That distinction is getting harder to ignore. As software gets more automated, more agentic, and more embedded in the work itself, charging by clicks, seats, requests, or tokens starts to feel like pricing the machinery instead of the result.

Why usage breaks

Usage-based pricing made sense when software mostly helped humans do work. More usage usually meant more dependence, and sometimes more value.

But that logic gets weaker when software starts doing the work itself. A customer should not pay more just because the system had to try harder to solve the problem using more tokens. They should pay for the problem being solved.

That is the appeal of outcome-based pricing. Not activity. Not access. Result.

Outcomes are the unit that matters

An outcome is not a page view or a token. It is a business result that someone actually cares about: a ticket resolved, a lead qualified, a workflow completed, a fraud case prevented, a deal accelerated, a hire made, revenue captured.

That is the real test. If the product is valuable, it should be able to connect its price to something the customer already recognizes as value. Otherwise, the vendor is charging for motion and calling it progress.

Measuring impact the consulting way

If you want to price on outcomes, you have to measure outcomes like a consulting firm would or the users within the company are measured for their success, Start with the business question. Define the hypothesis. Map the causal chain. Separate activity from output, output from outcome, and outcome from impact.

A useful framework is simple:

• Objective: What result are we trying to improve?

• Hypothesis: What do we believe will drive that result?

• Baseline: Where are we starting?

• Initiative: What did the software actually do?

• Output: What was delivered?

• Outcome: What changed?

• Impact: What value was created?

• Attribution confidence: How sure are we that the software caused it?

That matters because outcome-based pricing only works if both sides agree on what counts as a real result.

Measurable tools

The idea gets much more credible when it is tied to actual measures. Here are some examples for marketing teams:

Example 1: Lead qualification

• Output: 500 leads scored automatically

• Outcome: 35% of scored leads reach sales-qualified stage (vs 18% baseline)

• Impact: $1.2M pipeline created from what would have been $450k

Example 2: Campaign optimization

• Output: 12 A/B tests run across channels

• Outcome: Best-performing creative gets 28% higher conversion

• Impact: $750k incremental revenue from reallocating $200k ad spend

Other measures work the same way:

• Time saved. Hours avoided per week reviewing reports.

• Resolution rate. Percent of issues solved without escalation.

• Cycle time reduction. Days shaved from lead-to-opportunity.

• Error reduction. Fewer bad leads passed to sales.

• Revenue impact. Dollars created above baseline performance.

These are not vanity metrics. They are the bridge between software activity and business value.

The agentic shift

This is where the idea gets more interesting. If the AI works well enough, the user may not need to log in at all. The system can monitor what matters, surface what changed, and deliver the relevant information directly to where the user lives. Ecosystems like OpenClaw/Hermes are already delivering value with no user interface. KaaS companies have the deep domain knowledge to deliver a higher quality result but the interface might be just chat.

That changes the value proposition completely. The platform is no longer just a place to go. It becomes a layer of confidence - confidence that the system is tracking the issues that matter, watching for exceptions, and bringing the right things to the user without making them do the work.

The value is not in making people spend more time in the product. The value is in making sure they do not have to.

Difficulty of outcome based pricing

Outcome-based pricing sounds elegant until you try to implement it. Especially when Agentic risk management is added to the mix.

You need instrumentation. You need agreement. You need trust. You need a metric that is fair, auditable, and hard to game. That is why many companies end up with a hybrid model first: base platform fee plus usage- or outcome-based add-ons.

Why this fits KaaS

This is where KaaS gets interesting. If the product turns knowledge into action, then pricing should reflect whether that action produced value.

A KaaS system should not just store intelligence or surface insights. It should help produce decisions that matter. If it creates better judgment, the vendor should be paid for better judgment — not for how many times the customer had to look at the interface.

The larger shift

This is bigger than pricing. It is a shift in what software thinks it is selling.

Usage pricing says: pay for access and activity.
Outcome pricing says: pay for impact.
Agentic software says: pay for confidence that the work is being handled.

That is a harder model to build, but a more honest one. And once software can reliably own part of the outcome, usage starts to look like an implementation detail, not the business model.

KaaS platforms might have to borrow from industries like insurance (for risk management), finance (for outperformance based pricing)

My sense is that over the next 24 months, we will start seeing business models evolve to fit this approach. What do you think?

If SaaS was built to manage tasks, KaaS is built to manage thinking. In a world of vibe coding, the vibe may build the UI, but KaaS builds the brain behind it.

The important shift is not that software disappears. It is that the center of gravity moves. The old SaaS stack of UI, business logic, database, integrations still exists, but it starts to feel thinner when the real value is in how a system retrieves, interprets, and applies knowledge.

What replaces the SaaS stack

KaaS does not fully replace the SaaS stack so much as reorganize it around a different core. In knowledge-heavy products, most important layer is the one that decides what information is relevant, how it should be framed, and what should happen next.

In many cases, if the database is the commodity, the Retrieval Layer is where differentiation begins by applying the right precedent to the right moment.

Atomic insights

KaaS works best when knowledge is broken into atomic insights rather than buried in long documents or endless dashboards. Each insight should capture four things: hypothesis, context, action, and outcome.

Let us take a simple example of a marketing lead. SaaS database would store the details of the event. But, KaaS would store the logic behind it, what message and constraints produced that specific result. Once that is understood, it can autonomously create a new effective campaign. This is not about storing giga bytes of data, but to reduce this to sets of hypothesis and context. That helps the next marketing campaign to be more effective.

