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. 

‍

Traditional product research 

‍

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.

‍

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. 

‍

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.

‍

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

‍

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

‍

• 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.

‍

• 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.

‍

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. 

‍

AI is Changing How Teams Conduct Product Research

‍

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. 

‍

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.

‍

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. 

‍

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. 

‍

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. 

‍

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.

‍

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. 

‍

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

‍

• 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 

‍

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.  

‍

Apology

‍

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

‍

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. 

‍

Discount

‍

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. 

‍

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

‍

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. 

‍

Cost Cutting

‍

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 

‍

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.

‍

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.  

‍

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

‍

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

‍

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.

‍

Surveys still fall short

‍

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. 

‍

Social media monitoring - meh

‍

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.

‍

Focus groups flop

‍

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.

‍

The trends to watch for when building a modern VoC program

‍

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:

‍

AI-driven signals 

‍

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! 

‍

Automation

‍

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. 

‍

Data integration 

‍

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. 

‍

It’s time to modernize your VoC program 

‍

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. 

‍

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.).   

‍

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. 

‍

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.

‍

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.

‍

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.”

‍

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…

‍

I responded:

‍

“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?”

‍

“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.”

‍

“Now” before “Next”.

‍

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)

‍

More Examples

‍

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!”

‍

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”.

‍

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!”

‍

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.

‍

And we’re building this right now.

‍

We’re fired up to announce the launch of two new Signals designed to help customers gain more insights about their customers. Inspired by Sturdy’s existing customers and developed by our data engineering team, the new underlying language models detect when end users are confused and having trouble with billing-related matters. 

The addition of these two new Signals brings the total number of Signals available to Sturdy customers to 20. Sturdy customers will be able to take advantage of these new Signals on Feb 1, 2023.  

Confusion

This signal detects when a customer indicates that they are confused about what is happening or unsure about how to accomplish something.

For example, when a customer says something like, “we have no idea what is causing this,” in a support ticket, a signal is being sent. The customer is confused. They are asking for help. Maybe this is an isolated issue. Maybe this customer needs more training. Regardless, it’s an opportunity to engage. Furthermore, if your customers are often confused, it indicates opportunities to improve both your product and services. 

Billing Issue

This signal detects when there is an issue regarding billing or payment processing.

For example, when a customer gets or responds to a message like, “this is to inform you that our attempt to collect your payment has failed”, a signal is being sent. Maybe they didn’t receive their invoice, and it’s a matter of having the wrong billing information. In this case, a simple fix is in order. Otherwise, this could indicate a larger problem associated with the relationship of the account.  Or, if your company receives lots of billing issue Signals, it likely means that you have an internal process that needs to be revamped. 

Discovering, classifying, and escalating customer Signals at the right time helps teams understand what needs attention — now. Move over surveys, sentiment, and health scores. This is real actionable stuff— the stuff your team needs to work on now.  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.  

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

‍

Customer Intelligence (CI) has become a critical tool for organizations looking to gain a competitive edge in customer engagement and satisfaction. By collecting, analyzing, and leveraging customer data at scale, businesses can make informed decisions that will help them better understand their customers’ needs and preferences. With the rise of advanced technologies like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), customer insights have become more accessible than ever before. As a result, the number of Customer Intelligence Platforms available today proliferates, with more sophisticated tools emerging each year. This article will discuss the top 13 customer intelligence platforms in 2023 across various subcategories, such as sales intelligence, product intelligence, health score tools, productivity tools, and support intelligence.

‍

What is Customer Intelligence?

Customer Intelligence (CI) collects and analyzes key customer-generated data to glean crucial insights, risks, trends, and opportunities. CI is heavy on integrations and often uses advanced data sciences like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP).

CI is about data — some you may have already been using and new data now available thanks to technological advances. To grasp the magnitude of Customer Intelligence, imagine if you could unite and analyze all your customer interactions — emails, tickets, chats, call transcripts, and community data. Now imagine harmonizing this new knowledge stream with data in your CRM, CSPs, and usage tracking systems to create new analytical frameworks, reports, dashboards, and critical workflows. That is the essence of Customer Intelligence.  

It goes without saying that core to any commercially viable CI solution is a sophisticated data privacy element. While our customers want you to use their feedback, suggestions, and more to improve the value they derive from your products and services, they also expect solutions built for the privacy-first era. They want you to fix bugs, make your product less confusing, build critical features and service them better. CI means better listening — active listening.

‍

The proliferation of Customer Intelligence platforms doesn’t come as a surprise. Customer Experience has emerged as a top concern amongst business leaders, with more than 87% of senior business leaders indicating that customer experience is the leading growth engine for their businesses. The investment community has also taken a keen interest in Customer Intelligence-related startups pumping billions of dollars into the space in the past 48 months. The funding has been distributed across a variety of categories and line-of-business-focused segments. Let’s break CI down into a more digestible conversation. 

