Firefox

Firefox is Getting a Major Redesign, and Some AI Controls

Firefox is Getting a Major Redesign, and Some AI Controls

Firefox is redesigning its browser with easier AI and privacy controls. That says a lot about where browsers are headed.

Browser redesigns usually focus on aesthetics. New tabs. Rounded corners. Slightly different icons that people complain about for two weeks before forgetting.

Mozilla’s upcoming Firefox redesign, called Project Nova, does include those changes. But the more interesting update is buried in the settings menu. Firefox wants to make privacy controls and AI settings navigable, including a switch to disable all current and future AI features.

That detail stands out because most tech companies are moving in the opposite direction.

AI is increasingly becoming the default. It shows up in search bars, email apps, browsers, and operating systems, often with limited control over how much users actually want. Firefox seems to be betting that giving people clearer choices could become a competitive advantage.

The redesign itself is significant. Project Nova is Firefox’s biggest visual overhaul in years- rounded tabs, updated colors, more customization options, and easier access to features like split view and vertical tabs. Compact mode is also returning after users pushed back against previous changes.

But underneath the design refresh hides another challenge.

Firefox has been losing ground to browsers built on Chromium- the technology behind Chrome and other competitors. Standing out is difficult when many browsers increasingly feel similar. Mozilla appears to be responding with a mix of customization, privacy, and user control.

There’s something unusual about that strategy right now.

Most companies are trying to convince users that more AI is automatically better. Firefox is effectively saying: use AI if you want, but we’ll make it easier to avoid it too.

Whether that attracts users is another question.

Convenience often wins in technology. Defaults win even more.

Still, Project Nova suggests Mozilla sees an opening. If people become exhausted by software deciding things for them, a browser built around control instead of automation might feel surprisingly different.

Not because it has less AI.

Because it allows users to choose how much AI belongs in their browser.

Sales Sequence Examples

Sales Sequence Examples: 4 Proven Outreach Frameworks to Increase Reply Rates and Pipeline

Sales Sequence Examples: 4 Proven Outreach Frameworks to Increase Reply Rates and Pipeline

The sequences that generate the pipeline in 2026 are built around buyer behavior, not rep convenience. Here is what they look like.

What Is a Sales Sequence? Definition, Strategy, and Why Most Outreach Fails

Most outreach sequences have the same problem, especially when teams rely on outdated sales techniques that prioritize volume over relevance.

They were designed by someone optimizing for volume. More touches, more channels, more automation. The logic is that if enough messages go out, some percentage will convert. And that logic works well enough to keep the approach alive while being wrong enough to explain why most pipelines are anemic.

80% of sales require five or more follow-ups. Most reps give up after two attempts, which is why structured sales follow-up email templates matter in modern outreach. That is not a persistence problem. It is a design problem. Reps stop following up because the follow-ups they are sending feel like noise. When you know your next email adds nothing, it is hard to send it. So you do not.

The sequences below are built differently. Each one has a structure, a logic, and a note on the specific failure mode that kills it. Use them as starting points. The best version of any sequence has details only you could have added.

Why High-Performing Sales Sequences Work: Core Principles Behind Better Outreach

3 Principles of High-Converting Sales Outreach Sequences

Three things. Get these right, and the specific templates become secondary.

Relevance over volume. Signal-based outreach to ICP-matched accounts reaches 10 to 15% reply rates. Generic cold email sits at 3 to 5%. That gap is not about writing quality. It is about whether the rep had a real reason to reach out. A reason that is specific to this company, at this moment.

Channel mix, not channel obsession. Modern outreach combines email, phone, and social for maximum visibility through a coordinated sales cadence. Each channel has a different job. Email scales and creates a record. Phone creates real-time conversation. LinkedIn builds presence over time. Teams that rely on one channel are leaving the others as free space for competitors with stronger outbound sales playbooks.

Message length discipline. Initial touch emails should be under 120 words. Shorter emails with a single clear ask consistently outperform longer feature-heavy messages. Save depth for the middle of the sequence when partial rapport already exists. The first email is not the place to explain everything. It is the place to earn the next conversation.