Retrieval systems vs databases

A database answers, “What is stored?” A retrieval system answers, “What matters now?” That distinction is the real architectural shift. KaaS is less about accumulating more information and more about surfacing the right pattern for the present moment.

That is why retrieval matters so much. A database holds raw material. A retrieval system turns that material into context. And context is what makes knowledge actionable.

Still, retrieval is not magic. It only works if the underlying data is trustworthy, well-structured, and representative enough to be useful. Good retrieval can surface precedent. It cannot fix bad inputs.

Where LLMs fit

LLMs sit on top of this architecture as a reasoning and synthesis layer. They help interpret, summarize, compare, and explain. But they are not the architecture itself.

That distinction matters. Models are powerful, but they are also fluid. The durable part is the knowledge system underneath: the retrieval layer, the structure of the insights, and the governance around what gets stored and reused.

The LLM is the interface to intelligence. The KaaS architecture is what makes that intelligence dependable.

Why this matters

This is what makes KaaS more than a new label for software. SaaS sold tools. KaaS sells better judgment.

That is a bigger promise, but also a harder one. Not every product needs this architecture, and not every decision can be reduced to retrieved precedent. Some knowledge is tacit. Some context is political. Some judgment only exists in the room.

But for products built around compounding knowledge, the shift is real. The system becomes less like software you operate and more like a memory layer you rely on. Over time, that may matter more than another prettier interface or another smarter dashboard.

Next in the series: Operating model of KaaS companies.

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Chasing Dissonance is back!

It’s been quiet for a while—partly because the noise level spiked, and partly because we’ve been navigating a merger. But underneath that noise, the ground has shifted.

Open any feed and you’ll hear the same refrain: “Vibe coding” is the new frontier. The narrative suggests that because AI can now generate a functional demo over a weekend, the era of the software engineer is over and SaaS is dead. This “vibe coding” era treats software like a magic trick—if it looks like an app and acts like an app, it must be a viable replacement for enterprise infrastructure.

The panic has even spilled into public markets, where solid companies are being priced as if they can be disrupted by a few prompts. But there is a massive gulf between a high-fidelity demo and a durable business.

A demo doesn’t handle edge cases, SOC2 compliance, complex implementation, or the grueling reality of maintenance. AI is changing the economics of writing code, but it isn’t erasing the hard parts of creating value.

However, just because “vibe coding” can’t replace an enterprise system doesn’t mean SaaS is safe. The real threat isn’t that someone will rebuild your UI; it’s that your UI is becoming a commodity. When computation and data access are no longer scarce, “doing a better report” is no longer a moat.

What remains scarce? Institutional memory and verified expertise.

The Flaw in the SaaS Playbook

Traditional SaaS is excellent at tracking what happened, but notoriously poor at capturing why.

Every dashboard is essentially a reset. You see the outcome of a marketing campaign or a closed sales deal, but the reasoning, the hypotheses, and the tribal knowledge that led to those decisions are discarded. SaaS captures the event, but loses the experience. This is where the “vibe” fails: a generated dashboard can show you data, but it can’t tell you the logic of the expert who curated it.

In a world where AI can “vibe code” a functional interface in seconds, a tool that merely “stores and displays” is no longer a competitive moat. It is a commodity.

Enter KaaS: Knowledge-as-a-Service

The next generation of industry leaders won’t just be software providers; they will be Knowledge-as-a-Service (KaaS) companies.

While “vibe coding” focuses on the surface level—the interface and basic workflow—KaaS focuses on the depth—the accumulated understanding of the domain. While SaaS solves for access, KaaS solves for synthesis.

Key Pillars of a KaaS Organization:

• Structured Thinking over Data Storage: Instead of just rows of data, KaaS systems store reasoning. They maintain a living record of hypotheses and the context surrounding them.

• Compounding Intelligence: In standard SaaS, the millionth user gets the same experience as the first. In KaaS, the system gets smarter with every interaction, learning from previous decisions to improve future recommendations.

• The Expert-in-the-Loop: This is the antidote to the “vibe.” KaaS bridges the gap between human intuition and machine scale. It encodes the “logic” of top-tier experts, ensuring institutional memory persists even when people move on. It enables KaaS companies to bring in best practices and global knowledge to enhance the experience.

From Workflows to Outcomes

We are moving away from buying “tools to do work” toward buying “the ability to achieve the result.” This is the difference between a tool that lets you check boxes for compliance (SaaS) and a verified, logic-heavy layer that interprets new regulations against your specific history to tell you why you are compliant (KaaS).

Building Context, Not Retrieving It

You can’t just dump your data into a model and hope for the best. Context windows are finite, and more information isn’t better—the right information is better.

Instead of storing documents, KaaS breaks thinking into “units of analysis.” When a new problem arises, the system doesn’t just retrieve history; it builds a specific context from distilled patterns. It’s not about “what happened,” but “what tends to happen under these conditions.”

The same structure is what makes cross-company learning possible without crossing lines. You’re not storing sensitive data. You’re storing patterns.

That becomes a shared memory—grounded in real outcomes, protected from the “vibes,” and anchored in verified expertise.

Next Week: What this actually looks like when you try to build it.

If this resonates, or if you’d like to explore how to implement a KaaS solution for your company, let’s connect.

