possible through the embedded Einstein Prediction Builder on customer contact pages. NBA technology can also benefit contact center agents who often lack visibility into the channels customers have used prior to reaching out for help. As a result, agents may inadvertently frustrate customers by asking for information they have already provided. By using AI-guided interactions, Salesforce Einstein can prescribe the appropriate path forward, leveraging knowledge of successful resolutions to similar issues in the past.

In both examples, Next Best Action AI technology plays a crucial role in enhancing customer experiences, optimizing marketing efforts, and guiding sales and service interactions based on data-driven insights. Whether it’s through personalization, timely recommendations, or intelligent assistance for contact center agents, AI and machine learning technologies are transforming the way businesses interact with their customers.

AI Next Best Action models

Reinforcement Learning (RL) is a machine learning paradigm that is highly effective for determining the Next Best Action in various scenarios. It is based on the principle of learning from interactions with the environment, where an agent takes actions and receives feedback in the form of rewards or penalties. The goal of the RL agent is to learn a policy that maximizes the cumulative reward over time.

In RL, the decision-making process is modeled as a Markov Decision Process (MDP), which is a mathematical framework for modeling decision-making problems where outcomes are partly random and partly under the control of the decision-maker. An MDP consists of five main components: states, actions, rewards, transition probabilities, and discount factors.

• States represent the possible situations that the RL agent can encounter.
• Actions are the decisions that the agent can take in each state.
• Rewards are the immediate feedback the agent receives after taking an action in a state.
• Transition probabilities describe the likelihood of transitioning from one state to another after taking a specific action.
• Discount factors determine the importance of future rewards compared to immediate rewards.

The RL agent interacts with the environment by taking actions, observing the resulting states and rewards, and using this information to update its policy. The policy is a mapping from states to actions, and the agent aims to find the optimal policy that maximizes the expected cumulative reward.

There are various approaches to solving RL problems, including value-based methods (e.g., Q-learning), policy-based methods (e.g., Policy Gradient), and model-based methods. Deep Reinforcement Learning (DRL) combines RL with deep neural networks to learn complex policies from high-dimensional input data. DRL has been successfully applied to a wide range of applications, including robotics, recommendation systems, financial optimization, and game playing (e.g., AlphaGo).

In the context of Next Best Action, RL can be used to determine the most effective action to take for each customer in real-time, considering the customer’s historical interactions, current context, and potential future interactions. This can lead to improved customer satisfaction, increased revenue, and more efficient resource allocation.

How does Next Best Action AI work?

Next Best Action AI is a complex process that requires several key elements to function effectively. It operates by combining various technologies and strategies to provide tailored recommendations for customer engagement.

Single Customer View (SCV): The SCV combines all the data available about a customer, aggregating their interactions, transactions, and preferences across all touchpoints, creating a unified customer profile. This includes demographics, purchasing history, browsing habits, and even social media activities. By integrating data from CRM systems, marketing databases, transaction records, and more, organizations can gain a holistic view of their customer, allowing them to make more informed decisions and tailor their approach.

Real-time feedback loop: This involves capturing and ingesting new customer interactions as they happen, adjusting insights based on these updates. Each customer interaction, such as website visits, emails opened, links clicked, is recorded and timestamped. This information is then fed into the machine learning models that underpin NBA systems, allowing these models to continually learn and update themselves, thus improving the quality of recommendations over time.

AI and ML: The heart of the NBA system is AI and machine learning. These technologies process vast quantities of data and identify patterns in customer behavior, preferences, and interactions. Machine learning models can be trained on historical data and then tested on new, unseen data, ensuring they can make accurate predictions. These models use algorithms that can analyze data points in real time and make informed decisions on preferred channels, the best times for engagement, and the content, message, or offer to present.

Recommendations: These are predefined responses or actions that are presented to agents (or automated systems) as potential next steps to take in their interaction with a customer. Recommendations are stored as standard objects containing an image, text values, custom fields, and an assigned flow. It is crucial to plan the conditions under which they should appear and to whom they are directed. Also, an automation plan should be defined in case of acceptance.

Flows: Flows are the automation aspect of the NBA system. They are responsible for displaying the right recommendations to the right people at the right time, based on business criteria. These flows are typically designed and created using a flow builder, a visual interface that allows users to automate processes by defining triggers and actions without writing code. For example, a flow might be triggered when a customer visits a particular product page, prompting a recommendation for a related product.

