Can You Count on ChatGPT Customer Experience Survey Questions?

It seems like the internet is full of ChatGPT “hacks” these days. We are all inundated by articles and webinars that start with  “How to Use ChatGPT to…” I have also had way too many conversations with my Gen-Z son and millennial colleagues about how they use the tool to make everyday tasks go by more quickly. And I wouldn’t be the true customer experience nerd that I am if I didn’t ask: “Could we customer experience (CX) professionals leverage ChatGPT customer experience survey questions?”

On the surface, it seems like an obvious application for a ChatGPT customer experience approach. A survey is pretty straightforward, correct? Not so fast.

Keep reading to find out what happened when I tested this approach and why it may not be the best way to go when it comes to your customer listening approach.

Testing ChatGPT for Customer Experience Questions

I started off with a simple question for ChatGPT, hoping for a simple customer satisfaction survey, typing in, “Write me a survey.” You can see the screenshot of the output below.

ChatGPT customer experience survey- Should you use ChatGPT to write customer experience surveys?
A ChatGPT Customer Experience Survey

After reviewing the generated answer, you may be asking, “what’s missing?” Well, to the untrained eye, there could be little to no difference between a traditionally written survey and a ChatGPT customer experience survey. After all, there are demographic questions, the typical “How satisfied were you with your experience,” and other basic survey asks.

But here is what stands out to me as a glaring absence. What is missing is pretty much the most important part of any survey: the link to the business questions you are trying to answer by launching a survey in the first place!

Quick PSA from Jim: Creating surveys is an important topic,  but I would be remiss if I didn’t mention that while surveys are a tried-and-true method of collecting customer feedback, they are not the only way (or the best way, in many cases) to hear from customers. With so many channels available for you to monitor the voice of the customer, to restrict yourself to surveys alone is to limit your insights. This is another topic for another day (but if you’re interested, you can learn more about other listening channels here). End of PSA.

 For now, let’s talk about the risks of using AI like ChatGPT to write surveys!

ChatGPT Customer Experience Risks & Best Practices You Need to Know

ChatGPT Customer Experience Questions Miss the Point

Let me ask you a question: Is the point of your CX program to launch surveys? Now, many of you are likely rolling your eyes at me, but I promise, there’s a point to this. Hopefully, you answered no. Because the point of customer experience is not to ask questions, but to listen to customers and the market to help guide your path to achieving business goals. The questions are simply a vehicle to gain insight into what will help or hinder your business on the way to realizing those  goals.

When you look at the output of ChatGPT customer experience questions in the screenshot above, these questions really miss the point. Yes, they are generic questions that we have all likely seen in surveys before, but what are they getting at? The only results I can see this survey gleaning is a scoreboard metric and some customer demographics that we might already have access to via other data sources. 

When you craft surveys, the first questions you should ask should be for you and your team. Do you have a set of northstar goals (GOALS not scores!) for your customer experience program already? Great! If not, start that conversation with your executive stakeholders and team. Only then can you truly design your program, surveys, and other initiatives with the end goal in mind. 

Once you have agreed upon a desired end goal, then you need to ask:

  • What are we hoping to learn?
  • Who are we hoping to learn from?
  • Do we already have access to this data?

If you want to gut-check your surveys, you can check out this CX survey assessment my colleagues developed to help you optimize your surveys!

ChatGPT Doesn’t Know Your Customers Like You Do

Context is everything. And when it comes to ChatGPT customer experience questions, they won’t have any of the contextual data that you do. If your CX program has been around for a while, you likely have a mountain of customer data around. And that existing data will shape what you already know, and what questions you still need to ask. 

(Additionally, you might be tempted to feed ChatGPT some of your customer data, but that can unearth a whole boatload of security complications. Do you really want every ChatGPT user having access to your customer data? Didn’t think so.) 

An effective customer listening strategy is personal and targeted. Speaking to the customer in their language is critical. Many brands have worked hard to develop a brand persona. Asking customers for feedback in a sterile, canned voice will not yield the best results or further endear your brand to your customers. I don’t believe your brand personalization  can be accomplished by a ChatGPT survey—at least not today.

