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The latest from Sturdy — product news, insights, and resources.
Sturdy's MCP Server: One Call. Every Source. Already Resolved.
Another Step to Unlocking AI Outcomes: Resolve the Data First
The bottleneck is not your AI model. It’s the data it has access to. Sturdy’s MCP server delivers pre‑resolved, canonically organized context so your LLM can reason over it instead of guessing around it.
Another Step to Unlocking LLM Outputs: Resolve the Data First
For years, the problem was that data lived in silos. Different systems for sales, support, and calls. But the worst offenders were email and Slack. Email isn’t one silo; it’s as many silos as there are people on your team. Every rep, every CSM, every exec running their own inbox, none of it visible to anyone else. Slack is no different. Conversations buried in channels and DMs that nobody ever sees again.
What Changes
"Your LLM now has a single, usable data layer any user can query to inspect the full context of every prospect and customer."
“Every team now works from a single view of the relationship, not fragments of it. Sturdy gets everyone on the same page, no matter what screen they use.”
MCPs were a material step forward. They give LLMs a standardized way to reach outside their context window and pull live data from external systems without a human copying it in manually. An account record, an open ticket, a call summary, all accessible at query time without a custom integration.
Today, teams are dealing with a different version of the same problem. Every MCP server exposes a slice of the picture. The LLM can pull structured records, read a ticket, or fetch a call summary. What it cannot do is answer a question that requires all of them at once, because the data across those systems was never resolved against each other.
The entities don’t match. The timeline is fragmented. The thread that started the conversation often isn’t there at all.
The question every revenue team actually needs answered isn’t “what does this system say about the account?” It’s the question that requires the full picture: what has every person at our company said to every person at this company, across every channel, and what does that tell us about where this relationship actually stands right now.
No single MCP server can answer that. Most LLMs, handed raw data, will approximate an answer and present it with false confidence. That’s not intelligence. It’s a good guess.
That answer doesn’t live in any single system. It lives in the relationship between all of them. And if the LLM has to call multiple MCP servers to piece it together, resolve duplicate records, and reassemble a coherent account state on every query, the fragmentation problem hasn’t been solved. It’s just been moved into the inference layer.
What Sturdy’s MCP Does
Sturdy ingests from all of it. Email, call transcripts, support tickets, Slack, CRM, and meeting tools. Every channel where communication happens.
Before any of that reaches an LLM, Sturdy does the work that makes it usable. Entities are deduplicated and matched to canonical records. Interactions are classified. Signals are enriched, permission‑scoped, and source‑referenced. The relationship between interactions across systems is established once upstream.
Not inferred at query time. Resolved in advance, maintained continuously, and auditable.
That last part matters more than it sounds. LLMs are getting better at fuzzy matching, but revenue decisions cannot rely on it. “Probably the same account” is not good enough when you’re making retention calls, forecast commits, or expansion bets.
Then Sturdy exposes all of it through a single MCP server. One call. Pre‑resolved context with citations. The LLM starts from the signal, not the raw material.
The Token Cost Nobody is Talking About
There’s a practical consequence to raw MCP that most teams haven’t priced in yet. When an LLM has to reconstruct account context from scratch on every query, it burns tokens doing work that shouldn’t need to happen at query time.
Pulling from multiple sources. Resolving conflicts. Traversing relationships. Figuring out what it’s looking at.
At low volumes, this is invisible. At scale, it isn’t. The rediscovery tax on a raw MCP call runs roughly 60 to 80 percent of total token consumption per query. That’s the LLM figuring out context, not reasoning over it.
Sturdy removes most of that overhead. The context arrives already structured. The LLM starts from a position of knowing. The inference budget goes toward answering the question, not reconstructing the data.
What This Means for Teams Building on it
Sturdy’s MCP is designed for teams that have already provisioned an LLM and are now trying to make it useful. CTOs deploying models across their organization. Heads of Data and AI are trying to get real answers out of them. Operations teams are building agents that need reliable account intelligence.
The properties that matter:
Canonically resolved
Entity deduplication and matching happen upstream. The same account appears as one account regardless of how many systems it lives in.
Permission‑aware
Access controls are baked into the data layer. What a user can see reflects what they’re authorized to see in the source systems.
Source‑referenceable
Every signal comes with a citation. When something surfaces, the underlying interaction is linked.
Model‑agnostic
The data layer doesn’t change based on which model you use.
Nobody wants to spend 12 to 18 months normalizing data before they can build something useful. Resolving that data upstream changes what your LLM can do on day one.