Action strategies: These guide and determine which recommendations to show to agents based on data and business logic. Action strategies can be created for different situations, users, and departments. They can be created in both Flow builder and Strategy builder. Strategy builder has unique elements, such as recommendations, recommendation logic, and branch logic, enabling the declarative creation of recommendations.

Here’s a more detailed explanation of the process:

• Planning recommendations and automations: This initial step involves planning where and to whom the recommendations will appear, the conditions for each recommendation, and the path to follow upon acceptance. This planning stage is critical, as it helps to ensure that the NBA system will provide relevant and timely recommendations.
• Creating flows: Flows are created using a flow builder. These flows define the automated processes that will be executed if a user accepts or rejects a recommendation. For example, a flow could be designed to send a follow-up email with a discount code if a customer leaves a shopping cart abandoned.
• Creating recommendations: In this step, recommendations are created based on business rules, predictive models, and other data sources. These recommendations are context-specific, taking into account a customer’s recent interactions, their preferences, and even the current market conditions. Machine learning models can be used to optimize these recommendations further, ensuring they are as relevant and timely as possible.
• Creating action strategies: The action strategy is the logic that determines when and how to present a recommendation. It is created using either the flow builder or the strategy builder. The strategy takes into account the data and business logic, helping to ensure that the recommendations are shown to the right people at the right time.
• Displaying recommendations: Once the action strategies are created, the final step is to choose the page or interface to execute them and display the recommendations. This could be on an e-commerce site, within a customer support chat, or any other customer interaction point.

Benefits of Next Best Action AI

Improved acquisition

AI enhances the acquisition process in the media industry and other sectors by making it more efficient and cost-effective. When marketing teams aim for 1:1 personalization, they often face challenges due to limited data on individual prospects’ needs and preferences. Moreover, the expense of finding and engaging prospects can be considerable. However, AI can significantly optimize acquisition efforts by refining ad targeting and placement.

Traditional segmentation models tend to be updated infrequently due to the time and effort involved in manually analyzing and categorizing prospects. AI offers a more dynamic approach to segmentation by continuously analyzing prospect behavior at scale and automatically updating segments. By identifying emerging themes and trends in prospect behavior, AI can promptly add or remove prospects from designated segments, allowing for more precise targeting.

Beyond segmentation, AI plays a crucial role in selecting relevant messaging. AI can infer a prospect’s intent by analyzing their content consumption and behavior, enabling more tailored messaging. AI-powered recommendation engines take this personalization a step further by analyzing browsing and purchasing behaviors, demographics, and even look-alike models to generate product matches that are unique to each prospect. These engines use machine learning algorithms to identify patterns and predict the products that a prospect is most likely to be interested in.

Enhanced customer relationships

Existing customers provide a wealth of data that can be harnessed to make personalized decisions that strengthen customer relationships. Next Best Action AI technology has the potential to significantly enhance customer engagement by offering tailored recommendations and experiences across multiple channels.

Take, for example, a leading national bank that has leveraged NBA technology to improve the customer journey and experience. During every inbound call, call center support agents are presented with the next best product recommendations, which are informed by historical data on what has been successful or unsuccessful in the past. This targeted approach ensures that customers receive relevant offers, thereby increasing the likelihood of conversion and fostering deeper engagement.

In addition to product recommendations, the bank employs intelligent/predictive call routing (IVR) to enhance the customer experience. By analyzing the customer profile and the topic of the call, the IVR system matches the customer with a care agent who possesses the specific skills needed to address the customer’s needs. This targeted approach reduces the time spent on hold, increases first-call resolution rates, and creates a more personalized and satisfying experience for the customer.

The bank’s commitment to deepening customer engagement extends beyond the call center. Following every ATM transaction, customers receive a product recommendation that aligns with their preferences and needs. Subsequent follow-ups are conducted through the customer’s preferred communication channels, further enhancing the sense of personalization and demonstrating the bank’s commitment to meeting the customer’s unique needs.

The benefits of using Next Best Action AI technology for deeper customer engagement include:

• Tailored interactions: AI algorithms analyze customer data to provide personalized recommendations, making interactions more relevant and valuable to the customer.
• Enhanced customer experience: Intelligent call routing ensures that customers are matched with the most suitable care agents, reducing wait times and increasing the likelihood of a successful resolution.
• Multi-channel engagement: By offering recommendations and follow-ups across multiple channels (e.g., call center, ATM, email, SMS), the bank ensures consistent and personalized engagement regardless of the customer’s preferred touchpoints.
• Increased loyalty: Personalized experiences foster a sense of being valued, which can lead to increased customer loyalty and retention.
• Improved conversion rates: Tailored product recommendations are more likely to resonate with customers, increasing the probability of conversion.
• Data-driven insights: Next Best Action AI technology continually analyzes customer data to identify trends and patterns, enabling the bank to refine its engagement strategies and further improve the customer experience.