Customer Intelligence is quickly growing into a broad category. Our research taught us that a burgeoning ecosystem of CI categories and segment-specific platforms go deep to solve unique customer-related challenges. Nearly every Customer Intelligence solution leverages advanced data sciences to provide a missing layer to today’s B2B GTM stack. Based on conversations with over 100 B2B product and customer leaders, the most beneficial systems are those that create a System of Intelligence. But no matter the application, it is clear that leaders are looking for deeper insights with which to create more durable and profitable customer relationships.  

‍

Customer and Product Intelligence

‍

US-based Sturdy represents a strong example of an innovative, commercially-ready, Customer Intelligence solution. Sturdy collects unstructured data sources like customer emails, tickets, chats, meetings, community data, and more via public APIs. It then restructures the data while also anonymizing it to address privacy concerns. The “clean” data is combined with other data sources like CRM data and then is unified into one searchable system that every team can use. Sturdy consolidates hundreds, sometimes thousands, of data silos, then employs AI, NLP, and ML to surface essential signals and themes that help teams improve products, relationships, and revenue. The platform has a no-code automation engine and a suite of APIs (Sturdy’s Data Exhaust) to route essential data and insights to the people, teams, and systems that need them most.  

CI systems like Sturdy can transform massive amounts of unstructured data (think email) into knowledge delivered autonomously to any business unit, team, person, or system. Sturdy makes insights accessible to end users and back-office analytics teams alike. Leaders are investing in AI-forward systems of intelligence because they see it as paving the path to taking customer-centricity to the next level. 

Who buys Sturdy?  

Customer and product leaders.

Pricing

Sturdy doesn’t list pricing on their website, stating, "Sturdy’s business plan is based on the volume of data you process and the Signals you use. We tailor our plans to best fit your needs, so please contact us for a custom quote.” It’s also worth noting that Sturdy has enterprise and SMB “quick start” plans. 

‍

Sales-Focused Solutions

‍

The most mature category of CI products are those designed for sales and other pre-revenue teams. The leader in the space, Gong.io, has pioneered the Revenue Intelligence category, which is closely related to Customer Intelligence. Sales-focused CI solutions primarily analyze recorded sales calls for coaching opportunities and conversational insights about customer buying behaviors. 

Gong makes mention that their platform can support customer success and marketing teams by focusing on moving them “closer to revenue.” Gong also can help managers use conversational insights to identify coaching opportunities for remote workers, as it seems with this entire category. 

Who buys Gong.io?  

Sales and RevOps Leaders at SMB and enterprise companies with significant BDR and corporate-level sales teams. 

Pricing

Gong has a lot of great content on their site for sales and RevOps pros, but, like most others, they don’t provide pricing information. However, their site says pricing is based on an annual platform fee and the volume of recorded calls. Others to watch in this category are Invoca and Databook. Both are taking innovative approaches to provide sales teams with Customer Intelligence.

‍

‍

Invoca, like Gong.io, is a sales-focused platform that analyzes transcripts from sales calls to surface opportunities. The Invoca solution is called center-ready, and they list large customers like Verizon, Robert Half, and 1-800-Junk on their website. AI-forward technology provides the power to analyze all sales conversations, and the user interface provides multiple views of the overall prospect's journey and, often, beyond.   

Who buys Invoca?  

Sales, Call Center, and RevOps Leaders at B2C companies with larger agent-based, sales call centers.

Pricing

Invoca offers plans for both brands & agencies and pay-per-call marketers. They offer Pro, Enterprise, and Elite tiers in the former and Performance Professional and Enterprise in the latter. Neither list pricing on the website. 

‍

Another player in the sales-focused category is Databook. Databook provides “strategic enablement for account-based selling,” allowing teams to focus on more “doing” and less “planning.” Databook’s website classifies strategic enablement as “the art of leveraging information, process, and technology to successfully craft the strategies needed to drive effective sales execution.” This is all to say that they provide data to better inform and optimize your account-based sales process. 

To accomplish this, Databook leverages its proprietary data sciences tech to analyze publicly available data. It crawls all your accounts to provide and finds and ranks prospective accounts. Databook positions itself as an Enterprise Customer Intelligence Platform — another system of intelligence — to help you close more deals. 

Who buys Databook?  

Sales and RevOps Leaders at B2B companies with account-based sales and marketing motions.

Pricing

Databook does not provide any pricing information on its website. You can request a free demo on their contact us page.

‍

Support / Contact Center Intelligence

In addition to sales-focused CI, the support-focused call center category is very well represented in funding and product maturity. Companies like Observe.AI, Balto, and Forethought have raised $358MM to analyze interactions like support tickets and agent-managed phone calls. These solutions seek to reveal coaching opportunities, quality of service issues, sentiment, and compliance matters. 

‍

Observe.ai is a noteworthy solution in the Support / Call Center Intelligence subcategory. The platform analyzes agent calls and tickets. Then, using its proprietary conversation intelligence engine, it looks for what they call Moments, out-of-the-box and customer-defined themes. Consolidated views of all agent conversations and Moments give leaders good visibility into coaching/training and quality of service issues. 

Who buys Observe.ai?  

Call Center, Support, and Service Operations Leaders at B2C and B2B companies with larger agent-based support call centers.

