AI tips & trends

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. 

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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.

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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. 

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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.

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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. 

What is AI for business?

AI is not a fad. It’s the news. It dominates the talk of every business conference. And it is the number 1 topic of countless leadership meetings. CEOs are challenging their teams to leverage AI in every facet of their businesses to increase their productivity. gain deeper insights into user behavior, automate mundane tasks, and drive deeper insights with which to make critical decisions. It’s here. The big question is: How can you use it in your business right now?

GPT has catapulted AI into the spotlight, but does it translate into measurable productivity and revenue gains? A recent study by the  National Bureau of Economic Research suggests that even in these early innings of AI for business, it’s moving the needle for early adopters.The study’s authors, Erik Brynjolfsson, Danielle Li Lindsey, and R. Raymond, concluded that AI “disseminates the potentially tacit knowledge of more able workers and helps newer workers move down the experience curve.” Our takeaway from the study is that AI is already helping humans do things faster with often better results.

AI provides businesses with numerous automation opportunities, which can help save time and money while increasing accuracy at the same time. Automation technologies such as natural language processing allow machines to interpret human speech or text input without any manual intervention required from humans. This eliminates tedious data entry tasks from employees’ workflows so they can focus their energy on higher-value activities. According to a recent Zapier study, “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 trying to find information or a particular document.” Additionally, automated processes reduce errors due to human factors like fatigue or distraction, which could otherwise lead to costly mistakes. The same Zapier study confirms this, arguing that “83% of workers said they spend 1 - 3 hours a day fixing errors.” That’s a full workday moving data around, looking for information, and fixing human errors. Imagine what you or your team could do with their day if that were a thing of the past.

The fact is that your company will never generate less data than it does now. The issue is that traditionally, 90% of data generated and collected by businesses is dark—untapped, and often completely unknown. Thanks to applied AI, companies can now use advanced algorithms to easily analyze large unstructured data sets, allowing them to understand consumer and customer behaviors better. This application spans the organization from marketing to engineering, product to customer experience, business intelligence to RevOps, etc. Imagine if these teams had their very own AI teammate that organized every interaction with your prospects and customers and turned it into knowledge and insights to help make everyone’s job easier. Now that’s possible with AI. 

Harness the power of AI today. Simple, smart, and safe.

On a more grounded note, AI will have growing pains along the way. It has problems of its own, especially for businesses that need to leverage it to stay competitive. 

It’s clear that adopting AI is no longer a question of “if” but “when.” It’s an opportunity facing every leader in every industry. And as we confirmed in our research for this report, there are huge incentives to move quickly. But before you leap into AI-powered solutions for your business, you must evaluate them properly—not only from a technical standpoint but also from a functional perspective.

Let’s be honest, millions of dollars will be wasted trying to “roll your own” AI. Before attempting a bespoke AI project, it is important to understand the challenges.

Cross-modal data integration

Cross-modal data integration refers to the process of combining and analyzing data from different modalities or sources, such as email, voice, chat, CRM data, ticketing data, and more. This is one of the biggest blockers for teams trying to build their own AI solutions. Cross-modal data integration aims to extract meaningful insights and knowledge from diverse data sources that may provide complementary or redundant information.

Cross-modal data integration involves several steps, including data preprocessing, feature extraction, alignment, and fusion, or what we call data joining. During data preprocessing, the data from each modality is cleaned, standardized, and prepared for the AI to analyze. Feature extraction involves extracting relevant features or characteristics from each modality, such as text features. Alignment involves mapping the features from each modality onto a common feature space, which enables them to be compared and combined. Finally, fusion involves combining the features from each modality to generate a unified representation of the data. 

​​Cross-modal data integration is integral for AI for Business. For example, when you want to know the most commonly requested feature for a specific cohort of your customers, you need to combine data from multiple inboxes, systems, your CRM, and other modalities to provide a comprehensive and accurate answer.

Data cleansing and normalization

Data cleansing and normalization are critical steps in preparing data for AI applications. In simple terms—garbage in, garbage out. They are also insanely laborious. AI algorithms rely on accurate and reliable data to make predictions or generate insights. Data cleansing and normalization are critical to ensure the data used to train AI models is accurate, consistent, and complete. Think of it this way, you can improve the performance of AI algorithms by reducing noise like duplicate data and other inconsistencies. This can help AI models to make more accurate and reliable predictions. And cleaning and re-structuring data helps to make AI models more interpretable by reducing the complexity and variability of the data. This can help identify the most important features or variables driving the predictions or insights generated by AI models.

One of the most common reasons AI projects fail is due to data cleaning and normalization or, rather, the lack thereof. Business data can come in different formats, such as email, tickets, call transcripts, and more, each with unique characteristics and challenges. Normalizing and cleansing data across these different modalities is a complex task. Now consider that the amount of business data available for analysis is growing rapidly, making it difficult to manage and process data efficiently. This can be particularly challenging when cleansing and normalizing data, which can be time-consuming and computationally intensive.

