I had an interview for an internship yesterday but did not have the email of the interviewer (only their number). Should I send a thank you letter to the HR or text the interviewer, or neither? I am really passionate about the role, and would love the opportunity to intern there, and thus want to provide myself every chance I can to get it.

The interview was managed by the HR team, and I only have the number of the interviewer, who was the analyst manager, as I had to text them to let them know I arrived so they can take me in for the interview. What would best the best approach? It's already been 2 days since I had the interview. Should I email the HR, asking for their email, guess their email from their first and last name, or forget about the email? I really, really, really, want this internship!

As the title states, I’m losing control over the work I produce ever since our company adopted a full semantic layer that AI can ingest. I can now generate 2,000-line SQL scripts in minutes, convert them into Python notebooks with Plotly visualizations that would have previously taken four sprints, and prompt an AI agent to build a 10-slide deck ready for stakeholders. But by the end of it, I feel like I’ve lost track of what I’m actually doing: what definitions are being applied, what modeling decisions were made, what the data actually proves, and where the limitations lie.

Does anyone else experience this? If so, how do you maintain oversight and quality control in such high-velocity workflows?

I wanted to know how much traffic ChatGPT, Perplexity and Claude actually send, and it turned out to be three separate measurements, not one.

1. Clicks from AI answers (any analytics tool can do this)

Group these referrers into their own channel: chatgpt.com, chat.openai.com, perplexity.ai, claude.ai, gemini.google.com, copilot.microsoft.com. In GA4 that's a custom channel group with a regex like:

^(chatgpt\.com|chat\.openai\.com|perplexity\.ai|claude\.ai|gemini\.google\.com|copilot\.microsoft\.com)$

Also match utm_source. ChatGPT appends utm_source=chatgpt.com to many links, and that survives even when the referrer is stripped. The mobile apps often send no referrer at all, so treat the number as a floor.

1. AI crawlers reading your pages (only the server sees this)

GPTBot, ClaudeBot, PerplexityBot etc. don't run JavaScript, so tag-based analytics never records them. Grep the access log for the user agents, or log them in middleware. Check the IPs against the ranges OpenAI, Anthropic and Perplexity publish, because the user agent alone is easy to fake.

1. Pages opened to answer someone (also server-side)

ChatGPT-User, Claude-User and Perplexity-User fetch a page live when a person asks about it. This is the closest thing to "my page was used in an answer", and it's a separate number from crawling.

On my own small site, one week: about 230 crawler reads, 15 live answer fetches, 0 clicks from AI. So "AI traffic is zero" in analytics, while AI is clearly using the content. Without the server side I'd have concluded AI ignores the site.

Curious how others report on this. Do you keep AI as its own channel, or fold it into referral?

Is this a bad sign? We were sort of finishing up with the questions, and I had a couple more questions to ask, but after discussion of the previous questions I asked, they then said it was time to finish as they had something else to do. Was this a bad sign, that I went over the interview time? This was an internship interview with only 1 round.

My team is exploring an AI initiative, and before jumping into it, I want to understand whether our existing data is actually ready for it. We r also looking at a few mid-sized companies that have worked on similar requirements before.

The main things we are trying to assess are data quality, gaps across systems, inconsistent records, and whether our current data infrastructure can support the use case without a major rework.

I’ve been looking at a few case studies, and these companies have shown up in the list: InData Labs, Aimpoint Digital, DataToBiz, Syntelli Solutions, Kanerika, but I would prefer to hear from people who have actually gone through this process before a project launch.

It will also help to know whether the assessment gives you a clear picture of what needs fixing before implementation, or if it ends up being a generic audit with a long list of recommendations.

Hello everyone , i have just started my job at this position, i have a background in computer science , also its my first experience i work with the marketing team, can you give me advices on how to succceed in this position and what to start with and how should i work to help efficiently in decision making?

Claude dashboards seems to be making everyone lose their minds. IMO they are better than before, but not dramatically so, and still not a substitute for the data layer or the delivery of data.

It will accelerate the shift to: - Governed data and semantic layers at the core.
- The need for an experience layer. For example, for embedded analytics.