Cold Outbound Sales Sequence Example for ICP-Matched Accounts

Best for: Accounts you have never spoken with, where a recent event or behavioral signal justifies outreach as part of a focused B2B sales prospecting strategy. Timeline: 14 days, 7 touches. Conversion target: 8 to 12% reply rate when the signal is specific and the messaging reflects it.

Day 1 — Email 1: The Signal Reference

Subject: [Specific observation about their company]

Hi [Name],

[One sentence referencing the signal: new hire, funding round, job posting, LinkedIn post, product announcement sourced through tools like LinkedIn Sales Navigator. Specific enough that they know you actually looked.]

We work with [type of company] on [problem this signal suggests they are facing]. Usually takes about 15 minutes to figure out whether it is relevant.

Worth a quick call this week?

[Your name]

Note: Under 80 words. One ask. No pitch. The signal is the reason for the email and it should be obvious.

Day 3 — LinkedIn: Connection Request

No pitch. Send a connection request with a one-line note referencing the same signal or something from their profile. The goal is to become a known name before the next email arrives.

Day 5 — Email 2: The Problem Reframe

Subject: The hidden cost of [problem]

Hi [Name],

Most [their role] I speak with measure [problem] in terms of [obvious metric]. What they often miss is [second-order consequence that is less obvious but more costly].

I have seen this play out in [briefly describe one scenario from a similar company, not a full case study, two sentences].

Happy to share what others in your position have done about it. 15 minutes?

[Your name]

Note: This email does not mention your product. It demonstrates the kind of sales personalization that earns the conversation before a pitch begins.

Day 7 — Phone Call

Start with email to set initial contact on prospect terms. Follow with phone two to three days later to deepen the conversation.

Leave a voicemail only if you have something specific to say. “Following up on my email” is not a voicemail. “I sent you a note about [signal] and wanted to add one thing I could not fit in the email” is.

Day 9 — LinkedIn: Engage Their Content

Comment on something they posted recently. Not “great post.” An actual observation. One sentence that adds something. This is relationship infrastructure, not outreach. They notice it even if they do not reply.

Day 11 — Email 3: Social Proof, No Pressure

Subject: How [similar company] handled [problem]

Hi [Name],

Worked with a [their industry] company facing similar challenges last year. [Two sentences on what changed and what the result was. Specific enough to be credible, not so long it becomes a pitch.]

Not sure if the situation maps to yours. Happy to share more if it does.

[Your name]

Day 14 — Email 4: The Honest Close

Subject: Closing the loop

Hi [Name],

I have sent a few notes over the past couple of weeks and have not heard back. Not going to keep sending emails that are not landing.

If timing is off, I will check back in [specific month]. If this is not relevant, no problem at all.

Either way: [one line of genuine value, a resource, an insight, something worth having regardless of whether they ever reply].

[Your name]

Note: The break-up email gets more replies than most reps expect. The removal of pressure is what does it.

Warm Inbound Sales Sequence Template After Content Engagement

Best for: Contacts who have engaged with your content: downloaded something, attended a webinar, visited specific pages multiple times through your B2B sales funnel. Timeline: 10 days, 5 touches. The logic: These buyers have already expressed interest. The goal is to meet them where they are, not to restart from zero.

Day 1 — Email 1: Acknowledge What They Did

Subject: You looked at [specific content] — a thought

Hi [Name],

Noticed you [downloaded / attended / read] [specific content piece]. That usually means [the problem it addresses] is on your radar.

[One sentence of genuine insight that goes one layer deeper than the content they consumed. Not a pitch. Something they might not have considered.]

Happy to talk through it if useful.

[Your name]

Note: Referencing the specific piece of content signals that this is not a mass email. That matters more than any personalization token.

Day 3 — Phone Call

If they are a strong ICP fit, call before the second email and move them toward becoming sales qualified leads. The buyer who visited your pricing page three times is ready for a conversation sooner than the one who opened a newsletter.

Day 5 — Email 2: Deepen the Value

Subject: One thing most [their role] miss about [topic]

Hi [Name],

Since you engaged with [content piece], thought this was worth passing along.