Talk to us about connecting Sturdy to your existing AI deployment.
Another Step to Unlocking AI Outcomes: Resolve the Data First
The bottleneck is not your AI model. It’s the data it has access to. Sturdy’s MCP server delivers pre‑resolved, canonically organized context so your LLM can reason over it instead of guessing around it.
Another Step to Unlocking LLM Outputs: Resolve the Data First
For years, the problem was that data lived in silos. Different systems for sales, support, and calls. But the worst offenders were email and Slack. Email isn’t one silo; it’s as many silos as there are people on your team. Every rep, every CSM, every exec running their own inbox, none of it visible to anyone else. Slack is no different. Conversations buried in channels and DMs that nobody ever sees again.
What Changes
"Your LLM now has a single, usable data layer any user can query to inspect the full context of every prospect and customer."
“Every team now works from a single view of the relationship, not fragments of it. Sturdy gets everyone on the same page, no matter what screen they use.”
MCPs were a material step forward. They give LLMs a standardized way to reach outside their context window and pull live data from external systems without a human copying it in manually. An account record, an open ticket, a call summary, all accessible at query time without a custom integration.
Today, teams are dealing with a different version of the same problem. Every MCP server exposes a slice of the picture. The LLM can pull structured records, read a ticket, or fetch a call summary. What it cannot do is answer a question that requires all of them at once, because the data across those systems was never resolved against each other.
The entities don’t match. The timeline is fragmented. The thread that started the conversation often isn’t there at all.
The question every revenue team actually needs answered isn’t “what does this system say about the account?” It’s the question that requires the full picture: what has every person at our company said to every person at this company, across every channel, and what does that tell us about where this relationship actually stands right now.
No single MCP server can answer that. Most LLMs, handed raw data, will approximate an answer and present it with false confidence. That’s not intelligence. It’s a good guess.
That answer doesn’t live in any single system. It lives in the relationship between all of them. And if the LLM has to call multiple MCP servers to piece it together, resolve duplicate records, and reassemble a coherent account state on every query, the fragmentation problem hasn’t been solved. It’s just been moved into the inference layer.
What Sturdy’s MCP Does
Sturdy ingests from all of it. Email, call transcripts, support tickets, Slack, CRM, and meeting tools. Every channel where communication happens.
Before any of that reaches an LLM, Sturdy does the work that makes it usable. Entities are deduplicated and matched to canonical records. Interactions are classified. Signals are enriched, permission‑scoped, and source‑referenced. The relationship between interactions across systems is established once upstream.
Not inferred at query time. Resolved in advance, maintained continuously, and auditable.
That last part matters more than it sounds. LLMs are getting better at fuzzy matching, but revenue decisions cannot rely on it. “Probably the same account” is not good enough when you’re making retention calls, forecast commits, or expansion bets.
Then Sturdy exposes all of it through a single MCP server. One call. Pre‑resolved context with citations. The LLM starts from the signal, not the raw material.
The Token Cost Nobody is Talking About
There’s a practical consequence to raw MCP that most teams haven’t priced in yet. When an LLM has to reconstruct account context from scratch on every query, it burns tokens doing work that shouldn’t need to happen at query time.
Pulling from multiple sources. Resolving conflicts. Traversing relationships. Figuring out what it’s looking at.
At low volumes, this is invisible. At scale, it isn’t. The rediscovery tax on a raw MCP call runs roughly 60 to 80 percent of total token consumption per query. That’s the LLM figuring out context, not reasoning over it.
Sturdy removes most of that overhead. The context arrives already structured. The LLM starts from a position of knowing. The inference budget goes toward answering the question, not reconstructing the data.
What This Means for Teams Building on it
Sturdy’s MCP is designed for teams that have already provisioned an LLM and are now trying to make it useful. CTOs deploying models across their organization. Heads of Data and AI are trying to get real answers out of them. Operations teams are building agents that need reliable account intelligence.
The properties that matter:
Canonically resolved
Entity deduplication and matching happen upstream. The same account appears as one account regardless of how many systems it lives in.
Permission‑aware
Access controls are baked into the data layer. What a user can see reflects what they’re authorized to see in the source systems.
Source‑referenceable
Every signal comes with a citation. When something surfaces, the underlying interaction is linked.
Model‑agnostic
The data layer doesn’t change based on which model you use.
Nobody wants to spend 12 to 18 months normalizing data before they can build something useful. Resolving that data upstream changes what your LLM can do on day one.
