Founder @ 42 - Paid Media, Creative, SEO & RevOps for B2B

Help B2B companies scale @ 42 Agency
Helping inter-generational families connect with learning @ Little Robin Hood
Build better exclusions for online ads @ exclusion sites 
Publisher and writer  on B2B marketing insights @ 42slash.com 
Various Projects at rext.in

Kamil Rextin

I have been hacking away at Claude Code a lot over the holidays and it's official: I'll be hosting a workshop on Claude Code with Exit Five  Thank you so much to Matthew Carnevale  for making it happen.

If you're curious about how Claude Code can help you as a marketer, doing anything from data analysis to generating content, building custom agents, would love to see you there. Let me know if you have any questions you want me to address or use cases that are most interesting / curious about!

Attribution said paid social drove 257 leads for a B2B SaaS client.

The real number was closer to 2,137.

That's an 8.3x blind spot.

Here's how we found it. We matched companies that had any LinkedIn ad exposure against their CRM data—not just clicked. Not just converted. Any exposure.

Companies with LinkedIn exposure had a 6.72% opportunity rate. Companies without? 0.83%.

That's an 8.1x lift. Statistically significant.

Your crm model is lying to you. Not because it's broken. Because it was never designed to measure what paidsocial actually does.

paidsocial doesn't convert people in-session. It makes your brand familiar. It warms up accounts. It does the work that later appears as "direct" or "organic" or the sales team taking credit.

If you're measuring paidsocial the same way you measure search, you're measuring it wrong.

The question isn't whether LinkedIn is working. It's how much credit you're giving to channels that are just harvesting what LinkedIn planted.

I don't want to explain my content system every time I use it.

So I saved it as a Claude Code agent.

It knows the 5 themes. It knows the tease-publish-promo rhythm. It knows each essay spawns 6-9 LinkedIn posts and 1-2 carousels. It knows to hit the ClickUp API with the right tags, due dates, and assignments.

Next time I need content? I invoke the agent. It doesn't ask clarifying questions about structure. It just builds.

This is the shift most people miss about AI. Everyone's focused on "prompting better." That's a trap. You're doing the same work over and over.

Build systems, not prompts.

We spent the time once to encode our entire content engine - 10 months, 20 essays, 200+ derivatives - into something reusable. Now it's a tool, not a task.

The first time takes effort. The second time takes seconds.

What if your best processes were preserved and reusable instead of stuck in someone's head?

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I built a paid media co-pilot this weekend with Claude Code. Here's what actually matters.
Most of what it does is table stakes - unified dashboard across LinkedIn/Meta/Google, performance tracking, ad variation generation, approval workflows.

But there's one piece that fundamentally changes how I think about audience building:
Natural language audience targeting for LinkedIn.
Here's the problem we've all experienced:
You know exactly who you want to target. "Marketing leaders at mid-market B2B SaaS companies focused on demand generation."
But translating that mental model into LinkedIn's targeting interface means:

Clicking through job function taxonomies
Searching job titles one by one
Guessing which seniorities to include
Hoping you didn't miss relevant segments
No way to validate you captured your actual ICP

The new workflow:

Describe your audience in plain language
The tool analyzes your website + description
Queries a database of LinkedIn's complete targeting taxonomy (job functions, titles, seniorities, skills)
Matches and ranks relevance (highly relevant, partial matches, not relevant)
Shows you the API targeting script it will generate
Saves the audience for reuse
Pushes it directly to LinkedIn as a draft campaign

Why this matters:
When you stop treating ad platforms as the primary interface and start treating APIs as the interface, your workflow changes from searching to describing.

You're no longer constrained by platform UX. You're expressing intent and letting systems translate that into platform-specific parameters.
This is the same shift that happened with SQL → natural language for databases.
The platform interface becomes a validation layer, not the creation layer.
What I learned building this:
LinkedIn's API doesn't return audience penetration (annoying).
Demographics require a separate API that returns URNs but not company names (very annoying).
But the targeting taxonomy is comprehensive if you structure it properly.
And Claude Code made building this dramatically faster than traditional development would have been.

Shout out to the 42 Agency team, especially Ma Cherrie Dee Gonzales and Alejandra Céspedes, for creating the custom box and designing the it

Vanessa Griffiths & team  awesome partners to work with on this .

If you want an awesome giftbox, send me a DM (if you need RevOps or Demand Gen support) & I will ship you one personally.