[One specific piece of insight, data point, or observation directly relevant to the problem the content addressed. Not a product pitch. Something genuinely useful.]

Still happy to talk through it in more detail if it is relevant.

[Your name]

Day 7 — LinkedIn: Connect

Connection request with context: “I sent you a note about [topic] after you engaged with our [content piece]. Wanted to connect in case it is useful to stay in touch.”

Day 10 — Email 3: Direct Ask

Subject: One question before I close this out

Hi [Name],

I have followed up a couple of times since you engaged with [content]. Before I stop reaching out, one honest question: is [the core problem the content addresses] something you are actively working on right now, or is it more of a background concern?

No pressure either way. Just helps me know whether reaching out again in a few months makes more sense than now.

[Your name]

Note: A direct question that requires a one-word answer gets more replies than another value pitch. People respond to honesty when it asks less of them.

Re-Engagement Sales Sequence for Reviving Stalled Deals

Best for: Opportunities that went quiet after initial interest during long enterprise sales cycles. The champion stopped replying. The deal slowed without a formal close. Timeline: 21 days, 5 touches. The logic: Something changed internally. The goal is to surface it, not to pretend nothing happened.

Day 1 — Email 1: Acknowledge the Silence

Subject: Checking in on [Company] — honest note

Hi [Name],

Things went quiet on our end after [last touchpoint]. That usually means one of a few things: timing shifted, priorities changed, or there was an internal blocker we did not get ahead of.

Any of those ring true? Happy to adjust the conversation if so.

[Your name]

Note: Naming the most likely reasons for going quiet signals that you understand how buying processes work. It removes the awkwardness of the silence rather than pretending it did not happen.

Day 5 — Email 2: New Angle or Insight

Subject: Something that made me think of your situation

Hi [Name],

[One sentence: a market development, competitor move, regulatory change, or data point that is genuinely relevant to the problem you were discussing.]

Not sure if this changes anything for your timing. Thought it was worth passing along regardless.

[Your name]

Day 10 — Phone Call

Call before the next email. Leave a voicemail that references what you sent on Day 5, not the original pitch. The buyer who has gone quiet needs a new angle, not a reminder that they ignored you.

Day 15 — Email 3: Internal Champion Enable

Subject: Something that might help the internal conversation

Hi [Name],

If the challenge is getting internal alignment, I have something that might help through stronger sales collateral. [Brief description of a one-pager, ROI calculator, or case study directly relevant to the concern that was holding things up.]

Not trying to push the conversation forward before it is ready. Just want to make it easier when it is.

[Your name]

Day 21 — Email 4: Clean Close

Subject: Putting this on hold from my end

Hi [Name],

I have followed up several times without hearing back. I am going to put this on hold and check in around [specific month].

If circumstances change before then, you know where to find me.

[One line of value: a resource, an insight, something genuinely useful with no strings attached.]

[Your name]

Multi-Thread Sales Sequence for Buying Committees and Stakeholders

Best for: Expanding into an account where you have one contact but need to reach other committee members using a multi-threading in sales approach. Timeline: 10 days, 4 touches. The note: This sequence only works if your primary contact has indicated it is acceptable to reach out to colleagues. Permission and awareness are different things.

Day 1 — Email 1: Warm Introduction

Subject: Introduction from [Primary Contact’s Name]

Hi [Name],

[Primary contact] suggested I reach out directly. We have been speaking about [topic], and they felt your perspective would be valuable given your role in [specific area they oversee].

I do not want to assume the conversation is as relevant to you as it is to them. A short call to hear how you think about [problem from their vantage point] would tell me more than me guessing.

Does [time option] work?

[Your name]

Day 3 — LinkedIn: Connection

No pitch. Reference the same topic. One sentence. The goal is for your name to appear in more than one place before the next email arrives.

Day 6 — Email 2: Their Specific Frame

Subject: The [their role] side of [problem]

Hi [Name],

Most of the conversations I have about [problem] are with [primary contact’s role]. What I hear less often is how it looks from [their role’s] perspective.

[One sentence on what typically matters most to their function in this type of decision. Demonstrate that you have thought about their evaluation job, not just the general pitch.]