ChatGPT Is a Starting Block, Not the Finish Line

Now you may be thinking, “Jim, you’ve made a good case for the risks of using ChatGPT for customer experience surveys. But there has to be some way I can use it.” I’m glad you asked and yes, there is! 

I know we have all heard the fear-mongering conversations about AI taking jobs. And if we’re being realistic, AI will eliminate some jobs, but it will also create new ones. Those who will be safe from that chopping block are those professionals who learn how to leverage AI to increase efficiency and  perfect skills that AI alone just can’t manage without human input.

In the customer experience space, this could be leveraging ChatGPT as a starting point, then leveraging the additional context you have about your customers and your brand’s identity to perfect its suggestions. 

For example, ChatGPT can give you phrasing ideas for your survey questions as long as you are very specific in your prompts. It can also help you to think of other ways to ask questions you’ve been posing to customers for a long time, giving your same old relationship and post-transaction surveys a refresh. 

It’s not about AI or humans. It’s about humans using AI to improve and become more imaginative and efficient.

I will end with this. I do not want to come off as a “debbie downer” or, even worse, as naive. AI is going to have an increased role in customer experience and in creating the listening posts that practitioners create to capture customer insight. But, I believe true value will be well beyond simply crafting a survey. 

The real power of ChatGPT and other AI tools will be to help understand the data that comes from a survey or the multiple direct and indirect data sources that make up the voice of the customer. And, just to validate this statement, I asked ChatGPT why the voice of the customer is important? In this case, ChatGPT was spot on:

I think we can all agree that ChatGPT is right on target with that answer.

Unlocking Customer Satisfaction: The Role of Predictive Analytics in CX

In an era where customer expectations continually morph, businesses must always be a step ahead to keep their clients thrilled and engaged. And the most effective tool brands can use to stay ahead of the curve? Predictive analytics. “What is predictive analytics,” you may ask. Well, by thoroughly analyzing historical data, predictive analytics software can predict future customer needs and behavior, forging a proactive customer experience (CX) strategy.

A Deep Dive Into Predictive Analytics

Before we explore its role in CX, let’s unravel the concept of predictive analytics.

What Is Predictive Analytics?

Predictive analytics is a sophisticated method that combines data, machine learning techniques, and statistical algorithms to predict future happenings based on past data. It provides a glance at the likely future by examining patterns and trends in the current data. Deployed across sectors, from finance to healthcare, predictive analytics helps drive informed decisions and actions.

The Methodology and Technologies of Predictive Analytics

Predictive analytics revolves around three main stages: data gathering, statistical analysis, and deployment. This process starts with accumulating vast amounts of relevant data. This data then undergoes processing and analysis through advanced statistical techniques. Finally, the results are deployed in a practical form—be it a detailed report, a visual data representation, or an automated business operation process.

The Intersection of Predictive Analytics and Customer Data

Customer data forms the foundation for predictive analytics. By scrutinizing past customer behaviors, preferences, and interactions, predictive analytics can forecast future customer actions, preferences, and potential issues. It equips organizations with answers to key questions: who are their most valuable customers, what are their customers’ needs, or which customers are at risk of moving away.

The Role of Predictive Analytics in the Customer Experience

In the maze of business strategies, predictive analytics shines as a beacon, illuminating the path towards superior customer experience. It’s an incredibly powerful tool that can turn a plethora of data into insightful narratives about the customers, allowing businesses to not just meet but anticipate their needs. Let’s delve into how this transformative power reshapes the landscape of customer experience.