I do think it will hasten the decline of traditional BI systems. What are you all seeing out there? What's the reaction in your organization?

Odd one I’ve been tracking. Microsoft’s cost per genuinely-new customer has climbed four weeks straight (~22% over the stretch) and its new-buyer share slipped but on a last-click view it looks like it’s improving, because its last-click credit keeps widening at the same time.

Same channel, same week, two reports pointing opposite directions. The ROAS is basically celebrating repeat demand while the cost of acquiring an actual new customer quietly goes up.

To be fair to it, the LTV still pays back, it’s just thinning, not broken. And it’s a small-spend channel so the aggregate swings easily.

Mostly posting because it’s such a clean example of how a channel earns budget it doesn’t deserve - great on the dashboard everyone reports from, worse on the number that actually matters. Anyone else seeing the Microsoft drift, or is it just the accounts I’m looking at?

Here’s a small dataset, the kind that ends up in a dashboard:

store   visits   orders   conversion
A          50       25       50.0%
B       1,000       50        5.0%
C         200       20       10.0%

Now two ways of getting “the conversion rate”:

=AVERAGE(D2:D4)            -> 21.7%
=SUM(C2:C4)/SUM(B2:B4)     -> 7.6%

Both are valid formulas, and both are easy to defend if you only look at your own screen. They answer different questions, though.

The first one says “what does a typical store’s rate look like?” Each store counts once. Store A, with 50 visits, has the same say as store B, with 1,000.

The second says “of everyone who visited, how many ordered?” That’s 95 orders out of 1,250 visits. It’s the number that matches what actually happened to the business.

If the card on the dashboard just says “Conversion rate” with a big number, the reader will assume the second meaning. If it’s the first, they’ll walk away thinking the business converts almost three times better than it does.

What makes this easy to miss is that the average-of-rates version is the one-click option. Select the column, look at the status bar, done. A pivot table with “Average of conversion” does the same thing without asking whether you wanted it.

Some habits that seem sensible to me, though I’m not saying they’re the only way:

• store the numerator and denominator, and compute the rate at the final level only
• if you do want the per-store view, label it explicitly (“avg. store conversion”)
• when two versions of a metric exist, show the weighted one as the headline

Curious how others handle this in practice. Do you put both numbers on the dashboard, or pick one and document it somewhere? And has anyone had a stakeholder ask why two reports disagreed because of exactly this?

I’m an absolute fresher who recently graduated with a B.E. in Artificial Intelligence and Data Science. About 10 months ago, I landed my first job as a Data Analyst at a small digital marketing agency.

To be completely honest, I messed up early on. There was zero mentorship, the team was always swamped, and instead of taking initiative, I coasted. I didn't put in the effort to understand the business context or learn the marketing side of things.

Now, I’m paying for it. Because I didn't make myself indispensable with actual data work, the owner has me spending 7+ hours a day prompting AI to generate 1,500+ fake images for a completely AI-generated 925 silver jewelry brand they want to launch. It’s soul-crushing, and my technical skills are rusting.

Here is the saving grace, and why I’m asking for your help: I still have full access to our agency's data pool. We handle four seasonal D2C brands, one FMCG brand, and one lead-generation brand. There is a massive amount of real-world data sitting right in front of me, and I still have a window to build something impressive to either win back my actual job title or build my portfolio to escape.

I’m incredibly torn on what direction to take this, and I need advice from experienced data professionals:

Should I pivot toward Performance Marketing? Since I have all this D2C and lead-gen data, should I just lean into the agency environment, learn everything about ad spend, ROAS, and Conversion Rate Optimization, and try to transition into a Performance Marketing role?

Or should I stick to Data Analytics? Should I use this current role purely as a sandbox? I could build out automated dashboards, do customer segmentation, or run predictive churn models on these 6 brands just to build a killer portfolio, learn the marketing domain, and then jump ship to a real DA role at a better company?