Fractional CMO for Seed to Series B startups | Founding marketing leader at Float & CrowdRiff 📈 | Builder of standout startup brands

I already thanked them privately but nice touch from 42 Agency on their client gifting program. You don't have to be a big company to do things well. (And of course the bigger thanks is for their help untangling some thorny MOPS problems for me!). Well done Kamil Rextin Alejandra Céspedes.

NEW ESSAY: Paid Media Isn't Your Problem (Coming January 20th)

Last quarter, a B2B SaaS company came to us: "Our paid media isn't working."

We pulled their CRM. 47% of leads were sitting in "open" status. Never worked.

Their paid wasn't broken. Their system was.

This essay breaks down the 3 problems disguised as paid media problems—and introduces Compound GT

We just published a new essay on our Substack: 

Why most paid media problems are not actually paid media problems but they're more measurement or GTM stack problems. 


Check out the link below. Let me know if it interesting or if you have any feedback

Peter Caputa

CEO at Databox

I help the leaders of professional services firms leverage data to get the respect, power and money they deserve -- in that order. (Sorry Lil' Kim, you had it backwards.) 

I have been building systems that help professional services firms get to the next level for nearly 20 years now. As the founder and 9-year leader of the HubSpot Solutions partner program, I helped thousands of marketing agencies increase recurring revenue and margins by teaching them how to sell and deliver a measurable and compounding ROI -- helping many go from their dining room table to multi-million dollar exits. 

As CEO of Databox, my team & I have established a system (software, training, support) that actively helps 1200+ professional services firms use data to acquire, retain and expand their client engagements.

I believe in giving value first. 

So, if you're a leader at a professional services firm, you can start by 

1️⃣ Leveraging us to get the RESPECT you deserve by taking 5 minutes to contribute a quote for some of our upcoming research articles (https://databox.com/become-a-contributor) or inquire with my team about our other free co-marketing programs, 
2️⃣ Leveraging us to get the POWER you deserve by taking our free course on how to leverage benchmark data in your marketing, sales and client services processes, or
3️⃣ Leveraging us to get the MONEY you deserve by taking our free course on how to leverage data to predictably improve your (and your clients') performance. 

Both free courses are here --> https://academy.databox.com/

Of course, if you're ready to explore how we can help you the most, start a trial of our analytics software. More than 1200 professional services firms use us to efficiently monitor, manage, report, benchmark and forecast performance for their clients -->  https://databox.com/analytics-software/agencies

I also write and network a lot on Linkedin. While my inbox is usually overflowing, don't be afraid to ask me for help.

At Databox, I am currently 100% focused on helping marketing agencies automate their reporting. But, thousands of companies have figured out how to use our software on their own. They use it to set numeric marketing and sales goals, auto-track progress by pulling performance data from 100s of other software systems and visualizing it on any device.  The mobile app is slick and we even have a free version that allows you to track performance from up to 3 different apps. www.databox.com

Stay tuned...

About 18 months ago, I fully took on weekly grocery shopping & cooking dinner every night. I made it my mission to reduce food waste, our weekly spend & get rave reviews from my wife and son. 

Here's a few tips for dudes who aren't pulling their weight around the house yet. 

1️⃣  It's the least I/you can do. 

If you're male, don't brag about cooking or shopping like it's something special. I especially have no grounds for claiming that I'm some kinda hero. My wife did the majority of housework for the better part of our 21 years of marriage & still carries most of the load. Even when she went back to work full-time, I was a bit late to doing my part. I justified it because my job has never been a 9 to 5. But, that's a bad excuse. 

2️⃣ Reducing cost

a. Each week, we start with a list. (Going to the store without one is a good way to buy too much.) 

b. I decide what I'll cook each night on Sunday. Knowing the 5-6 meals I'm making (we usually get pizza on Friday), is key to not overbuying. 

c. I don't do a lot of comparison shopping, but don't buy ribeye every week either. (I did for awhile.) But, now I'll buy beef cubes and make stew once a month, or different cuts that are more affordable and then make chimichurri to flavor it up. 

d. Very little pre-packaged foods. I skip most of the middle aisles. Fruit, vegetables, meat, other proteins (beans, tofu), dairy. This is healthier, of course, but also cheaper.  