To compound matters even more, business data is always stored in different systems or databases, which are infrequently compatible with each other. Just think how many different people are emailing your customers. Daunting.

Data cleansing and normalization are important but challenging steps in preparing data for AI applications. It requires domain expertise, technical skills, and a deep understanding of the data and the business problem.

User interface

Let’s say you can get clean, structured data into an AI or large language model. Now what? Now you need a user interface (UI), so business people can inform decisions and workflows with AI-generated insights. This isn’t trivial. You’ll need another team of developers because the people cleaning and preparing the data aren’t the same people who will build an end-user UI. Now you need to build some more software.

A well-designed UI helps users understand an AI system’s capabilities and benefits and increase their willingness to adopt and use it. The UI communicates the outputs and insights generated by the AI system in a way that is easy to understand and interpret. The UI can provide a way for users to input data, provide feedback, or customize the AI system to their needs. This can help improve the AI system’s accuracy and relevance and increase user value.

Data permissions

What next? Sigh. Don’t forget about data permissions. You are going to need them.

Data permissions refer to the rights and permissions required to access and use the outputs from your AI project. They are a critical component of data governance and must ensure that data is accessed per your organization’s policies. Think of this as who can see and use what.

Depending on the nature of the business and the data being used in the AI project, there will likely be requirements around data access and use. Ensuring that the AI project has appropriate data permissions can help to ensure compliance with relevant laws and regulations, such as data protection laws like GDPR.

Simply put, you must build more software to manage who can see what. Data permissions are often reviewed and updated to ensure ongoing compliance with and changing business needs, so your permissioning software has to be commercial grade.

Automations and exhaust

Insights from operational AI systems involve integrating AI models and insights into existing business processes and systems. You must get AI-generated insights to the humans and systems needing the knowledge. This may involve building APIs that allow the model to be called from other applications or systems or integrating the model into your teams’ existing tools like your CRM, email, etc.

Once the AI model is integrated, you have to automate the generation of insights. This may involve setting up automated alerts or reports triggered based on specific events or data conditions or integrating the insights into a dashboard that provides real-time insights (data UI).

By automating insights from AI systems, businesses can gain real-time, actionable insights that can inform decision-making and drive business outcomes. Automating insights can also help improve businesses’ process efficiency and effectiveness by reducing the need for manual analysis and decision-making. Vernon Howard, Co-Founder & CEO at Hallo, exclaims that with AI,

I can automate 20 to 30% of my work now.”

The takeaway is that if you can generate insights, you must autonomously get them to the right place.

Integration requirements

One of the main failure points for AI-related projects is unsustainable methods of extracting and converting data. In the past, this was done manually by interns, business analysts, and data engineers. Technological advancements like APIs make modern data capture processes instantaneously and consistently. This frees data and BI professionals from arduous entry work, focusing their efforts on more rewarding, core business responsibilities.

When selecting any technology solution, it’s important to ensure that it has integration APIs (Application Programming Interfaces) to enable seamless integration with your other systems and applications. Integration APIs allow different software applications to communicate with each other, exchange data, and perform tasks without the need for manual intervention.

Ideally, look for solutions that build direct integrations to platforms instead of using third-party integration platforms. Third-party platforms offer a fast way to connect systems but often lack configurability and data classification controls. Also adding a third-party data integration solution can also introduce another data processor to consider.

Data privacy

The subject matter experts we interviewed for this report agree that privacy concerns are the number one reason AI initiatives fail to launch. Xiaoze Jin, the Lead AI/ML Solution Architect at Rackspace Technology, states,

We’re in a very early inning of cloud AI as a SaaS offering. Privacy, above all else, is most important. Beyond privacy lies responsible AI/ethics, federated learning, and zero-trust framework security.”

Due to privacy concerns and stringent compliance regulations, functional leaders are often stymied by infosec and privacy teams reluctant to allow access to collect or process user data, preventing them from taking full advantage of AI-driven insights. Yacov Salomon, Founder & Chief Innovation Officer at Ketch, states,

Be aware of privacy, the ethical use of data, and the governance of data.”

He continues,

...the world is evolving, and if ML and AI are involved, you need to scrutinize. Governing around data makes a big difference.”

Data privacy laws are designed to protect individuals’ privacy and personally identifiable information (PII). PII refers to any data or information that can be used to identify a specific individual, directly or indirectly. PII can include any information that can be used to identify an individual, such as their name, email address, social security number, phone number, home address, date of birth, driver’s license number, passport number, biometric data, or any other unique identifier.

