CX Strategy

Customer experience trends: 5 to explore in 2023 | Sturdy.ai

By
Joel Passen
December 20, 2022
5 min read

We’re in the decade of data. Data products like Snowflake, AWS, Azure, and Google Cloud have created more market cap than any other segment of SaaS in the last five years. Unfortunately, the ripple effects have been slow to reach every business unit, especially customer experience teams — couple this with macroeconomic malaise, layoffs, and customers’ changing needs. The demand for deeper customer insights will be a top priority for B2B SaaS companies in 2023.

Dirty data continues to be a drag.

Let’s face it; when you don’t believe in the data, you quickly lose faith and are often forced to rely on your intuition to make decisions. The old expression “garbage in, garbage out” was coined nearly 50 years ago. However, we still struggle with data quality or what we refer to as dirty data. Dirty data is inaccurate, incomplete, or inconsistent data sets. 

So what stands in the way of gaining more timely and accurate customer insights? You guessed it — dirty data. Brian Hall, President & Founder of Carema Consulting, states, “Dirty customer data is the biggest threat to successfully realizing a land & expand strategy.” Here’s how dirty data plays out for many customer experience teams. The current customer health score shows the customer is green. Usage levels are normal. The account manager had no risks flagged, but there’s no connection to the ticketing system to let the AM know there has been a spike in tickets over the past two weeks. Disconnected data. Unmatched data. Dirty data makes it nearly impossible for many teams to identify the early warning signs associated with risks. 

Your customers aren’t going to tell you it’s about the data when they complain. Dirty data certainly isn’t going to show up in survey responses. But bogus data is the root cause of your customer problems. 

In 2022, the team at Sturdy spoke with over 100 CX leaders and attended several CX-focused conferences. What we heard was consistent — teams don’t have the correct data to look through the windshield. Instead, we are still looking through the rearview mirror. Here are some of the most common challenges. Sound familiar? 

1. The focus is on retention, but there’s a lack of available data to identify risks consistently.

2. Everyone wants higher unit margins, but most fail to employ automation effectively. 

3. More teams want to leverage customer data, but reliable sources are scarce.

You probably saw plenty of 2022 predictions about sexier topics. “This is the year of digital transformation!” “The year of value creation!” 

   

While those things are essential, it’s just lip service without the right data. I predict that the data decade will continue roaring in 2023, fueled by the further adoption of data management solutions and the growth of Customer Intelligence Platforms. Such platforms will turn data into the insights teams need to create more long-lasting customer relationships. 

Here are the top trends I am watching for in 2023. 

Dirty customer data gets its day.

Dirty customer data is the root cause of most customer-related issues. And to compound the pain, tech solutions that would typically solve data issues, like artificial intelligence and machine learning tools, require access to accurate, high-quality data. The old chicken and the egg problem. 

This year we’ll see more B2B SaaS companies taking a more strategic and systematic approach to customer data management. While it will primarily be a people and process challenge, more customer intelligence companies will enter this space, especially as the need for tools to provide solid data quality and analytics solutions continues to grow.

Customer data democratization grows. 

More businesses will adopt customer intelligence solutions to provide self-service-oriented insights across multiple business units. The trend here will take the pressure off traditionally under-resourced CX leaders to be the clearinghouses for all post-sale customer-related data. 

This year BI and CX ops teams will focus on building new analytical frameworks with corresponding data. Product teams will start to have access to the unbiased, unabridged voice of the customer. Marketing teams will more intelligently identify customers willing to be priceless advocates.

Data-driven insights overshadow surveys.

More and more, a leading B2B CX strategy is to use data to discover what customers are doing and saying every day instead of focusing on surveys. Like many metrics we use today, interviews and surveys rely on customers’ recollections, which are always backward-looking and biased. Data-driven insights provide an impartial lens into customers’ actual words in near real-time. 

I predict more leaders will dispense with the survey charades. We’ll see more teams begin to rely on insights derived from customer ecosystem data to identify risks and opportunities to improve the customer experience. 

The activation of AI-fueled automation.