3️⃣ Reducing waste 

a. Leftovers are for lunch. I work from home and have no car to go out-- 3 drivers, 2 cars. It forces me to eat leftovers for lunch. I try to make a little bit extra when I'm making things so my lunches don't suck. Not too much extra, tho. Definitely made that mistake before, which ends up causing waste. 

b. Freeze fruit and vegetables before they go bad. Every week, I take fruit that's past its prime and I freeze it. That goes into smoothes I eat for breakfast. 

c. Make my own stock. I make my own stock from chicken/turkey bones and past-prime vegetables. Organic stock is surprisingly not that cheap. Homemade is so much better too. But, I love it because it feels like I'm using everything. 

d. My son doesn't love soups for some reason, but making soup is a good way to use veggies up from the previous week that we didn't cook. It's also a great way to use extra chicken, sausage, etc. 

e. Compost scraps. I started composting in high school. Most people think it's disgusting, but it takes stuff we're not going to eat and turns it into fertilizer for the garden. Plus, it keeps it out of landfills which reduces methane gas production, a driver of climate change.

f. I look at what's in the fridge/pantry before deciding what to cook the following week. 

The pic below is from last night's dinner.  I cooked the shrimp separately since my wife is a vegetarian, but this was just two roasting pans. So, it was an easy one to clean up too. 

Any tips for me?

#OffTopicFriday

We're making Search/Answer Engine Optimization (SEO/AEO) a top priority again in 2026 at Databox. And I'm wondering if we're doing the right stuff?

In public, I spent the last year+ whining about our dropping search traffic. In private, we've been talking to people, experimenting and building out our new approach and systems. 

I think a big switch is in the approach to the content: instead of asking, “How do we optimize our content for keywords?”, we're asking, “How quickly can AI understand and trust our answers?”

Once we made that shift, the approach suddenly became clearer.

I recently attended a Databox-hosted webinar where Rick Kranz (from the AI Marketing Lab) explained how AI decides what to surface.

He said, “AI doesn’t want to read your story. It wants the answer.”

If the answer isn’t immediately clear, the model moves on.

That's a lot different than we used to write our content. 
• Long intros
• Stories (like this post, haha.) 
• Educational content to attract the right audience

But in a world where AI is deciding what information to surface to others, this stuff matters a lot less. AI is providing the education that we used to provide. So, we need to influence AI so it will influence our prospects. To do that, as Rick explained, it’s scanning for structure, relevance, and proof.

So, we're changing our approach to content up in a few ways: 
• way more product content (a lot less focused on educating our customers on topics that are only tangentially related to our product or one of our integrations)
• more competitive comparison content
• making it real clear who we're for (and not for)
• lots of FAQs
  
Plus: following the rest of the advice in Rick's webinar: https://bit.ly/4pJoInP

Is this the right approach?

If I were running a professional services firm, I'd be very concerned about AI disrupting the model. 

Software, data, and now AI are changing what “doing the work” looks like in very real ways. Tasks that once required teams of people -- planning, implementation, analysis, even creative execution -- are becoming faster, cheaper, and increasingly automated. 

Across marketing agencies, RevOps firms, accounting practices and many other types of professional services firms, this shift is already visible in the work itself. 

Ad creative, landing pages, emails, and social content are now generated and tested by AI in minutes instead of weeks. CRM cleanup, lead scoring, forecasting, and pipeline analysis that once took analysts days are increasingly handled automatically by customer relationship management platforms. Even in accounting, reconciliations, variance explanations, and first-pass financial reviews are being produced continuously by software. What used to require junior teams, manual workflows, and recurring billable hours is now embedded directly into tools, running faster and more consistently than human delivery ever allowed.

We're beyond theory at this point. I'm hearing from firms that clients are leveraging AI to in-source or shopping around for firms that better leverage AI and will do the work cheaper. We're doing this ourselves. Whereas a year ago, we'd ask, "Can we find an expert to help us with this?" Now, we ask, "Can we build a custom GPT or agent that incorporates our smartest person's approach, so that the team can do this work independently?"

I don't expect professional services will go away. But, I don't expect that selling time, tasks, or fixed deliverables will last. (Nor will the bundling of a menu of deliverable-type services into a "retainer".) 

I'm also hearing that as execution work becomes easier to automate, clients are starting to ask different questions. They're struggling with knowing how to navigate this new world. In an increasingly complex tech stack, with more and more data, systems and tools, they're struggling to figure out up from down. 

So, I think that the shift for professional services is away from "output" and more about "providing direction." Less about activity and more about giving confidence that clients are spending time and money on the right things. Less about next month’s deliverables and more about how to improve the system next week.  Clients want to know what’s working and what’s not, when and where to intervene, how to improve the system, and how confident they should be in the results they’re expecting. 