Worth 15 minutes?

[Your name]

Day 10 — Email 3: Direct and Brief

Subject: One last note

Hi [Name],

Sent a couple of notes. Not going to keep reaching out if the timing is off.

If [core problem] becomes more pressing in the next quarter, happy to pick it up then.

[Your name]

How AI Improves Sales Sequences Without Replacing Human Judgment

The most effective sequences are AI-assisted systems that adapt to buyer behavior. Using AI for sales prospecting helps teams prioritize, tailor messaging, and determine next steps using engagement data and modern sales prospecting tools. opens, replies, content interactions, meeting activity, and buying-group behavior.

The sequences above are structures. AI earns its place in filling them with the specifics that make them feel real, not in generating the structure itself.

What AI does well here: researching the signal that powers the Day 1 email. Finding the market development that gives a stalled deal re-engagement a reason to exist. Identifying which content the buyer engaged with and surfacing what related insight is most relevant right now. Suggesting the right send time based on historical engagement data from similar accounts.

What it cannot do: write the sentence that references something specific the buyer said in a discovery call three months ago. Replicate the tone of a rep who has been building this relationship over six months. Know when a buyer’s silence means they are evaluating quietly versus when it means the deal is dead. Those judgment calls are the rep’s.

AI personalizes the content within each touch. You set the sequence structure: which channels, what timing, what order. The platform ensures repeatable execution with AI-enhanced messaging supported by effective sales enablement strategies.

The division of labor is clear. AI handles scale and research while the rep handles judgment in modern digital sales transformation strategies. When organizations invert this, using AI for judgment and reps for execution, the sequences lose the one thing that makes a buyer pause on a cold email: the sense that a real person paid attention to their specific situation.

Sales Sequence Best Practices: What Every Effective Outreach Workflow Has in Common

Every sequence in this piece operates on the same underlying logic.

Give before you ask. Add something every time you show up. And when you have nothing new to add, do not show up.

That last part is the hardest. Sequences are designed to run automatically, which means they run even when the rep has nothing new to say. The buyer on the other end notices. Not consciously. But it accumulates into an impression of the sending organization, and it is not a positive one.

The buyers described in the email pieces from this library are the ones going with the vendor that has burned them the least. Every empty follow-up is a small burn. Every touch that brings something real is a deposit.

Sequences that generate pipeline are the ones that improve overall sales performance from the buyer’s perspective. Not from the rep’s quota perspective. The buyer’s.

Build them that way and the reply rate takes care of itself.

NVIDIA

NVIDIA Keeps Breaking Records. At What Point Does AI Growth Stop Looking Surprising?

NVIDIA Keeps Breaking Records. At What Point Does AI Growth Stop Looking Surprising?

NVIDIA’s AI revenue surged again. The strange part? Numbers this massive are starting to feel expected.

NVIDIA reported another blockbuster quarter this week. And the biggest takeaway may be that people are no longer shocked by numbers like these.

The company published $81.6 billion in revenue for Q1 fiscal 2027- up by 85% from a year earlier. Its data center business grew 92% to reach over $75.2 billion.

These figures would have dominated headlines for months, a couple of years ago. But now they almost feel expected.

That says something significant about where AI is today.

The conversation around AI often revolves around products people directly use- ChatGPT, Gemini, Claude, and AI search. NVIDIA’s results are a reminder of the enormous demand that is hidden under all these tools. Someone has to build and power the systems behind it before AI can answer questions or generate images.

NVIDIA remains one of the companies benefiting most from that demand.

Its chips have become so central to AI development that nearly every major tech company relies on them in some form. That puts NVIDIA in an unusual position. Most technology companies fight for users. NVIDIA profits from everyone else fighting for users.

The company also forecast $91 billion in revenue for the succeeding quarter, suggesting executives believe spending on AI infrastructure still has room to grow.

There are obvious reasons to be cautious. China’s restrictions continue to obstruct growth, while Google and Amazon are investing in their own chips to reduce reliance on NVIDIA.

But that future feels distant compared with the present reality.