Boosting retention

AI’s continuous monitoring of customer behavior can be a powerful tool for proactive churn management, significantly bolstering customer retention efforts. Many organizations employ Next Best Action AI technology to combat churn by analyzing customers’ behavior patterns and providing targeted suggestions to call centers and retail outlets to help retain them.

Through a focus on higher-value customers, AI enables brands to pinpoint the primary causes of customer attrition, understand the factors contributing to customer retention, and optimize retention spending. The key benefits of this approach include:

• Proactive churn prevention: Instead of reacting to customer churn after it occurs, AI allows companies to proactively address the risk of churn by identifying behavioral patterns and triggers that indicate a customer might be considering leaving. Brands can then take targeted action to address customer needs and concerns, improving satisfaction and retention.
• Targeted customer retention efforts: By analyzing customer data, AI can help identify which customers are more likely to churn, enabling organizations to focus their retention efforts on higher-value customers who represent a more significant loss if they were to leave.
• Understanding attrition drivers: AI algorithms can examine vast amounts of customer data to identify the primary drivers of customer attrition, such as product dissatisfaction, poor customer service, or competitor offerings. With this information, brands can take targeted actions to address these issues and prevent future churn.
• Optimized retention budgets: Next Best Action AI technology can provide insights into the effectiveness of retention efforts, allowing brands to optimize their retention budgets by allocating resources to the most successful strategies.
• Improved customer experience: By understanding the factors influencing customer retention, brands can enhance the customer experience, increasing customer loyalty and reducing the likelihood of churn.
• Increased customer lifetime value: By retaining higher-value customers, brands can increase customer lifetime value, resulting in long-term revenue growth and profitability.

Next Best Action AI case studies

Case Study 1: Improving the insurance quote process with AI Next Best Action

Background: An auto insurance company wanted to enhance the experience for customers who were initiating a new insurance quote. They had invested considerably in bringing these customers to their homepage and were intent on making the quote process as smooth as possible. However, analysis revealed a high drop-off rate during the quote process. Customers often left the form incomplete, either because they didn’t have the needed information on hand, or they encountered questions that were irrelevant to their situation. This posed a dilemma for the insurer, who needed a complete questionnaire to generate an accurate quote.

Solution: The insurer decided to utilize AI to streamline the quote process and personalize the customer journey. They implemented Next Best Action AI strategies at three key stages:

1. Dynamic question generation: The first intervention used AI to dynamically generate the subsequent questions in the quote form based on the customer’s previous responses. The AI used data points from the customer’s existing responses to present the most relevant next question. As a result, the insurer could use the answers to the dynamically generated questions to provide a quote price range rather than a specific quote.
2. Optimized follow-up channels: In the next stage, the insurer used AI to determine the most effective channel for following up with the customer after the initial quote. Depending on factors like the nature of the quote and the customer’s preferred method of communication, AI would recommend a follow-up through email or a phone call.
3. Tailored product recommendations: Finally, the insurer deployed AI to analyze the myriad product options available and the customer’s propensity to choose certain options. Using this analysis, the insurer could make a targeted next best product recommendation that aligned with the customer’s initial quote and specific needs.

Outcome: The AI-powered interventions led to a marked improvement in the quote process. Customers enjoyed a more tailored and less frustrating experience, which translated to fewer drop-offs and more completed quotes. By offering dynamically generated questions, optimized follow-up methods, and tailored product recommendations, the insurer succeeded in making the quote process more engaging and customer-centric. The Next Best Action AI strategies provided the tools to guide customers seamlessly through the quote process, ultimately leading to increased customer satisfaction and a higher conversion rate.

Case Study 2: Next Best Action AI in retail

The scenario Consider an online retail scenario. A customer visits the website, browses products, reads product reviews, and adds items to their cart. However, they get distracted and leave the items in the cart without completing the purchase. This is where Next Best Action AI decisioning can play a crucial role. By leveraging past and current customer behaviors, NBA can drive the most suitable business action for this user.