The Transformative Power of Predictive Analytics in CX

Predictive analytics revolutionizes customer experience in various ways, leading to impactful changes:

  • Proactive Approach: Predictive analytics transforms CX from being reactive to proactive, enabling businesses to anticipate and address customer needs even before they’re expressed. This proactive stance results in a more streamlined and satisfactory customer journey.
  • Tailored Customer Interactions: By providing insights into customer behaviors and preferences, predictive analytics allows for personalization at an individual level. The result is finely tuned interactions that resonate with customers on a personal level, increasing engagement and loyalty.
  • Improved Product Recommendations: With the help of predictive analytics, businesses can create more accurate and appealing product suggestions. By understanding the preferences and purchasing habits of each customer, product recommendations become significantly more relevant and effective.
  • Timely Customer Service: Predictive analytics can also help in detecting potential issues or queries a customer might have, enabling customer service to address these proactively. This results in timely resolution of issues, improved customer satisfaction, and an enhanced reputation for the business.

Reaping the Rewards of Predictive Analytics in CX

Predictive analytics brings an array of benefits to customer experience management, each one contributing to a more successful business strategy:

  • Enhancing Customer Loyalty and Satisfaction: By predicting what customers want before they even ask for it, businesses can provide a proactive and personalized experience that increases satisfaction and fosters loyalty.
  • Boosting Customer Lifetime Value: Predictive analytics helps identify the most valuable customers and understand their behavior, allowing businesses to implement strategies that maximize the value these customers bring over their lifetime.
  • Reducing Customer Churn: By identifying patterns that indicate a customer is at risk of leaving, businesses can take proactive measures to retain them, thereby reducing customer churn.
  • Enriching Cross-selling and Up-selling Opportunities: Predictive analytics can identify which customers are most likely to be interested in additional products or services, creating more opportunities for successful cross-selling and up-selling.
  • Catalyzing Overall Business Growth: By enhancing the customer experience and making operations more efficient, predictive analytics contributes to accelerated business growth and increased profitability.

Addressing the Complexities and Solutions of Implementing Predictive Analytics

While predictive analytics represents a potent force in sculpting remarkable customer experiences, the path to its successful implementation is not devoid of complexities. These challenges need to be recognized, understood, and navigated strategically to truly unlock the transformative potential of predictive analytics. Let’s first uncover the common hurdles that organizations face on this journey.

Unraveling Common Hurdles in Implementing Predictive Analytics

Navigating the path of implementing predictive analytics involves tackling several challenges:

  • Data Privacy Concerns: Organizations must handle vast amounts of customer data, raising critical concerns about data privacy, security, and compliance with regulations such as GDPR and CCPA.
  • Lack of Skilled Resources: Predictive analytics requires a unique blend of skills in data science, statistical analysis, and machine learning – a skill set that may be scarce within an organization.
  • Integration Issues: Organizations often struggle with incorporating predictive analytics systems into their existing infrastructures, leading to compatibility issues and inefficient operations.
  • Real-Time Analysis and Scalability Problems: For many organizations, processing large volumes of data for real-time insights or scaling their analytics initiatives to accommodate increasing data loads can be a daunting task.

Roadmap to Overcoming these Challenges

Addressing these challenges calls for strategic solutions:

  • Building a Skilled Team: Investing in hiring and training employees in data analytics can help build a proficient team capable of harnessing the power of predictive analytics.
  • Data Quality Assurance: Prioritizing data quality is crucial – cleaner, well-structured data can significantly improve the accuracy of predictive models and forecasts.
  • Investing in Scalable and Integrable Analytics Platforms: Selecting analytics platforms that can seamlessly integrate with existing systems and scale with increasing data volumes can ensure smoother operations and long-term success.
  • Establishing a Robust Data Privacy Policy: Developing a comprehensive data privacy policy, complying with all relevant regulations, can assuage data privacy concerns and safeguard the organization from legal repercussions.

Wrapping Up

In the customer-driven era, predictive analytics has emerged as a linchpin to enhance customer experience. By harnessing data to forecast customer behavior, companies can deliver personalized experiences, leading to heightened customer satisfaction and loyalty.

InMoment is leading the way in integrating predictive analytics into CX strategy. The predictive customer analytics in InMoment’s XI Platform unlocks profound insights into customer behavior, helping businesses create not just reactive but proactive and personalized experiences. Investing in such technology will undoubtedly place companies on the path to sculpting a more engaging and gratifying customer journey.

The horizon of CX lies in predictive analytics. Is your business ready to seize it?

