Value is a capability.

AI maximalism in value isn’t about prompting. It’s redesigning the way we uncover, structure, and communicate value so AI can accelerate it.

Real leverage comes from embedding value into the workflow.

You can ask AI to “turn this into a value story,” but that only removes friction in one moment, for one deal. It doesn’t scale across reps, regions, or industries.

Scale comes from structure and context.

That’s why I’ve been building the semantic structure of value here at Semrush. Use cases, personas, industries, metrics, ROI logic, and the relationships between them. When value is modeled this way, we get AI activated more naturally and with higher confidence.

With this, AI can:
- build a value narrative
- create directional before/after models
- adjust assumptions for each stage
- surface insights as the work happens

If you’re unsure where to start, begin with structure. AI amplifies what you make clear.

Value requires human judgment because value is inherently bias. AI just lets us bring that judgment to more deals, more consistently, at a higher standard.

The graph I copied above is pulled from this new report: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

AI Accelerating Value Discovery

Love this opening to 'Damodaran on Valuation' 👏🏽

After his unapologetic dedication, @AswathDamodaran jumps straight into something I think about constantly: the biases we bring into valuations (of technology investments).

The one I wrestle with most is inputs.

I’ve too often seen (even guilty of) models packed with so many assumptions that by the end of it, you risk losing what actually drives value.

More inputs don’t create better outputs. 

Every useful strategy I’ve worked on eventually comes back to a small set of forces that enable efficiency, drive growth, and cut costs. Back to first principles, it makes sense. Technology offers true business value when it changes how teams build, ship, support, and collaborate. But it’s only realized when clearly understood and adopted.

The best models help leaders act.

I’ve approached achieving this through this lens...
- Which levers meaningfully enable/accelerate the outcomes for a business?
- What assumptions about future ways of working make these outcomes attainable?
- How do we translate a cloud/platform investment into clear value paths executives can act on? 

In the end, clarity beats complexity.

I’ll be sharing more frameworks soon on how teams identify value, how technology creates/unlocks it, and how organizations actually realize it.

If there are topics you want me to unpack next, I’d love to hear them.

For those interested: https://a.co/d/93IgIW6

Biases in Valuation Inputs

Came across this listing from @Molson_Hart of annual rate increases from @FedEx and @UPS going back to 2006.

Same percentage. Every single year.

In 2022, raised to 5.9%. In 2023, both lifted prices to 6.9%. In 2024 and 2025? Back to 5.9%.

You don’t get a pattern like that by accident. It's really interesting to see game theory playing out so well. This a repeated prisoner's dilemma. Both have cooperate because defecting could trigger a race to the bottom.

So they move together. Playing nice :)

As I've learned more about game theory at a technical level at Kelley, the more I see it play out at work. Not everyone is setting broader pricing moves commonly associated with it. But you could be setting product priorities, making roadmap bets, advising on go-to-market moves, or shaping partnerships.

In any of the above, knowing the game you're playing is valuable.

The questions I find myself coming back to:
- Are we in a one-off decision, or a repeated pattern?
- Who are the other players and what are they optimizing for?
- What do our decisions signal (to competitors, customers, own teams)?

Forgetting everything else I just wrote, the core concept is simple: your market decisions both influence and are influenced by others. Potentially more closely than you give it credit. Being aware of the signals and responses you may encounter is, at its most basic level, strategic.

Here's a good article breaking down the UPS/FedEx pricing: https://fideres.com/fedex-and-ups-investigation/#:~:text=,more%20expensive%20than

Playing a Repeated Game

Trust scales faster than traffic.

There's lots of themes in our latest research, but one that is standing out to me is the established (or engineered) credibility. 

Affiliate ecosystems and review sites are becoming the quiet connectors between brands and what AI learns about them. Every mention, review, or structured data point adds another layer to how AI understands who to trust.

What’s interesting is how cross-functional this is...
Marketing creates the story.
PR builds the relationships.
Partnerships expand reach.
Ops keeps the data clean.

This shifts (or realigns for some) how to strategize/quantify success. It's not only "how much traffic we drive?" but "how much authority did we earn?" 

If so, we update the (simple) math: visibility = authority x ecosystem reach

Tracking authority is becoming a shared signal. It bridges marketing, partnerships, and brand strategy under a common goal.

It's no where near solved. For anyone measuring success in this space, how do you define authority internally? Have you made any adjustments to your ecosystem buildout?

