AI Business Use Case in Sale - Overview Part 2

For part 1, please refer here - AI Business Use Case in Sale - Overview Part 1

4. Churn prediction

Learn more about Churn prediction here.

Churn prediction- Historical Data Analysis

Description

Analyzing historical data to identify patterns or trends among customers who have churned in the past. This method often involves looking at usage patterns, customer feedback, and other relevant metrics.

Use Case

Imagine a telecommunications company, CommuniTech Inc., leveraging AI to analyze historical data for churn prediction. The AI system sifts through years of customer data, identifying patterns associated with churn such as reduced usage, frequent complaints, or changes in payment behavior. By comparing current customer behavior against these historical patterns, the AI predicts which customers are at risk of churning. For instance, if a customer's monthly data usage suddenly drops or there's a spike in complaint calls, the system flags them as churn risks. CommuniTech Inc. can then proactively address these customers' concerns, offer tailored promotions, or engage in other retention strategies to prevent churn. By employing AI to analyze historical data, CommuniTech Inc. significantly reduces churn rates, leading to increased customer loyalty and sustained revenue streams.

Churn prediction- Survival Analysis

Description

This statistical method estimates the time until an event occurs (e.g., customer churn). It's particularly useful in understanding not just if, but when customers are likely to churn.

Use Case

T-Mobile, a notable telecom entity, leveraged Survival Analysis to gain insights into customer retention amidst a fiercely competitive market. With the telecom landscape evolving, T-Mobile and other providers aimed to enhance customer experiences and reduce churn. By understanding the subscription lifecycle and identifying customers at risk of churn, they could devise strategies to prolong customer engagement. Databricks facilitated this endeavor by introducing the Telco Customer Churn Predictor Solution Accelerator, which employed Survival Analysis methods like Kaplan-Meier, Cox Proportional Hazards, and Accelerated Failure Time on sample telco datasets to build churn prediction models. These models helped in calculating the lifetime value of subscribers, thereby enabling more informed decision-making towards minimizing churn

Churn prediction- Sentiment Analysis

Description

Analyzing customer feedback, reviews, and social media mentions to gauge customer satisfaction and predict churn. Sentiment analysis can provide insights into how customers feel about a product or service, which can be indicative of churn risk.

Use Case

Imagine a streaming service provider, StreamFast, noticing an uptick in churn rates over the past few months. To understand the underlying issues and predict future churn, they deploy an AI-powered Sentiment Analysis tool. This tool scans through thousands of customer reviews, social media mentions, and customer service interactions to gauge the sentiment towards their service. Over time, the AI identifies negative sentiment trends associated with certain content changes and service interruptions. By correlating these trends with churn data, StreamFast can predict when and why customers might cancel their subscriptions. With this insight, they proactively address identified issues, tailor their content strategy, and engage with at-risk customers to mitigate dissatisfaction and reduce churn, thereby successfully leveraging Sentiment Analysis for churn prediction.

Churn prediction- Cohort Analysis

Description

Grouping customers into related cohorts and analyzing their behavior over time. This method can provide insights into how different groups of customers behave and how likely they are to churn.

Use Case

Picture an online subscription-based fitness platform, FitLife, experiencing a surge in churn rates. To unravel the underlying issues and foresee potential churn, they employ an AI-driven Cohort Analysis tool. This tool segregates their user base into distinct cohorts based on sign-up dates, engagement levels, and demographic factors. By analyzing the behavior and churn patterns within each cohort over time, the AI uncovers that younger users who signed up during the pandemic exhibit higher retention rates compared to older demographics. Furthermore, it identifies that users who engage with the platform's community features are less likely to churn. Leveraging these insights, FitLife refines its marketing strategies to target similar demographics and enhances community engagement features to bolster retention. Through AI-empowered Cohort Analysis, FitLife gains a nuanced understanding of its user base, enabling proactive measures to mitigate churn and foster a more loyal customer base.

These methods can be used individually or in combination to build a more accurate and comprehensive churn prediction model, which in turn helps businesses take proactive steps to retain customers and improve their services.

Searching for the optimal AI solution for Churn prediction issue? Consult with Stevie AI!

5. Upselling and Cross-Selling

Learn more about Upselling and Cross-Selling here.

Upselling and Cross-Selling – Recommendation Systems:

Description

AI-powered recommendation systems, like those used by Amazon, analyze customer purchasing history and browsing behavior to suggest additional products or more expensive items.

Use Case

Tinder: Unlike Amazon and Netflix, which incorporated recommendation systems into their existing applications, Tinder was designed from the outset to reap the benefits of this technology. The app utilizes a recommendation system to suggest potential matches to users based on various factors like location, mutual interests, and preferences

Upselling and Cross-Selling – Email Marketing Automation

Description

AI can automate email marketing campaigns by analyzing customer behavior and preferences to send personalized product recommendations, discounts on higher-value items, or suggestions for complementary products.

Use Case

Dell utilized AI for their Email Marketing Automation. They automated their email newsletters, which led to a 50% increase in click-through rate (CTR). This effort demonstrated that despite email marketing being a traditional channel, the incorporation of AI could significantly enhance its effectiveness, making it a more potent tool in reaching and engaging customers

Upselling and Cross-Selling – Chatbots and Virtual Assistants:

Description

AI-driven chatbots and virtual assistants can engage customers in real-time on websites, suggesting additional or higher-value products based on the customer’s current selections.