For now, the AI boom is still translating into extraordinary spending, and NVIDIA is sitting near the center of it. The strange thing is no longer the company’s growth.

It’s how quickly the industry has accepted growth at this scale as business as usual.

Anthropic

Anthropic’s $15 Billion SpaceX Deal Shows AI’s Biggest Battle Has Changed.

Anthropic’s $15 Billion SpaceX Deal Shows AI’s Biggest Battle Has Changed.

Anthropic’s huge SpaceX deal reveals AI’s next fight may not be smarter models. It may be compute and infrastructure.

The AI race used to feel straightforward: build a better model, attract users, win.

That story is changing.

Anthropic, the company behind Claude, has reportedly agreed to pay SpaceX around $1.25 billion every month for access to AI computing infrastructure. That’s roughly $15 billion a year. The number sounds absurd at first. Then you realize what it says about where AI will head.

The biggest AI conversation revolved around who had the most intelligent chatbot. OpenAI. Google. Anthropic. But intelligence alone may no longer decide the winners.

The real bottleneck is becoming compute.

Training and running advanced AI demands massive data centers brimming with infrastructural nitty-gritties. And that demand keeps rising. Supply struggles to keep pace.

That changes the nature of competition.

The question is no longer only, “Who has the best AI?” It’s increasingly, “Who can afford to keep powering it?”

SpaceX is an unexpected player in that conversation. Known for rockets and satellites, the company is becoming part of the infrastructure layer supporting AI through its Colossus computing systems. Anthropic appears willing to spend heavily because building equivalent capacity independently could take years.

There’s also irony here.

AI companies compete aggressively in public. Behind the scenes, rivals may depend on the same infrastructure to survive.

The economics are also hard to ignore.

Consumers interact with polished AI products and expect quick answers. What they don’t see is an industry spending billions merely to maintain the machinery behind those responses.

This deal suggests something beyond one partnership.

AI may not ultimately be won by the company with the smartest model. It could be won by whoever controls the resources needed to run intelligence at scale.

The AI industry often gets compared to an arms race. Increasingly, it looks more like an energy race.

Marketing Attribution

Is Marketing Attribution About ROI or Value? An Insight

Is Marketing Attribution About ROI or Value? An Insight

Your attribution model is optimizing the measurable and ignoring what matters. Here’s the ROI vs. value question every marketing team gets wrong.

Most marketing teams are solving the wrong problem.

They’ve spent years building attribution models, fighting over last-click versus first-click, defending channel spend in quarterly reviews, and trying to prove that marketing “works.” And somewhere in that process, the actual question got lost.

Attribution was never supposed to tell you which channel deserves the credit. It was supposed to tell you something useful about how your customers make decisions. Those two goals sound similar. They produce completely different outcomes.

What Marketing Attribution Actually Is (And Why Most Teams Use It Wrong)

At its core, marketing attribution is the practice of connecting a customer’s eventual buying decisions with the touchpoints that preceded it. A prospect reads a blog post, clicks a LinkedIn ad three weeks later, attends a webinar, googles the brand name, and converts on a retargeting ad. Attribution is the framework that decides which of those moments “caused” the sale.

The problem is the word “caused.” It’s doing a lot of work that it can’t actually support.

No attribution model tells you what caused a purchase. They tell you what happened before one. That’s a correlation, not a mechanism. But because marketing teams need to justify budgets, correlation gets dressed up as causation, and suddenly the retargeting ad is “driving” revenue rather than just happening to be the last thing someone clicked before buying something they’d already decided to buy.

Last-click attribution is the most common version of this mistake. It hands all the credit to the final touchpoint before conversion. Which means the blog post that introduced the product, the LinkedIn content that built trust over six months, the webinar that answered the objection that was blocking the deal- none of that shows up in the model. The retargeting ad gets the trophy. The rest of the funnel gets cut.

Teams that run on last-click attribution don’t have a measurement problem. They have a visibility problem. They’re making budget decisions with one eye closed, especially when they fail to account for broader full-funnel marketing efforts that influence buyers long before conversion.

The ROI Trap

Here’s what happens when attribution becomes purely about ROI.

