AI & ML

Your employees spend most of their workday searching for information and moving data from one place to another

By
Joel Passen
May 9, 2023
5 min read

In today's world, the power of AI is undeniable and, in many cases, is yet unknown. Businesses are leveraging this technology to increase their productivity and efficiency in ways that were never before possible. From semantic search to content generation, AI has enabled teams across the globe to work smarter, faster, and more effectively than ever before. But what really takes AI-powered productivity to the next level is converging information. Zeya Yan and Kristina Shen contend,

To date, generative AI applications have overwhelmingly focused on the divergence of information. That is, they create new content based on a set of instructions. In Wave 2, we believe we will see more applications of AI to converge information…While Wave 1 has created some value at the application layer, we believe Wave 2 will bring a step function change.”

In other words, this synthesis or convergence of information promises to revolutionize how businesses operate. In this blog post, we'll explore how much time teams are currently wasting with mundane tasks and how Synthesis AI can ameliorate that. 

Generative and Synthesis AI

Before we do that, let’s first define what Synthesis or Applied AI is. In simple terms, it is the use of artificial intelligence (AI) in a practical context. It involves using AI algorithms and techniques to solve real-world problems or create new products or services by synthesizing vast volumes of data into insights. Examples include self-driving cars, facial recognition software, natural language processing, and machine learning. Applied AI can help businesses save time and money by automating routine tasks and providing better insights into customer behavior. For example, Sturdy is an Applied AI vendor using several AI techniques to help businesses understand and act on customer and prospect interactions more effectively.

Let’s take a deeper look at how Applied AI can power the productivity of your team. We’ll begin by taking a close look at the standard American work week. According to a 2023 Zippia study, the average American adult works 38.7 hours weekly. That is roughly 8 hours a day during the standard work week. There seems to be plenty of time to accomplish meaningful work, but there’s a catch. Workers spend more than half of their workday searching for information and doing manual data entry. A 2022 Coveo report found that “the average employee spends 3.6 hours daily searching for information.” The report highlights an increase in one hour per day over the last year, a “trend heading in the wrong direction.” Imagine the impact that has on your business. To keep the math simple, if you have 100 employees working 262 days a year, that’s nearly 100,000 hours of wasted time a year. And as the old adage goes, time is money. 

wasted hours in a workday

But it doesn’t end there. Your employees spend nearly as much time simply moving data from one place to another as they are searching for information. A 2021 Zapier study concluded,

76% of respondents said they spend 1-3 hours a day simply moving data from one place to another. Additionally, 73% of workers spend 1-3 hours just trying to find information or a particular document.”

Put that into perspective for one moment. That means your workforce is potentially spending up to 75% of their time looking for information or moving data from one location to another. Thanks to AI, it doesn’t need to be this way.

Sturdy doubles your team's productivity

For example, Sturdy uses Applied AI to make data entry and searching for information unnecessary. Sturdy collects all unstructured interactions with your prospects and customers—the stuff stuck in various silos—emails, chats, tickets, call transcripts, etc. It cleans it up, synthesizes it with different data sources, and gets it into one searchable system every team will use. And let’s be honest, your business will never generate less data than it does now. With Sturdy, the dark data trapped in your business finds the people, systems, and teams that need it most without requiring data entry. Now organizations can route cancellation insights to account managers—surface unbiased feature requests to product teams—send bug reports to the engineering team, and more. Sturdy automatically delivers the insights to answer the “why?” and “what next?” to the teams and systems that need it most. Yesterday you searched for information. With Sturdy, it will find you. 

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Improving Revenue Retention in 2025

Joel Passen
November 15, 2024
5 min read

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.

Customer Retention

Burton's Broken Zippers

Steve Hazelton
November 15, 2024
5 min read

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.

AI & ML

Navigating AI Ethics

Joel Passen
October 14, 2024
5 min read

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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