Stop “listening” and start anticipating your customers’ needs.
The following contribution comes from Forbes and is by Carmine Gallo, Senior Contributor, Harvard Professor, and bestselling author of leadership communication books.
Listening is overrated when it comes to creating an exceptional customer experience. Your customers will only tell you what they think they need, but how you meet their unspoken needs makes all the difference. As a communications expert, I focus on what leaders say and how they say it. However, in studying brands considered benchmarks for customer service, I’ve discovered that what customers don’t say is just as important—perhaps even more so—than what they do say. Brands and people who deliver radically superior customer service stand out because they anticipate unspoken needs and desires.
I recently took my family to the Grand Del Mar, a 5-star resort in San Diego, named the best hotel in the United States by TripAdvisor. It’s located on a beautiful property in the hills, but San Diego has so many other lovely spots. The attentive service that TripAdvisor highlighted in its review is what earned me its loyalty. But what exactly makes the staff so special, and more importantly, what can all businesses learn from their customer service techniques? The «secret» to the Grand Del Mar’s customer service became crystal clear to me on this recent visit: the staff finds little ways to pleasantly surprise their guests, anticipating their unspoken desires. Here are a few of the many examples I observed:
-My daughters discovered a small sandy area near the pool. Within seconds, not minutes, a staff member casually walked by and, without saying a word, placed sand toys there. The girls looked up, and there they were, as if by magic.
girls’ beach
-The valet brought our car and asked us where we were going. «To Legoland!» the children shouted. By the time I finished loading the trunk, the valet had already placed four bottles of water in the car. “It’s hot today. You’ll need them,” he said.
Vanessa and I decided to treat ourselves to a special dinner at the hotel’s upscale restaurant. The hotel offered an attractive children’s play area called The Explorers Club. Dinner lasted a little longer than the kids’ club was open, and the restaurant was a five-minute walk from the main hotel. “I saw you have courtesy cars in the lobby. Can we have one pick us up as soon as we’re finished?” I asked the waiter. “It’s been arranged. The car is waiting,” he replied. “And we’ve let the club know you’re on your way.”
At the end of our stay, the receptionist asked if we had our boarding passes and if we needed directions. I asked the person why everyone seemed to anticipate guests’ needs. “It makes us stand out,” he replied. The employee was absolutely right. The reason this level of service leaves a positive impression—and why you, as a leader, should encourage it—is because it happens so rarely that customers are willing to pay a premium for it. I’ve studied the top brands in customer service, and they all train their employees to anticipate unspoken needs. It’s a key component of an exceptional customer experience.
The Ritz-Carlton is probably the most famous brand that employs this technique. All employees are trained to “anticipate each guest’s needs.” Anticipation is the second of three steps employees take to earn a guest’s loyalty (the first step is “a warm and sincere greeting,” and the third step is “a cordial farewell”).
One of the consistent steps in the Apple Store’s customer service model is “listening to and addressing both expressed and unexpressed needs.” For example, many customers buying a Mac for the first time feel anxious about transitioning from the PC-based environment they’re used to. A salesperson (specialist) trained in active listening would detect this reluctance and proactively recommend Apple Store services, such as free data migration and free courses.
I recently wrote about a new hospital in Dallas that is revolutionizing the patient experience. In “The Hospital Steve Jobs Would Have Built,” I described the service steps at the new Walnut Hill Medical Center. One of the key steps is «Addressing the patient’s concerns, questions, and needs, both expressed and implied.» A diagnosis or a hospital visit is often a confusing time for most people, who continue to dwell on their concerns long after returning home. Anticipating questions improves the experience, puts the patient at ease, and reduces follow-up calls, allowing staff to dedicate more time to the care of their current patients.
I recently spoke with a group of hundreds of in-home interior designers working for a well-known brand. Before my presentation, I interviewed some of the brand’s top salespeople. The company’s number one salesperson told me that the secret to their success was «listening to implied needs.» For example, a client requested a consultation. Due to scheduling conflicts, the salesperson couldn’t meet with the client for another five days. The client verbally stated that this was fine, but it was clear from their tone of voice that they wanted the consultation to happen as soon as possible. The saleswoman rearranged her schedule, called the client again, and met with him that same day. That consultation earned her the highest commission of her 13-year career with the company.