5 Things We Learned from the Customer Experience Speakers at the Sydney XI Forum

After 14 customer experience speakers, 250 delegates, two hands-on workshops, and hours of networking on the Sydney Harbour cruise, the 2022 Sydney XI Forum is done and dusted. That means it’s time to take what you’ve learned and start doing the work to elevate your experience program! 

We heard from award-winning customer experience speakers from some of Australia’s biggest brands—Craveable Brands, The NRMA, Rest Super, Foxtel, and JAX Tyres & Auto—not to mention two of InMoment’s global leaders. The day was filled with practical tips that you can apply to your program from day one.

If you missed out on the event, don’t worry—here are five key takeaways you can use to apply to your experience program today! 

5 Pieces of Advice from Our Customer Experience Speakers

#1: Managing Experiences Is Not Enough—The Future Is Experience Improvement

InMoment’s Global CMO, Kristi Knight, took us through the evolution of customer experience (CX). CX started out in the golden age of advertising, market research, and understanding consumers. Then, the internet was born, and online surveys were created to collect customer feedback in a timely manner. Next, we started managing experiences, and we recognised that the total experience a customer has is a collection of moments and interactions along their journey. The idea of simply “managing” metrics tells your business where you are and where you’ve been, not necessarily where you’re going. The future of CX is moving past managing experiences, to actually improving them through experience improvement

#2: Instead of Collecting More and More Data, Take Action On the Data Your Already Have

The CX industry has made big promises to brands; Essentially, if you listen to customers and act on that feedback, you’ll see results like loyalty, retention and other positive business outcomes. The XI Forum challenged our perspective on the traditional model of listening to feedback and collecting endless data. The ultimate goal for brands is to move beyond collecting operational and customer feedback, toward building differentiation from competitors, and ultimately designing and innovating new revenue streams and customer segments for the future. Make sure your CX platform is equipped to layer all types of feedback, whether that be direct (surveys) data, indirect data, or inferred (CRM) data.

#3: Make a Plan to Leverage AI in Your Experience Program

Like most industries, customer experience leaders are currently challenged to integrate AI into their programs to free up some of the manual tasks of improving experiences. If done correctly, AI can power your natural language understanding capabilities to show your business which actions to take to move the needle.  Ideally, every CX  platform should tell brands WHAT to do next.

To do that—survey data is not enough for AI to work properly, and there isn’t a robot sitting behind the platform making sense of your customer data and creating business insights for you. What you can do today is create bespoke AI models that will help make your platform smarter—for one, you can train your platform on what a churning customer looks like, and set up triggers to reach out to valuable clients when they are signaling dissatisfaction. 

#4: When It Comes to CX and the C-Suite, Optimise Your Dashboards

The C-Suite of any business is typically one of the most important stakeholders in your CX program. They need to be informed about what kind of CX initiatives are happening, where the CX problems are, and the plan to tackle them—AND they are extremely time poor. 

To solve this, optimise your C-Suite CX dashboards using these principles: 

  • Purpose: make the purpose of the dashboard crystal clear
  • Relevance: make sure each element of the dashboard will resonate with your audience, which may require bespoke configuration 
  • Engaging: these data visualisation on a C-Suite dashboard, should be simple and straightforward—take the guesswork out of convoluted charts and diagrams. Add branding, colours, and themes to make it visually appealing. 
  • Story: create and place the widgets in isolation and then decide the consumable order—the ultimate goal should be an overarching CX story that your C-Suite can easily understand 

#5: Level Up Your Experience Program by Marrying Together Multiple Voices

In the last keynote of the day, the CEO of JAX Tyres & Auto, Steve Grossrieder, described how their business is layering together voice of customer, voice of employees, and even voice of franchisees for a complete view of the customer journey. In doing so, the entire culture of the business is focused on the customer. For true customer centricity, it might be time for your brand to consider adding in another data set to further understand your employees, franchisees, partners, or other stakeholders.

Keep an eye out for our Q&A interviews with the speakers in the coming weeks, and check out the full post-event wrap up here!

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