CX Strategy

How to build a modern voice of the customer program

By
Joel Passen
February 8, 2023
5 min read

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.).   

  1. 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. 

  1. 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.

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What Is a QBR? (And Why Most of Them Are Broken)

Alex Atkins
January 15, 2026
5 min read

Quarterly Business Reviews (QBRs) were invented with good intentions: get out of the weeds, meet with your customer, and align on outcomes every quarter.

In practice? Many QBRs have become 40-slide product monologues that take weeks to build, bore executives, and don’t change much of anything.

As Aaron Thompson argues in his widely shared post “QBRs are Stupid” [1], the traditional way we do QBRs is often more about checking a box than driving real business value. But when done right—and when modern tools are involved—a QBR (or more broadly, an “Executive Business Review”) can still be one of the highest leverage motions in Customer Success, Sales, and Account Management.

This post breaks down:

  • What a QBR is (and what it’s supposed to be)
  • Who uses QBRs and why they matter
  • The traditional steps to creating a QBR
  • How QBRs are evolving (less “quarterly,” more “business review”)
  • How Sturdy.ai can run QBRs for any account in seconds—not hours or days

What Is a QBR?

A Quarterly Business Review (QBR) is a structured, typically executive-level meeting between a vendor and a customer to:

  • Review business outcomes and value delivered
  • Align on goals, strategy, and risks
  • Agree on a plan for the next period (not always a quarter anymore)

Unlike a status meeting, a QBR is supposed to focus on outcomes, strategy, and impact, not tickets, small features, or sprint updates.

Industry bodies like TSIA (Technology & Services Industry Association) and customer success leaders (e.g., Gainsight, Winning by Design) have consistently emphasized that effective business reviews should be outcome-based, data-backed, and jointly owned by vendor and customer [2][3].

Who Are QBRs For?

QBRs are heavily used across:

  1. Customer Success (CS) / Account Management (AM)  
    • To prove ongoing value
    • Reduce churn and expand accounts
    • Align on adoption, usage, and business outcomes
  2. Sales / Strategic Accounts / Customer Directors  
    • To maintain executive relationships
    • Surface expansion opportunities
    • Show roadmap alignment to strategic initiatives
  3. Professional Services / Consulting / Agencies  
    • To connect deliverables to business impact
    • Discuss ROI, timeline, and next phases
    • Reset expectations where needed
  4. Product & Executive Teams  
    • To hear voice-of-customer at the highest level
    • Validate product direction with strategic accounts
    • Identify common themes and risks across the portfolio

In modern SaaS and B2B, QBRs have shifted from a “CS-only” ritual to a cross-functional motion that spans CS, Sales, Product, and Leadership [4].

Why QBRs Matter (When They’re Done Right)

When they’re not just slidedecks for slidedeck’s sake, QBRs can:

  • Prove value
    Tie your product directly to metrics your customer’s executives care about: revenue, cost savings, risk reduction, NPS, time-to-value.
  • Protect and grow revenue
    Well-run business reviews correlate with higher renewal and expansion rates because they build trust and keep your solution aligned with evolving needs [2][5].
  • Align on strategy and roadmap
    They create formal space to talk about: “Where is your business going?” and “How does our roadmap support that?”
  • Surface risk early
    Adoption gaps, champion turnover, budget changes—QBRs are where these get raised and addressed proactively.

The problem is not the idea of a QBR; it’s the way traditional QBRs are executed.

The Traditional QBR: Steps, and Where They Go Wrong

Let’s walk through the typical (old-school) QBR workflow and why it’s so painful.

Step 1: Define Objectives and Audience

What’s supposed to happen:

  • Clarify the purpose of the review:
    • Renewal risk?
    • Proving ROI?
    • Expansion discussion?
    • Strategic alignment with a new initiative?
  • Confirm who will attend: executive sponsors, day-to-day users, procurement, etc.
  • Tailor the content to those people, not a generic template.

Why it matters:
McKinsey and Gartner both emphasize executive conversations that center on the customer’s business priorities, not your internal agenda [5][6]. If you don’t decide the objective and audience upfront, you end up with a “kitchen sink” deck that satisfies no one.

Where it goes wrong:
Teams often skip this step and reuse the same template for every account, regardless of size, segment, or lifecycle stage.