See the report here: https://ai-visibility-index.semrush.com/

Trust in AI Ecosystems

Conversational commerce is coming of age...

Etsy jumps in first to lead the shift of AI buying journey. You can now buy in-chat from Etsy in ChatGPT. 

Some of the reasons feel obvious...
- new sales channel to expand gross sales and buyer base
- Etsy's offsite ads program captures extra revenue (seems to be 12-15%)
- being the first mover on the clear leading competitor to search

When I saw the release I thought I might have seen this before...

Amazon’s Alexa voice ordering was hailed as transformative. In reality, voice shopping never took off. Their data showed only ~2% of users made a purchase by voice, and 90% didn’t try it again.

While it's labeled as the "future", Etsy’s move is probably more about discovery than changing how people fundamentally shop. 

Yes, explore these emerging channels but track your metrics closely:
- is the AI channel driving more visibility?
- are sales net-new or shifting customers from your app/site?
- are these customers engaging long-term or churning?

Over the next 6–12 months, we’ll see whether this conversational commerce hype translates to sustained behavior. I had the chance to chat with Gerald Murphy and Peter Brusa on the broader impact (my go to experts!). 

Reality will probably fall between base and best case. 

But the signal for marketers is to ask yourself, “Are our products and services discoverable through GenAI platforms?” If you’re in e-commerce, consider how your catalog or feed can plug into these channels.

The headline here will fade but the ranking wars inside AI are just starting.

Etsy Pioneers Chat Commerce

Now eats next for breakfast.

We’re wired to protect how we work today. Urgent tasks and a way of doing things that just work today feel safer than uncertain bets on tomorrow.

I think these are stories we all know...
- Blockbuster maximized late fees while Netflix built streaming.
- Kodak protected film margins while others scaled digital.
- BlackBerry doubled down on keyboards while Apple redefined the phone.

Contrast that with Microsoft moving to cloud, Adobe pivoting to subscriptions, or Disney betting big on streaming.

They paid some short-term costs to unlock long-term relevance.

This is very much what we're doing at Semrush, expanding into full online visibility and rebuilding solutions with AI from the foundation that help firms adapt to the market.

And there's one more area to consider. Talent. Comp aside, they leave when they don’t see, or don’t get a stake in, your “next.”

Leaders today don’t fail because they can’t build.
They fail because they won’t.

If you're waiting until the technology feels safe, neat, and proven, you'll be left behind. AI years should be counted every three months. Things are moving that fast.

It's worth the reflection...

What’s your version of late fees, film margins, boxed software, or traditional approach that’s keeping you from “next”?

Now Eats Next for Breakfast

Solve the 'right' problem not the 'obvious' one.

We've been working with dozens of teams in Fortune 500, agencies, and scaling startups looking to transform for the future. "Future" being the biggest shift in search in over 20 years: AI answers.

That's the backdrop, so what's the problem?

One simple example is a case where a B2B firm was seeing drastic drops in leads YoY. There's the obvious answer that 'everyone is using ChatGPT'.

But when narrowing in we saw...
- Yes, AI search lowered total traffic from traditional 'Google'
- Those same AI overviews were recommending their competitors
- Clickstream data signaled missed opptys and increasing dead-ends
- Site errors were triggering higher bounce rates (though recent)
- Content teams were reactive to catch up vs. leading the message

We could go down multiple levels of why, but I'm already going long on this post. I'll point out the obvious: firms face challenges from multiple angles. The bigger challenge is figuring out what to solve for and avoiding just patching the symptoms we just saw.

To do this, we zoomed back out...
- No single, queryable model of demand or performance.
- Tool overlap (research, content, testing, etc.) and conflicting KPIs
- High manual effort/hand-offs and decision latency

So now we have a clearer sense of how these higher order problems are connected down to the teams' everyday operation and impacting business value. Connecting these dots makes all the difference. It's here where the solution becomes viable. It's here where your business case and ROI to solve it become credible (better yet, attainable).

This exercise of working up/down the problem tree might feel like common knowledge, but it's not common practice.

Solving the Right Problem

Officially been a year since starting at Kelley…

26 credits down, 34 to go.

Tomorrow I start the spring term. Back to where i kicked off my journey last spring. 

I remember joining the new cohort kickoff event and we had so many questions. Back then I was just trying to get a handle of it all (and it took a minute).

It’s amazing to see what a year can do. 