Any commercial AI solution should have a detailed and thorough approach to privacy. Ask for a copy. Otherwise, look for solutions that automatically de-identify data. The de-identification process can involve removing or masking certain data elements, such as names or addresses. Of course, there are different levels of de-identification, ranging from removing obvious identifiers to more advanced techniques that involve complex algorithms and statistical analysis. The effectiveness of de-identification methods depends on the type of data being processed and the acceptable re-identification risk threshold. Patricia Thaine, Co-Founder & CEO at Private AI, argues,

The easiest thing to do is remove PII as early as possible in the pipeline... At ingest or as soon as you possibly can in your system in order to minimize risk.”

Pro Tip: Sending customer data that includes PII to large language models (LLMs) like ChatGPT is a bad idea and will likely compromise user and confidentiality agreements with your customers.

Security

AI solutions may require access to sensitive data. Ensure that any solution provider maintains a comprehensive Information Security Management program to manage Sturdy’s systems and products’ security, availability, confidentiality, integrity, and privacy risks. The vendor’s program must be independently audited and certified to meet the requirements of Trust Services Criteria SOC2 Type II. You’ll also want to ensure that all data communications into and out of a platform are encrypted-in-transit. That data is stored encrypted-at-rest using industry-standard encryption mechanisms.

Any solution vendor should have a current SOC 2 Type II for your team to review. This is the most comprehensive SOC protocol and attests not only to the suitability of a vendor’s processes and systems but their operational effectiveness of sticking to those controls over a period of time.

Considering these technical requirements when evaluating AI solutions, you can ensure that the solution is compatible with your existing infrastructure and meets your performance needs.

AI is the future wave, and those who do not embrace this ever-evolving technology in their business are already falling behind. Jin argues,

We’ve already begun to see the paradigm shift, creating a new way of living and working with AI as a co-pilot... And yet, we’re still living in the wild west of AI, with land-grabbing occurring left and right.”

Those who do understand the immense power that AI can begin to automate processes, secure insights and increase customer engagement across multiple channels reap immeasurable rewards. As a CEO and business owner, Howard exclaims,

This is a big deal... this is huge.”

Unsurprisingly, AI has become one of the most aggressive sectors in business today. Still, fortunately, there are now plenty of resources and expert advice on how to safely and successfully deploy AI into your company. It’s time to get ahead of the competition and take advantage of this revolutionary force before it zooms us into an entirely new world of business opportunities!

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“100% of this article was written by a human” (True)

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We’ve all seen large language models (LLMs) rapidly move into the mainstream, and many of us are already using them at present to generate blog posts, sales emails, and marketing campaigns (sadly, not yours truly). 

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Indeed, there is no shortage of ideas and blog posts explaining how businesses will use LLMs to generate content and remove routine or laborious tasks. Some say LLMs will render coders and lawyers obsolete. (BTW, they won’t). While there will certainly be job disruptions, we believe the change will be one of radical productivity improvement and, as a result, much more interesting and fulfilling work for many of today’s knowledge workers.

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Before continuing, let’s clarify. LLMs are just one type of AI, a “tool.” If you think that auto-creating a sales email is a kind of “meh” endgame of billions of funding, you won’t hurt our feelings. That’s because we feel the future of AI in business will be utilizing many AI tools that will go far beyond creating content. They will help you think and take action. They will help you do things that are almost impossible or are very expensive to do today.

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If you love manually updating JIRA with bug reports, typing QBR results into spreadsheets every Friday, or getting five people in a room to discuss your best customer, then the adoption of AI for your business is going to be a bummer. For the rest of us, AI will make us much more productive, informed, and, ultimately, employable.

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Before continuing, I would like to say that I have spoken to many companies that have “started using AI in our business.” This always means dumping a healthy serving of emails and support tickets into GPT and generating summaries. And many now say, “That was cool, but now what?”

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They’ve just scratched the surface. At the risk of sounding like an AI hype machine, answering “What’s next?” is really difficult because we’re still trapped in an impossibility box: today, there are things we think are impossible but aren’t anymore. 

At Sturdy, business for AI will go beyond Generative AI, fundamentally changing how businesses collect, organize and synthesize their information. For now, I will call this AI Harmonization (AIH).  Again, this goes far beyond writing sales emails. Beyond the LLM, AIH will illuminate previously dark sources of data and harmonize that information across other models, thereby creating previously unimaginable strategic visibility and scale.

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The pieces of this AIH pie are the following: 

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1. Collect and flatten metadata, structured data, and unstructured data into a privacy-compliant, permissioned, and normalized structure;
2. Autonomously identify themes, topics, and insights inside this information and at its intersections. 
3. Automatically deliver “stuff” to, and synchronize with, other systems, workflows, and people who need it.
4. All of the above will be done automatically, in real-time, without supervision.
5. (extra credit): Your interface to this information will fundamentally change from “click here, then click here” to “tell me what you want.”