Clients may be asking firms to do more work for less. But, what they really need from these firms is guidance, structure, and judgment. 

I see lots of professional services firms scrambling to learn the latest and greatest in AI. That's necessary. That's good. But, I think what they really need to be doing is embracing their role as consultants, advisors and systems builders.

If you're a fan of strategy, planning and execution books, check out my book collection below. 

Last year, I read or re-read all of these books looking for an answer to a simple question: how should a company connect their strategy, planning and execution processes?  I never found what I was looking for. 

Individually, every one of these books is worth reading. But, none of them bridge the gaps. 

Books on the left are a bunch of best-sellers on building a differentiated strategy, that set up a company for high-growth and scale. Playing to Win, Good Strategy/Bad Strategy and Blue Ocean are must-reads. 

In the middle are some amazing books on building a focused plan: Measure What Matters, e-Myth (esp for smaller businesses), Traction (esp for medium-sized firms), Scaling Up (esp for bigger or 'getting-bigger' companies). 

And on the right, are some awesome books on how  to execute agile-y using data: Revenue Acceleration Framework, Competing on Analytics, Shape Up. 

And to be fair, some of them bridge the gaps to some degree. Traction and Scaling up touch on the importance of strategy, even though they don't really walk you through how to build a great one.  They also provide meeting cadence and structure and touch on the importance of using data. Patrick Lencione talks about the how it's near impossible to execute well without strategic clarity and alignment.  The Science of Scaling talks about how focus is critical for execution, even though most of the book is about selecting a strategic focus and setting an audacious goal. 

The problem?

I found that none of them provided an actionable, step-by-step process for connecting all 3 things: strategy-setting, planning and execution into one system. I also found that almost all of them were really light on teaching companies how to use data to make more informed decisions when developing (or refining) a strategy, a plan and when executing.

Don't get me wrong: I love all of these books. They are amazing. The authors are amazing. I quoted many of them in my course. 

But, the reason I put my Predictable Scale course together is because no one else (that I found) really made these necessary connections.  And today, more than ever, as processes aren't just getting digitized, but automated via AI... performance measurement, analysis and quick decision-making will be a massive competitive advantage for organizations that can do it. And that can't be done unless there are clear connections between the strategy, the plan and execution processes. 

If you're trying to answer the same question I was and love these books too, here's the free course: https://lnkd.in/eScK5VX9

You can watch the videos and/or download the ebook and workbook.

Not enough people are talking about how impressive Wistia is as a business: well north of $60M ARR, profitable, founders in full-control and growing. 

I caught up with Wistia co-founder, Chris Savage, yesterday to interview him for our podcast, Move the Needle. 

He shared: 

• The key, yet counter-intuitive strategic decisions they've made to get them where they are now. Hint: Narrowing their target market was one of them. 
• How product bets rarely work out right away and how important it is to get customer feedback all of the time to base early decisions on, often before usage data rolls in
• How they turn strategy into an annual plan. 
• How they use Objectives and Key Results (OKRs) to ensure they're executing
• The unique frequency in which they revisit their strategy and plan (3 times per year!) including who and how they adjust when things aren't working and how they scale up what's working 
• How he empowers his team to innovate in ways he never would have thought of himself

I probably could have pulled more wisdom from him. But, not in an hour. 

Subscribe to the Move the Needle podcast or newsletter so you don't miss it. It will be published soon.  Sub here --> https://lnkd.in/ej9dMvnt (15k subscribers can't be wrong!) 

Or comment below if you want us to ping you when it's live.

Based on what I see in my inbox, most sales teams use AI and tools like Clay to send a high volume of poorly personalized prospecting messages.
 
After fully implementing Clay into our tech stack, I’m confident it’s not the tech—it’s the operators.
 
There are a lot of things I dislike about the emails I receive from salespeople, but what kills me most is how poorly personalization is being used. With a tool like Clay, you can gather deep insight about a company and its people. Yet most teams stop at company type and job title. They fail to connect that personalization to the problem they solve.
 
Most emails I receive reference something about me that AI found online but has zero relevance to what the company actually sells. For example: “I see you're doing a great job as CEO of a mid-sized SaaS company. We help SaaS companies scale faster with XYZ.” That’s not personalization—it’s just a weak bridge between my title and their pitch.
 
At Databox, we’re approaching prospecting with Clay and Custom GPTs very differently.
 