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As I discussed a couple of weeks ago, investor misalignment can cause pricing challenges for a public company. Continuing on that conversation, finding investors that align can be challenging, but a methodical process can make it easier. Another part that is

Company and Investor stratification

Global

To identify where you company is at investible universe level, identify financial items that are common over large universe of stocks like revenue, net income, their growth rates, valuation associated with those elements.

Based on these parameters, IR team can identify investors who invest in companies with similar investment profiles. More importantly where the company is expected to be in next year.

Industry/Sector

Within industry, identify investors who are investing in your competitors and do not own your company currently. Evaluate what operational drivers your peers are reporting and whether those parameters are relevant to you.

Geography

Investor locations determine, to some extent, styles that are unique - Identifying if a particular geography is a better fit on where the company will be in the next couple of years can be helpful

Index Relativity

Investor ownership can sometimes be dictated by the index exposure of a company and investors who have more passive exposure to companies in your industry just for index neutrality.

ESG requirements

A portion of investors care about ESG parameters of the company, some investors might focus on environmental concerns and others only on governance issues.

Other consideration

Investors also have other mandates that include avoiding investing in particular industries or geographies.

Reaching out to the new investor base

Data disclosure and guidance requirements are different each investor base and also based who the peers that you want to be compared.

Identifying the right disclosure metrics

Start with finding peers that have similar profile of where you want to be in a year. Identify who their investors are. If those are the same investors you are expecting to own your stock, they would be expecting similar disclosure as your peers.

Providing guidance and color

As your business matures, you might get more generalist institutional investors whose risk aversion might be higher than investors who are deep into an industry. This requires the IR team be clear and prepare the investor base if there is natural volatility in any of the operational parameters due to seasonality, cyclicality.

ESG Disclosure

It is important to have control over the disclosure and present your ESG factors in the way it reflects the company’s position in the best light. It helps to present ESG data that highlights the factors important for the industry. Making a case on the website and presenting information in an easily consumable fashion can help find the right investors with ESG priorities.

Evaluating investor portfolios

Measure investor portfolios to the risk metrics like growth, value, momentum and identify the portfolios that closely represent the expected characteristics of the company. This can help identify the investors who can provide the best value to the company.

What’s next

At Virtua we work with public companies providing a platform for publishing ESG data along with financial data providing a comprehensive view for investors. We also work with you to understand your industry position from investor point of view. If any of this is of interest, reach out to us.

Investor misalignment can distort the valuation of even the most disciplined public companies. Achieving long-term pricing power is not simply a matter of reporting more data—it requires a purposeful strategy for finding and engaging investors whose objectives match the company’s trajectory.

Strategic Investor Stratification

Global Universe Analysis

Begin by objectively assessing where your company sits in the broader investible universe. Analyze revenue, net income, growth rates, and the corresponding valuation multiples against a wide array of public peers. This benchmarking should include both current metrics and projected performance for the coming year. These insights help identify investors who regularly allocate capital to companies at your life cycle stage and with similar outlooks.

Industry and Sector Targeting

Within your sector, identify institutional investors who have equity in your competitors but not in your firm. Pay close attention to which operational drivers (such as ARR, gross margin, or retention trends) these investors respond to. Align your disclosures accordingly, ensuring you highlight comparable and relevant strengths. Regularly evaluate peer disclosures to maintain relevance and competitiveness in your investor engagement materials.

Geographic Segmentation

Investor priorities can vary significantly by geography. Segment your outreach by region to reflect distinct investment styles and preferences. Continually reassess which geographies may offer a better alignment with your expected evolution over the next several years.

Index Inclusion and Passive Ownership

Some investors allocate capital passively based on index composition. Track index-related shareholder shifts and communicate proactively with index-conscious funds. Recognize that index inclusion or exclusion can trigger substantial flows, and adapt your outreach accordingly.

ESG Considerations

A growing segment of investors prioritize ESG factors—though their specific interests may focus on environment, social policy, or governance. Develop a sector-relevant ESG narrative supported by robust data. Make these materials readily accessible and align them with respected frameworks (such as GRI or TCFD) to ensure transparency and credibility.

Other Investment Mandates

Respect investor constraints related to industry, geography, or other negative screens. Strategically targeting audiences who are not restricted from investing in your niche increases efficiency and engagement rates.

Tailored Disclosure and Guidance

• Peer-Centric Benchmarking: Identify the investors of companies with similar current or aspirational profiles. Observe and align your disclosures to support comparability.

• Contextualize Volatility and Risk: As you attract diversified or generalist investors, provide clear guidance on seasonality, cyclicality, and other sources of potential volatility to set appropriate expectations and reduce uncertainty.

• Regular, Transparent Communication: Maintain a disciplined cadence of engagement—quarterly updates, direct calls, and investor Q&As.

Portfolio Alignment and Analytics

• Factor-Based Targeting: Analyze investor portfolios for core attributes like growth, value, and momentum. Prioritize investor targets whose portfolios best match your projected financial and operational characteristics.

• Quantitative Evaluation: Use portfolio tools and dashboards to systematically evaluate investor-company fit, aiding in the efficient allocation of IR resources.

ESG Disclosure Best Practices

• Control the Narrative: Publish clear, measurable ESG goals and progress, highlighting factors that are most material for your industry.

• Multi-Channel Distribution: Ensure ESG data is consistently accessible via your IR website, regulatory filings, and presentations.

• Proactive Stakeholder Access: Offer engagement opportunities such as webinars and direct Q&As with company ESG leaders.