Every dollar has to prove itself. Every channel must have a return that can be traced, in a straight line, back to revenue, which is why many teams become overly dependent on ROI-focused performance marketing metrics. Anything that can’t be measured gets deprioritized. Brand awareness spend gets cut. Content gets gutted. Thought leadership disappears. Events are killed because the pipeline attribution is fuzzy.

And for a quarter or two, the numbers look fine. Efficiency goes up. Cost per acquisition tightens. Leadership is happy.

Then the pipeline starts drying up. The top of the funnel, which nobody was feeding for twelve months, stops delivering the volume the bottom of the funnel needs. Suddenly, the “efficient” marketing org is scrambling to explain why growth has stalled, and the answer is sitting right there in the attribution data they trusted too much.

ROI-focused attribution optimizes the measurable at the expense of the important. It’s not that ROI doesn’t matter. It does. But ROI is a lagging indicator. It tells you what already happened. It says nothing about whether what you’re doing today is building the conditions for revenue six or twelve months from now.

Value-focused attribution asks a different question entirely. Not “which touchpoint gets the credit?” but “what is actually moving this customer toward a decision, and are we present at those moments?” This is where a structured marketing funnel becomes more useful than isolated attribution metrics.

The Models on the Table

Understanding the difference between attribution models isn’t just academic. The model you choose shapes how your team allocates budget, which channels get investment, and what stories you tell stakeholders.

Last-click gives everything to the final touchpoint. Fast to implement. Easy to explain. Almost always misleading in a multi-touch buying journey.

First-click is the opposite problem. It credits the channel that created awareness but ignores everything that happened between introduction and conversion. Useful for understanding top-of-funnel reach. Useless for understanding persuasion.

Linear attribution splits credit equally across every touchpoint. More honest than single-touch models. Also kind of a cop-out. Not all touchpoints are equally influential, and treating them like they are doesn’t tell you anything actionable.

Time decay gives more credit to touchpoints closer to conversion. The logic is that recency implies relevance. Sometimes that’s true. Often, it just over-weights retargeting and under-weights the content that built the case for buying in the first place.

Data-driven attribution uses machine learning to assign credit based on which combinations of touchpoints actually correlate with conversion across your specific customer base, similar to how brands are increasingly using AI in marketing to improve decision-making. It’s the most sophisticated model. It’s also the one most dependent on data volume and data quality. Garbage in, confident-looking garbage out.

No model is neutral. Every one of them has a bias baked in. The question isn’t which model is “correct.” It’s which model’s biases you understand well enough to make useful decisions with.

The Dark Funnel Problem Nobody Talks About Enough

Here’s what every attribution model misses. The conversations that happen outside your tracking infrastructure.

A CFO mentions your product in a board meeting. A customer posts about their experience in a Slack community with 8,000 members. A prospect asks their peer network for vendor recommendations, and three people say your name. A podcast episode plants the idea six months before the contact form ever gets filled out.

None of that shows up in your attribution model. Not one touch. The model observes a direct visit and a form submission and calls it a two-touch deal. The actual buying journey involved fifteen moments of influence that you can never fully reconstruct.

That is the dark funnel. And for B2B companies, especially, it drives a significant share of the pipeline. The buyers who already know who you are before they even engage with your tracking are often the easiest closes and the most valuable customers, which is why omnichannel marketing matters more than many attribution models acknowledge. They show up “out of nowhere,” according to the model. In reality, they did months of research in places you can’t see.

The implication isn’t that attribution is useless. It’s that attribution data should always be read with humility. What the model shows you is a partial map of a territory that’s larger than the map.

B2B vs. B2C: Why Attribution Works Differently

In B2C, buying cycles are short. One or two people make the decision. The touchpoints are mostly digital and mostly trackable. Attribution models work reasonably well here because the gap between “influenced” and “converted” is small enough that you can actually close it with data.

B2B is a different animal entirely.

Average enterprise deals involve six to ten stakeholders. Buying cycles extend across six to eighteen months. Decisions occur in meetings that your marketing team was never present at. The champion who pushed for your product internally saw a LinkedIn post, attended a webinar, read a competitor comparison, had a conversation with a sales rep, and then spent three months convincing procurement. Each of those stakeholders had their own journey. The attribution model sees one company name and one conversion event.