1️⃣ We start with an offer, not a product pitch.  Marketing creates offers like:
 
• A comparison page (Looker Data Studio vs Databox): https://lnkd.in/edDz_JsA
• A research report on attribution (plus dashboard templates that help with attribution): https://lnkd.in/esxG-Mi7
• A webinar on Answer Engine Optimization (AEO) with a real dashboard example: https://lnkd.in/eHQUjJKv
 
2️⃣ We use a Custom GPT to pull out pain points: 
 
• Extracts the pain points from the offer 
• Identifies which personas and companies likely experience those pains
• Suggests personalization angles tied directly to those pains. It also determines what data Clay can realistically pull to support that personalization. For example, Clay can check whether a company uses Looker Data Studio, is spending $$$ on multiple ad platforms, or if their website uses markup relevant to AEO.
 
3️⃣ We use a second Custom GPT to write the emails. It:   

• Takes the offer, persona, and pain points and drafts multiple email variations.
• Is trained on prospecting best practices (since I've written extensively on this) and focuses on asking permission to share the offer. It's not pitching our product. 
  
When someone replies, we send the offer and ask follow-up questions. Since the offer naturally positions Databox, some prospects self-convert or engage sales. 

The goal is simple: only email people who will genuinely find our offer valuable. We’re not pitching a BI platform just because someone fits our ICP.
 
Clay experts say a 1–3% reply rate is “good.” That bar feels way too low. I think that's the best they're achieving because they're 'blasting product pitches at' and 'begging for sales calls from' people who aren’t in-market.
 
Our approach should still capture in-market buyers—but also engage people who aren’t actively shopping.
 
We’ll see. Follow me if you want to see a future posts with our results. It could be that email prospecting is basically just dead.

Gotta love a little Vintage Linkedin automation. 

I feel so celebrated. 

You might think that this type of automation is dated, but you gotta give it to Brad for really stepping up the personalization in 2025.

PS. Vintage linkedin automation templates available on my Etsy store: https://lnkd.in/ghrwJedd

Pretty slick how Vendr lets you access their SaaS pricing data now via a chat interface. 

They're calling the bot "Ruth" which reminds me more of my deceased Great Aunt vs the coolest, new way to research SaaS pricing. 

See pic below of me testing it out on one of Databox's bigger SaaS bills, Intercom ...

It seems quite smart. And I know vendr has access to lots of real invoice data, so it's gonna be more accurate than review sites and other places where people self-report their software costs. 

You can try it free here: https://agent.vendr.com/ (You just have to sign up with your work account.)

I made $1,180 from Peep Laja's Wynter from about 20 minutes of work. Here's how I did it. 

See the image below for proof. 

If you're not familiar with Wynter, here's what they do: B2B marketers use Wynter to gather feedback from people who fit their ideal client profile. They load up a survey or logo variations or home page mockups and then ask for written feedback. Then, they invite people from their panel to give feedback, in exchange for $35. 

I wrote a post a few months ago and 59 people signed up to to the Wynter panel from that post and then got invited to give feedback. 

They each made ~$35. 

I made $20 for referring them. 

59 x $20 = $1,180. 

If you want to make a quick $35 bucks, sign up here to give feedback. https://lnkd.in/eSv3_Pve

Given that 59 people did it just from my one post, I can't imagine the amount of cash Wynter is sending out to their panel.  

It usually just takes just a few minutes to give the feedback. Plus, and here's the cool part: I usually learn something. Since marketers are usually asking for feedback on messaging and branding, it's interesting for me to think about and apply the learnings to our own work at Databox. 

If you want to join their panel (ie people who give feedback) and get paid $35 a pop, go here --> https://lnkd.in/eSv3_Pve

If you want to get paid for referring people to their panel (this is how I made the $1,180), here's the link for that. Their referral program is powered by my buddy, Josh Ho 🤷🏽 's, business, Referral Rock --> https://lnkd.in/eGJfGk7N

Anyone else a member of the Wynter panel? What's your experience?

#OffTopicFriday

"I don't use the word strategy because it gets so over-used." 

In the last few months, I've heard this on a number of occasions when I'm talking to agencies and consultants about why they should sell strategy first to their clients. (And not let their clients off the hook from developing a differentiated strategy, since most clients want to skip this and will say "we have a strategy" when in reality they don't.) 

I've also had lots of conversations with agencies/consultants where they agree with me, but asked me, "What do you mean by strategy?" 