Practical Implementation

Solution Integration

Platforms like Virtua enable companies to display financial and ESG data cohesively, giving investors a comprehensive view that facilitates better alignment and engagement. By adopting structured investor stratification, refined disclosure practices, and data-driven targeting, your company can cultivate a durable, value-enhancing investor base.

If your organization is navigating these challenges, Virtua offers resources and expertise to support your journey in optimizing investor alignment and engagement.

Earnings calls are a staple of the public markets. Every quarter, companies release their numbers and then host a call where executives walk through the results and answer questions from analysts and investors. But let’s be honest: these calls often feel routine, scripted, and sometimes frustratingly opaque.

What if there was a better way? What if earnings calls could become truly informative, interactive, and accessible — without the usual noise and delays?

The Real Purpose of Earnings Calls

At their best, earnings calls serve three critical functions:

• Transparency: Explaining what drove the financial results, from revenue and costs to market dynamics.

• Strategic Communication: Sharing the company’s vision, operational highlights, challenges, and growth prospects.

• Investor Engagement: Providing a forum for analysts and investors to ask questions and get clarifications directly from management.

Yet, in practice, these calls often fall short. The dialogue can feel rehearsed, the answers vague, and the timing restrictive.

How AI Can Elevate the Experience

Imagine a company website as a dynamic platform, that goes beyond static PDFs and press releases, where investors and analysts can ask questions anytime, via text or voice, and receive detailed, accurate answers powered by AI trained on the company’s disclosed data.

When new or unexpected questions arise, platform can inform the company of those questions, and management can update the underlying knowledge base, enabling the AI to continuously improve and provide richer insights over time. This creates a cumulative, evolving knowledge system that supports ongoing dialogue — without waiting for the next quarterly call.

Addressing the Challenges

Of course, there are important hurdles to overcome:

• Accuracy and Compliance: AI must be carefully constrained to only provide information that has been publicly disclosed, ensuring no contradictions with management’s messaging and full adherence to Regulation FD.

• Equal Access:All relevant information should be available to everyone simultaneously, avoiding any selective disclosure.

• Authenticity: The platform needs robust verification to ensure that questions come from genuine investors and analysts, not bots or bad actors.

The Future Is Now — And We’re Building It

At Virtua Research, we’re developing solutions that empower companies to manage their financial messaging more effectively — including AI-driven platforms like this one. Our goal is to transform investor communications into a more transparent, interactive, and scalable experience.

If this resonates with you, or if you’d like to explore how to implement such a solution for your company, let’s connect. The way we communicate financial results is ripe for innovation — and AI could be the key to unlocking it.

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There’s a moment every public company faces—sometimes in the afterglow of a strong quarter, sometimes in the shadow of a missed target—when you realize your fate isn’t entirely in your own hands. It’s in the hands of the investors who show up, quarter after quarter, call after call, each with their own playbook and their own vision for your future.

The Investor Gallery: A Moving Target

In public markets, your shareholder base is a living, shifting ecosystem. Some investors want you to sprint, others want you to jog, and a few are ready to call a cab if you stumble.

The Growth Crowd

These are the funds and retail traders who love a good story and a steeper chart. They’ll forgive your losses if you’re blitzing new markets and stacking double-digit growth. They’ll even reward you for big, bold bets—until the narrative slips. Then, volatility becomes the price of admission. They want access, updates, and a sense that you’re always chasing the next big thing.

The Disciplined Middle

Think GARP funds and the big mutuals. They want you to grow, but not at the expense of discipline. They’ll reward you for threading the needle between ambition and execution, for scaling with purpose, and for showing that you know where the bottom line lives. For them, operational slip-ups are less forgivable, and guidance is gospel.

The Passive Giants

Index funds, pensions, and insurance companies: the ballast of the public market. They’re not here for the drama. They want stability, predictability, and governance that won’t make headlines. They’ll stick with you through slow quarters if you keep the dividends coming and the boardroom steady. But don’t mistake their patience for indifference—miss their expectations, and you’ll feel it in your valuation.

The Fixers

Distressed and activist funds are the ones who show up when things get rough. They’re not here for the ride; they’re here for the overhaul. Cost cuts, asset sales, new leadership—whatever it takes to get the ship upright. They’ll push for drastic change, and they won’t wait long for results.

Investor Alignment: The Hidden Lever of Public Company Success

Here’s the uncomfortable truth: most public companies don’t spend enough time really understanding who owns them—and whether those investors are the right fit for where the company is headed. It’s not enough to know the names; you need to know the styles, the holding periods, and the expectations lurking behind every quarterly call.

• Style ≠ Stated Preference: Just because an investor claims to be long-term doesn’t mean they won’t bail at the first sign of trouble. Watch their portfolio moves, not just their words. Style factors include momentum, growth/value, size, leverage.

• Industry Savvy Matters: Investors who know your sector will give you more rope when things get bumpy. If your base is full of generalists, expect less patience for industry-specific storms.

• Holding Periods Shape Volatility: A mix of long-term and short-term holders is healthy. Too many traders, and your stock becomes a rollercoaster. Too few, and you risk illiquidity and stagnation.

• Engagement is a Two-Way Street: The best IR teams don’t just broadcast, they listen, adapt, and build real relationships. In a world of family offices and algorithmic funds, this is both harder and more important than ever.

What Should Public Company IR Teams Actually Do?

• Target with Intention: Don’t just “meet the Street”—seek out investors whose style matches your phase. Growth story? Court the funds that love risk. Turning profitable? Find the GARP crowd.