Listening to the customer is fundamental and guarantees excellent service, even above average. But five-star brands know something most don’t: what customers don’t say is often more important than what they do say. Surprise your customers in unexpected ways, and you’ll earn their loyalty.
Carmine Gallo is a communications coach for the world’s most admired brands, a popular speaker, and the author of *Talk Like TED* and *The Apple Experience: Secrets to Building Insanely Great Customer Loyalty*.
Customer Churn Prediction: How to Identify Early Warning Signs in the Data?
The following contribution comes from the Fresh Proposals portal, which describes itself as follows: Sandeep and I have been friends since university. We share some interests, but we particularly enjoy hiking and jungle treks. Together, we’ve camped on the peaks of the western mountain ranges of the Deccan Plateau (India). Despite the difficulties of getting there, the clear skies, fresh air, and silence made it all worthwhile. Whether it was summer, winter, or the rainy season, we continued hiking and trekking for years.
Our paths diverged; we worked in different roles and with different responsibilities, at various companies and in different countries. We stayed in touch and continued to talk about startups, technology, markets, products versus services, and similar topics.
While reflecting on the challenges of managing sales proposals in my other project—and developing a sales proposal tool and evaluating progress so far—I asked Sandeep what he thought about a proposal builder and managing its lifecycle. He had several interesting ideas, so I asked if he’d be interested in taking on this challenge.
Co-authored.
What is churn prediction?
Currence is the silent enemy of businesses. According to Harvard Business Review, acquiring a new customer is 5 to 25 times more expensive than retaining an existing one. Yet, most companies focus more on acquiring new customers than retaining the ones they already have.
That’s where churn prediction comes in. By identifying early warning signs in your data, you can prevent churn before it happens, keeping customers engaged and increasing your revenue.
Table of Contents
What is Churn Prediction?
Customer churn prediction is the process of analyzing customer data to determine who is most likely to stop using a product or service. Just as meteorologists use atmospheric data to predict storms, businesses use behavioral patterns, transaction history, and engagement levels to anticipate when a customer might leave.
A customer churn prediction model not only identifies potential customers but also helps businesses act before churn occurs. By detecting early warning signs, businesses can refine their sales communication, improve customer service, or introduce retention incentives to keep their customers engaged.
Why is customer churn prediction crucial?
Retaining customers is not just an added benefit but a fundamental driver of profitability. Research shows that increasing customer retention rates by as little as 5% can boost profits by 25% to 95%. This is because repeat customers tend to spend more, refer others, and require less effort to retain than acquiring new customers.
Customer churn prediction isn’t about reacting after a customer leaves, but about preventing the loss before it happens. Businesses that proactively address churn signals can build stronger customer relationships, reduce lost revenue, and foster long-term loyalty.
Early Warning Signs: What the Data Reveals
Customer churn doesn’t happen out of nowhere. It often manifests through subtle changes in behavior that, if caught early, can give you the opportunity to intervene and make a difference. The data you collect doesn’t just reflect what’s happening; it tells a story about how customers feel, their level of satisfaction, and their intentions. Here are some key churn warning signs and analytics that indicate a customer might be considering leaving:
Warning Signs
Decreased Engagement
Fewer Support Interactions
Negative Comments
Pricing Page Visits
Lack of Feature Adoption
Payment Declines
What It Means
Decreased Usage, Fewer Logins, and Reduced Activity
Customers Stop Asking Questions or Seeking Help
Complaints, Bad Reviews, or Expressed Frustration
Customers Look for Downgrades or Cancellation Options
Customers Not Using Your Product’s Key Features
Late Payments or Failed Transactions
Decreased Engagement
When a previously active customer starts using your product less frequently, it’s a clear sign that something isn’t working correctly. If you notice a decrease in logins, fewer transactions, or less time spent on important features, it’s time to pay attention. Whether they’re exploring other options or simply no longer finding the value it once did, this change requires your immediate attention.
Fewer Support Interactions
It may seem strange, but a decrease in support requests isn’t always a good sign. Engaged customers typically reach out when they have problems. If a customer suddenly stops asking questions or seeking help, it could mean they’ve lost interest or found another solution that better suits their needs.