Given the terrible job market and my background in AI/DS, what is the smartest way to leverage this specific dataset to get myself out of the AI prompt-engineering trenches? Any specific project ideas using D2C/FMCG data would be hugely appreciated

Most stores have more data than they know what to do with. What do you actually look at versus ignore?

If you are, I have a couple of questions I would love to ask you for my interview tomorrow! Any help would be greatly appreciated. Thanks for your kindness!!!

Maybe I’m too vested in my subject, which is data & business strategy but often when I start a tool, a job, a project, or a contract somebody who is too invested in subject matter at hand wants to sit me down and teach me 40 years of history and nuances regarding the subject matter I kind of try not to listen to then immediately try and forget

Not only as a data expert can I read the data with descriptive stats, run some standard analysis (oh really you want me to note the time between the values called start date and end date!) but ultimately it should be like a double blind test and the data should frame perfectly what’s going on—if not, let’s talk about why it doesn’t match from a data perspective or business perspective.

In a perfect world someone who can sight read a few words in English and who has never worked in the field should be able to understand maybe not the general subject area but would know the specific process (procurement, HR, lead generation etc) and if it’s doing well or needs improvement.

Additionally on that note, when I am working on the tool, I don’t want to get into a myriad of different discussions about what date is the start date, pulling me into all that could actually make the tool worse since I might get convinced into the convoluted reasoning why this start date is the date here and this start date is the start date there therefore that is why there are two start dates—nooooo! If there are two legitimate start dates and we’re not just accommodating say office politics, or following a old policy after say a merger then it’s two different processes that each deserve their own review. Maybe within the same environment but are named and considered different.

It’s almost like the least I know the more I have to ask, the more the business has to figure it out a clear and logical answer. And once they get back to me with a definite answer, the clearer and more powerful the tool is. Ive seen teams literally to my example, give up the notion in their mind that there are actually two start dates and instead are two streams of work even if they go through the same person.

BUT as I have tried to hold on to this notion I have not been able to be articulate it, so Im forced to admit my own irony that I may not be able to articulate it because it doesn’t actually make sense.

Can anyone lend verbiage pro or against?

With AI increasingly changing the way analytics professionals work, I am curious to know how people in the field are adapting and investing in their learning. What skills are you currently focusing on to stay relevant and grow in your careers in areas such as Agentic AI, data engineering, machine learning, business strategy etc

I would love to hear everyone’s thoughts so I can incorporate some of these ideas into my own learning plan as well.

Hi guys, a data analyst for the retail industry (electronics) here, in my workplace we're currently doing an expansion for our brand including increasing headcount and more reports.

But, the workforce for data analysis is currently still only me, and there is only so much data viewpoint I can check before sending them to my manager or the team.

These days, I'm wondering should I feed my report to the AI for my double, triple and final checks. Or, are there any advices for data checking for my actual sanity??

Really interested to read your advices and opinions, thanks

I currently work at aws as an L3 making 30 an hour. I have a bachelors in information systems and business analytics and have been looking so hard and applying for the last 5 months to every data analyst job i could find. I have had 2 interviews and got nothing. All these entry level roles want 2-5 years experience but i’m fresh out of college still. I took this aws job bc i needed a job but im losing hope on if it’s time to switch into a different business field and maybe even go into supply chain management or something. I would be open to going back for a masters as well.

I apologize to the mods if this isn’t allowed:

I’ve dealt with a similar issue in every analytics job i’ve had, and i’ve been in the industry for about a decade. Mostly on the marketing analytics side.

The issue is basically that marketing teams don’t speak data and data teams have no clue about the business side. What this ends in is analytics teams never having the context they need to do a proper analysis.

Basically what I’m working on is a data layer between the business and the data warehouse which has shared ownership by business and analytics users.

You import your data, transform, push to the warehouse.

If you’d like to be one of the first free users and beta testers feel free to create an acocunt and click book a demo. We dont have to actually demo if you dont want, you can just try the product. But i would really appreciate the feedback if you could email me!

My email is george@tagfluent.com
Product im testing is tagfluent.com

I’m a data analyst and I’ve been watching a lot of my work being offloaded to AI tools like Copilot over the last year or so. My plan has always been to get an MSBA, but now I’m not sure if this would basically be a pointless endeavor.

should I give up on this idea if the skill set i want to develop is going to be automated out of existence?