• Own Your Narrative: Your IR site, your earnings calls, your investor days—these are your stages. Use them to tell your story, not just recite your numbers.

• Leverage Tech and Transparency: Host webinars, use AI platforms that can enable investors get answers their questions, and keep your disclosures crisp. The more accessible and transparent you are, the more trust you’ll build.

• Proactively Diversify: Reach out to new geographies, family offices, and long-term retail. Don’t let your base get stale or overly concentrated.

• Engage nay-sayers: Talk to the investors who are not buying your stock, the shorts who do not like you. They can help with the blind spots.

The Real Game: Building the Investor Base You Deserve

The public markets are unforgiving to companies that ignore their shareholder mix. The wrong base can push you into strategies that don’t fit, or punish you for moves that make sense in your industry but not in their models. The right base? They’ll give you room to breathe, to grow, to stumble and recover.

So, ask yourself: Who owns you today—and who do you want to own you tomorrow?

The answer might be the difference between a fleeting pop and lasting value.

Reach out if you would like to see how Virtua helps address these challenges.

What next?

What happens when alignment breaks—and how to turn investor dissonance into opportunity. Until then, let’s keep the conversation going. Where do you see the biggest disconnect between your company and your investors?

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Investor relations teams are perpetual tightroe walkers, balancing the market’s thirst for insight with the rigid guardrails of regulation. The challenge isn’t just about what information to share, but how to share it—without falling into the traps of selective disclosure or leaking material non-public information (MNPI). The SEC’s Regulation Fair Disclosure (Reg FD) isn’t just a rulebook; it’s a reminder that transparency and fairness must coexist, even when the market clamors for more.

Transparency Isn’t Just a Box to Tick

True credibility with investors isn’t built by doing the bare minimum. It’s earned by consistently publishing financial and operational metrics in both regulatory filings and press releases—making sure everyone gets the same information at the same time. When you share a data point in one channel but not another, you’re not just risking confusion; you’re inviting skepticism. In a world where trust is fragile, consistency is your most valuable currency.

The Hidden Dissonance of Private Meetings

Investors always want more—private meetings, exclusive insights, a whisper of what’s coming next. But IR professionals know: every conversation is a potential minefield. Leak MNPI, and you’re not just breaking the rules—you’re undermining the very trust you’re trying to build. Insider trading policies, trading blackout periods, and pre-clearance processes aren’t just formalities; they’re the guardrails that keep everyone honest.

Setting Expectations: The Art of Realism

It’s tempting to guide low and beat expectations, or to paint an overly rosy picture. But dissonance creeps in when guidance doesn’t match reality, or when the tone shifts dramatically between venues—optimistic in the annual report, cautious in the earnings call. This inconsistency doesn’t just create volatility; it erodes investor confidence and can make even small mistakes feel much bigger.

AI: The Conductor of Consistency

AI isn’t just a tool for the future; it’s already here, helping IR teams monitor peer disclosures, compare messaging across documents, and spot discrepancies in reported numbers. Think of AI as your conductor—helping you keep the orchestra in tune, so the market hears a coherent story, not a cacophony of mixed signals.

Leaning Into Dissonance

At the end of the day, chasing dissonance isn’t about eliminating every contradiction. It’s about knowing when to lean into the tension—acknowledging the market’s hunger for information while holding firm to the principles of fairness and transparency. It’s about bringing the market back in key, even when the notes don’t always align.

What’s next

Of course using AI comes with its own challenges. You need to make sure each document is properly labeled and be able to orchestrate where the questions should be directed to avoid sharing privileged information. At my company Virtua Research, we built a process to avoid these security issues. Reach out if you would like to further discuss how we implement our secure AI solution.

Next week, I will delve into another risk management topic . For now, if any of this resonates, or you think I am missing something, don’t hesitate to reach out or share your thoughts.

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Let’s be clear: investors don’t value your company in isolation. Every stock is measured against its peers, with growth rates and valuation multiples under constant scrutiny. If your company’s narrative diverges too much from the broader industry story, expect some uncomfortable questions.

Peer Risk

Public companies don’t operate in a vacuum. When you issue guidance on revenue, margins, or any other metric, you’re adding your voice to an ongoing industry conversation. And so are your competitors. If your guidance stands out (for better or worse), analysts will notice but perceptions can shift quickly.

For Investor Relations professionals, this means vigilance is essential. Tracking every update from your peers isn’t just best practice, but critical. When a competitor revises their outlook up or down, it sends a signal. These changes shape how the entire sector is viewed. If your own guidance doesn’t keep pace, you risk becoming the outlier.

Supply Chain Signals

But don’t stop with the competition. Suppliers and customers are also part of the story, and their guidance often contains early clues about validity of internal forecasts. If a key supplier lowers their forecast, or a major customer becomes more optimistic, these are signals worth noting. Bringing this intelligence back to your FP&A team can provide a valuable external perspective and help anticipate shifts before they impact your own results.

Investor Feedback

Investors are rarely shy about sharing their perspectives - on your strategy, your product launches, and your financial outlook. Listening closely to their feedback provides real-time insight into what’s resonating and what isn’t. It’s also smart to pay attention to the questions investors are asking your peers. Sometimes, the most pressing issues for your industry surface in someone else’s Q&A first.

Turning External Insight Into Internal Strategy

At its core, Investor Relations is about connecting external signals to internal decision making. By tracking peer moves, supply chain developments, and investor sentiment, IR professionals help management stay attuned to the broader industry narrative. It’s not just about telling your company’s story, it’s about ensuring that story fits within the larger context, and that you’re prepared for whatever comes next.