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Of course, what I just laid out vastly simplifies the challenge. Yet, I can’t help but feel super excited about what it means…here are some examples:

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If you are an Account Manager, your day might start with, 

“Here are three accounts you need to look at right now.”

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Or, let’s say you’re a VP of CS, 

“Let me know anytime an enterprise customer has an issue within 90 days of their renewal date. Check this every day at 9 am.”

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Or, imagine being a new VP of Products at a SaaS business, 

“Every Tuesday, send me an XLS with a product roadmap with all bugs, organized by topic, reported by our enterprise customers worth more than 500k in ARR ranked by unhappiness generated and estimated engineering complexity.” 

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Sure, many of the examples above are already completed in many well-run companies today. They are just being done with manual labor. A lot of manual labor. In fact, our Sturdy data shows us that as much as 45% of an Account Manager’s day is data entry. Logging tickets, updating salesforce, writing call summaries (that number doesn’t even count the “account review” meetings). 

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Yes, AI will eliminate almost all this manual labor masquerading as knowledge work. 

It will automate almost all data collection… reporting…and, eventually, much of the response. 

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But, worker productivity will skyrocket and allow our teammates to focus on much more meaningful tasks.

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(Shameless plug: you could buy Sturdy today and next week 3 of the examples shown above are now part of your business. (What, you think I write blog posts for my health?))

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In short, the people interacting on a day-to-day basis with customers will have about twice as much time to do high-value things, like talking to customers. If I had to guess, I would say that this probably means companies will have fewer Account Managers, but it also means that they’ll become twice as valuable (because they’ll be 2x more productive).

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If you were hired as an Engineer in 1970, it is likely that for the first decade of your professional career, you spent your days using a slide rule to double-check someone else’s work. Did a computer take the engineer's job? No. Did their jobs get better, more productive, and more important? You betcha. 

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Let’s do this. 

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Account views in Sturdy, the leading AI for Business platform, are specific views or displays of all the interaction data related to a particular customer account or group of accounts.

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Account views are designed to provide a comprehensive and consolidated view of all interactions and touchpoints the business has had with specific customer accounts or groups of accounts. This allows cross-functional teams to better understand the customer's needs, preferences, and behaviors, which can help teams develop more targeted and effective strategies to engage, expand, and retain the account. 

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Account-based views in Sturdy are helpful for several reasons:

1. Focused view of customer data: Account-based views provide a focused view of all the data related to a specific account or customer. This allows teams to understand the customer better and personalize their approaches.
2. Better alignment of cross-functional efforts: With account-based views, sales, CX, support, product, marketing, and operations teams can work together to identify the needs and pain points of each account and develop tailored strategies to address them. 
3. Improved collaboration among team members: Account-based views enable teams to share information and collaborate more effectively. Having all relevant customer data in one place allows team members to communicate easily and work together, giving the customer a better experience. 
4. A new collective reality: Account views create a shared understanding of the customer's history, preferences, needs, situation, and requirements. This can be especially important when dealing with complex or long-term customer relationships, where multiple individuals or teams may be involved in the sales and service processes. 

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Overall, account views in Sturdy will play an important role in creating a shared understanding of customer accounts and helping organizations work together more effectively to meet customer needs and achieve desired outcomes.

Every business wants to stay ahead of the competition. We’ve got a saying here at Sturdy; “your customers are either growing with you or away from you.”  And, if you think about it, you are just trying to develop a relationship with your customers to create trust and loyalty. To that end, one of the fundamental tenants of any healthy relationship is listening. Yup. It’s that simple. 

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One of the clearest paths to maintaining a competitive edge is simply listening to your customer’s feedback. The next step is acknowledging that their feedback matters. And the way to solidify the relationship with your customers is to implement their suggested changes in a timely manner.  

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At the end of the day, listening is a choice in a relationship. Whether or not you listen to your customers is up to you. But one thing is for sure. If you don’t listen to your customers, somebody else will.

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Unfortunately, listening to your customers is harder than it sounds — especially at scale. We wouldn’t write this blog post about it if it were easy. While we all agree that customer feedback can give you a competitive edge, implementing the suggested changes is not always easy. After all, customer feedback is often subjective and open to interpretation. It can be hard to take a risk on an idea that may or may not pay off – especially when your competition is doing something different. But the truth is, taking customer feedback seriously and incorporating it into your everyday processes will be hugely beneficial. Not only will you gain customer loyalty and loyalty from potential new customers, but you’ll also stand out in a crowded marketplace. Taking customer feedback on board might be difficult, but it’s worth it. 