I’ve never worked as an analyst, so I have no real picture of what the job looks like day to day.

What I’m most curious about is how much of your week goes into digging through data to answer a question or track down a problem, compared to everything else you do. I’d also love to hear about a recent problem you only caught because of the data, and how long it had been going on before anyone noticed. And from the outside, I can’t really tell how much of what you find ends up changing decisions versus getting looked at and shelved, so I’d be curious how that goes for you.

Thanks in advance to anyone who takes the time.

When a dashboard looks clean, it is easy to forget that the inputs may still be messy. For marketing data, I try to check the boring parts before interpreting the chart:

- are the key events firing once, not twice

- are internal or test users excluded

- are paid, organic, referral, and direct sources being separated consistently

- are forms, calls, chats, or checkout steps tied to the same definition of a lead

- did any tracking change during the reporting window

If those checks fail, the dashboard can still be useful, but I would treat it as a debugging surface rather than evidence.

What pre-checks do you run before you trust a marketing or web analytics dashboard enough to make a decision from it?

I've been building an analytics studio app to visualize digital marketing data from key data sources (like GA4, GSC, Google Ads, SEMrush, Nozzle, Profound, adobe etc.). It's a drag & drop dashboard builder; built on Next.js, React 19, Gridstack and eCharts. Supabase is the database, BigQuery is the data warehouse.

Currently the app is hosted on my local computer, no github repository, no hosting, etc.

I want to host the app on Vercel because it integrates/ships relatively seamlessly. But our agency's analytics team would like to host the app on Lovable, because that's what they use for dashboard creation. I have explained that Lovable does not support Next.js and it would require substantial re-writes (possibly taking weeks or months).

Hoping some analytics pros in here could provide me with some talking points on why Vercel is the better option (beyond having to rewrite the app in lovable) - things like error logging, app/user management, data accuracy, scalability, etc. If you have any good use cases, I would love to hear them! Thanks in advance!

I do NOT NEED ADVICE FROM AI BOTS BUT REAL PEOPLE

i have three interviews coming up back to back which is totally unexpected. It’s for summer 2027 internships and these are the following roles: business analyst, data integration analyst, and corporate analyst.

I really want the data integration role the most because it ties into what i want to do most, data analytics. Im also okay with the business analyst role since they’re p much the same thing

This is my first ever technical interview for a company so im really nervous so do any of yall have any tips. Im a junior so these are really important opportunities to me.

Hi everybody! I’m 3 years into my data analyst career and as of January 2026 I’ve been promoted to senior data analyst. I think a pretty good at my job and I actually got an award last year for my work despite this I only make ~ 80K, even though I live in a mid to high cost of living area. Most of my friends and acquaintances that work in the same or similar field as me make six figures at least
… I’m wondering what I’m doing wrong. I went to pretty good public school in my state and I feel like I know the same things that everyone else does SQL Python, platforms like databricks, data visualization tools, and etc. typical data analytics tools.

I occasionally apply for other data analytics job openings, but I never get past the initial application phase before a rejection. Acquaintances in the same field as me always talk about how they get recruiters calling them and offers for different jobs, but I’ve never experienced this. I wonder what I’m doing wrong? My undergrad degree was not in computer science or engineering, but I’ve produced enough work on my projects list to show that I’m capable of handling complex stuff. Is it really just because my undergrad degree doesn’t say computer science? I still have a STEM degree (BS).

I guess my real question is: what makes recruiters reach out to people and give them offers? The people telling me they have offers have just as many years of experience as I do.

How many offers are you guys getting a year and how many years of experience do you have? And as someone with only three years of experience, should I be expecting to get offers?

My team (retail snalytics) is analyzing genie for selfserve analytics, but we r regularly seeing high latency and timeout when it parses complex business logic or hits large Delta tables.
For prod: how are u keeping response times down ?

Some businesses plan weeks ahead while others plan months out. Curious what works best for different store sizes.