That is why B2B companies that over-rely on digital attribution models end up systematically undervaluing brand, undervaluing events, and the kind of long-form content that builds credibility over time, despite the proven impact of strong B2B content marketing strategies. Those channels influence the buying committee in ways that are real but not trackable. Cutting them because they don’t show up cleanly in attribution data is how companies accidentally hollow out their pipeline while watching their digital efficiency metrics improve.

What Value-Focused Attribution Actually Looks Like

Shifting from ROI-focused to value-focused attribution doesn’t mean abandoning measurement. It means measuring more things and being honest about what each measurement can and can’t tell you.

Self-reported attribution is underused and surprisingly accurate. Ask people how they heard about you. Ask what made them decide to reach out. These insights often reveal gaps that traditional closed-loop marketing systems fail to capture. Ask which content they remember. Customers will tell you things your tracking pixel never could, and the answers are often more useful than a last-click model’s verdict.

Influenced pipeline is a more honest metric than attributed revenue. Instead of asking “which touchpoints caused this deal?” ask “which touchpoints were present in deals that closed?” That’s a softer claim but a truer one, and it gives you a more defensible way to measure the contribution of channels that live in the middle and top of the funnel.

Holdout testing, running campaigns for some audience segments and not others, and comparing outcomes, is harder to set up but much more rigorous than any attribution model. It actually tests for causation rather than just observing correlation.

And brand tracking, periodic measurement of awareness, consideration, and preference among your target audience, captures the long-run equity that attribution models can’t see, especially in complex B2B SaaS marketing environments. If 60% of your target market has never heard of you, your performance marketing is working against a headwind that no amount of retargeting will overcome.

ROI and Value Aren’t Opposites. They’re a Sequence.

The real answer to whether attribution is about ROI or value isn’t either/or.

ROI matters. Marketing has to generate returns. But ROI is a harvest metric. It measures what came in. Value is a planting metric. It measures what you’re building toward.

Teams that only measure harvest eventually run out of things to harvest. Teams that only think about value eventually run out of money. Sustainable growth requires balancing attribution with broader growth marketing strategy planning. The ones doing it well use attribution to understand what they’re harvesting today and separate thinking to make sure they’re planting enough to harvest tomorrow.

That’s not a measurement framework. That’s a mindset shift. And it starts with being honest about what attribution models can and can’t actually tell you.

Google

Is What We Truly Need Another Google Search Makeover?

Is What We Truly Need Another Google Search Makeover?

Google wants Search to stop showing links and start thinking. The internet’s biggest habit is changing.

Using Google has always meant one thing: type a question, get a page full of links, and choose where to click.

Google thinks that process is too slow.

At its latest I/O event, the company doubled down on AI-powered Search, pushing toward a future where Google doesn’t just find information- it interprets, summarizes, compares, and increasingly acts on your behalf.

The biggest shift: AI Mode. It’s an experience designed to handle more complex questions with conversational answers. Need help planning a trip, comparing solutions, or researching? Google wants users to ask naturally and let AI piece together the response.

It sounds convenient because, frankly, it is. Most people don’t enjoy opening ten tabs to compare information. They want answers faster.

But there’s a trade-off hiding underneath.

Traditional Search pushed users toward websites. AI Search keeps users inside Google longer because the answer arrives before the click. That changes who gets attention online- publishers, creators, and businesses built around search traffic could feel the impact.

Google argues AI will help people explore topics more deeply rather than reducing discovery. Critics worry the opposite happens: fewer clicks, fewer original sources, and an internet increasingly filtered through one company’s interpretation.

The strange thing is, this shift already feels normal.

Millions use ChatGPT, Gemini, and other AI tools instead of typing traditional searches. Google isn’t creating the behavior but adapting to it.

That may be the real story here.

Google built an empire by organizing the Web. Now it’s betting the next era won’t revolve around organizing information, but delivering conclusions.

And if that happens, one of the internet’s oldest habits, such as browsing, may quietly start disappearing.