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You might ask, “how do I listen to my customers better?” Relying on outdated survey methodologies like NPS and CSAT can be tempting. After all, these methods have been around for a long time and are tried-and-true customer feedback techniques. But the truth is nothing is more valuable than the unsolicited, unabridged voice of the customer. Relying on tools of the past, like surveys, can mean missing valuable customer insights, alienating good customers, and wasting valuable internal resources that could be focused on more high-impact projects. As we mentioned in our previous post, 4 stars and frustrated | time to move beyond surveys and sentiment, surveys continue to fall short for many reasons:

1. Surveys are a backward-looking tool in an era where customers expect near real-time remedies.
2. Survey results are often ambiguous, failing to reveal the cause of customer frustration.
3. Survey data is often seen as unreliable and not contextually substantive enough to drive real business impact.
4. Surveys are often answered by users with exceptionally positive or negative experiences. (According to Forrester reports, surveys capture between 2% and 7.5% of customer interactions.)
5. Survey responses are limited to structured questions, so respondents cannot provide feedback about topics not covered. 
6. Surveys require significant customer time and effort and can be considered annoying.
   
   
   

Don’t get us wrong, surveys can be a relatively simple and inexpensive way to collect customer feedback. But the truth is, they’re over the hill. The NPS was first published the same year the camera phone was created. Think that’s wild? The CSAT was created the same year the internet was invented. You heard that correctly, the world wide web kicked off the same year the CSAT was first administered. Feel old yet?

You might be thinking, “Okay, but what about the other methods of gathering customer feedback? What about focus groups, customer interviews, and journey mapping, for example?" Good question! These are decent ways to collect detailed customer feedback without relying on traditional questionnaires and surveys. There’s still one glaring issue, however… These methodologies are still looking through the “rearview mirror.” These reports, interviews, and maps capture what’s happened in the past. Your team needs to look forward through the “windshield” and see around the corners along the way.

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Today, deploying a commercial-ready artificial intelligence solution is the key to staying ahead of customer needs and competitors. It fills in the knowledge gap between customer feedback and your team by gathering and making sense of the  unbiased, unabridged, and unsolicited voice of the customer. By leveraging AI, you can gain insights that traditional customer feedback techniques simply can’t provide – like specific signals. For example, today’s AI solutions have language models that understand specific scenarios and integrate with large language models like ChatGPT to summarize what customers are saying autonomously. Surveys aren’t going to surface risks and opportunities in real-time. You and your team will have to sit down and read the results or pay someone to do it. AI is the only way to understand what best action needs to be taken in real-time. 

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Think about the potential application of a technology like this! This goes beyond customer success and truly impacts all aspects of a modern business. For example, AI solutions let your product team maintain product-market fit by autonomously capturing product feedback like feature requests, user confusion, frustration, etc. Customer intelligence can also discover and inspect product-related topics like performance issues, bug reports, access issues, security alerts, etc. Your RevOps and BI teams can access an entirely new structured data source to create analytical frameworks. Your marketing team can tap into your pool of happy customers for testimonials and case studies. The list goes on…

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In short, customer feedback should always be taken seriously. While outdated survey methodologies like NPS and CSAT can still provide insights, these techniques should only be used to supplement more modern strategies like AI-powered resources. By taking customer feedback seriously and relying on customer-centric methods, you’ll ensure your customers grow with you, not away from you.

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Churn is the biggest threat to growth for B2B businesses having recurring revenue models. Therefore, keeping a watchful eye on key revenue metrics like  account growth and retention is critical for executives.  Real-time dashboards are essential for executives as they provide visibility into their business performance. Dashboards help executives quickly gain insights into their key performance metrics and spot potential trends or issues before they become major problems.

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We identified a trend after speaking with dozens of executives at customer-obsessed companies over the past year or so. Leaders and board members want key revenue metrics available with one click. They neither have the time nor the need to go into the deepest levels of data. They want a quick way to access topline revenue stats and relevant data to inform conversations with revenue teams. 

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The all-new Sturdy Executive Revenue Dashboard, now in beta, makes powerful revenue analysis accessible anytime. It provides a quick way for the management to visualize and understand the following:

• account growth
• cancellations 
• month over month cancellation trends
• churn rate
  

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Sturdy’s new Executive Revenue Dashboards allow execs to automatically gather, organize and analyze the revenue metrics that are most important to the organization in one simple dashboard. Benefits include:

• A concise executive revenue summary – Executives get a consolidated report of key revenue metrics in one pane of glass. 
• Visualize trends –  A quick and effortless way for executive management to visualize the most critical trends, including growth, monthly cancellation trends, churn rate, and retention rate.  
• On-demand - With Sturdy, execs never have to wait for monthly or quarterly reports on the business's health. Access critical revenue-related trends anytime on demand with one mouse click. 

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Interested in learning more about how real-time revenue dashboards and churn dashboards can help executives? If so, book some time with one of our experts. During the demonstration, our expert will show you how our dashboard solutions can provide visibility into your business performance and enable you to take proactive steps toward reducing customer attrition and driving long-term growth.