Pricing

Observe.ai does not provide any pricing information on its website. Instead, the company offers live demonstrations to walk prospective customers through the platform and its features based on various use cases.

‍

Leaders evaluating Observe.ai should also consider evaluating Balto. Balto’s conversational intelligence solutions offer benefits to agents, supervisors, and leadership with the goal of improving agent performance. Their AI enables companies to train and onboard their agents faster with prescriptive content suggestions and triggers that alert supervisors of critical moments and coaching opportunities. Balto promises to ensure that “your agents will say the right thing on every call,” real-time guidance is programmed to assist agents with the next best actions and workflows. Balto’s secret sauce is the real-time alerts that managers receive when agents need assistance allowing teams to be as proactive as possible.    

Who buys Balto.ai?  

Call Center, Support, and Service Operations Leaders with larger agent-based call centers at B2C and B2B companies.

Pricing

As with the norm, Balto does not provide specific pricing information but allows prospects to elect for personalized demos.

‍

Product Intelligence

Product Intelligence is another healthy category of the Customer Intelligence space. These solutions aim to serve product and user experience teams with customer-generated insights related to product adoption and roadmap suggestions. Pendo and Aha! have been at it the longest and focus on collecting usage data and surveys. While an up-and-comer, Enterpret is building the next generation of customer feedback intelligence by leveraging the voice of the customer.

‍

Pendo is a category leader in the Product Intelligence segment. It combines your product’s feedback, analytics, and in-app guides into one workspace. Pendo solicits and collects qualitative and quantitative data to understand customer engagement and product efficacy. With tools to impact and measure product engagement to deliver content to users at critical junctures like onboarding, Pendo is a feature-rich product intelligence solution. This maturity extends to Pendo’s commercial motions. In short, they have plans and associated feature bundles to fit small start-ups and enterprises alike.

Who buys Pendo?  

Product Management, Product Operations, Product Marketing, and Operations leaders at small and large B2B and B2C companies. 

Pricing 

Pendo is one of the few vendors that offers detailed pricing information on their website featuring four separate plans: Free, Starter, Growth, and Portfolio. While the freemium offering allows users to get a taste of the power of Pendo, it offers a scant limit of 500 monthly active users (meaning your product users), product analytics, and in-app guides. 

The Starter package increases monthly active users to 2,000 and adds their Net Promoter Score (NPS) tool. This package costs $7,000 a year. In addition to these offerings, Pendo’s Growth plan provides Sentiment analytics and can be used in a single web or mobile app. And finally, Pendo’s Portfolio package allows users to use the software across unlimited web and mobile apps. In addition to sentiment analytics, it provides cross-app reports and portfolio summaries. 

‍

Where Pendo focuses on customer feedback, Aha! provides a platform for product road mapping. More of an ideation and product creation platform for product managers than feedback analysis play, it’s a surprise to us that Aha! doesn’t integrate out-of-the-box with Pendo. Integrating Pendo data requires a Zapier integration.

The Aha! suite offers a collaborative seven-step framework for the product development process The first step establishes a clear vision and goals. The Ideate phase captures brainstorms and crowdsourced ideas. The Plan phase helps users prioritize, estimate value, and manage capacity. Showcase allows users to share roadmaps and go-to-market plans. The Build phase allows users to deliver new functionality through agile development. The Launch step brings these new features to market. Lastly, the Analyze phase allows you to see your product come to life by tracking customer usage. 

Who buys Aha!?  

Product Management and Engineering leaders at small and large B2B and B2C companies. 

Pricing

Like Pendo, Aha! also offers a freemium option for their Aha! Create, a digital notebook for product builders. Interestingly enough, Aha! offers a free 30-day trial for its premium products. This allows users to access all features, easily invite colleagues to collaborate, and does not require a credit card upfront. Following the free trial, the Aha! Develop offers an agile tool for healthy development teams at $9 per user per month. Aha! Ideas is a comprehensive idea management tool that starts at $39 per user per month. Last but not least, the Aha! Roadmaps offering starts at $59 per user per month. 

‍

Enterpret, similar to Pendo, is building a customer feedback platform. Unlike Pendo’s approach, which leverages data from surveys and other solicitations, Enterpret looks at external reviews and internal interactions like support tickets. The platform then allows users to create and search a taxonomy to find and track product insights. Enterpret is equipped with semantic search capabilities making it easy to query keywords and topics. Their core offering aims to help teams prioritize product roadmaps, discover product gaps, and detect quality issues. The company was founded by software engineers and backed by notable investors.    

Who buys Enterpret?  

Product Management and Engineering leaders at SaaS companies. 

Pricing 

There is no pricing information available on the Enterpret site. Like many others listed above, prospective customers can fill out a demo form for more information.

‍

Productivity Tools

Productivity-focused CI apps like Theysaid.io (FKA ‘Nuffsaid) and Retain.ai help customer success teams understand which customers need the most attention and which are black holes for your resources. For example, Theysaid.io uses a proprietary engine to prioritize tasks that matter most and log information to other systems without app-switching. This might be particularly useful to teams that use an “at scale” or “one to many” approach to manage customers. 