Step 2: Gather Data (Usage, Outcomes, Support, Voice-of-Customer)

What’s supposed to happen:

  • Pull product usage data (logins, key feature adoption, utilization vs. license)
  • Capture business outcomes (KPIs, ROI estimates, improved cycle times, etc.)
  • Summarize support data (tickets, escalations, time-to-resolution)
  • Incorporate voice-of-customer: NPS, CSAT, survey results, call notes, emails

Why it matters:
Data-backed QBRs are more credible and effective. TSIA’s research on outcome-based engagement models shows that value evidence (data plus narrative) is a core driver of renewal and expansion [2].

Where it goes wrong:

  • Data is scattered across CRM, helpdesk, product analytics, call recordings, Slack, and email
  • CSMs or AMs spend hours to days cobbling it together manually
  • Important context (like that frustrated email from the VP last month) gets missed because it lives outside the “official” systems

Step 3: Build the QBR Deck

What’s supposed to happen:

A concise, outcome-focused structure such as:

  1. Executive Summary  
    • Key wins this period
    • Key risks and challenges
    • Recommended next steps
  2. Your Goals & Strategy  
    • Recap of the customer’s stated objectives
    • Any changes in their business (M&A, leadership, budget shifts)
  3. Value & Outcomes  
    • KPI trends
    • ROI or impact stories
    • Before/after comparisons where possible
  4. Adoption & Usage  
    • Feature adoption
    • Usage by segment/team
    • Gaps and opportunities
  5. Support & Experience  
    • Ticket trends
    • NPS/CSAT highlights
    • Themes from feedback
  6. Roadmap & Alignment  
    • Relevant roadmap items
    • How they map to the customer’s goals
  7. Joint Plan / Next 90 Days  
    • Clear action items, owners, and dates
    • Milestones for the next review

Why it matters:
This structure keeps the meeting focused on the customer’s business—not on an endless product tour. Gainsight and other CS thought leaders consistently recommend an “outcomes-first” format that leads with business results, not feature lists [3].

Where it goes wrong:

  • The deck is 40–60 slides of feature screenshots and charts
  • The story is missing: data with no narrative, or narrative with no data
  • It’s built from scratch every time, burning hours of CSM and AM bandwidth

Step 4: Internal Review and Alignment

What’s supposed to happen:

  • CS, Sales, and sometimes Product or Leadership review the QBR deck together
  • Align on:
    • Renewal / expansion posture
    • Risk areas to probe
    • Who will say what in the meeting

Why it matters:
Cross-functional alignment ahead of the call means you present a unified front. Research on strategic account management underscores the importance of coordinated communication across all vendor stakeholders [7].

Where it goes wrong:

  • Internal prep is rushed or skipped
  • Different people show up with different agendas
  • The customer experiences a fragmented, reactive conversation

Step 5: Run the Meeting

What’s supposed to happen:

  • Start with outcomes and their priorities, not your agenda
  • Spend more time on discussion than on presenting slides
  • Ask questions like:
    • “What’s changed in your business since we last met?”
    • “What would make this partnership a no-brainer for you next year?”
    • “Where are we falling short of expectations?”

Why it matters:
Harvard Business Review and other executive communication research shows that senior leaders want vendors to:  

  1. understand their business context, and
  2. co-create solutions, not just present information [6].

Where it goes wrong:

  • It’s a monologue; the vendor talks for 80–90% of the time
  • The “review” is mostly a product tour or roadmap dump
  • Action items are vague or never captured

Step 6: Follow-Up and Execution

What’s supposed to happen:

  • Share a succinct recap:
    • Decisions made
    • Action items, owners, and due dates
    • Updated success plan
  • Track progress and refer back to it in the next review

Why it matters:
Without follow-up, QBRs become “nice conversations” that don’t change outcomes. TSIA and Forrester both highlight the importance of codifying customer outcomes and success plans as part of a recurring cadence [2][8].

Where it goes wrong:

  • Notes live in someone’s notebook or a random doc
  • No shared source of truth for the success plan
  • The next QBR starts from scratch, again

How QBRs Are Evolving

Several trends are reshaping how leading teams approach QBRs:

1. From “Quarterly” to “Right Cadence”

Not every account needs a formal review every quarter. Many organizations now use:

  • Tiered cadences:  
    • Strategic: monthly / quarterly
    • Mid-market: 2–3x per year
    • Long-tail: automated or one-to-many reviews
  • Event-based reviews:  
    • Post-implementation
    • Pre-renewal
    • After major org or product changes

This aligns with best practices in scaled customer success, where engagement is driven by value moments and risk signals, not arbitrary calendar quarters [3][4].