This is the main problem with strategy. Every one thinks it's something different. 

It's not. And if you're going to sell strategy, you gotta stop winging it or customizing the approach for every client. 

I have a 30 second rant that makes it real clear what I mean by strategy.

It's a step-by-step process that must be done annually that...

1. Starts with identifying and defining (or refining) who a company's ideal client profile (ICP) is, then
2. Identifies the strengths and weaknesses of the companies that compete for that ICP's business, then...
3. Identifies 'opportunities for' and 'threats to' the business because of external trends, factors, then...
4. Defines/refines a vision, mission and ambition that guides a business long term towards creating the future they envision for themselves, customers and partners, then...
5. Defines annual objectives to execute in the short-term (1 year) that helps them do as many of these as possible given their resources: a) better serve their ICP b) more handily beat their competitors c) leverage their strengths and/or overcomes their weaknesses relative to those competitors and relevant to their ICP, d) capitalizes on opportunities, avoids/overcomes threats and e) moves the company in the direction of achieving their vision, mission and ambition.
6. Defines key metrics/goals to measure success of those objectives

I have said this so many times now that I can literally say this without taking a breath or stuttering a word.

If you need a shorter way of saying it or remembering it, use...

1. Who they're for
2. Who they're against
3. What they're operating environment is
4. Why they exist, when and how much impact they want to have
5. What to do now
6. How to measure success of it

That's strategy.

And if it's not obvious from my rant, you can't skip ANY of it. One step is a pre-requisite for the next. If you skip any of it so that you "can get started executing", I can guarantee you that you'll be executing a sub-par strategy.

Why is advertising everyone's go-to growth lever? 

We asked marketers a simple question: “If you suddenly had an extra $25K/month to invest in marketing, where would it go?”

52.6% said paid marketing.

Not because it’s the most efficient channel. But because it’s the easiest one to defend when someone asks, “What did we get for this spend?

And that is the trap of digital advertising.

It becomes the default. Not necessarily because it is the best option, but because it’s most measurable.

To make things worse, ads don’t necessarily maintain effectiveness and profitability as budgets grow. 

Search ads are often the last click before someone buys, capturing demand created elsewhere.  So when a company runs out of branded and competitive ad clicks, clicks get expensive.  

So when expanding ad budgets, companies end up…

• Paying more for lower-intent leads
• Measuring success by CPL, not lifetime value
• Growing acquisition while ignoring efficiency
  
The teams getting this right are doing something different.

They’re managing the profitability of their ads, not just another traffic source. A system that increases efficiency as budget increases: 

• Eliminates wasted spend hiding inside “good” campaigns
• Maximizes high-intent leads through better campaigns and targeting, not necessarily more budget
• Measures success through a full ROI calculation by tracking CPL, CAC, and ROI in one place
  
Devin Littlefield, CEO at MarketVantage will share his exact approach in our upcoming webinar on making paid ads profitable again: https://lnkd.in/eVBFUpSW

If you’re responsible for pipeline and are planning to increase your ad budget, you might want to attend.

We've been pulling companies away from Looker Data Studio  a lot more recently, especially agencies with a large number of accounts. 

Here's a direct quote from an early Databox customer, who has come back to Databox after using Looker for awhile, "So, we moved away from Databox to a custom solution on top of Looker Data Studio using [3rd party tools] and APIs, etcetera. But what has happened is that we have scaled up a couple of our accounts [with lots of data feeds and] the manual cost [of maintenance, including] manual intervention in managing the APIs no longer make sense." 

He continued, "So you name any BI software in the market, I have tried it. But, ultimately,  it was Looker Studio with manual [data feeds] because I'm good with IT and everything. So I used to make all those API calls, and then pop it in a Google Sheet, pull that data, present it in Looker Data Studio. But eventually, we have grown to a point that I cannot be doing those manual efforts. So I'll happily pay, a $200, $300, $400 price point now. As long as something gives me clarity in my process and my systems, we are happy to invest back."

One of Databox's main advantages is that we have built 130+ integrations with popular software platforms, making it easy for users to get their data into the system. 

Over the last year, we invested in making these integrations so robust that the full datasets can be accessed inside Databox, which ultimately enables advanced analysis of the data, similar to what's feasible in even the most advanced business intelligence platforms. 

This combo of ease of setup without compromising analytical power is what's pulling people back to Databox from Looker. Looker Data Studio itself is a free product. But, it's not free once you start needing to analyze data that's not coming from a Google product.