What’s Next?

Next week, I will delve into another risk management topic . For now, if any of this resonates, or if you’re facing your own IR challenges, don’t hesitate to reach out or share your thoughts.

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The Stock Price Mirage

Here’s the thing: your stock price is a mood ring, not a mirror. It’s supposed to reflect what your company’s worth, but more often it just reflects what people think you’re worth after a few too many cups of coffee and a quick scan of your last earnings call.

Market value? That’s just the stock price times the number of shares—simple math, but behind it is a swirling mess of trust, hope, skepticism, and sometimes, pure speculation. Your job is to keep that number tethered to reality, or at least to something resembling it.

Living at the Extremes

Some companies are the teacher’s pet—trading at the high end of the industry’s valuation range. Others are the class outcast, stuck at the bottom. Why? Maybe investors trust your management, maybe they believe in your growth story, or maybe they just like your logo. The point is, if you’re at the extremes, the stakes are higher. One slip, one missed quarter, and the market’s love (or loathing) can turn on a dime.

When the Street Brings the Wrong Ruler

Here’s where things get weird: the metrics that matter change as your company grows up. Early on, it’s all about revenue—“Look how fast we’re growing!” Later, it’s EBITDA, and eventually, the grown-ups in the room start talking P/E ratios. But if analysts are measuring you with the wrong ruler, chaos ensues. Suddenly, you’re being judged on profitability when you’re still in growth mode, or vice versa.

Chasing Dissonance: How to Keep the Market in Tune

So how do you keep the market from playing the wrong song?

• Watch the Estimates: Keep an eye on what analysts are saying—not just about you, but about your peers. What metrics are they obsessed with this quarter? Are you being compared to the right companies, at the right stage?

• Tell Your Story (Loudly): Don’t let the market fill in the blanks. If your key metric is temporarily off (seasonality, anyone?), get ahead of it. Explain, contextualize, repeat.

• Spot the Dissonance Early: If you see a gap between how you think you should be valued and how the market is actually valuing you, don’t ignore it. That’s your cue to step in and recalibrate expectations, before the price chart turns into a rollercoaster.

• Build Trust, Not Hype: The market can smell spin a mile away. Be transparent, be real. Investors reward companies that tell it straight—even when the news isn’t perfect.

The Bottom Line

Valuation isn’t just about numbers—it’s about narrative, trust, and timing. If you’re in IR, you’re not just managing information. You’re managing dissonance, trying to keep the market’s story about your company as close to reality as possible. And when the music gets out of tune? That’s your chance to step up, set the tempo, and get everyone playing in harmony again.

Because in the end, chasing dissonance isn’t about eliminating it—it’s about knowing when to lean in, and when to bring the market back in key.

Our analyst team and AI tools at Virtua Research honed over 15 years of working with IR teams can help monitor and manage this process for you.

What’s Next?

Next week, we’re peeling back another layer of risk—because if you think valuation is complicated, wait till you see what’s lurking underneath. But for now, if something here doesn’t sit right, or if you’re wrestling with your own IR headaches, don’t be shy. Reach out, drop a comment, start the conversation. This is just the opening act.

Investor Relations teams wear many hats. They’re the company’s voice to investors and sell-side analysts, the internal detectives digging into numbers with finance and operations teams, and the storytellers who weave all this into a narrative that makes sense — even when the world around us doesn’t.

Getting the Basics Right: Internal Collaboration

It starts inside. The IR team leans heavily on finance, accounting, and ops to get a real-time sense of how the quarter is shaping up. This isn’t just about numbers on a spreadsheet — it’s about understanding the story behind those numbers, the KPIs that matter, and the subtle shifts that might signal something bigger.

Reading the Room: Market and Industry Context

But numbers alone don’t tell the whole story. The IR team also has to keep a finger on the pulse of the industry, the economy, and the competitive landscape. What are peers saying? What are suppliers and customers signaling? Macro trends, regulatory shifts, even geopolitical events — all of these can change the game overnight.

Aligning with the Big Picture

And then there’s management’s vision. Guidance isn’t just a forecast; it’s a statement of intent. It needs to reflect where the company wants to go, not just where it is. The IR team has to balance optimism with realism, ambition with credibility.

The Reality Check: When Plans Meet the Real World

“The best laid plans of mice and men…” — yeah, that old chestnut. Once guidance is out there, the real challenge begins. Actual results rarely line up perfectly with expectations. Sometimes they’re better, sometimes worse. The IR team’s job? Keep a close eye on the numbers and be ready to course-correct.

If trends start to diverge, it’s time to craft a clear, honest message for investors. No one likes surprises, but everyone respects transparency.

The Wild Card: Change

The economy shifts. Industry conditions evolve. Competitors move. What was true last quarter might not hold this quarter. That’s the reality IR teams live in.

How IR Teams Navigate the Noise

Watch Your Peers, Customers, and Suppliers

Tracking guidance changes and consensus estimates from companies connected to yours is like having an early warning system. If your peers are lowering their bars or if customers are signaling trouble, that’s a signal worth noting.

Model Check: Internal vs. Analyst Estimates

Building your own model based on analyst data and comparing it with your internal numbers can reveal blind spots. Where do estimates diverge? Which line items have the widest range of forecasts? Sometimes, a stable line item suddenly becomes unpredictable — why? Peers’ reporting patterns can offer clues.