2. From “Slide Deck” to “Shared Workspace”

Instead of a static PowerPoint, teams are moving toward:

  • Live dashboards (usage, outcomes, health)
  • Shared success plans (in CRM or CS platforms)
  • Collaborative docs with real-time notes and ownership

The review becomes a conversation anchored in live data, not a one-way presentation of stale screenshots.

3. From “CS-Only” to Cross-Functional

Sales, Product, and Leadership are increasingly:

  • Joining key business reviews
  • Using them to validate roadmap, gather voice-of-customer, and shape account strategy
  • Treating QBR artifacts as input into forecasting, product planning, and exec reporting

This shifts QBRs from a “CS ritual” to a company-wide motion for strategic accounts.

4. From Manual to AI-Accelerated

The most important evolution: how the QBR is created.

Instead of:

  • Manually pulling data from 6+ systems
  • Rebuilding decks from scratch
  • Hoping someone remembered that critical email or call

Organizations are now using AI and automation to:

  • Aggregate all customer interactions and signals
  • Summarize risks, opportunities, and sentiment
  • Auto-generate QBR-ready narratives and visuals

This is where tools like Sturdy.ai fundamentally change the game.

How Sturdy.ai Can Run QBRs for Any Account in Seconds

Traditional QBR prep can easily consume 5–10+ hours per account once you factor in:

  • Data gathering
  • Deck building
  • Internal alignment
  • Revisions

Multiply that across a CSM’s portfolio and it becomes obvious why QBRs either get skipped or watered down.

Sturdy.ai flips this on its head.

At a high level, Sturdy.ai:

  1. Ingests your real customer data  
    • Emails
    • Call transcripts
    • Support tickets
    • CRM notes
    • Product usage and other signals (where integrated)
  2. Understands what matters  
    • Themes and topics (requests, bugs, risk signals)
    • Sentiment and urgency
    • Stakeholder changes and escalation patterns
    • Outcome-related language (ROI, time savings, revenue impact, etc.)
  3. Auto-builds QBR-ready insights in seconds
    For any account, Sturdy.ai can surface:
    • What’s going well (wins, positive feedback, adoption signals)
    • What’s not (repeated complaints, unresolved issues, risk indicators)
    • Which outcomes you’ve actually helped drive
    • Concrete recommendations and action items for the next period
  4. Generates QBR artifacts instantly
    Instead of starting with a blank slide, you start with:
    • An executive summary tailored to that account
    • Key metrics and trends pulled from your systems
    • Highlighted quotes and examples from real interactions
    • A suggested agenda and next-steps section

What used to take hours or days of manual prep becomes a seconds-long operation:

“Run QBR for ACME Corp.”

…and you have a structured, account-specific review ready to refine and deliver.

Why This Matters for Modern CS, Sales, and Account Teams

When QBRs are no longer time-prohibitive:

  • You can run them for more accounts, not just the top 10%
  • You focus on quality of conversation, not on slide assembly
  • You capture real, holistic context, not just what’s in one system
  • You can standardize excellence, instead of relying on heroics from your best CSMs

Instead of asking, “Do we have time to do a QBR for this customer?”, the question becomes:

“Given we can generate a review in seconds, what’s the right cadence and format for this account?”

That’s the shift from QBRs-as-admin-work to QBRs-as-a-strategic-advantage.

Bringing It All Together

  • QBRs were created to align on outcomes, prove value, and co-create a plan—not to be product demos with extra steps.
  • Traditional QBRs are broken because they’re manual, generic, and often misaligned with what executives actually care about.
  • The fundamentals still matter: clear objectives, data-backed story, joint success plan, and strong follow-up.
  • QBRs are evolving toward flexible cadence, collaborative formats, cross-functional ownership, and heavy use of data and AI.
  • With Sturdy.ai, you can run QBRs for any account in seconds, pulling from the full reality of your customer interactions—not just the few metrics someone had time to find.

If you’re spending hours or days preparing for each QBR, you’re paying the “old tax” on a motion that no longer has to be that painful. The value of the QBR is in the conversation, not the manual labor behind the slides.