Talk to us about connecting Sturdy to your existing AI deployment.
Our articles
Sturdy releases new Signal - How To
Sturdy's Data Engineering team has been hard at work developing new customer Signals. Late last month, the team added a new customer signal to our AI-powered customer intelligence platform called “How To”. This signal detects when your customers ask, “How do I do this?”. By listening for this type of interaction, SturdyAI users get immediate access to insights like:
- Which new or existing features do your customers need help with, either because they are confused by them or because they are very interested
- When seemingly small UI/UX issues become trends
- Which customers could benefit from more training, helping you to develop your champions
- How to improve your knowledge base, help text, and self-serve content
Next up, we’ll launch our new Expansion Signal. This signal will help to identify when your customers express purchasing intent like adding more users, buying services, or upgrading their plan.
Unhappy news from sturdy 😢
It’s weird to be happy to announce a new customer signal called ... Unhappy. Strange but true. New to Sturdy’s AI-powered customer intelligence platform is the Unhappy signal. The new model detects negative sentiment and customer frustration in emails, support tickets, chats, and video calls.
The Unhappy Signal is the first of many new Signals to come.
Unhappy is one signal in a series of new signals that Sturdy’s Data Sciences team is developing. The team is also exploring innovative ways to correlate causes of the negative sentiment with specific signals. For example, Sturdy will be able to show how specific bugs or account leadership changes impact customer sentiment. For those less familiar with what we are developing, the Sturdy platform scans emails, chats, support tickets, and other related customer communications. It then automatically detects signals that impact revenue, product roadmaps, references, and more.
Customer success, account management, customer marketing, and product teams use Sturdy.
Customer success, account management, customer marketing, and product teams can now more easily surface what is occurring, but also discern why it is happening because the platform automatically provides contextual highlights. That’s critical because all too often, the onslaught of customer communications is smothered by the sheer volume of messages. These large unstructured data sets stored in multiple systems in the cloud are not easy for companies to use on their own
No training, no change management required.
We aren’t here to reinvent and change the way teams or companies work. And that is what is so exciting about what we do. SturdyAI is the force multiplier for your business. If you already have a cutting edge BI tool, we just give it better data. If you have a killer CX app, we make it more insightful. If you have great Customer Success, Account Management, Operations, Marketing, and Product teams, we make them more efficient and provide them with better data.
More about us.
Led by a team of seasoned founders and B2B SaaS experts, Sturdy.ai is unlocking massive value from data hiding in plain sight. Using AI, Sturdy helps P&L holders preempt customer issues before they spiral and seize revenue opportunities in time to improve this quarter's results. Sturdy’s AI-powered customer intelligence platform detects critical signals from your customers and routes them to the right people at your company in real time, unlocking value and reinforcing process execution.
Sturdy is joining the Colorado Customer Success Community for CS Tech Day
Sturdy.ai is joining fellow SaaS technology innovators Prodoscore and Update.ai to share our solutions with the Colorado Customer Success community. Members will learn about the newest technologies available to customer success teams in an engaging format featuring live product demonstrations.
Customer success leaders and team members will briefly kick the tires on some exciting new offerings that can help lift customer retention rates, deliver better customer experiences, and increase productivity.
When: Wed, October 13, 2021 at 4:00pm MDT
Where: Register for free here
Who should join?
If you're a cloud computing (SaaS, IaaS, PaaS, MSP, mobile) manager whose mission is to onboard, serve, retain, and grow customer relationships, this regional community is for you! Meetups feature networking, learning, and sharing ideas to combat customer churn and increase loyalty. This is a local chapter of the Customer Success Association (http://www.customersuccessassociation.com). Topics include new technologies, "best practices," management systems, and people dynamics. Attendance is free and all are welcome.
About Sturdy:
Led by a team of seasoned founders and B2B SaaS experts, Sturdy.ai is unlocking massive value from data hiding in plain sight. Using AI, Sturdy helps P&L holders preempt customer issues before they spiral and seize revenue opportunities in time to improve this quarter's results. Sturdy’s AI-powered customer intelligence platform detects critical signals from your customers and routes them to the right people at your company in real time, unlocking value and reinforcing process execution.
Lose your executive sponsor, save your customer
It happens all the time, and you’re often the last to know. Your sponsor, once your economic buyer and advocate, is on the move. Gone. Losing an executive sponsor or senior point of contact is a catalyst for churn. Often “Executive Change” is reported as unavoidable churn. But is it?