Negative Feedback
Frustration often precedes a customer’s decision to leave a service. Complaints, negative reviews, or even subtle signs of dissatisfaction in surveys or conversations can be red flags. Ignoring this feedback may lead these customers to start looking for alternatives that offer a better experience.
Frequent Pricing Page Visits
If a customer is constantly checking the pricing page, especially the sections about plan downgrades or cancellations, they’re likely reconsidering their decision. This is a crucial moment for you to step in with personalized communication, offering discounts, upgrades, or tailored solutions that address their concerns.
Lack of Feature Adoption
When customers don’t use key features that provide real value, they may not be getting the most out of your product. This can lead to disengagement and, ultimately, cancellation of their subscription. Monitoring feature adoption and guiding users toward the highest-value functionalities can help improve retention.
Canceling and Late Payments
Billing issues can be a significant early warning sign of customer churn, especially for subscription-based businesses. A failed transaction, multiple late payments, or a customer contacting you to cancel automatic renewals could indicate dissatisfaction or financial hardship that might lead them to unsubscribe.
If a customer’s behavior starts to change, such as logging in less frequently, skipping support calls, or repeatedly checking the pricing page, it’s time to take action.
How to Create an Effective Churn Model
A robust churn model not only identifies who is unsubscribing but also allows you to stop them before they do. Achieving this requires a structured approach that combines data collection, predictive analytics, and proactive action. Here’s how to create a churn model that truly makes a difference.
Creating a Churn Model
Step 1: Gather the Right Data
The accuracy of churn prediction depends on the quality of the data collected. Without the right data, even the most sophisticated AI tools can miss the real reasons why customers leave the service.
Product Usage: Monitoring how often customers interact with your platform reveals a lot about their level of engagement. A sharp decrease in logins, a reduction in time spent on key features, or a sudden drop in interaction with core tools could indicate disengagement.
Support Interactions: A noticeable change in customer service interactions can be a red flag. A decrease in support inquiries could mean they’ve stopped using the product, while an increase in complaints could indicate growing frustration. In either case, monitoring these interactions provides early warning signs.
Financial History: Payment behavior can indicate the likelihood of a customer churning. Frequent payment delays, subscription downgrades, or refund requests can indicate that customers are reconsidering their commitment.
Customer feedback: Surveys, reviews, and support tickets are direct ways for customers to express their opinions. Negative comments or a decrease in survey responses can signal dissatisfaction before customers decide to leave. By consistently tracking these metrics, businesses can build a comprehensive dataset that drives accurate predictions about customer churn.
Step 2: Use predictive analytics to detect patterns. Raw data is only useful if it can be interpreted. This is where predictive analytics comes in. Advanced tools like Fresh Proposals (for document analysis), HubSpot, and Salesforce analyze historical customer behavior and identify trends that indicate a higher risk of churn.
Machine learning algorithms take historical churn data and apply it to current users, recognizing subtle changes that indicate a customer is disengaging. These AI-based models continuously refine their predictions, becoming more accurate over time.
For example, if an e-commerce platform observes that customers who abandon their carts multiple times without completing a purchase tend to churn, predictive analytics can identify similar users before they leave for good. Similarly, a SaaS company could identify that customers who don’t adopt key features within the first 30 days are much more likely to cancel their subscriptions.
The main advantage of predictive analytics is its ability to prioritize at-risk customers, allowing companies to take action before it’s too late.
Step 3: Act Before It’s Too Late
Even the most accurate churn prediction is useless if it doesn’t translate into action. Once you identify at-risk customers, the next step is to implement specific strategies to win them back.
Reactivation campaigns: If a customer’s engagement drops, sending personalized emails, in-app messages, or even direct calls can remind them of the value they’re missing. Instead of a generic email like «We miss you,» showcase relevant features they haven’t yet used or offer personalized support based on their past behavior.
Exclusive offers: Some customers consider unsubscribing due to pricing. Offering loyalty discounts, extended free trials, or upgraded features at no extra cost can make them reconsider. However, discounts should be a strategic tool, not a superficial fix for a deeper problem.