Potential business actions For the hypothetical customer in this scenario, here are several possible actions:

• Sending an abandoned cart email
• Sending a push notification suggesting a product to purchase
• Making a customer representative call
• Sending a targeted offer or discount
• Displaying a personalized discount banner on the website
• Sending an SMS
• Engaging a Chatbot
• Encouraging the customer to sign up for the newsletter
• Doing nothing

The challenge lies in selecting the optimal decision for each unique user, considering the vast number of paths and actions they might have taken in the past. This is where NBA technology shines.

Real-time machine learning for NBA To implement NBA effectively, a combination of historical and real-time data is essential. Many NBA techniques solely rely on historical data due to the complexity of processing real-time consumer behaviors within a machine learning framework. However, real-time user information is crucial for a successful NBA experience.

With a decisioning pipeline configured for real-time inference, new decisions can be generated for every user based on their real-time behaviors. These decisions enable dynamic NBA targeting across user channels. Moreover, real-time inference allows decisions to be made for first-time or anonymous users, enhancing the overall customer experience and engagement.

Use cases of Next Best Action AI

Customer service

Customer service is a critical component of any business, and with the incorporation of Next Best Action AI strategies, there are numerous ways that customer service can be enhanced.

Here are two important use cases:

Improving customer satisfaction

Next Best Action AI tools can significantly enhance customer satisfaction in several ways:

• Real-time assistance: AI-driven models can analyze customer interactions in real-time, identify any issues, and suggest the best possible responses to customer service representatives. This allows representatives to address customer concerns swiftly and effectively.
• Proactive problem-solving: By leveraging historical customer data and ongoing interactions, AI can anticipate potential customer concerns before they occur and offer solutions. This proactive approach not only solves customer issues but also strengthens customer trust and loyalty.
• Consistent service quality: AI models can analyze vast amounts of data to identify patterns, preferences, and trends, helping customer service teams offer a consistent, high-quality experience to all customers.
• Feedback analysis: AI can help businesses understand customer feedback across multiple channels, identify areas of concern, and implement changes to enhance customer satisfaction.

Personalizing responses and interactions

Next Best Action AI tools can make customer interactions more personal and relevant by:

• Personalized communication: AI models can analyze customer behavior and preferences to tailor communication specifically to individual customers, making interactions feel more personal and relevant. Personalized communication can include product recommendations, targeted offers, or addressing customer concerns in a tailored manner.
• Context-aware responses: AI tools can assess the context of customer interactions, considering factors such as customer history, recent interactions, or transaction history, and provide relevant responses. This can lead to more meaningful and productive conversations with customers.
• Optimal channel selection: AI models can help identify the most effective channels for communication, considering customer preferences, engagement history, and response rates. This ensures that interactions occur through the customer’s preferred channel, whether that’s email, phone, chat, or social media.
• Intelligent chatbots: AI-driven chatbots can handle routine inquiries and deliver personalized responses, freeing up customer service representatives to focus on more complex issues. Chatbots can be designed to engage with customers in a conversational manner, creating a more human-like experience.

Marketing and sales

Next Best Action AI strategies have the potential to enhance the way marketing and sales teams operate. Here are two specific use cases of how Next Best Action AI strategies can be applied to marketing and sales:

Optimizing campaigns and targeting

Next Best Action AI tools can significantly enhance the effectiveness of marketing campaigns by identifying and suggesting the most appropriate actions to take. This can include determining which customers should be targeted, what channels should be used, and what message should be communicated.

• Customer segmentation: AI can analyze vast amounts of customer data to identify patterns and trends, creating customer segments based on factors like purchase history, demographics, or engagement patterns. This allows marketers to tailor campaigns to specific groups of customers with similar characteristics.
• Channel selection: AI models can help marketers identify the most effective communication channels for specific customer segments. This could include social media, email, text messages, or traditional mail. By targeting customers on their preferred channels, marketers can increase the effectiveness of their campaigns.
• Message personalization: AI can help marketers customize their messages for individual customers or customer segments based on previous interactions, preferences, and behavior. Personalized messages are more likely to resonate with customers, leading to higher engagement and conversion rates.
• Dynamic content optimization: AI tools can assess the performance of different marketing content in real time and automatically adjust campaigns to feature the most successful content. This enables marketers to continually optimize campaigns and achieve better results.

Cross-selling and upselling

Next Best Action AI strategies can significantly enhance sales by identifying opportunities for cross-selling and upselling. This involves recommending related or higher-value products to customers based on their preferences, purchase history, and browsing behavior.