How's Looker working for you?

What's the right way for SaaS companies to add AI to their product and who is doing it well?   

On our most recent Move the Needle podcast episode, Rodrigo Fernandez from ProductLed shared a story about how a SaaS company's activation rate dropped by 20% when they added AI to their product. Hear him tell the story -> https://lnkd.in/ez5zsU7G

Every SaaS company is racing to call themselves “AI-first” or "AI-native," present company not excluded. 

It’s expected. It’s 2026. A Databox investor recently told me, "Software companies aren't raising funds right now unless they have an AI story where customers are getting value out of it." 

It's not about slapping a name or making an announcement.  The AI needs to provide value to the customer that they couldn't get without AI. 

The mistake Rodrigo sees over and over:

• AI added as a feature, not as a value accelerator
• Cheap models & shallow prompts
• “AI onboarding” that asks questions… and does nothing meaningful with the answers

This shouldn’t come as a news flash, but users aren’t impressed by AI existing.

What impresses them is AI removing work or doing something they couldn't do before.

Here’s a better pattern:

• Start with the outcome the customer wants
• Use AI behind the scenes to configure, personalize, and guide
• Reduce time-to-value instead of adding another layer to learn

Rodrigo made a great point, "Your users already use great AI tools every day. Their expectations are high, and getting higher.”

In our product, we've rolled out a few AI-powered capabilities last year that deliver value and have happy users.   

1️⃣ AI performance summaries: They summarize performance in words --> https://lnkd.in/exaGPvYf

2️⃣ AI for building metrics from advanced datasets. Most companies struggle to turn their data into metrics. It requires an understanding of how the data is stored (tables, common identifiers between tables, field names, etc), how and if it can be aggregated, how it should be filtered, etc. AI can assist in this process. 

3️⃣ Analysis by AI. Our MCP server allows LLMs (like Claude, chatgpt) to do analysis that would take a human a lot of time to do: https://databox.com/mcp

We have much more AI-powered capabilities launching imminently that will make it even easier for our customers to generate insights from their data, as well as setup and use the rest of our product more easily to visualize, monitor and report performance. 

But, as we roll these things out, I'm very curious: which SaaS companies are doing this well?

According to Rodrigo Fernandez from ProductLed, 97% of SaaS companies never reach $20M ARR because SaaS companies don't define activation correctly. 

Ali Orlando Wert recently interviewed Fernandez on Databox's Move the Needle podcast to go deeper into what companies usually do and how to fix it. 

His advice, "Activation is an equation. Not an event." 

Too often, Fernandez explains that most companies pick a single “aha moment” that doesn’t necessarily map to the user receiving value.  

Here’s the short version of what Rodrigo suggests instead:  
• Define activation based on jobs-to-be-done, not a user's clicks
• Treat activation as an equation that measures whether that job gets done, not a single event
• Map the full journey to value before pouring fuel on growth 
  
If you want the full breakdown, including how Rodrigo thinks about activation as a “solar system” of events, here’s the full episode: https://lnkd.in/ep9rMRUB

And the longer, more thorough recap: https://lnkd.in/ez5zsU7G

The most valuable service any professional services firm can offer isn’t a deliverable. It’s alignment.

We all know leadership intent doesn’t automatically turn into measurable priorities a team can execute on. You’ve seen it: smart people, lots of motion, but no shared definition of what matters this quarter.

When that alignment is missing:
• teams chase too many “important” things
• goals drift away from metrics
• reviews get skipped or run on stale numbers
• performance improvement feels random, instead of expected

This is where professional services firms can help: not by doing more work, but by making success unambiguous.

On your next client call, listen for misalignment signals. 
• priorities changing mid-quarter
• nebulous goals that aren’t tied to specific metrics and targets
• statements like “we’ll review when we have time”
• stakeholders pushing in different directions 
  
Any one of those is a sign that your client doesn’t have a shared definition of success. If you hear this, this is a good time to introduce clients to OKRs. OKRs force annual and quarterly choices, tie objectives to measurable goals, and lock in a review cadence so priorities don’t drift mid-quarter.  

This reinforces a good working relationship, where all of the following are true: 
• clear outcomes for the quarter
• owners and initiatives tied to those outcomes
• a simple rhythm to review progress
• visibility so everyone sees what’s working (and what isn’t) 

If you’re not familiar with OKRs, we put together a free OKR Services Blueprint for professional services firms who want to productize this into a client offering (what to sell, how to price it, and how to run the cadence without spreadsheets).