‍

TheySaid bills itself as a modern approach to customer success platforms. Customer interactions are consolidated in a single workspace. The analysis is done on the aggregate data to find trends. Customers are asked questions as they interact with products gathering inputs that make up quantitative trends. When a trend hits defined thresholds, workflows are kicked off. This can be particularly helpful for teams that employ a one-to-many approach. 

Users of TheySaid create role-specific questions vetted by third-party experts and sent at specific times during the customer journey. Risks are then scored and given a label. TheySaid state on their website that getting started takes just a few hours.

Who buys TheySaid?  

Customer Success Leaders are at SMBs that have not leveraged a traditional customer success platform.

Pricing 

Although no pricing information is offered on the website, the demo form states that prospective customers can try TheySaid for free.

‍

Like Theysaid, Retain.ai aims to create a single source of record for every customer. And, like TheySaid, getting started is quite easy. Just select what applications, workflows, pages, and attributes you want Retain.ai to track. Have your teams install a browser plugin, and the system starts tracking things like time-to-serve, engagement, team productivity, and more. Customers receive a holistic view of customer engagement across all systems view dashboards. Retain.ai has some sample case studies on its website, but it's unclear what market segment the product is geared towards.

Who buys Retain.ai?  

Customer Success Leaders at B2C companies (based on their sample case studies).

Pricing

The Retain.ai website does not provide any pricing information. Those interested in learning more can fill out their demo form.

‍

Health Score Tools

Arguably, customer health score solutions appear more as an output of Customer Intelligence than a category. These solutions target SMB buyers who haven’t adopted a more robust customer success platform. Companies like Akita and Involve.ai analyze product usage, NPS, the number of support tickets, and customer sentiment and then, with the help of data science, ascribe a health score to your accounts. Similar to Theysaid, Involve.ai takes it further by recommending playbooks once an account reaches a certain health threshold.

‍

Akita is the go-to customer success software for SaaS businesses. Akita provides a hub for telemetry-based customer data, activity, and metrics. Beyond storing all the information, it lets customers set up unlimited alerts when certain criteria are met. Like Involve.ai, automated playbooks can be triggered in response to customer behaviors or attributes. This frees up valuable time to focus on high-value tasks. Beyond this automation lies Akita’s task management capabilities, built to provide a single and simple interface for workflows. Thinks of this as a workspace for CSMs

Who buys Akita?  

Customer Success Leaders 

Pricing

Akita offers three transparent pricing options. Start, Connect, and Customize offerings can be purchased on a monthly or annual subscription. Prospective customers are incentivized to go annual by saving 20% after 12 months. The Start plan offers basic features and costs $160/month (if billed annually) for up to three users. Each additional user costs $47.20 per month. The Connect Plan offers “powerful integrations for a scalable customer success strategy.” This plan costs $480 per month (again, if billed annually). Similar to the Start plan, this plan includes three users, with each additional user costing $63.20 per month. Last but not least is the Customize plan. This option requires connecting with an Akita representative to learn more about their advanced integrations. Before committing to any of these plans, however, prospective customers can test Akita out on a free 14-day trial. This free trial includes unlimited user licenses, playbooks, custom segments, and health scores.

‍

Involve.ai touts that they’re an early warning system to predict churn and upsell opportunities. Their platform is built to help customers capture and analyze customer sentiment. After organizing and analyzing customer sentiment, Involve delivers actionable insights regarding retention, churn risk and upsell opportunities. Additionally, Involve provides customers with an actionable customer health score powered by their proprietary AI model built to analyze customers’ qualitative and quantitative data. Like Akita, Involve provides automated workflows and playbooks to maximize team efficiency.

Who buys Involve.ai?  

Customer Success Leaders at SMBs that have yet to adopt a customer success platform 

Pricing

Involve.ai doesn’t provide a specific pricing breakdown but a tool that hints at potential costs based on the number of clients and revenue. For example, a company with a $5MM ARR, 2% Annual Churn Rate ($100,000), and fifty customers can expect to pay $12,000 annually for Involve.ai.

By now, it’s clear that Customer Intelligence is a diverse and quickly evolving market. This list is not exhaustive. The common theme for all the systems mentioned here is data centricity. They all hinge on getting data in one place and analyzing it to provide better insights about customer behaviors.   

Whether you’re already sold on the value of Customer Intelligence or looking for ways to take your customer relationships to the next level, check out these key considerations you need to know about choosing the right Customer Intelligence platform to accelerate your goals. 