Here’s How It Happens
Surviving an executive change is possible - even likely
Surviving an executive change is more realistic if you have a plan. Winging it and leaving a save to chance is not a winning solution. Your plan needs to start well before you receive news that your sponsor has departed. Ideally, you need to start by understanding your customers’ organizational structure and power chain. You need to understand how decisions get made. Post-sale teams should continually blueprint accounts looking for additional executive-level advocates. Also, risk is mitigated when you leverage your champion to create co-champions that will advocate for you when there is a shake up. A good rule is to create and foster at least three key advocates within each customer account. Ideally, these stakeholders should be cross-functional representing finance, IT, and functional teams.
Even when you do have a process in place to address loss of sponsor, the news is often blindsiding. More likely than not, executives don’t share their transition plans with anyone outside their org with advance warning. Otherwise, signals of change are often unconsciously ignored due to the sheer volume of communications your team is dealing with. Worse yet, what if requests like our example above land with a teammate that simply responds with a copy of the contract unaware of the gravity of the situation?
If your heart is racing and your palms are sweaty, you’re not alone. We’ve been there. That is why one of the first language models that we developed and trained when we started Sturdy was executive change.
Detecting customer Signals
So how do you detect executive change signals? There are some hacks out there. The easiest to implement is one that leverages LinkedIn Sales Navigator. If you have a paid account, set up “Career Change” alerts in LISN. This will work for smaller companies with 20-50 customers but gets too noisy at any kind of scale. The big constraint is that you can't filter the alert by decision makers only. This would be a good feature for LISN though by the time your DM updates their profile with a new role, the window of opportunity to save the account likely will have closed.
At Sturdy, we use our own product to detect executive change signals. Sturdy analyzes emails, tickets, chats, and video calls listening for signals of executive change. When it detects language synonymous with the loss of a sponsor, it flags the conversations and alerts our stakeholders immediately. Our alerts are sent to a Slack channel called #executive-change. At our stage, this is quite effective and still manageable. Eventually, we’ll connect Sturdy to our case management tool creating a more sophisticated closed-loop process.
Below is the same message from the top of this post but this one was run through the Sturdy AI Inference Engine. It’s been accurately flagged with customer signals indicating executive change and a high probability of churn. This message triggered a real time alert to our customer operations team.
Reacting to an executive change
We think about signals as lead generation for inquiry and action. And, as with sales leads, acting with urgency yields the best outcomes. Borrowing from our sales / marketing SLA, our requirement is to follow up on executive change signals inside of 1 hour. This makes us seven times more likely to schedule a meeting with the customer in the same week as the signal was received. Having a set timeline, we prevent procrastination and promote action.
Otherwise, we have a defined play that we run. The play has 3 phases and we train our workmates on this and other plans on an on-going basis. Here is an outline from our post-sales playbook for executive change.
The loss of an executive sponsor is a red-level risk event. Winging it doesn’t save customers. You need a defined process in place to mitigate account churn and solution downgrades. Team members need to investigate the account vitals quickly. Information should be gathered from other client stakeholders. If a new sponsor is in place, a briefing should be scheduled ASAP. Show the new leader what’s in it for them. Clearly emphasize the value your solution delivers. Minimize their risk. Show them the future. Give them an easy win.
A reminder of why it matters
The B2B SaaS industry is maturing quickly. Competition is fierce. Category leading post-sale teams focused on customer retention and monetization are building capabilities to significantly contribute to top line growth. For example, A $100M ARR Company with 2000 customers saves 30 customers in Year 1, dropping its churn from 8 to 6.5%. By maintaining this churn rate, its revenue in year 1 will be $1.6m higher. By year 5, $25m, and by year 10 almost $170m higher (50k ACV, 5% upgrade rate, 30% growth rate). Look at these numbers through an investor’s lens where some companies are valued at 25x earnings. Those are some real numbers. Saving a couple dozen customers a year really adds up.
Summary
The loss of an executive sponsor is a red-level risk event but it doesn’t need to be fatal.
- Preventative measures like fostering multiple executive-level relationships to develop cross functional advocates significantly mitigates risks. Go wider. Go cross-functional. Have no less than three key executive contacts at every account.
- Building a process or deploying technology to detect risk is key. Knowing is more than half the battle in this instance.
- Creating a defined process to manage a loss of sponsor event is imperative as is training team members to respond with urgency.
- Creating a culture that reinforces the importance of retention and customer monetization is a key to motivating high performance post-sales teams.