This year, I’m lucky enough to be hosting the same kickoff (as VP of Networking).

So what’s it all been like…

I can’t sugar coat it… it’s been challenging. 

I chose Kelley for its rigor (damn I got what I asked for).

I’ve had so many people ask about my experiences. It’s something I value having candid answers to, especially for those considering it.

Here’s my view of the Kelley program one year in…
- 📚 Courses: 
application to my day-to-day work has far exceeded my expectations. I credit that to my role, what I learn in mgmt, quant, finance, and strategy courses can be leveraged at work immediately. Additionally, while it’s online, the live instruction is executed very well. Attendance is required with additional group work.
- ⏰ Time commitment: 
more than I expected. This really depends on learning style. For me, I allocate ~20hrs to get everything done and need it really grasp the material. It’s also important to note the back-to-back terms. Spring > Summer > Fall > Winter… all with only one week break in between. Shoutout to my peers doing this fulltime while working and kids. 
- 💰 Price: 
a massive investment of both money and time.  I do believe it’s worth it, but it’s still quite expensive. 
- 🤝 Network:
an underrated value of Kelley is its network. You are surrounded by professionals from the worlds premier companies in industry from tech to health to manufacturing. I appreciate the requirements/opptys for in-person courses every term.

Overall I have to credit to the Kelley not only for delivering the program but doing so with such high standards. They can stand by their #1 ranking.

Ready for year 2!

1 Year at Kelley

ROI models without probabilities are fantasies, not forecasts.

Many ROI models assume static conditions and perfect execution. This never happens. So why would you tell a client they can achieve it?

In our value consulting practice, we help clients quantify and articulate the real value of their digital marketing investment. 

Not just in theory, but in measurable financial impact. 

And one way we ensure it's reliability is through 'expected value' (EV), a method that accounts for probabilities and potential outcomes, not just best-case scenarios

This might be a better breakdown:
👉 Good: ROI model assumes everything will go as planned.
👉  Better: ROI model factors in failure rates, market shifts, execution risks.
👉  Best: ROI model that uses expected value - weighing all possible outcomes by their probabilities to get a more realistic, data-driven forecast.

Now for the EV calculation:
👉 If a campaign has a 60% probability of generating $1M but a 40% chance of only adding $200K, the EV is: (60% × $1M) + (40% × $200K) = $640K EV

Of course, this is just the simple formula. You'd likely have multiple other factors unique to your product's value that need to be considered (or different models entirely). 

If you're working in value consulting or a value-selling rep, consider leveraging the "expected value" concept to build better ROI analyses that account for uncertainty. 

One final note that can't be overlooked - this is a team effort. It's a result of deep collaboration with our partners. We work relentlessly to give our partners the tools, data, and confidence they need to drive change inside their own organization.

Despite my preference for solo sports 😅, what we do as a team to support our partners fires me up! 

If you're looking to measure and maximize the value of your digital marketing efforts, let's connect. Drop a comment or DM me.

#digitalmarketing #valueselling #consulting

Embracing Realistic ROI Forecasts

What prevents buyers from well, buying?

A study of 2.5M recorded sales conversations (shoutout 'The Jolt Effect'), 40-60% of deals are lost to buyer indecision. 

Here are 3 key reasons behind buyer indecision and tips on addressing each...

1️⃣ Solution valuation 

Buyers don't just want the best option, but the one best for them. It's a seller's job to help them make that decision.

Tips:
- Offer personalized recommendations
- Provide comparison charts
- Share customer reviews/industry insights

2️⃣ Lack of information

Not fully understanding the solution, implementation, and it's impact can make decision-makers feel uneasy.

Tips:
- Provide comprehensive product descriptions
- Include specifications, FAQs, and demo videos
- Ensure CS/SE is available to answer questions

3️⃣ Fear of not getting full benefits

What if the product doesn't deliver on its promises? That's a direct impact on their goals, budget, and ultimately reputation.

Tips:
- Be transparent in your marketing
- Offer trials/demos
- Share case studies that demonstrate real benefits

Each of these reasons has its nuances. The best way to address them depends on understanding your buyers' concerns preventing them from making a decision and providing clear, honest, and detailed information.

Hope this helps you convert more hesitant buyers into happy customers!

Note: the study is from 'The Jolt Effect'. An amazing book on how reps can identify and overcome indecision. I've never recommended a book on here, but this one is worth the read for reps, managers, and even those who develop sales strategy (like me).