US consumer spending dashboard built on data from 50M+ cards now on Snowflake
app.snowflake.com
app.snowflake.com
Users likely not reimbursed for their data getting packaged and resold by merchants, payment networks, or credit card issuers
Reading up on programmatic advertising from a perspective of learning it vs resources that critique it is illuminating.
You’re describing how it works. Use cash and buy offline with your cellphone left at home (yes, all 3), or get your data sold for other people’s products.
I used to be more vocal about privacy but reviewing the innards of the tech make me think this dynamic will never change. Too much money and too well built.
What's the angle on this?
Gist of it is if your cellphone says you went wherever, there’re intuitions about what you bought. For example, stores have Bluetooth and Wi-Fi beacons on top of aisles - if your phone beacons but doesn’t connect, the store can pull a device ID (so to speak), and again correlate.
Similarly interesting is “retargeting” that tracks you’ve unsubscribed from emails and the retargets a similar ad.
The Dominik Kosorin books on adtech for a technical audience (Amazon) were illuminating and also broke my spirt on privacy. This stuff is everywhere and there’s an identity graph out there that has your life. No dodging it until regulations change or you go offline.
Edit - some other interesting angles. Third party cookie blocks in browsers harmed the industry. One of the main suggested recovery approaches is building graphs on authenticated logins. Every resource accessed post-login has a good chance of being that user. This is behind all the “sign in with Google/FB,” and known for a while as part of adtech. The primary issue with authn’d tracking is the friction of logging in, and the power it gives to closed networks that can track post-login easily like Google, FB, Apple.
What is the new, de-frictioned login flow pushed widely now as a way to go passwordless and solve the many security problems? All do-good messaging? Passkeys. What is also a de-fricituoned post-authentication user graph, if you use it to sign into everything? Passkeys. Adtech is a cancerous industry.
https://heyirys.com/ https://www.redmob.io/ https://echo-analytics.com/ https://www.factori.ai/ among others.
It would be indeed great if Snowflake allowed us to preview the data. That said, if you have a Snowflake account, you can mount it and automatically get a trial (you can run arbitrary queries against it).
The data is aggregated at a weekly level, by category, merchant, and demographics. It is not single individuals' data if that is what you are after.
- Different data sources - More accurate (though this is of course debateable, our plan is to publish benchmarks, accuracy, transparency, etc.) but this will always be debateable - Focus on data scientists & Snowflake users (rather than a SaaS platform)
Obviously, this is a very early product. The key is to join types of datasets together, while maintaining accuracy (see vision outlined here: https://magis.substack.com/p/datanomics)
Or Argus?
Or the various merchant exchanges (e.g. WMX/Walmart, 8451/Kroger)?
Genuinely curious.
That said, we obviously have competitors. We are trying to differentiate with our data accuracy, focus on data scientists, and focus on Snowflake approach.
Obviously, this is a first product: we hope to add to it. See the vision here: https://magis.substack.com/p/datanomics
So what’s your actual business name and marketing site for me to read more?
https://docs.cybersyn.com/our-data-products/consumer/consume...
For the record, cancer is an appropriate metaphor to describe the data broker industry
The nonsense “we care about privacy” wrappers go away and they start talking product-speak about the details. I don’t think privacy regulations change until adtech product leads get comfortable enough talking publicly about their deadpan views on what they can find out about users.
https://www.thecut.com/2015/02/vicemo-collects-all-your-sket...
This is the first that I’ve seen “in the wild” (i.e. outside of an interview with a start-up that yet had to find a lot of paying customers).
Is that common? Do you know of other companies doing that? Do they typically offer additional individual data (say: lead generation), inferred insights (say: likely spending by post-code based on local shops and disposable income), or something else (custom dashboard)?
Cybersyn is obviously a startup explicitly trying to do just this -- AFAIK, we're the first "Snowflake Native" startup but it is hard to tell, there are more than 2200 data products in Snowflake Marketplace already so I easily could be wrong.
The top of the paid listing is the usual suspects: weather, geographic (GeoIP and matching elements), and you, CyberSyn, but nothing really exotic yet.
What sort of exotic data would be useful to you, out of curiosity?
I’ve seen B2B sales desperately trying to sling their solutions to so many companies that are not at the right stage to value or understand it, so things like “companies with one level of engineering management” vs. “two” vs. “more.” “Companies disillusioned by terrible attempts at Agile,” or “… where the security team is a candle they forgot to light,” or “Companies with a violently dysfunctional Product leadership,” or “Companies with an inexperienced/checked-out CEO” or “…looking for a buyer” might be relevant — assuming you manage to rephrase those into less hurtful categories. Details gained from LinkedIn, etc. would be helpful to train our funnel model to say: “We seem more likely to sell our LLMaaS to companies with a certain number of technical employees or CTOs that like open-source.”
Same for customers: I receive so many coupons for things I clearly do not need or care for. But there are things that I would be considering (my Amazon basket is a good indicator). You’d need to include relevant ideas and exclude things I’ve likely already bought, though… Given how Amazon Prime is unable to not insistently recommend TV series that I’ve finished and will hide what I’m actively watching even when I search for it by name, that could be a hard problem to fix.
Matching that with the political thing: how to convince people to buy certain things. There’s been some controversy in trying to use the OCEAN model to get people to vote for certain people, but I think there’s room for an ethical way of telling marketers: that person would subscribe to HelloFresh because it’s cheaper than GrubHub, that person because they need structure in their life, that person because they want to eat healthy, that person because they like the idea of learning something, that person because they need to have something nice to serve their dates and that person, they already subscribe, stop being weird.
But yes, the long term vision is to make this all joinable (https://magis.substack.com/p/datanomics)
To add to that, 2k/mo is pretty much the "go-to" pricing for most B2B SaaS products.
We're ingesting from S3 or FTPs usually.
This type of product is generally meant for very large enterprises, so this is already an entry-level product at best.
We do make a lot of related economic data available for free though that is published by government sources. I think often what is publicly available and collected by the government is under-appreciated - the level of granularity of government inflation statistics by product category is outstanding for example.
There’s nothing inherently wrong with Snowflake. But like any database it has a time and place.
For OLTP it would be unambiguously bad (although maybe there's hope with Unistore, which I haven't tried.)
Many workloads are a mix and so it can become ambiguous whether it's a great fit / great value / whatever you're defining good/bad-ness by
The Marketplace, Streamlit, and Native Apps stand out as particularly cool/useful.
More than anything, just the completeness of the platform (ie. data quality tools, governance, etc.) is super helpful to not have to cobble together.
The AI features they announced today also seem game-changing.
Of course, full diclosure, they are my lead investor but I chose to work with them for these reasons.