Your Analytics Are Blind to Where Growth Is Actually Happening
Streaming Ads Just Got Louder. Your Media Plan Should Get Smarter.
Ad minutes are up 18% on top U.S. streamers, but more inventory doesn't mean better inventory.
What's the News:
Streaming ad loads are climbing fast. Ad minutes per hour rose 18% across top U.S. streamers between January and August 2026. As subscription prices rise, more viewers are shifting to cheaper ad-supported plans, and platforms are using that migration to grow ad revenue — which means more commercial time and more available inventory. Source: Business Insider
Why It Matters:
More inventory is not automatically better inventory. As streamers add more ads, connected TV becomes easier to buy…and easier to waste. Brands may get more reach, but they’re also competing inside a more crowded viewing experience. That makes buying discipline critical. Frequency, placement quality, creative strength, and incrementality are what separate smart CTV investment from expensive noise.
The brands that win will use connected TV to create demand, then measure whether that demand moves brand search, site activity, leads, and revenue.
Marketers Should:
Look for connected TV opportunities. More ad-supported viewing creates new ways to reach audiences outside crowded social feeds and search results.
Watch frequency closely and prioritize quality placements. Not all streaming inventory is equal. Know where ads are running and whether those environments support the brand.
Measure beyond video completion. Track branded search, direct traffic, site engagement, assisted conversions, and revenue.
Validate with Incrementality Testing and/or Media Mix Modeling. CRM-based click attribution will significantly undercredit CTV, while the CTV platform itself will likely overcredit it. Incrementality testing and Media Mix Modeling give a more confident read on CTV’s true return.
ChatGPT Cracked the Top 10. Your Analytics Still Can't See It.
AI referral traffic barely registers in GA4. That blind spot is growing every month.
What's the News:
AI usage is reshaping the U.S. web’s traffic rankings. A Semrush analysis of the most-visited U.S. websites over the past year found the top three unchanged — Google (#1, up 11%), YouTube (#2, up 37%), and Reddit (#3) — but the bigger story sits further down the list: ChatGPT climbed to 9th overall on a 48% jump in visits, officially passing Bing, which fell 50% year over year. Source: Search Engine Land
Why It Matters:
This continues to show the growing gap between actual AI usage and what GA4 is telling you about your traffic. As one industry analysis put it, relying on GA4 alone to measure AI’s search impact leaves marketers navigating something like a Bermuda Triangle of analytics: direct traffic keeps climbing, and GA4 can’t tell you why.
For the majority of brands, AI-attributed traffic shows up in GA4 as just 1–2% of sessions, and the common reaction is to write it off as a drop in the bucket. That reaction drastically underestimates the impact of AI search. Direct traffic, meanwhile, often accounts for 75–85% of overall traffic for most brands. That’s not a coincidence: direct traffic is what happens when users are already aware of a brand (increasingly through AI search) and it’s also the catchall label GA4 assigns to anything it can’t attribute.
Marketers Should:
Stop reading GA4’s AI channel as the whole picture. It’s undercounting by design. A growing share of AI-driven visits show up as “direct” instead.
Treat rising direct traffic as a signal, not noise. A jump in direct sessions alongside AI’s growth is a clue worth investigating, not a dead end.
Build visibility earlier in the consumer journey. If you’ve dismissed AI search as a rounding error, you’re already behind and need a strategy for showing up before someone opens a browser tab.
Bring in measurement built to see what GA4 can’t. Don’t let misleading attribution data undersell the opportunity. Audience intelligence tools and direct customer research help fill the gap.
Google Just Leaked the Blueprint for Local Search
72 ranking signals, 793 competing data sources, and one hard truth: a GBP edit is a vote, not a command.
What's the News:
A recovered Google Maps binary reveals Google runs local rankings through an internal system called Oyster Rank, which contains 72 signals (25 already deprecated) layered on top of a “Geostore” entity model that pulls data from 793 different providers. Source: Search Engine Land
Why It Matters:
This confirms what a lot of local SEOs have suspected but couldn’t prove: a Google Business Profile edit isn’t a command, it’s a vote that competes against hundreds of other data sources Google already trusts more or less. If a client’s category, hours, or NAP data keeps reverting or won’t stick, it’s not user error. It is the conflation system picking a different source as more trustworthy. The bigger shift is that ranking and map visibility are two separate systems, meaning a client can be technically well-optimized and still invisible on the map itself.
Marketers Should:
Treat GBP edits as evidence, not instructions. If a field keeps reverting, the fix is building a stronger, more consistent signal across every third-party source Google trusts, not resubmitting the same edit in the dashboard.
Kill the “72 ranking factors checklist” narrative before a client brings it to you. These are vocabulary terms in Google’s internal schema, not weighted factors you can optimize against one by one.
Audit category consistency for multi-location clients. Google doesn’t assume every location under the same brand shares the same primary concept. You shouldn’t either.
Treat location pages as entity evidence, not just landing pages for search traffic. That content now feeds the same system deciding how confidently Google can recommend the business in an AI answer.
Get ahead of Ask Maps and Gemini-driven queries now. Clients with only a clean listing will lose to competitors who’ve built out reviews, structured attributes, and web-level entity consistency that a conversational system can actually reason over.
Report proximity results with more nuance. Distance still matters, but the geographic search area itself flexes based on query type and density, so “why did a farther competitor outrank us” needs a different answer than “we’re too far away.”
Google Ads Wants Your In-Store Sales Data. Here's Why That's Good News.
Two new Performance Max and Data Manager tools aim to close the loop between clicks and walk-ins before the holidays.
What's the News:
Google is rolling out two new features for multi-location retailers, restaurants, and local service businesses ahead of the holiday season. The first, Local Customer Optimization, is a campaign-level toggle in Performance Max for store goals campaigns that prioritizes budget toward consumers who are actively in-market nearby across Google Maps, Waze, and local Search. The second, Store Sales in Data Manager, simplifies how businesses share offline transaction data with Google Ads by allowing direct CRM or Google Sheets connections — reducing the technical lift required to feed in-store revenue back into the platform for measurement and Smart Bidding optimization. Source: Search Engine Land
Why It Matters:
Both updates are aimed at closing the loop between digital spend and physical store revenue. Local Customer Optimization gives multi-location businesses a simpler lever to prioritize proximity-based intent without restructuring campaigns. Store Sales in Data Manager lowers the barrier to offline conversion imports, which have historically required developer resources or third-party integrations to maintain consistently. For clients with brick-and-mortar locations heading into Q4, both features are directly relevant.
The Store Sales in Data Manager update is the more important of the two. Offline conversion imports have always been the right way to train Smart Bidding toward customers who actually buy in-store rather than just walk in. But the technical friction of maintaining that pipeline has kept most advertisers from doing it consistently. Easier CRM and Sheets connections remove that excuse.
Marketers Should:
Check whether in-store transaction data is flowing into Google Ads at all. For any client with a physical retail presence, that’s the first question to answer before the holiday season.
Audit how stale that data is, if it exists. A pipeline that updates monthly won’t train Smart Bidding the way a near-real-time feed will.
Test Local Customer Optimization on store goals campaigns now. It’s rolling out already, and holiday in-market intent is only going to get more competitive.
Prioritize the CRM/Sheets connection setup before Q4 ramps up. Store Sales in Data Manager is expected within weeks. Retail marketers should get their pipeline ready so it’s live when demand peaks.
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