Automation is the future for many business units. As more mundane tasks are automated by machine learning and AI, humans have increasingly more time to devote to developing relationships with customers. AI can also simplify data unification by providing more streamlined, intelligent processing. 

With its ability to comb through big data sets like customer emails and tickets at faster speeds, AI-forward Customer Intelligence Solutions will enable leaders to service the one-to-many segments successfully. Brian Hall exclaims, “email and tickets represent a largely untapped treasure trove of customer insights for B2B SaaS companies. Those companies that leverage these insights have the best chance to consistently grow customer lifetime value.”

Get ready to leave dirty data in the dust. 

Over the last few years, we have seen some paradigm-shifting evolutions in data technology. The near ubiquity of unified data management systems (data cloud products as described above) has made it possible for many of us to collect, combine and consume new data sets. 

Businesses will put a dent in dirty data this year, driven by AI-fueled innovation. Here is how:

• More business units will benefit from customer intelligence solutions that provide democratized customer insights. 

• The unabridged, unsolicited voice of the customer will ring with accuracy, replacing the stagnant straw polls and surveys that teams have long relied on. 

• AI will surface contextual data in real time, making automation a reality for scaled customer experience teams. 

Data-driven power shifts are redefining customer experience. It doesn’t matter if your business has bought into them yet, the shifts are happening either way. 2023 is the year that many CX leaders will start to leave dirty data in the dust.

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If improving revenue retention is a key priority in FY25, here is some food for thought. If you believe data is the essential foundation for improving retention, imagine the possibilities with 50-100x more data about your customers. Here’s the thing: Every business has this customer data, but 99% of businesses are sleeping on a data set that could change their business. It’s the unstructured data that’s sitting in ticketing systems, CRMs, chat systems, surveys, and the biggest silo by volume - corporate email systems. Most of us still rely on structured data like usage, click rates, and engagement logs to gauge our customers' health. However, structured data provides only a partial view of customer behavior and revenue drivers. Unstructured data—like customer emails, chats, tickets, and calls —holds the most valuable insights that, when leveraged, will significantly improve revenue outcomes.

Why Unstructured Data is Essential for Revenue GrowthImproving Customer Retention: Unstructured data helps businesses identify early warning signs of dissatisfaction, allowing them to create proactive interventions before customers churn. Repeated mentions of poor experiences, response lags, product-related frustration, and more in call transcripts, cases, and emails indicate potential churn risks. By identifying these trends while they are trending, businesses will improve retention.

Fueling Product Innovation: Let’s face it: Our customers bought a product or service. Post-sales teams don’t develop products and are limited in what they can directly impact. Product teams need more unbiased, unfiltered contextual customer data, and they need it consistently. Unstructured data provides real-time feedback on how customers use products and services. Businesses can analyze customer feedback from multiple channels to identify recurring requests and pain points. This data fuels product innovation and informs customer-led roadmaps that lead to higher engagement rates and more profound value. Developing products that directly respond to customer feedback leads to faster adoption, better advocacy, and a competitive advantage.

Identifying Expansion Opportunities: Unstructured data reveals customer needs and preferences that structured data often overlooks. Businesses can uncover untapped expansion opportunities by analyzing email, chats, and case feedback. These insights help identify additional products or services that interest customers, leading to new upsell or cross-sell possibilities. To drive immediate improvements in revenue retention, the key isn't pouring resources into complex churn algorithms, chatbots, or traditional customer success platforms—it's being more creative with the data you're already collecting. Start listening more closely to your customers, identify the patterns in their pain points, and share this knowledge with your peers who can improve your offerings. This is the year to start thinking outside of the box.

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Burton's Broken Zippers

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Last year, I bought a pair of ski pants and the zipper fell out on the first chair lift. I called Burton, and they offered an exchange. New pants, first chair, same problem. Support informed me that I was required to return the pants for repair. The repairs would be completed after ski season. For the inconvenience, Burton offered me a 20% discount on my next purchase of skiwear. The next time I am in the market for skiwear that I can't wear during ski season, I will use that coupon.