• Understand where there might be deviation from analyst models to internal estimates

• Identify where there might be a lot of dispersion on a particular line item - this could be because there is no clarity on how to forecast that particular line item - an item that normally stable, but it is off for a particular period.

• Identify what line items peers who have lower dispersions are reporting and when they started reporting those line items.

Bring It Back Inside

Armed with external insights, the IR team can work with finance to adjust expectations and goals. It’s a two-way street — external signals inform internal planning, and internal realities shape external communication.

What’s next

Next week, we’ll dive into another layer of risk. But for now, if you see gaps in this approach or want to talk through your own challenges, reach out or drop a comment. The conversation is just getting started.

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Leader-Follower

Not all analysts have the same impact. Some set the pace, releasing estimates first and shaping the initial market reaction. Others follow, echoing or challenging the consensus. It’s essential to know who’s leading: track whose commentary consistently moves the stock, whose estimates are closest to reality, and whose updates drive volume. When an influential analyst (say, Analyst 8) weighs in, that’s a signal to engage directly and ensure your message is clear.1

Earnings call dynamics: Decoding Tone and Themes

Earnings calls are more than Q&A-they’re a window into what analysts are really focused on. What questions are they asking your peers? Are the concerns about growth, risk, or broader industry issues? Track the themes, note the shifts in tone, and prepare for the questions that matter most. After each call, review how the narrative has changed and adjust your strategy accordingly.

Estimate Accuracy History by Analyst

Patterns matter. Some analysts consistently overshoot, others underestimate. Track estimate accuracy by analyst and by line item. Who’s always at the low end? Where are the biggest misses? This data isn’t just for scorekeeping-it informs how you engage with each analyst and where you may need to clarify or reinforce your guidance.

Analyst engagement Measurement

Analysts do more than publish reports-they connect your company to the investment community. Track the quantity and quality of meetings, conferences, and introductions. Not all interactions are equal: prioritize those who bring new investors to the table and foster meaningful engagement.

Ranking and Incentivizing Analysts

Develop a ranking system based on estimate accuracy, influence, and engagement. Reward the most insightful analysts with greater access to management and deeper conversations. This not only incentivizes diligence but also ensures your time is spent where it matters most.

Putting it all together

This is where technology elevates the process. AI can identify subtle shifts in sentiment, flag inconsistencies, and highlight emerging trends across analyst commentary and estimates. It’s about catching the points of dissonance-where market perception and your narrative diverge-before they become risks.

At Virtua, we see the sell-side as a complex, evolving landscape. Understanding it requires both quantitative rigor and qualitative insight. The value is in connecting the dots and anticipating where the next divergence might occur.

What’s next?

Next week, I’ll explore another dimension of risk. If you think there’s a blind spot in this approach, or want to discuss how we can help you identify and address your own points of dissonance, reach out or leave a comment. The conversation continues.

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Why are sell-side analysts important?

Imagine a room. In one corner: management, spinning their narrative. In another: investors, hungry for insight. In the middle, holding a stack of spreadsheets and a red pen, stands the sell-side analyst. Their job? Translate, interrogate, and sometimes, instigate.

Sell-side analysts-those folks at investment banks and research shops-are the ones who pick apart public companies and industries, then stitch together reports with price targets, buy/sell/hold calls, and financial forecasts. But their real power? It’s in the consensus. Their models feed into the consensus estimates that investors use as the North Star. Miss the consensus, and your stock price might tumble. Beat it, and you’re a hero (for a day, at least).

But consensus isn’t just a number. It’s a living, breathing benchmark that reflects not only management’s guidance, but also the collective wisdom (and folly) of the analyst crowd. When those estimates are dialed in, the market narrative feels grounded. When they’re scattered, uncertainty creeps in-and that’s where risk starts to whisper.

The Analyst as Interpreter and Instigator

Analysts aren’t just number crunchers. They’re interpreters, relaying management’s story to investors and providing the context that helps everyone make sense of the noise. Their questions on earnings calls, their focus in reports-these are signals, telling IR teams what’s on investors’ minds, what’s misunderstood, and what’s keeping people up at night.

And the relationship? It’s not a one-off transaction. Analysts show up quarter after quarter, at conferences, on roadshows. They’re long-term partners (and sometimes, worthy adversaries) in shaping how the market sees a company.

When Consensus Goes Off the Rails

Here’s where things get interesting. Sell-side estimates are aggregated by platforms like IBES and FactSet, and investors watch them like hawks. Miss by a penny, and you might see your valuation rerated. But what happens when the estimates themselves are all over the place?12

Academic research tells us that when analyst estimates are dispersed-when there’s no clear consensus-returns tend to be lower. Uncertainty is the enemy of premium multiples. The market doesn’t like not knowing.3

So, what’s an IR pro to do? First, get curious. Compare the consensus with your internal numbers. Where’s the dissonance? Which line item is out of whack? Drill down: Which analyst is the outlier? What’s driving their model? Did they mention something in their report that explains the deviation, or is it a formula error lurking in a spreadsheet cell?

This is detective work, not just data entry or just monitoring and reporting the numbers to upper management. The goal isn’t to browbeat the analyst, but to understand the narrative their model is telling. Maybe there’s a good reason for the divergence-or maybe it’s just a blank cell throwing off gross margin by 30 percentage points.