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Product research is a crucial component of successful software product development. By understanding customer needs, preferences, and behaviors, technology companies can create products that create value for their customers and differentiate in the marketplace. Research helps businesses learn more about their target audience and users’ desired outcomes to develop features and functionality that increase customer engagement and dependency. Let’s face it, the name of the game is getting your customers addicted to your tool or platform. In addition, software product research provides valuable data that businesses can use to optimize customer acquisition and retention motions. 

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Traditional product research 

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To date, product research has been conducted through surveys, focus groups, and customer interviews. Traditionally, surveys have been emailed to customers immediately to gather qualitative and qualitative feedback. More recently, product experience platforms have given product researchers access to more dynamic in-app surveys, product usage analytics, and the ability to launch traditional surveys with fewer resources.

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Customer interviews allow one to ask specific questions and dig deeper into customer motivations, pain points, and specific use cases. Interviews can be extremely useful when businesses try to develop new products or determine how to enhance existing ones. Customer interviews can also provide valuable insights into desired integrations, services, and more. 

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Focus groups allow companies to observe how customers interact with products and better understand the user experience. Observing customers using the product can provide valuable insights that are unavailable through surveys or customer interviews. Additionally, observational research, such as shadowing customers in their own environment, can help uncover valuable insights that would otherwise remain hidden.

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Here are a few other common ways teams conduct product research:

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• Examine competitors: Analyzing competitors' products and marketing strategies can give you valuable insights into customer preferences and behavior trends in the market.

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• Track sales data: Tracking sales data such as purchase histories, customer feedback, and website analytics can help you pinpoint which products are selling well and which are not so you can adjust your product design accordingly.

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• Monitor social media: Utilizing social media channels like Facebook, Twitter, LinkedIn, and Instagram can help you monitor customer conversations about your product or service and see what users are saying about it.

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At the end of the day, what do all of the traditional product research methods have in common? They are labor-intensive, expensive, and time-consuming, requiring intricate expertise and specialization to operate. Another drawback to traditional product research methods is that the data and insights generated are typically used by a small group and not leveraged across the enterprise. 

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AI is Changing How Teams Conduct Product Research

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ChatGPT, the AI-powered natural language understanding (NLU) platform that helps automate conversations has catapulted AI into the business mainstream. Aside from being all the rage, business leaders are adopting AI now more than ever because of technological advancements that have made it more accurate and faster to deploy. Additionally, AI is becoming increasingly affordable, allowing businesses of all sizes to benefit from the latest advances in artificial intelligence. Furthermore, the increased availability of data has allowed for more sophisticated algorithms and models to be used, enabling better decision-making and providing a competitive edge for businesses that use AI. 

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Product leaders recognize that customer expectations are changing rapidly, and AI can help them stay ahead of the curve. While AI and its practical applications are evolving quickly, here are a few ways that advanced data sciences are already impacting product research.

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1. Automating the data capture and cleaning processes

AI automation can take over mundane tasks such as data collection and normalization (cleaning or standardizing data for reuse and analysis), freeing up teams’ time to focus on more strategic initiatives. AI also facilitates the data cleaning and preprocessing (data joining and integration) activities required to glean knowledge from the raw data. 

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2. Eliminating privacy concerns

Privacy issues are often a roadblock for product researchers. Teams must be careful how they use personal data (PII) to discover product insights. Privacy restrictions and personal data limitations challenge legacy experimentation and research methods. AI is paving the way to alleviate these concerns so teams can move quickly. New advances in  PII Identification, de-Identification, synthetic PII generation, and pseudonymization provide teams with tools to iterate and innovate faster than ever without jeopardizing privacy regulations. 

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3. Making sense of previously untapped data sets

AI-powered platforms are making it possible to sift through data using natural language processing (NLP) and machine learning algorithms to quickly analyze large amounts of customer-generated information like email, tickets, call transcripts, and more. These data sets have, for the most part, been hard to access given, among other things, their unstructured nature. AI-based tools can search for patterns and recognize key signals that might be difficult and even impossible for humans to spot, especially at scale. 

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AI is already accelerating product research by enabling teams to quickly and accurately collect, clean, and identify trends in customer behaviors related to product usage and specific future use cases. AI-based platforms can analyze vast amounts of data in real time, helping companies make decisions faster while reducing costs associated with human labor. Additionally, using natural language processing (NLP), companies can automate text-based research tasks, such as discovering specific product-related insights, which would otherwise take an immense amount of time and resources. With the help of AI, teams can gain valuable insights into their products more efficiently and more effectively than ever before.

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More Signals! More insights! More knowledge!  Today, we’re excited to announce the release of three new Signals designed to help our customers better understand their customers and what to know, now. As always, the new Signals were inspired by Sturdy’s existing customers and their feedback. 