Use Case:

1. Domino's: Domino’s Messenger Bot allows customers to place orders via Facebook Messenger, demonstrating how chatbots can simplify and personalize the ordering process2.
2. Google, Facebook, and Microsoft: Tech giants have also developed chatbots; for instance, Google's Meena, Facebook's BlenderBot, and Microsoft's Tay and Xiaoice are designed to engage users in conversation, showcasing the potential of chatbots in various interactive applications2.
3. E-commerce and SaaS Companies: Numerous e-commerce brands and SaaS companies have deployed chatbots for a range of purposes like creating awareness, engaging new audiences, and increasing customer engagement. For instance, chatbots like MobileMonkey have been used for engaging new audiences, and others like Kiehl’s chatbot assist customers in finding the right products3.
4. Impact of AI Chatbots: AI Chatbots have proven to significantly impact businesses, as seen in a study showing a 87% higher conversion rate, 70% service cost reduction, and a 91% satisfaction rate when employing conversational AI chatbots, indicating the substantial benefits they bring to companies across various industries

Upselling and Cross-Selling – Chatbots and Virtual Assistants:

Description: AI-driven chatbots and virtual assistants can engage customers in real-time on websites, suggesting additional or higher-value products based on the customer’s current selections.

Use Case:

1. Domino's: Domino’s Messenger Bot allows customers to place orders via Facebook Messenger, demonstrating how chatbots can simplify and personalize the ordering process2.
2. Google, Facebook, and Microsoft: Tech giants have also developed chatbots; for instance, Google's Meena, Facebook's BlenderBot, and Microsoft's Tay and Xiaoice are designed to engage users in conversation, showcasing the potential of chatbots in various interactive applications2.
3. E-commerce and SaaS Companies: Numerous e-commerce brands and SaaS companies have deployed chatbots for a range of purposes like creating awareness, engaging new audiences, and increasing customer engagement. For instance, chatbots like MobileMonkey have been used for engaging new audiences, and others like Kiehl’s chatbot assist customers in finding the right products3.
4. Impact of AI Chatbots: AI Chatbots have proven to significantly impact businesses, as seen in a study showing a 87% higher conversion rate, 70% service cost reduction, and a 91% satisfaction rate when employing conversational AI chatbots, indicating the substantial benefits they bring to companies across various industries

Searching for the optimal AI solution for Upselling and Cross-Selling issue? Consult with Stevie AI!

6. Training and Onboarding of Sales Personnel

Learn more about Training and Onboarding of Sales Personnel here.

Training and Onboarding of Sales Personnel – Inconsistent Training

Description

Without AI, training can be inconsistent as it largely depends on the trainer's skills, knowledge, and ability to communicate effectively. This inconsistency can lead to varied performance levels among sales personnel.

Use Case

A retail chain named RetailSuccess faced a challenge where, despite having a solid training program, the results across their sales managers were inconsistent. Some managers excelled while others struggled to apply their learning on the sales floor. The introduction of AI in their training program helped bridge this inconsistency, ensuring a more uniform level of competency across all sales managers.

People.ai developed an “Activity Trend Graph” tool that allows sales leaders to track sales rep activities by account, opportunity, emails, phone, meetings, and more. By using this tool, sales leaders could identify where reps weren't spending the right amount of time and provide coaching nudges at the right moment, ensuring a more consistent training and coaching process.

Companies have started using AI sales training solutions that are always available for practice, unlike traditional training methods that might not be as accessible. This constant availability ensures sales professionals can practice multiple times to master the necessary skills, bringing a more consistent level of training among the sales personnel.

A managing director mentioned how his team developed an innovative training format that mixes AI and real videos to simulate a virtual customer situation. This blend of AI and real-life scenarios helped in creating a more consistent and engaging training experience, aiding in better preparation of sales personnel for real-world customer interactions.

Training and Onboarding of Sales Personnel – Time-Consuming Process

Description

Traditional training and onboarding processes can be time-consuming, which could delay the readiness of sales personnel to perform their tasks effectively.

Use Case

A real-life example illustrating how AI can alleviate the time-consuming nature of training and onboarding sales personnel is provided by the company Beyond Retro. They needed to swiftly upskill their salespeople to handle a significant portion of store responsibilities due to downsizing prompted by COVID-19. The training process had to be both quick and scalable, especially as they were planning to expand with new stores, showcasing the potential of AI in expediting training processes and ensuring they are well-adapted to the company's evolving needs

Searching for the optimal AI solution for Training and Onboarding of Sales Personnel issue? Consult with Stevie AI!

Conclusion

The integration of Artificial Intelligence (AI) within the sales domain has significantly revolutionized traditional processes, enabling businesses to navigate challenges and optimize performance. From enhancing lead scoring and qualification through behavioral and demographic scoring to predicting churn with historical data analysis and sentiment analysis, AI's data-driven insights empower sales teams to make informed decisions. Furthermore, AI has proven instrumental in augmenting sales forecasting via time series and regression analysis, ensuring resources are adeptly allocated. Techniques like Cohort Analysis, enabled by AI, provide a nuanced understanding of customer behaviors, facilitating personalized marketing strategies.

Moreover, AI's role extends to mitigating common challenges in sales personnel training and onboarding, addressing issues like inconsistency and time-consuming processes, as illustrated by real-world examples. AI-driven chatbots and virtual assistants, alongside automated email marketing and recommendation systems, exemplify AI's capability in enhancing customer engagement and personalizing interactions. Through these myriad applications, AI not only propels sales efficiency and effectiveness but also cultivates a more customer-centric approach, paving the way for sustained business growth and competitive advantage in the evolving market landscape.

Stevie AI works with you to define your requirement, find the AI app that meets your requirement, and guides you on your Al implementation journey.