• Product recommendation: AI models can analyze customer data to suggest relevant products for cross-selling or upselling, considering factors like purchase history, browsing behavior, or similar customer preferences. This helps sales teams recommend products that are more likely to be of interest to customers.
• Personalized offers: AI tools can help sales teams create tailored offers for individual customers, considering their preferences, purchase history, and interaction history. This increases the chances of successful cross-selling or upselling.
• Identifying the right time: AI can help sales teams identify the best moments to approach customers with cross-selling or upselling offers, considering factors like recent interactions, buying patterns, or seasonal trends. This ensures that offers are presented at the most opportune time.
• Price optimization: AI models can help sales teams determine the optimal price points for cross-selling or upselling offers, considering factors like customer sensitivity to price changes, competition, or market trends. This helps sales teams find the right balance between maximizing revenue and maintaining customer satisfaction.

Financial services

Next Best Action AI strategies have a considerable impact on financial services. Here are the two significant use cases of how Next Best Action AI strategies can be applied to financial services:

Risk assessment and fraud detection

Next Best Action AI tools can transform the way financial institutions assess risk and detect fraudulent activity. By leveraging AI to analyze a vast amount of data, financial organizations can identify and respond to potential risks or fraud more effectively and efficiently.

• Real-time risk assessment: AI can analyze vast amounts of data in real-time, including transaction history, credit scores, and other relevant financial data. This allows financial institutions to quickly assess the risk associated with a particular customer or transaction and take appropriate actions.
• Fraud detection: AI models can be trained to recognize patterns of suspicious activity and detect potential fraud in real-time. By analyzing patterns of transaction data, AI can identify anomalies and trigger alerts for further investigation, enabling financial institutions to take appropriate actions quickly.
• Behavioral analysis: Next Best Action AI tools can analyze customer behavior, such as spending habits or account activity, to assess potential risks. By identifying patterns of behavior that may indicate increased risk, financial institutions can take appropriate actions to mitigate that risk.
• Predictive analytics: AI can use historical data and predictive modeling to forecast potential risks or fraud. This enables financial institutions to take proactive measures to prevent future issues before they occur.

Investment and portfolio management

Next Best Action AI tools can significantly improve investment and portfolio management, helping financial institutions optimize their clients’ investments and achieve better returns.

• Investment recommendations: AI models can analyze a vast amount of market data, including historical trends, economic indicators, and company performance, to recommend suitable investment opportunities. By leveraging Next Best Action AI tools, financial institutions can offer personalized investment advice to their clients based on their specific goals, risk tolerance, and market conditions.
• Portfolio optimization: Next Best Action AI tools can help financial institutions optimize their clients’ portfolios by recommending asset allocations that maximize expected returns while managing risk. AI can also consider factors such as tax implications, liquidity needs, and market volatility when optimizing portfolios.
• Real-time portfolio monitoring: AI models can monitor portfolio performance in real-time, allowing financial institutions to respond to market changes quickly. By using Next Best Action AI tools, financial institutions can recommend adjustments to portfolios based on current market conditions, maximizing returns and managing risk effectively.
• Predictive analytics: Next Best Action AI tools can use historical data and predictive modeling to forecast market trends and asset performance. By leveraging AI-powered predictive analytics, financial institutions can make more informed investment decisions, helping their clients achieve better returns.

Healthcare

Next Best Action AI strategies have substantial applications in healthcare. Here are two significant use cases and explanations of how Next Best Action AI strategies can benefit healthcare:

Personalized treatment plans

Next Best Action AI tools can transform the way healthcare providers develop and implement treatment plans. By analyzing a wide range of patient data, AI can help healthcare providers personalize treatment plans to the specific needs of each patient.

• Data-driven diagnosis: AI can analyze vast amounts of data, including electronic health records, lab results, and medical history, to identify patterns and trends. This enables healthcare providers to make more accurate diagnoses and develop more effective treatment plans.
• Treatment optimization: AI models can assess the effectiveness of various treatments and recommend the most suitable treatment for each patient. By considering factors such as age, medical history, and current medications, Next Best Action AI tools can help healthcare providers optimize treatment plans.
• Predictive modeling: Next Best Action AI tools can use predictive modeling to forecast potential outcomes of different treatment options. This enables healthcare providers to assess the potential benefits and risks of each treatment and make more informed decisions.
• Personalized care: Next Best Action AI tools can help healthcare providers tailor treatment plans to the unique needs of each patient. By analyzing patient data, AI models can recommend treatments that are more likely to be effective and less likely to cause adverse effects.