‍

When choosing a CI platform, consider the following:

• Insights for various teams: Customer Intelligence isn’t just for customer success teams. Product and engineering teams can immediately benefit from learning more about customer frustration, confusion, and wants directly from the voice of the customer. Marketing teams can transform positive insights into customer references. Revenue operations and business intelligence teams can create new analytical frameworks from previously unavailable data. Choose a system that helps you democratize customer insights and one that helps to create a collective reality for every team that wants to better understand your customers.
• ‍Fast time to value: Let’s face it, we’ve all bought platforms that were oversold, hard to implement, and even harder to administer. Look for solutions that can deliver insights to your specific use cases quickly. Understand the resources required to start receiving value and what resources are needed to maintain the program in the future.
• ‍Tech stack: When choosing a Customer Intelligence platform, the platform you select must integrate deeply with the critical components of your current GTM tech stack. And don’t forget about customer email. More than 50% of B2B customer-to-business communications start with an email.
• ‍Avoid duplicate functionality: CI platforms often have similar functionality to systems you already have, like customer success platforms and CRM systems. Look to compliment your existing system with rich data from a Customer Intelligence solution.
• ‍Security: Does the platform have a clear and transparent take on data security? Ensure that any system you choose is SOC 2 Type II ready.
• ‍Data privacy: How does the platform handle data privacy? What is the technical approach to safeguarding your customers’ PII? Will the solution meet the security and privacy requirements of your infosec and data privacy teams?

In conclusion:

We’re still in the early innings of CI. The challenges to achieving the potential are eroding as quickly as the technical capabilities are evolving, creating a new must-have system for the modern post-sale tech stack. Many organizations aren’t aware of how rapidly it’s evolving and may not realize the benefits Customer Intelligence can bring to various teams in their companies.

As we look ahead to 2023, it's clear that Customer Intelligence will continue to be one of the most essential tools businesses can use to stay competitive and understand their customers better. By leveraging customer data through CI platforms, companies are able to make informed decisions that will help them improve customer engagement and drive sales and revenue retention. They ultimately increase customer satisfaction levels across all channels to ensure your customers grow with you, not away from you.    

‍

Customer retention is the ultimate force multiplier in any B2B SaaS business. It involves building strong relationships with existing customers, ensuring they stay loyal to your brand, helping them use more of your product or service, and becoming advocates who bring in more customers through word of mouth. By investing in customer retention and ultimately increasing your customers' lifetime value (LTV), SaaS businesses unlock tremendous potential for growth and profitability.

‍

Sometimes the SaaS world seems like alphabet soup. Lots of acronyms. As a reminder, Lifetime Value (LTV) is an essential metric for SaaS businesses. It measures the profitability of a customer over their entire lifetime of their contract or subscription. LTV provides an indication of how much revenue can be expected from a customer within any given point in time. 

Here’s how I suggest calculating LTV. First, determine the average revenue per user (ARPU). This is calculated by dividing total revenues by the number of users over a specific timeframe. Then, divide this result by the customer churn rate for that same period — this will estimate how long each customer’s subscription lasts on average. Multiply the ARPU and estimated lifecycle together to get your lifetime value. Doing so will allow you to accurately measure customer loyalty and help you devise meaningful customer retention strategies. 

‍

Over the course of my career, I’ve learned that sometimes the best strategy is to stop doing something rather than create a new process. Making changes and implementing new processes and workflows can be time-consuming, lead to more complications, and cause confusion for your teams and customers. Simply put, here are a few things you can do to stop pissing off your customers because we can all agree that pissing off customers is a bad strategy.  

‍

Stop ignoring customer feedback

‍

Ignoring customer feedback is more than a mistake; it’s negligence. Customer feedback is the single most valuable thing a customer can provide — arguably more than their contract value. Insights about your products or services allow you to make improvements and create better experiences for every customer and every prospective customer. 

‍

I’ve written about the perils of relying on surveys to capture customer feedback. So as a modern business leader, it’s high time you establish the channels to capture it and share it with the teams that can benefit the most. Have a system for everyone in your organization to access and analyze customer feedback — make feedback a collective reality. Democratize it. 

‍

At one company where I served as the chief revenue officer, we provided hiring software to medium-sized employers, which helped them attract job applicants and manage the interview and hiring processes. We monitored customer feedback carefully. In fact, we monitored feedback so closely that it became a part of our culture and was more or less the genesis of my current company, Sturdy. 

‍

In addition to fielding and responding to occasional issues and concerns about how our service worked, we identified patterns within the feedback: features that were missing, UI that was confusing, bugs that caused frustrations, coaching opportunities for associates, and more. These patterns in the customer feedback informed the creation of very focused rules of engagement and playbooks that ultimately increased our LTV. This lift in LTV helped us successfully sell that business to one of the largest payroll providers in the world. 

‍

Stop overpromising

‍

Whether the account manager said “yes” when they should have said “no,” or what they said was accurate until someone else messed it up, overpromising often comes back to haunt post-sales teams. Poorly aligned expectations leave everyone involved feeling disappointed and let down. This fracture in the customer-to-business relationship is one of the leading causes of cancellations. It’s also one that often goes undocumented or improperly categorized. 

‍

Just as important as capturing the reasons why customers cancel, customer success teams should identify and document common trends and topics that indicate overpromises. By understanding the areas where false promises are made, you can enable customer-facing teams to consistently provide accurate information about the capabilities of your product and services. 

‍

Shameless plug for Sturdy — Our AI looks for Signals of overpromises in communications with your customers. This Signal detects when a customer indicates a discrepancy between the product or service they expected and the one they received.