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Introducing the “Apology”, “Discounting”, and “Cost Cutting” Signals. Designed and built by our data engineering team, the new language models detect the following:

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• When your internal teammates apologize to customers
• When discounts or price reductions are discussed with customers
• When customers ask to cut costs or reduce spend 

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Sturdy is the only customer intelligence platform with out-of-the-box, purpose-built language models. Adding these three new Signals brings the total number of Signals available to Sturdy customers to 23. Sturdy customers will be able to take advantage of these new Signals on Feb 15, 2023.  

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Apology

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This Signal detects when a teammate apologizes to a customer. This Signal takes directionality into account and only “signals” on outbound interactions. 

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For example, when a teammate says something like, “we sincerely apologize for just getting a response out to you now,” in a support ticket, a signal is being sent. The teammate apologizes for dropping the ball. Maybe this is an isolated issue. Or, if this is a common occurrence, it could be a problem and, ultimately, detrimental to the relationship. 

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Discount

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This signal detects when a discount or price reduction is discussed with a customer. This signal takes directionality into account and only “signals” on outbound interactions. 

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For example, when a teammate says something like, “I was approved to offer a 15% discount,” in an email, a Signal is sent. The teammate is providing a price reduction. Alone this may not be critical, but in the aggregate, discounting can be a bad habit for account management teams

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Otherwise, Sturdy shows details about specific accounts. It’s always informative to know if any teammate has offered a customer a discount — and when, and, most importantly, why. Sturdy surfaces this information in easy-to-read dashboards, so you don’t need to wade through your CRM, CSP, or ticketing system. 

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Cost Cutting

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This signal detects when a customer is looking to cut costs or reduce their spend. This is another directional signal that only fires on incoming interactions 

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For example, when a customer says something like, “We've loved the platform so much, but we are trying to reduce costs as much as possible” in an email, a signal is being sent, and often swift action needs to be taken to solidify a renewal, spot a trend, or answer questions like — what segments are asking for cost reductions, etc.

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Discovering and delivering customer Signals at the right time helps teams understand what needs attention — know, now. Survey and health scores don’t give teammates the knowledge of what to do now. Signals uncovered from everyday interactions with your customers are insanely relevant — a must have.  In today’s competitive SaaS environment, the most successful companies are learning to “listen” and interpret the Signals that their customers are giving them about their products and services. The category-leading companies are doing this at scale - automatically with Sturdy.  

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Catch your interest? Want to see how it works? Get in touch. 

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A guide to leveraging modern technology to build an actionable voice of the customer program.

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Every business benefits from knowing what customers think and feel. A Voice of the Customer (VoC) program can help you capture and leverage customer insights to improve your products, processes, relationships, and bottom line. VoC programs have been in existence since the dawn of marketing. However, until recently, they were limited to gathering data through surveys, interviews, or focus groups. Most VoC programs fail because they rely on yesterday’s tools to address today’s challenges.

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Surveys still fall short

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Most companies still rely on surveys to gather customer insights. Sure, surveying customers sounds like a good idea. To some extent, surveys are a good starting point for obtaining information about customer experiences. But let’s face it, we all know that survey response rates are low. According to Delighted, a good survey response rate ranges between 5% and 30%. This means that your analysis through surveys represents only a fraction of your customer base, and typically, only the dissatisfied or extremely satisfied customers take the time to respond. Unfortunately, most VoC programs still rely on surveys as the number one data source to influence decisions about products, marketing campaigns, service processes, and more. 

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Social media monitoring - meh

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While social media monitoring can be a great source of customer data and insights, it has flaws. There are several reasons why it may not always be the most reliable source for customer insights. First, as with surveys, social media users’ opinions change rapidly due to the nature of the platform. The same user may have different views or opinions at different times, which can lead to issues with reliability. Companies must ensure they are looking at a large enough sample of customers and not just basing their decisions on a few users' whims. Second, as we’ve learned from politics, all sources on social media are unreliable, and there is no way to verify their accuracy or truthfulness. VoC program managers can be misled if they rely heavily on these sources without doing extra research. And finally, as with surveys, monitoring conversations on social media is a time-consuming process. Companies must dedicate resources to this task to keep up with the latest trends and conversations about their brand or products, which can be costly in terms of both money and time.

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Focus groups flop

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For decades, businesses have relied on focus groups to learn more about their customers. Unfortunately, focus groups flop in many of the same ways that surveys and social media monitoring fail to deliver actionable insights. First, focus groups are typically limited in size and scope, making them unsuitable for gathering insights from a large customer base with diverse segments. Second, running focus groups is costly and resource intensive. This makes it difficult for companies with limited resources to benefit from them.

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The trends to watch for when building a modern VoC program

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Listen, if you rely on surveys, social media, and focus groups as the main inputs for your voice of the customer program, you are not alone. These methods are still the standard. But, there is a new trend emerging driven by advancements in technology.