Medication adherence and follow-ups

Next Best Action AI tools can help healthcare providers monitor and improve medication adherence and follow-up care.

• Medication reminders: Next Best Action AI tools can send automated reminders to patients, reminding them to take their medications at the prescribed times. This can help improve medication adherence, leading to better treatment outcomes.
• Follow-up recommendations: AI models can analyze patient data and recommend appropriate follow-up care, such as scheduling appointments or ordering lab tests. By leveraging Next Best Action AI tools, healthcare providers can ensure that patients receive the necessary follow-up care to achieve optimal treatment outcomes.
• Patient monitoring: Next Best Action AI tools can help healthcare providers monitor patient progress and identify potential issues. By analyzing data such as vital signs, lab results, and patient-reported symptoms, AI models can detect patterns and trends that may indicate potential problems.
• Adherence assessment: AI models can analyze patient data to assess medication adherence, identify potential barriers to adherence, and recommend strategies to improve adherence. By leveraging Next Best Action AI tools, healthcare providers can help patients overcome barriers to medication adherence and achieve better treatment outcomes.

E-commerce

Next Best Action AI strategies have considerable applications in e-commerce, helping online retailers improve their customer experiences and optimize operations. Here are two significant use cases and explanations of how Next Best Action AI strategies can benefit e-commerce:

Product recommendation and personalization

Personalization is crucial in e-commerce to enhance customer experiences and increase sales. Next Best Action AI tools can provide personalized product recommendations, ensuring customers find products that suit their preferences and needs.

• Individualized recommendations: AI models can analyze customer browsing behavior, purchase history, and demographic information to provide tailored product recommendations. By suggesting relevant products, Next Best Action AI tools can enhance the shopping experience and increase the likelihood of a purchase.
• Dynamic content: Next Best Action AI tools can personalize the content displayed on e-commerce websites based on customer data. By presenting personalized content, e-commerce platforms can engage customers more effectively and encourage them to explore and buy products.
• Behavior analysis: AI models can analyze customer behavior to identify trends and patterns, enabling e-commerce platforms to tailor their recommendations and content to current customer preferences and needs.
• Cross-selling and upselling: Next Best Action AI tools can help e-commerce platforms optimize cross-selling and upselling opportunities by recommending complementary or higher-value products based on customer data.

Inventory management and pricing optimization

Managing inventory and pricing efficiently is crucial for e-commerce platforms to minimize costs and maximize profits. Next Best Action AI tools can help online retailers optimize their inventory management and pricing strategies.

• Demand forecasting: AI models can analyze historical sales data, customer behavior, and external factors (such as weather and holidays) to predict future demand for products. By forecasting demand accurately, e-commerce platforms can optimize their inventory levels and reduce costs.
• Dynamic pricing: Next Best Action AI tools can help e-commerce platforms implement dynamic pricing strategies, adjusting prices based on factors such as demand, competitor pricing, and inventory levels. By optimizing pricing dynamically, online retailers can maximize their profits and remain competitive.
• Inventory optimization: AI models can analyze sales data, customer behavior, and external factors to optimize inventory levels for different products. By ensuring optimal inventory levels, e-commerce platforms can minimize stockouts and reduce carrying costs.
• Promotional strategies: Next Best Action AI tools can help online retailers optimize their promotional strategies by analyzing customer data and identifying the most effective promotions and discounts for different customer segments.

Endnote

Next Best Action AI demonstrates transformative potential for marketing and other industries, allowing for more efficient and effective actions that drive improved business outcomes. While implementing AI-driven NBA may require a multi-layered internal review process and investment in the beginning, the benefits of better response times, greater accuracy in content, and reduced costs outweigh the initial efforts. As AI technology continues to evolve, the capabilities of machine learning and deep learning will further strengthen the recommendations generated by NBA, making them more precise and impactful.

The realization of the full potential of Next Best Action AI doesn’t only rely on the technology itself, but also on the organization’s willingness to adapt and embrace this transformation. A solid strategy, a robust dataset for AI to learn from, strong executive sponsorship, and buy-in from the broader organization are essential for harnessing the power of AI-driven NBA. By embracing AI and taking a holistic approach, organizations can reap the rewards of optimized, intelligent decision-making, creating an environment that fosters growth, innovation, and customer satisfaction.

Streamline operations, boost efficiency, and transform customer experiences. Elevate your business today with LeewayHertz’s Next Best Action AI service.