The Human Side of Tightening Consensus

At the end of the day, it’s about relationships. The tighter and more accurate the consensus, the more confident the market feels-and the higher the potential for your stock’s valuation. That means staying engaged, keeping the dialogue open, and helping analysts refine their models so the story they tell is as close to reality as possible. This means identifying the root causes for differences in the numbers, it might be one assumption difference in an underlying line item to the main item like revenue or EBITDA that is causing the deviation and reviewing that with the analyst.

How we solve this problem using our analytics platform and AI

Virtua’s solution: Instead of wading through endless tables and charts, you ask a simple question-“Which analyst is causing the deviation, and why?”-and get a clear answer. Something like:

“Analyst #9 has projected revenue on the lower end compared to consensus. Their gross profit calculation references a blank cell, resulting in an unrealistically low margin. This overstates COGS, which drags down net income versus consensus.“

That’s not just data; that’s insight. That’s the kind of clarity that lets IR teams focus on what matters: telling a story the market can believe in.

Instead of going through endless screens of BI dashboards, excel and powerpoint files, ask our platform the question you care about and get answers that matter!

What’s next?

Next week, I’ll dig into another flavor of risk. Until then, if you think I’m missing a beat, or want to talk about how we can help you chase down your own dissonance, drop me a note or leave a comment.

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Share

If you ask an AI engine what an Investor Relations (IR) person does, you’ll get a textbook answer like: “The Investor Relations team manages communication between corporate management and investors, analysts, and the financial community, ensuring accurate and effective communication of financial standing and strategic direction, while maintaining regulatory compliance.”

Sounds impressive, right? Like the IR team is the company’s version of a secret agent—always working behind the scenes, keeping everything smooth and under control. And much like a risk manager at a bank or the head of maintenance at an airline, when things are going well, no one notices them. But the moment the wheels wobble or the market hiccups, they’re the first ones called to fix the mess.

The IR Role: Risk Manager Meets Opportunity Spotter

Here is my attempt to organize the different areas an IR person is expected to manage.

Each category comes with a checklist of things to monitor. The problem? That checklist can get so crowded it looks like a grocery list after a Black Friday sale. Trying to keep track manually is a recipe for missed signals and sleepless nights.

Enter AI: The IR Person’s New Best Friend

A well-designed AI platform can sift through mountains of data, applying business rules and metrics, and highlight of one or two things. that really need the IR person’s attention right now.

Imagine an AI dashboard that doesn’t just dump every number and alert on your screen but instead says:

“Hey, here’s the one risk that’s actually worth sweating today,” or “This opportunity just popped up, maybe time for a quick investor call?”

Let the AI tool find the dissonant chord and help monitor your customized variables!

What’s next?

I will be reviewing how we approach this problem at Virtua helping IR teams getting answers to these business questions every Thursday.

If you see a topic I am missing or you would like to engage us, please reach out to me.

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Dashboards Are Dead! Long Live Dissonance

For decades, data visualization dashboards have been a cornerstone of decision-making. Tools like PowerBI, Tableau, and others have become ubiquitous across industries, promising real-time insights into KPIs, trends, and performance metrics. Yet, in practice, dashboards often become overwhelming collections of graphs and tables, making it difficult for users to focus on what truly matters.

The Problem with Dashboards

Dashboards were designed to help users interpret complex data quickly. However, they frequently fall short because:

• Too Much Information: Dashboards often cram every possible metric into one view, creating noise that buries critical insights.

• Symptom, Not Cause: What appears on a dashboard is often just a symptom. Users need to navigate multiple dashboards to diagnose the root cause of issues.

• Changing Relevance: The importance of metrics shifts over time, but dashboards rarely adapt dynamically to highlight what’s most relevant in the moment.

• User Interpretation Required: Dashboards rely on users to spot anomalies and decide what to investigate, which can lead to missed or delayed insights.

What If We Could Do Better?

Imagine an AI system that doesn’t just display data but actively listens for the “dissonant chord” — the anomaly or outlier that disrupts the harmony of otherwise normal business metrics. This system would:

• Understand business rules and acceptable variances.

• Identify which changes are truly abnormal and impactful.

• Summarize these dissonant factors clearly.

• Suggest potential remedies where possible.

Why Is This Hard?

Every data point naturally fluctuates. Distinguishing between normal variability and meaningful anomalies requires deep contextual understanding — something traditional dashboards cannot provide.

Why Do We Have Dashboards in the First Place?

A quick look at the benefits dashboards promise:

• Improved Decision Making: Consolidate complex data into clear visuals for faster insights.

• Accountability and Collaboration: Promote transparency and alignment across teams.

• Tracking KPIs and Performance: Monitor progress toward goals continuously.

• Early Problem Detection and Forecasting: Spot issues before they escalate and plan accordingly.

All of these are valuable — but only if the user can easily find and interpret the right information.

How Can AI Help?

With advances in large language models (LLMs) and machine learning, AI can:

• Build Impact Maps: Outline factors influencing a target metric.

• Measure Relative Impact: Quantify how much each factor affects the overall goal.

• Detect Dissonance: Identify abnormal changes within each factor.

• Summarize Insights: Highlight the most critical dissonant factors.

• Recommend Remedies: Suggest actions to address issues where possible.

What’s Next?

At Virtua Research, we’ve distilled these ideas into practical AI-driven solutions, working with over 500 public companies to transform how Investor Relations teams manage risk and performance. In upcoming posts, I’ll share how we apply this approach to real-world challenges, starting with the factors impacting risk management in Investor Relations.