Sturdy announces listing of its AI-powered customer intelligence integration on the Zendesk App Marketplace
Sturdy, a revenue retention solution using AI-powered conversational analysis that identifies opportunities and preempts risks hidden in everyday customer conversations, is pleased to announce its integration on the Zendesk Marketplace.
Sturdy has developed an integration with Zendesk that enables Zendesk customers to tap into data that, for most, has been hiding in plain sight - the customer-generated content of tickets and chats within Zendesk.
Sturdy already works with Zendesk customers and helps them by:
- Increasing customer retention rates by .5-2%: Sturdy surfaces actionable insights that signal indicators of customer churn like executive and sponsor changes, contract requests, poor sentiment, and more.
- Increasing customer lifetime value by 5-15%: Sturdy amplifies the unbiased voice of the customer while detecting customer signals such as feature requests, bug reports, outages, renewals, and upsell opportunities. Use of these signals enables teams to better understand their customers’ needs.
- Increasing team member efficiency: Sturdy’s customer signals cut through the noise of email and tickets so team members can resolve the most revenue-sensitive issues quickly and with the relevant context.
- Increasing customer references by 10-25%: Sturdy listens for signals of referenceability and serves a lead-generation for customer marketing and customer advocacy teams.
The integration between Sturdy and Zendesk involves the use of Sturdy's AI technology to detect critical custom-generated signals from everyday communications like tickets and chat sessions. Once detected, customer signals are routed to the appropriate team members to take action resulting in revenue preservation, revenue generation and the gathering of critical trends that provide insights into customer behaviors.
"We are excited to partner with Zendesk and we share their mission to improve customer experiences," said Joel Passen, one of Sturdy’s co-founders. Leveraging AI and ML, we turn previously underutilized sources of customer content (tickets and chats) into actionable data that amplifies the voice of the customer and automates critical processes resulting in improved customer outcomes and, ultimately, revenue retention for SaaS enterprises.”
To learn more about Sturdy's products, please visit sturdy.ai
To learn more about Sturdy's integration with Zendesk, go to https://www.zendesk.com/apps/support/sturdyai/?q=mkp_sturdy
About Sturdy:
Led by a team of seasoned founders, Sturdy is unlocking massive value from data hiding in plain sight. Using AI, Sturdy helps P&L holders preempt customer issues before they spiral and seize revenue opportunities in time to improve this quarter's results. Sturdy’s AI-powered customer operations platform detects critical signals from your customers and routes them to the right people at your company in real time, unlocking value and reinforcing process execution.
Infographic: All about customer Signals
20% of all customer content contains a critical signal. For B2B SaaS businesses, these signals are immensely important. They often indicate if our customers are willing to grow with us or if they are growing away from us.
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What is a customer Signal?
Customer Signal
(noun) a gesture, action, or transmission delivered intentionally or unintentionally by a customer that conveys information, instructions, or insights.
Customers send Signals that help us predict churn, capture references, get in front of renewals, prioritize features, and just run our businesses better. Our customers are giving us this information in Slack, Email, Salesforce, Webinars, training sessions, quarterly business reviews, Zoom calls, etc.
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 five 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 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 sent. In a SaaS environment, the customer is likely signaling risk. Maybe they are evaluating a competitor. Maybe 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 important. 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.
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Where to find Customer Signals
Most of us have given our customers the ability to communicate with us using a variety of channels. After all, we want to hear from them. This allows us to gauge their health, status, and likelihood of buying more of our products and services.
Given the prevalence of multi-channel communications workflows, critical Signals are often trapped in layers of technology across multiple teams, gathering digital dust. The most common scenario for most businesses is that important customer Signals are hiding in plain sight. They’re trapped in email accounts, ticketing systems, call transcriptions, chat logs, and CRMs. And for most of us, the only way we utilize this information is if someone manually identifies, records, and escalates it.
How to use customer Signals
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 SturdyAI, teams can easily sign up for alerts on specific Signals, accounts, and even competitor mentions. For example, the most appropriate team member in any group can get an alert whenever:
- One of your customers requests a copy of their contract or asks about their renewal date
- An account has a new executive, point of contact, or executive sponsor
- A user asks for information about adding more users or adding a new product or service
- One of your customers mentions one or more of your competitors
- A user reports an outage or bug
- A customer is signaling satisfaction and, ultimately, referenceability
What’s exciting about customer Signals
Customer Signals undoubtedly help us understand our customers better. Specifically, by defining and leveraging Signals at scale, we can have a clear understanding 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 if they are growing away from us.