Just comment below and we'll send it to you.

If your firm's client acquisition is reliant on referrals from one, much larger partner, and you're worried about what would happen if that deal flow slows or stops for some reason, here's what you need to do now. 

1. Look through your client work and find one that you did something unique for. Let's call it solution x. 
2. Identify if solution x would be applicable to an identifiable, relatively small group of companies. Smaller is better because this means you'll attract fewer competitors. (The easiest way to identify prospects is by a market, so if solution x is perfect for companies in market a, that's ideal. But the solution could apply across markets. Just make sure you'll be able to identify a list of the companies who will value solution x and make sure they'll be able to recognize the brilliance of solution x immediately via your marketing.) 
3. Make sure that there isn't a bunch of other competitors (of yours) who have already built solution x. Solution x must truly be a unique solution to a unique problem. 
4. Focus all of your proactive direct sales and marketing efforts on this market for 6 months. 

Things you shouldn't do yet: 

1. Tell your much bigger partner that you're trying to narrow down your market. In fact, tell them the opposite: you'll continue to help them with the broad range of clients they're bringing you. 
2. Change your company's home page or positioning. Not necessary -- you can just build a landing page. 

Your objective should be to get to the point where x% of your clients have purchased solution x.

It could be 20%, 30%, 40%. You might get to the point where it's 100%, which would be amazing. But, anything is progress. 

If the number is below 40% in 6 months, though, do this next: 

1. Ask the clients who have hired you to implement solution x, what other challenges they have. Most likely, you will identify additional ways to help this narrower client list in ways your competitors won't. 
2. Prioritize building out the next solution for market a: solution y. 
3. Repeat

12, 18, 24 months from now, you'll have made the change that allows you to market your services directly to a segment of the market that neither your partner or your competitors are focused on, which will make it easy/inexpensive for you to acquire clients directly. 

You'll also become the go-to firm for this segment of the market. This'll actually get you more referrals from your much bigger partner because you'll be the first they think of when they see a company in market a. If you do this well, you'll even find that your current "competitors" start referring you for companies in market a. This'll also make it possible for you to survive if your much bigger partner decides to stop being your sugardaddy.

I'll be the first one to tell you to build partnerships and referral sources. But, you should NEVER be dependent on them.

Scaling is an over-used word that’s lost it’s meaning. 

So when we launched our latest course, I hesitated to call it Predictable Scale. 

Most of the time, people use the word "scale" to say “grow faster”.  But that’s only half of what it means to scale. 

This came up in a recent conversation on The RevOps Show with Doug Davidoff and Jess Cardenas from Lift Enablement when they asked me a simple question: “What does scale actually mean?”

My answer: Scale isn’t just growing revenue. It’s growing revenue without increasing costs at the same rate. In other words, you need both revenue and margins to grow at the same time. 

You can listen to the full episode here: https://lnkd.in/excg3sfg

Of course, knowing the definition isn't the hard part. The hard part is answering the question, “How do you scale?” 

For most companies, growing is relatively straightforward. You just do more of what’s already working. You invest more in ads, hire more salespeople, increase your marketing output, etc.  

But, if you want to scale, you have to find ways to multiply outcomes without multiplying effort or cost. 

This requires two things: innovation and discipline. 

Innovation to find new ways that help you grow more efficiently, and discipline to get more efficient at doing the things you’re already doing. 

This is why scale is the last step in the Predictable Scale methodology after strategy, planning, execution, adjusting what’s not working and repeating what is. 

Growing doesn’t always require strategy, thoughtful plans, focused execution or using data to measure and adjust. Doing more of what's already working doesn't require much thinking at all. 

But, scale does.

Scale requires a step-by-step process...

https://lnkd.in/eScK5VX9

I have not been able to make or receive calls in the last hour seemingly because Verizon is down.

My wife's phone stopped working about 10 minutes ago. 

But, Verizon is not reporting anything. 

Their "check status" page is reporting nothing. 

Their help page says "login" to your account to check your network status, but the "forgot password" page doesn't load. 

No announcement from them on their support handle on social media. 

The only way I know my wife and I are not the only ones is because people are manually reporting that their Verizon mobile service is down via DownDetector, a free service by Ookla. 

Looks like it's impacting at least 171k people, going up by a few thousand people every time I refresh the page! 

Props to Ookla. Not props to Verizon.

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