‍

Here are some overpromise signals that were detected in customer-business emails. Sound familiar? 

‍

"This is something that was promised in the implementation stage."

‍

"… even excited about the features that were promised. But do feel ... underdelivered on the capabilities."

‍

"Below is a list of things that were promised and hasn’t happened:"

‍

"That was promised, but I still have not received anything."

‍

"We can't use these services that were promised/promoted."

‍

Stop doing Silly QBRs 

‍

Ok. This may seem trivial and maybe even a little silly itself, but I can’t let this one go. For those unfamiliar with the term, a Quarterly Business Review (QBR) is a look into the performance and value of your service over the past quarter. The objective of a QBR is to identify areas of improvement and offer strategies for moving the relationship with your customer forward. As the name suggests, QBRs are typically conducted at least once per quarter and most often with a typical, boring format — a presentation on some slides.  The TLDR — 95% of the time, QBRs are awful. Personally, I loathe being on either end of them.

‍

I suggest taking a page out of Customer Success Keynote Speaker & Educator Aaron Thompson’s playbook and turning QBRs into something meaningful for your customers. Use them as an opportunity to strengthen your relationship. Don’t just go through the motions. Here are some other tips from Aaron’s blog post on LinkedIn titled “Stupid Is As Stupid Does...And QBRs Are In Fact Stupid

‍

1. Make them a conversation, not a presentation.
2. Come with more questions than statements.
3. Don't get into SLAs, IRTs, or anything tactical. The topic du jour is their business strategy, and you are there to learn, not to teach. 
4. Make them 50% retrospective and 50% prospective. 100% strategic still. 
5. Get Creative. Much like Spotify's #Wrapped2019 (and 2020 and 2021) campaign, they demonstrate value to their millions of subscribers at the end of each year at scale.

‍

‍

At several of the companies that I’ve started, advised, consulted for, and worked at, we’ve used the ‘stop, start, continue’ framework. If you aren’t familiar, the ‘stop, start, continue’ framework facilitates retrospectives. The outcome is improving future work performance through open communication and collaboration. In that vein, if you stop doing these things that damage customer relationships, you will open up the possibility of developing deeper relationships with your customers based on trust and value. Implementing even one of these changes can significantly impact your customer retention strategy. Which of these are you going to commit to first? 

‍

If you’ve ever heard the following - keep reading

‍

"This is something that was promised in the implementation stage."

‍

"We were excited about the features that were promised, but you’ve under-delivered on the capabilities."

‍

"Below is a list of things that were promised and hasn’t happened:"

‍

"That was promised, but I still have not seen or heard anything."

‍

"We can't use these services that were promised/promoted."

‍

TLDR

‍

Sturdy discovers signals in everyday customer interaction like email and more. The Overpromised Signal detects when a customer indicates a discrepancy between the product or service they expected and the one they received.

‍

Whether the salesperson or account manager said “yes” when they should have said “no,” or what they said was accurate until someone else messed it up, overpromising often haunts post-sales teams. Poorly aligned expectations leave everyone involved feeling disappointed and let down. This fracture in the customer-to-business relationship is one of the leading causes of cancellations. It’s also one that often goes undocumented or improperly categorized. 

‍

Just as important as capturing the reasons why customers cancel, post-sale teams should identify and document common trends and topics that indicate overpromises. By understanding the areas where false promises are made, you can enable customer-facing teams to consistently provide accurate information about the capabilities of your product and services. In short, take these trends back to sales leadership to address the problem systematically. 

‍

Intrigued? It works. See Sturdy in action.  

Before Sturdy, we worked for a B2B SaaS Software company called Newton. At Newton, we spent an enormous amount of time tracking and recording customer insights that came from customer feedback. 

‍

In fact, we had a training program, Alchemy, where every person at Newton was trained on what to do when they read or heard certain things like, “how do we download our data?” or “can we get a copy of our contract?”. We had a rule that every “happy” customer was sent to marketing for a potential reference. Every unhappy customer got a call from an executive. We thought we were a well-oiled machine. And yet, with all this, whenever we wanted to get on a call with an important customer, we needed to get several people in a room to discuss the account because we could never be sure what state the account was in.

‍

The challenge was that logging and identifying these important account triggers was entirely manual. If we logged every email, it just became noise. If we logged nothing, we had no idea what was going on.

‍

And at Newton, we realized that in a year, we generated 15,000 support tickets, 15,000 phone calls, and almost 100,000 customer conversations via email. 

‍

Email. Almost every executive knows they have data gathering digital dust in email inboxes. Unread messages, Bug Reports, Cancellation Requests, and Unhappy sentiment are just a few examples of critical business signals that flash in and out of inboxes daily. The challenge is, and always has been, to ensure that every signal is recognized and acted on.

‍

When we started Sturdy, the idea was simple, “the way we record and monitor customer feedback is insane. It has to change”. So we decided to tackle customer email first. Along the way, we realized we had built the first “Customer Email Intelligence Platform.” 