Innovative businesses are starting to use traditional channels of customer feedback in combination with unsolicited feedback to gain true insights into VoC.  

VoC programs have come a long way since their inception, from manually collecting data through surveys and interviews to leveraging AI-driven analytics tools today. Technology has revolutionized how organizations collect, analyze, and deliver customer insights to the teams that need them most. With modern tools and platforms, businesses can collect, analyze and leverage data on a larger scale and with greater accuracy than ever before. Here are some ways technology has changed VoC programs:

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AI-driven signals 

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AI has revolutionized analytics tools over the past few years by allowing companies to collect large amounts of data quickly while also uncovering signals about specific customer behavior that were not possible before. Going beyond just sentiment,  AI-driven signals help organizations develop strategies that meet customer needs better and lead to long-term success. But the real power of AI is to deliver the signals that are happening now — ones that can impact this quarter's results! 

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Automation

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Before, businesses had to manually enter data into various formats and generate time-consuming and backward-looking reports. But with the combination of AI-driven insights and automation, teams can now automate processes such as collecting the unabridged, unbiased, and unsolicited voice of the customer. Automation, in this sense, reduces costs and frees up resources while increasing the speed at which teams receive valuable customer feedback. 

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Data integration 

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Modern customer intelligence platforms can combine multiple data sources to help VoC teams get perspective, providing a richer understanding of customer signals and trends from multiple channels. Using multiple data sources in combination with machine learning algorithms, companies can create more accurate models and insights than they would have been able to do with just one data source. For example, imagine having a searchable interface on top of every inbox, video call, ticket, and survey — a single pane of glass, as it were — a window into a real-time understanding of your customers’ needs and preferences. 

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It’s time to modernize your VoC program 

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The success of any VoC program depends on selecting the right tools and technologies for collecting, analyzing, and interpreting data. Companies need to consider factors such as cost-effectiveness, scalability, accuracy, and speed when building and updating VoC programs. Here are the considerations to get you started. 

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1. Collect more relevant data sources

Don’t stop surveying, scouring social media, or conducting customer interviews. Gathering multiple data sources is key. But it’s time to add data sources. Customer intelligence technology is maturing quickly. Many of today’s systems allow you to create omnichannel customer experience insights by capturing and analyzing every customer interaction, regardless of channel (phone, email, chat, etc.).   

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2. Analyze and interpret customer data 

Once relevant data has been collected, teams must analyze it effectively to draw meaningful conclusions. This requires the effective use of AI technologies such as natural language processing (NLP) or computer vision (CV). Effective analysis helps uncover signals and patterns that wouldn’t be visible from just looking at raw numbers or statistics like the results of surveys. 

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3. Deliver what matters - now

Finally, companies should use the signals gained from the analysis process to take actionable steps to improve their services or operations to better serve customers’ needs. This could involve implementing changes based on customer feedback or altering marketing strategies according to changing trends in customer preferences.

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Overall, creating a modern VoC program is essential for businesses in today's competitive market. By understanding its fundamentals and leveraging advanced technology, companies can gain valuable insights that can help them succeed.

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I was talking with a VP of CS not long ago, and she said, “Our AMs need Sturdy to tell us what to do next.”

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Since VC firms love to ask things like,  “Does your product recommend Next Best Action?” and Sturdy just recently closed some funding, my judgment was cloudy…

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I responded:

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“Do you mean that you need Sturdy to tell your people what to do next? Like if they hear that their account had an Exec Change, then Sturdy needs to give them a playbook?”

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“Uhh, no, our people know what to do next. We need Sturdy to find out what to do now….For example, if someone contacts the billing team and asks for a copy of their contract, we want the CS person to get an alert because right now, they might never even know their account is at risk.”

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“Now” before “Next”.

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I couldn’t help but think of all the different events that are spread out in other people’s inboxes. All of those “Nows” waiting to be found. (FWIW, we know that at least 15% of all customer conversations have some sort of “Now” in them)

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More Examples

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If one of your Account Managers gets an email that reads, “Hey, this feature is really confusing and annoying!” your UX Designer has a “Now!”

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If a customer responds to a ticket, “That’s really disappointing, we were sold this feature, and now we’re learning it does not exist. Lame!” your Sales Team has a “Now”.

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If a customer contacts your billing team and asks, “Hey, can we cancel our contract three months early?” then that customer’s Account Manager has a “NOW!”

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So, what does this mean for Sturdy? Well, we need to rethink two parts of our product. First, we need to make it much, much easier to sign up for the “Nows” that are important to you. Second, we need to ensure that all those duplicate messages in inboxes, chats, cases, and tickets don’t create duplicate warnings. No noise, just Signal.

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And we’re building this right now.

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