References

[1] Aaron Thompson, “QBRs are Stupid,” LinkedIn Pulse (discussion of common QBR pitfalls and how they fail to deliver real value).
[2] TSIA (Technology & Services Industry Association), research and best practices on outcome-based customer engagement and Customer Success motions.
[3] Gainsight, Customer Success thought leadership on Executive Business Reviews and outcome-focused customer engagement.
[4] Winning by Design and similar SaaS consulting frameworks on recurring value reviews and customer-centric cadences.
[5] McKinsey & Company, research on B2B customer value, account management, and executive engagement strategies.
[6] Harvard Business Review and Gartner, articles and research on effective executive conversations and strategic vendor relationships.
[7] Strategic account management literature and SAM programs that emphasize coordinated, cross-functional engagement with key customers.
[8] Forrester, research on customer lifecycle management and the importance of measurable, recurring value communication.

Customer Churn

The Most Dangerous Threat to CROs

Joel Passen
July 1, 2025
5 min read

The most dangerous threat to CROs doesn’t live in the opportunity pipeline.

It's churn.

  • It doesn’t scream like a missed quarterly pipeline goal.
  • It doesn’t show up in dashboards until it’s too late.
  • It's rarely caught by a generic 'health score'.
  • It's the board meeting killer.

Retaining and growing our customers is the only repeatable, compounding, capital-efficient growth lever left in B2B businesses.

📉 CAC is way up.

📉 Channels are saturated.

📉 Talent is expensive.

📉 Competition is fierce.

📉 Switching costs are low.

The path to $100M used to be “sell, sell, sell.”

Today? It’s “land, retain, expand.”

No matter how strong your sales motions are or how slick your product or service looks during the sales process, if your customers are churning, you’re stuck in a leaky bucket loop of doom.

Every net-new dollar you win is offset by dollars you lose. It's just math.

Yet most GTM orgs still operate like retention is someone else’s problem. "That's a CS thing."

  • The CS team might “own” the customer post-sale.
  • Account Management may own the renewal and growth number.
  • Support is in the foxhole on the front line.
  • RevOps might model churn with last quarter’s data.
  • Marketing might send an occasional newsletter via email.
  • Finance may be leaning in on the forecasting.
  • Product is building things that supposedly the customers want.

But in reality, churn is the CRO's problem. We wear it - or should.

If your go-to-market motion isn’t designed to protect and grow customers from Day 1, you’re not just leaving money on the table — you’re setting fire to it.

Retention and expansion aren’t back-end functions. They’re front-and-center revenue motions.

The most valuable work these days starts after the contract is signed — not before.

We need to stop treating post-live as a department and start treating it as the engine of durable growth.

Software

Have you heard this from your CEO?

Joel Passen
April 29, 2025
5 min read

"How are we using AI internally?"

The drumbeat is real. Boards are leaning in. Investors are leaning in. Yet, too many leaders hardly use it. Most CS teams? Still making excuses.

🤦🏼 "We’re not ready."Translation: We don't know where to start, so I'm waiting to run into someone who has done something with it.

🤦🏼 "We need cleaner data."Translation: We’re still hoping bad inputs from fractured processes will magically produce good outputs. Everyone's data is a sh*tshow. Trust me. 🤹🏼♂️ "We're playing with it."Translation: We have that one person messing with ChatGPT - experimenting.

😕 "Just don't have the resources right now."Translation: We're too overwhelmed manually building reports, wrangling renewals, and answering tickets forwarded by the support teams.

🫃🏼 "We've got too many tools."Translation: We’re overwhelmed by the tools we bought that created a bunch of silos and forced us into constant app-switching.

🤓 "Our IT team won't let us use AI."Translation: We’ve outsourced innovation to a risk-averse inbox.

It's time to put some cowboy under that hat 🤠 . No one’s asking you to rebuild the data warehouse or perform some sacred data ritual. You don’t need a PhD in AI.

You can start small.

Nearly every AI vendor has a way for you to try their wares without hiring a team of talking heads to perform unworldly 🧙🏼 acts of digital transformation.

Where to start.

✔️ Pick a use case that will give you a revenue boost or reveal something you didn't know about your customers.

✔️ Choose something that directs valuable work to the valuable people you've hired.

✔️ Pick something with outcomes that other teams can use.

Pro Tip: Your CEO doesn't care about chatbots, knowledgebase articles, or things that write emails to customers.

What do you have to lose? More customers? Your seat at the table?

Any Account. Any Question. Any Time.

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