‍

‍

In building Sturdy, we learned that a customer email intelligence platform must do four things very well, all at once: 

‍

1. Safely and securely extract only customer emails while ignoring all other emails;
2. Accurately merge all of a customer’s information into one view, a “single pane of glass”; 
3. Classify, categorize and Identify critical themes, topics, and sentiments in each email;
4. Route and alert the teams and teammates who need to know.

‍

Safely and securely extract only customer emails while ignoring all other emails

‍

For a long time, technologists have developed technologies that attempt to extract customer email data from an inbox and put it somewhere more useful: Outlook plugins, BCC addresses, Salesforce logging, Activity Capture, and Do-Not-Reply Email Addresses. These systems often create more issues, like duplicated data, missing emails, and lost headers. 

‍

Modern CEI solutions will not rely on “hacks” like BCC to get customer emails. At Sturdy, we have a patent-pending suite of tools that ensure only emails from/to customers can be ingested. This toolkit also allows Administrators to ask Sturdy to ignore emails sent by certain people, or it can be restricted at the API-level. 

‍

Bottom Line: Extracting customer emails needs to be rock-solid, secure, and highly configurable.

‍

Accurately merge all of a customer’s information into one view, a “single pane of glass”

“Hey, I need to call Acme Corp. Let’s all get together for 20 minutes to review their account.” Having all your customer emails in one organized spot will make wonderful things happen. The most obvious and time-saving will be the virtual elimination of the “hey, what’s going on with this account meeting?” Getting together to discuss accounts will never go away. But, having a 20-minute meeting so everyone can share their email inboxes should.

‍

In fact, Sturdy estimates that in a typical B2B SaaS company, an Account Manager spends almost 30 hours per month in Account Review meetings. 

‍

Bottom Line: Moving customer email out of the inbox will vastly improve account management and add time to everyone’s day.

‍

Classify, categorize and Identify critical themes, topics, and sentiments in each email

‍

The third pillar of CEI is where the heavy lifting happens. Today, your business can convert and categorize every piece of customer feedback into something actionable or insightful, at scale, without manual labor.

‍

If you're considering using AI or machine learning, remember that almost all language models today are trained using consumer data. This means they weren’t trained using business language, which tends to be far more restrained and professional. 

‍

We have reviewed over 10 million customer emails at Sturdy and built language models identifying the key themes and topics driving B2B SaaS and Services businesses. We have found that over 20% of customer emails have an essential theme or topic relevant to another business team. 

‍

Bottom Line: Modern AI technologies will illuminate insights, topics, and themes in your customer base at scale.

‍

Route and alert the teams and teammates who need to know

‍

You have likely worked in a company that attempted an early version of email intelligence. It was just done manually.  “If you get a feature request in an email, log it to Jira and forward it to the engineering team.” Identify, Classify, and Route. Manual labor doesn’t scale.

‍

Imagine if every time a customer was confused by a product issue, it could be routed to the design team. Imagine if every bug report ever reported by a customer was searchable at its source. 

‍

As modern Customer Email Intelligence identifies and routes business themes and topics without requiring human interaction, the hidden costs of recording, saving, and logging customer requests will go to almost zero.

‍

Sturdy’s automation engine allows our customers to harmonize email intelligence with CRM data. So you can say, “If one of our top accounts requests a copy of their contract, let the CEO know.”

‍

Bottom Line: Customer Email Intelligence will ensure that the correct information gets to the right team every time.

‍

Customer email intelligence. The time is now.

‍

There’s never been a better time to upgrade your tech stack to include Intelligence solutions. Businesses can maximize productivity and accuracy by scaling these intelligence solutions while eliminating mundane and time-consuming tasks. This type of automation allows companies to scale quickly, adapt to changing markets faster, reduce costs and increase efficiency. New technologies like Customer Email Intelligence also allow for more intelligent decisions that can save time and money in the long run. Sturdy might be your solution if you want to understand your customers better at scale and remove manual labor from your business. Let us know.

‍

‍

Whether it’s a positive review or a scathing complaint, customer feedback is critical to the success of every business. It’s a window into the experiences buyers seek and a way for B2B software companies to improve their products, processes, and relationships.

‍

Customer feedback is information given by your customers about the quality of your products and services. Are you meeting customer requirements and delivering value? Whether good or bad, there is no better and more reliable data source about your company than customer feedback.

‍

With B2B buyers demanding more B2C-style experiences, it’s never been more critical to keep up with the changing needs of buyers and users. Unfortunately, many teams still rely on yesterday’s tools to solve today’s challenges. 

‍

To date, most companies have relied heavily on surveys to gather feedback. Others have coupled surveys with analytics tools that analyze customer sentiment. Unfortunately, both surveys and sentiment analysis fail to provide the necessary depth of qualitative data to build deeper customer relationships. Simply put, surveys and sentiment are often subject to broad interpretation. 

‍

Today’s most competitive B2B SaaS companies are putting deeper contextual insights about their customers to work. They are doing this by layering them into operations, processes, metrics, information flows, etc., to enable every team to make decisions based on specific, actionable signals. We’ll explore this more later.