I started my first business over 25 years ago. Since that day, I have lived in an almost constant state of fear that somehow, somewhere, things would get so broken that we'd treat a customer like this.

Let's be clear, no one who runs a business wants stuff like this to happen. Yet, it happens all the time.

If you run a software company, your engineering team will have usage tools and server logs to tell you when your product is "down" or running slowly. They can report which features are being used and which ones aren't. You'll learn that certain features in your product cost more to run than others, maybe because of a bad query, code, or something else. And you'll know what needs to be upgraded.

However, every time a customer contacts a business, they are "using" (or "testing") your product. If you sell ski pants, your product is ski pants, and your customer service team. If you sell software, your product is your tech and your customer service.

Yet, your customer-facing teams have very poor usage data, if any at all. Which feature of our service gets used the most (billing, success, support)? What are the common themes? Is one group working more effectively than the others? Does a team need an upgrade? 

(BTW, what costs more, your AWS bill or your payroll?)

The reason your customer-facing teams don't have usage data is because this data is "unstructured," and it is everywhere. Imagine if your engineering team needed to check 50 email inboxes, 1,000 phone recordings, a CRM, and a ticket system to get your product usage statistics. 

That's where your customer-facing teams are today. Until you can get answers from these systems as easily as an engineer can, you’ll continue to churn, annoy customers, and try to hire your way out of a retention problem. It won’t work.

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Joel Passen
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The question is no longer about whether you will use AI; it’s when. And no matter where you are on your journey, navigating the ethical implications of AI use is crucial. Ethical AI is not just a buzzword but a set of principles designed to ensure fairness, transparency, and accountability in how businesses use artificial intelligence. In the case of Sturdy, we’ve made ethical AI a core commitment. These principles guide our every move, ensuring AI benefits businesses without crossing the line into unmitigated risk.

What Is Ethical AI?

Ethical AI refers to developing and deploying AI systems that prioritize fairness, transparency, and respect for privacy. For businesses, this means using AI to make smarter decisions while ensuring that the data and technologies used do not cause harm or reinforce biases. The importance of this cannot be overstated—AI has the potential to either empower or exploit, and ethical guidelines ensure we remain on the right side of that divide.

Sturdy’s Commitment to Ethical AI

Sturdy's approach to AI revolves around several inviolable principles:

  1. Business-Only Data Use: Sturdy’s AI systems focus solely on improving how businesses make decisions. They don't delve into personal data or manipulate information for other purposes. The data processed by Sturdy comes from business sources like support tickets, corporate emails, or recorded calls—never from personal channels.
  2. No Ulterior Motives for Data: The data collected by Sturdy is knowingly provided by our customers, and the company doesn't use this data for any purpose beyond what's agreed upon. This ensures transparency and trust between the platform and its users.
  3. Privacy and Protection: One of the most critical aspects of Sturdy’s approach is its commitment to not allowing any entity—whether a business or government—to use its technology in ways that violate privacy. If a client were found to be doing so, Sturdy would terminate the relationship.
  4. No Deception: Our product is engineered to prevent deception. It never manipulates or deceives users, ensuring that the insights drawn from AI are used to enhance business practices rather than exploit loopholes.

Human Oversight and the Role of AI

At the core of Sturdy’s AI principles is the belief that AI should not replace human decision-making but augment it. Our Natural Language Classifiers (NLCs) are built to detect risks and opportunities based on the probability that a conversation indicates a particular issue. For example, when a customer complains about a "buggy" product, Sturdy’s AI might tag it as a "Bug" and label the customer as "Unhappy." However, humans remain in control—analyzing the situation and deciding the best action.

Final Thoughts

Sturdy's approach to AI exemplifies how businesses can responsibly use technology to drive growth and improve operations while safeguarding ethics. They demonstrate that AI doesn’t need to infringe on privacy or replace human decision-making. Instead, AI should be a tool that empowers teams, ensures transparency, and upholds ethical standards. Navigating the ethics of AI is not just a challenge—it’s an ongoing commitment, and Sturdy is setting a new standard for how it should be done.

How many customers will you have to lose before you try Sturdy?

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