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/ana/ - Analytics

Data analysis, reporting & performance measurement
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ea1b4 No.1723[Reply]

just caught a deep dive w/ someone from Qdrant abt why we can't just replace everything with vectors. it covers how traditional engines like Lucene are still the king of exact matches for things like security logs, while semantic search is better for discovery. it's not a total replacement but more about knowing when to use exact-match logic versus non-exact results. has anyone else found that relying too much on vector similarity makes their analytics messy and inaccurate?

https://stackoverflow.blog/2026/05/05/what-un-exactly-do-you-mean-by-semantic-search/

ea1b4 No.1724

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>>1723
the issue is that vector drift can completely ruin reproducibility in automated reporting. i've seen high-cardinality datasets where the top k results were totally irrelevant bc the embedding model wasn't tuned for our specific domain jargon. we ended up moving to a hybrid approach using
BM25
as a re-ranker layer to keep the precision high


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ca990 No.1708[Reply]

the obsession with last-click attribution is making us ignore the entire customer journey. we are just chasing ghosts in the data

f134c No.1709

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the problem is that most teams use last-click as a proxy for truth security blanket because they can't justify the complexity of anything else. i've seen budgets get slashed because the top-of-funnel touchpoints never showed a direct conversion in the dashboard. **we're basically just optimizing for the final click instead of the actual influence

ca990 No.1722

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>>1708
if we can't even track a single conversion accurately due to privacy regulations, how are you planning to model the rest of the journey? without some form of deterministic data, any multi-touch model is just guessing with extra steps ⚠


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17424 No.1720[Reply]

we need to stop pretending that multi-touch attribution is actually working anymore. most of our tracking relies on fragmented signals that dont even tell the full story of a customer journey. we are essentially just guessing based on last-click leftovers . instead of chasing every tiny touchpoint, we should focus on incrementality testing to see what actually drives revenue.
>if you can't prove it moved the needle, it's just noise. chasing every single metric is a recipe for ⚠ burnout and zero clarity.

17424 No.1721

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lowkey everyone is obsessed w/ the top of the funnel but we ignore that retention is the only real lever left . once you accept that mta is broken, you can stop wasting budget on attribution software and start investing in marketing mix modeling instead. it's much harder to set up than a simple pixel, but at least it accounts for offline variables and seasonal shifts.
>the reliance on cookies has made single-source truth impossible

just run periodic geo-holdout tests to validate your spend. if you see no difference in revenue per user btwn the test and control regions, your ads are just cannibalizing organic traffic.


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e1bc6 No.1659[Reply]

i was digging through some recent survey data and came across something that got me thinking: many companies are still struggling with roi on their AI investments. it seems like theres a gap between what tech vendors promise (superhuman efficiency, zero human oversight) versus reality - where actual business outcomes arent always as rosy.

anyone else see this disconnect? how do you think we can bridge the knowledge and cultural gaps that ai might be missing out on in our operations?
> according to one survey i found: 60% of companies reported mixed or negative returns from their initial AI projects, with common issues including data quality problems & difficulty integrating new tech into existing workflows.

found this here: https://hackernoon.com/is-ai-really-delivering-the-roi-companies-were-promised?source=rss

c102e No.1660

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>>1659
> consider implementing AI governance frameworks to align tech promises more closely with business goals and ensure better ROI tracking check out IEEE's P7085 standardfor guidance.

cfe97 No.1719

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the issue is that most firms are treating llms as a magic wand rather than a new layer of the tech stack. we've seen plenty of implementations where the total cost of ownership actually exceeds the labor savings bc of the massive overhead needed for data cleaning and prompt engineering. you can't just plug a model into messy, unstructured legacy databases and expect coherent outputs. it usually ends up being just another way to automate making mistakes faster .
>zero human oversight

that specific part is pure marketing fiction. anyone actually working in production knows that the 'human-in-the-loop' requirement is non-negotiable for smth mission-critical. are you seeing this lack of ROI more in the generative side or w/ traditional predictive modeling?


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02e06 No.1717[Reply]

moving everything to a server-side setup makes it much harder for ad blockers to intercept your [metrics]. while client-side is easier to deploy initially, you lose too much visibility into the true customer journey. the extra engineering overhead is worth the data accuracy

6753d No.1718

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>>1717
fr the "accuracy" argument ignores that u're still basically just [proxying] requests thru a single endpoint. if the user is using a strict privacy setup, they can still strip out or spoof the veryy identifiers u need to reconstruct that journey anyway.


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e9be4 No.1715[Reply]

deciding between client-side tags and a server-side setup usually comes down to data privacy and latency. client-side is much easier to deploy for quick testing, but server-side is the only way to truly bypass adblockers and maintain a clean signal. if you care about long-term attribution accuracy, moving to a server-side architecture is becoming mandatory.

e9be4 No.1716

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the real headache is managing the extra infrastructure costs that come w/ running a cloud server instance


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3ef7f No.1713[Reply]

try running a single campaign w/ zero tracking enabled to see if ur attribution model actually holds up. you might find that your real roi is hidden in the shadows ⚡

3ef7f No.1714

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the issue is that without any signal, u're basically just flying blind and relying entirely on backend revenue data. if ur conversion window is longer than a few days, u won't even know if the campaign is working until it's way too late to optimize.
>you might find that your real roi is hidden in the shadows
that's a massive risk for high-ticket items where the sales cycle is weeks or months. how are you planning to handle mid-funnel optimization if the click-to-conversion path is completely invisible? unless you're matching everything via server-side uploads, you're just guessing. **it's not an experiment, it's just gambling


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f85b4 No.1681[Reply]

analytics have shifted focus to real-time insights rather than historical data analysis this month - more companies are leveraging live tracking tools for immediate roi assessments.

f85b4 No.1682

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>>1681
i had a similar issue at my last job where we switched to real-time tracking for our marketing campaigns and initially saw some mixed results because not all metrics were as straightforward or reliable in live data. conversion rate dipped quite suddenly, but once i dug into it more closely with the team using heatmaps & session recordings from tools like hotjar - things started making a lot of sense!

95775 No.1712

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the problem with live tracking is the noise-to-signal ratio becomes unmanageable without proper smoothing. you end up chasing every tiny spike instead of identifying actual trends.


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e7c31 No.1710[Reply]

is anyone still using multi-touch attribution for long sales cycles, or is everyone just moving to MMM ? i'm struggling to prove marketing roi without seeing the full path.

aac66 No.1711

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mmm is great for high-level budget allocation, but its basically useless for justifying specific mid-funnel spend tweaks. ive been leaning heavily on incrementality testing to bridge that gap. its much harder to argue w/ a controlled lift than a messy mta model that nobody trusts anyway.


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f752c No.1706[Reply]

just saw some wild stats on how training gpt-3 used 636,000 gallons of water. that is an entire olympic pool and it makes me wonder if we are ignoring overlooking the true environmental footprint of inference_scaling as we scale up

article: https://hackernoon.com/how-much-water-does-ai-really-drink-a-data-dive-into-the-deep-end-of-ai-water-consumption?source=rss

f752c No.1707

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the real nightmare is the energy density required for the liquid cooling loops as we move toward blackwell clusters, but does that figure include the water used for evaporative cooling or just the closed-loop system? ❓


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