‍

Surveys are still the status quo

‍

Let’s face it, surveys are a relatively simple and inexpensive way to collect customer feedback. However, Forrester reports that surveys capture between 2% and 7.5% of customer interactions.

Given the importance of understanding our customers, SaaS businesses must expand their approach to collecting and curating customer feedback. This starts with expanding the data sources teams use to operationalize insights across the business.   

‍

Easier said than done. To date, B2B SaaS businesses haven’t invested heavily enough in tools and technologies to help them better understand their customers. Today, leaders still struggle to create a complete picture of customer needs, frustrations, and intent. To a large extent, this is due to a reliance on surveys.

‍

While many of us can’t rid ourselves entirely of surveys, they continue to fall short for these 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.
5. Survey responses are limited to structured questions, so respondents cannot provide feedback about topics that are not covered. 
6. Surveys require significant customer time and effort and can be considered annoying.

‍

Customer surveys are just one tool in the burgeoning field of customer intelligence. Sturdy defines it as the process of collecting and analyzing customer data from internal and external sources to unlock customer insights. Recently, many have turned to sentiment analysis to gain a deeper understanding of the consumer mindset. Sentiment analysis insights gathered from different sources lead to improved product features, pricing, customer experience, and overall customer satisfaction. 

‍

Sentiment alone is… OK

‍

Many companies are running sentiment analyses on their product or customer service feedback. But as with surveys, this isn’t enough. Sentiment analysis gives you the binary answer good/bad or extends the range with outputs like terrible/bad/OK/good/great. 

‍

Sentiment analysis requires machines to be trained to analyze and understand emotions as people do. Human language cannot be categorized into only three buckets (positive, negative, and neutral) in its intricacies and complexities. For example, Let’s say we determine that 68% of customers have a negative impression of our product. That still leaves us with many unanswered questions: Do we change the pricing? Do we make UX adjustments? Without more specific insights, we’re left, once again, to go with our guts. Think survey results. 

‍

Let’s put it differently: if 68% of your customers are expressing negative sentiment, you need to understand why the customer feedback is so negative. Your team will need contextual clues to solve this level of dissatisfaction. The answers are probably right there; you just need the qualitative layer below the actual sentiment. 

‍

Once you understand the qualitative data, you can design better products, adjust processes, and build better relationships based on specific data points that need less interpretation. To do this, companies are leveraging next-generation AI, NLP, and ML technologies that provide deeper, actionable insights about their customers. 

‍

Tapping a new source of customer feedback

‍

Customer insights programs are more successful when customer data and feedback are gathered from multiple sources to get a more complete, diverse look into customer needs and impressions. Companies realize that customers constantly send signals that help us predict churn, capture references, get in front of renewals, prioritize features, and run our businesses better. Our customers are giving us this information in Slack, Email, Salesforce, Webinars, training sessions, quarterly business reviews, Zoom calls, etc., daily.

‍

Customer Signal

(noun) A gesture, action, or transmission delivered intentionally or unintentionally by a customer that conveys information, instructions, or insights. 

‍

For B2B SaaS businesses, these signals are immensely valuable. For example, reducing churn from 10% to 9% in a $10 million ARR business means that every customer is worth $17k more in lifetime value (500 customers, $20k annual contract value). And reducing churn in this example is saving just 5 customers a year. 

‍

Examples of Customer Signals‍

‍

Identifying, classifying, and escalating customer signals to the right people at the right time empowers companies with information and insights to preempt issues before they spiral and seize revenue opportunities in time to improve the bottom line. 

‍

For example, when a customer asks, “Can I have a copy of our contract?” in a support ticket, a signal is being sent. In a SaaS environment, the customer is likely signaling risk. Maybe they are evaluating a competitor. Perhaps there has been an executive change or a shift in priorities. Regardless, every SaaS leader will agree that this signal needs to be escalated so action can be taken. 

‍

Below are a few other examples of customer signals. This is not an exhaustive list; every company will vary on what is essential. An interesting exercise is to sit down and list out the signals that your teams should be watching for. The output of this exercise can be used to improve operations, user experience, training workflows, and more.

Customer signals help us understand our customers better than surveys and sentiment alone. By defining and leveraging signals at scale, we can clearly understand if our products are delivering the value promised at the time of the sale. We can also better understand if our customers are willing to grow with us or are growing away from us. 

‍

“B2B companies historically lag behind their B2C counterparts in adopting and deploying commercial analytics, but the ones who engage with the tools already outperform their peers; their return on sales are up to five percentage points higher than that of their counterparts.” McKinsey

‍

New analytics tools like Customer Intelligence platforms reveal opportunities for cross-functional collaborations. And the insights often have significant implications for non-sales teams. Rapid advancements in technology, especially AI, are making it easier to help brands quickly and responsibly use data to understand customer behaviors and predict customer needs. We can better anticipate future decisions when we discover new patterns and insights in our data. Ultimately, going beyond surveys and sentiment by leveraging customer signals presents opportunities and incentives to deliver better service and find new ways to grow.

‍

‍