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. When we have the ability to discover new patterns and insights in our data, we are better able to anticipate future decisions. In the end, harnessing customer Signals presents opportunities—and incentives—to deliver better service and find new ways to grow.
Video: Sturdy featured on CS Insider
Our customers are telling us what they want and need every day - with nearly every message. Customers give us information to run our businesses better, predict churn, capture references, get in front of renewals, prioritize features, and more. Until now this data has been trapped and left to decay in dozens, if not hundreds of data silos.
This week SturdyAI is in the spotlight at CS Insider, a community comprised of all things customer success, curated for busy professionals. In this short video, we walk through how SturdyAI is helping businesses detect signals that empower teams to preempt risks and discover opportunities that impact the bottom line.
Highlights
- Analyze customer communication channels like tickets, email, chat, and voice for valuable insights.
- Accelerate data into action so stakeholders can quickly act on what’s most urgent.
- Tap into and leverage a valuable new data set that you’re likely ignoring today.
Interested in finding what's hiding in your customer communication data? Get in touch with us.
Sturdy announces Slack integration
Slack has become the de facto tool for internal communications for many teams. By shifting internal communications out of inboxes and into channels, teams can work more collaboratively while reacting to critical issues in real-time. This is why we are excited to announce that SturdyAI is now integrated with Slack. Now teams that rely on Slack to collaborate can create custom channels to receive important signals from SturdyAI.
For those less familiar with SturdyAI, we’ve built a product that analyzes business language(support tickets, emails, customer chat sessions, video and phone call transcriptions, etc.) for important signals that impact the bottom line. For example, when a user asks, “Can I have a copy of our contract?” in a support ticket, our product instantly recognizes this as a potential cancellation signal, flags it, and alerts the team in charge of triaging accounts. Today, our AI today recognizes 7 distinct signals, with dozens more currently in development. And the cherry on top is that SturdyAI gets smarter with every message.
Here’s how it works.
Step 1
First, a customer communicates with their vendor via email, support ticket, chat or recorded call. Below is an example of a ticket submitted through a ticketing system.
Step 2
Next, SturdyAI ingests and analyzes the ticket in real-time looking for important signals. In this ticket, there is a critical issue. The customer, Brightlight, is asking for a copy of their contract. This is a clear signal that this customer may be at risk. Furthermore, the customer is indicating that they have purchased a product that may have similar features reinforcing the urgency of this message. Below is the original ticket that SturdyAI analyzed and applied the Strong Churn signal.
Step 3
Now that SturdyAI is integrated with Slack, critical signals are “chirped” into Slack channels. SturdyAI’s customers create their own Slack channels to receive critical signals. Integrating SturdyAI takes minutes. When SturdyAI is integrated with Slack, critical signals are “chirped” into Slack channels. SturdyAI’s customers create their own Slack channels to receive critical signals. We've seen some great use cases already. Here are a few of our favorites.
#competitor-mentions
#customer-references
#executive-change
#feature-request
In this example, we’ve created a “Churn Alerts” channel. These customized channels provide teams and leaders with real time visibility into critical customer issues so the right people can take action before it’s too late. Below is a screenshot of our churn-alerts channel and the alert that was triggered by the original message that this customer sent about requesting a copy of their contract.
Why now?
Integrating with Slack was moved to the top of our feature roadmap as the pandemic has created new challenges for our customers related to remote operations. Less in person attendance by customer-facing teams means widening internal communication gaps at each stage of the customer lifecycle. Plus, integrating with Slack just isn't that hard. In the coming weeks, we will continue to refine the integration and, ultimately, the user experience making it easier for users of Slack to get mission-critical signals from customers in the apps that they use most.
Infographic: Creating a Self-Sustaining Customer Reference Funnel with SturdyAI
Customers willing to serve as references for your solution often make the difference between opportunities that result in closed/won or closed/lost. However, harvesting these references can be challenging, time-consuming, and resource-intensive. While critical to the bottom line, the responsibility for gathering new references often falls on marketing and product management teams who may not be as close to the individual users as their counterparts in sales, support, and customer success. This compounds the complexity of gathering new references.
While customer reference software platforms are often good at providing a go-to location to request references and to find approved reference content, they lack functionality to build a sustainable customer reference pipeline. Continuously building a pipeline of references is a key use case and value proposition for SturdyAI.
Using AI and machine learning while leveraging your existing tech stack, SturdyAI empowers customer reference teams to automatically capture authentic referenceability signals from everyday communications with your customers. It's automated lead generation for your customer reference program.
