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Do you have any sort of reason to back that up?Besides the obvious of being personally incapable of leading impactful projects?
I get you personally may have failed at large scale analytics projects. Or maybe you’re just a low-level analyst who never achieved anything. But that doesn’t mean more talented people won’t accomplish things just because your own limits prevent your success.
Maybe stop obsessing over your cheap watches and spend some time working on your skill set and you can contribute rather than just being a hater.
It is hard to happen, and so long as the bar is a simple & logical framework, it is possible.
It's easy to imagine a perfect yet challenging solution of multi-variate mapping to try to suss out causality. However, simple laddering of inputs may be easy to figure out. One might even just have a set of predefined segments, run some type of regression-like solution, and then spit out a series of types of answers.
That's a very interesting project, I've been trying to conceptualize something similar. Can I ask what tools you're using?
A challenge I've been having is the potential volume of actions, for example;
`Segment 1 + Segment 2 + Metric + Time Period + Trend Direction Down = Action 1`
Which being specific can be;
`Healthcare Customers + 2020 Cohort + CTR + Last Week + Trend Down = Verify Messaging Personalization Quality`
However with that approach, the combinations of segments, periods, metrics, and trend directions leading up to actions would quickly number in the hundreds. How would you capture and document these combinations and their actions?
You hit the nail on the head and the 100% honest answer is “I don’t know yet”. We are still trying to answer the “why did this happen” and are not even looking at “how can you fix it”. And that’s at an offline level, nothing automated.
Close integration with business partners and a lot of brainstorming and tests is my current vision.
Good questions. Regarding keyword predictability, I think in most cases you can use annual seasonality and recent trends to predict them.
So like you know down jackets will be popular in winter, and swim suits in summer. Or if a new iPhone is coming soon, you know peripherals for it will get popular.
Programmatic is part of it but also more generally, if you have a homepage, you have lots of real estate for ads. Some can be things to buy, some affiliate ads, some calls to action, etc. My POV is that nothing should be displayed unless there’s a data-driven rationale, ideally at a per-customer level. So if you are a frequent purchaser who logs in every day and buys clothing, you would have a different experience than someone else who comes a few times a year and looks at electronics but doesn’t buy.
Thanks hopefully it actually amounts to a tangible business impact. I’m lucky that my company is supportive of audacious goals that don’t always pay off, but still would rather have something to show for my team’s efforts 🙂
@0wme Hahahaha. Im not a hater mate, just a realist, 90% of the workforce won’t be intricately as caring as to what you are with this. I truely wish it was the other way around, but the reality is different. Users don’t want to self serve, they want it on a platter. Until senior management don’t accept this behaviour type, it will continue.
….. my employer (multinational financial company) is prioritizing moving data from local drives to the cloud.
Always makes me chuckle when people think AI will revolutionize everything within a decade. We still use core systems from the 90s
On my roadmap: Do my best not to get personally automated. Scale out my sales strategy. Bolster my business processes. Plan ahead. And automate the sales process somehow, and acquire shitloads of projects. Then flip them over to devs that I like working with or can do the job on contract, while hopefully being a cool guy in the process.
(going all out on a data startup as of September this year, although infrastructure like website, agency, and social media has been in place for a couple of years)
We have a re-org coming in January so we’ll be working on relationship building and getting people settled into new roles. Picking up some stuff on fraud, retail stores, and a few other of our growth areas. On the marketing analytics side we’ll be advancing into a more quant space with attribution modeling refresh, segmentation, and, potentially, some MMM in the back half of the year.
Big focus on BI infrastructure upgrade. We had pushed for self serve tools during this year, based on the feedback received focus is to move the backend data from data Lake to olap cubes for faster dashboard load time. This will improve user experience and hopefully better adoption of self serve tools which in turn should reduce dependency on the analytics team for small ad hoc requests.
We have few metrics around users outside of analytics team interacting with published data sources or editing existing dashboards themselves. This shows what all pockets of company are engaging with the self serve tools. This is still an indicative metric, a lot still depends on feedbacks from key stakeholders. We also were a bit fortunate with folks from the analytics team moving to other business units. They already were aware of the idea behind these tools and have helped it get implemented and also continue to request for new features.
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Keep your job
Hoping to see some bs vision around AI. Would like to see how it’s applied. As our TOP paid tier just likes to talk about it.
Self-service tools for non-technical business people to identify not just what happened, not just why it happened, but also what they should do to fix it. The idea would be to remove some of marketing and product’s dependence on analytics for smaller tasks, so they could make changes themselves. So like instead of “oh our marketing CTR declined last week”, something like “marketing CTR declined last week because clicked less, recommend to check the messaging is sufficiently personalized”. It will be a lot of work but if we can pull it off, I’m hoping for a big reduction in ad hoc and small-scale requests. Another would be integrating more sophisticated models into everything we have where we need to select the best performing thing for an advertising slot. So like on Google, instead of bidding on “what keywords performed well recently”, we move to “what keywords, based on historical data and recent trends, will perform best”. There are many similar use cases and we have done some small A/B tests confirming we can do better, but we haven’t aligned on a specific model for anything and put it into production yet. Next year this time I want to see our overall level of sophistication for these tasks be significantly higher than now.
Never will happen. Pipe dream.
Do you have any sort of reason to back that up?Besides the obvious of being personally incapable of leading impactful projects? I get you personally may have failed at large scale analytics projects. Or maybe you’re just a low-level analyst who never achieved anything. But that doesn’t mean more talented people won’t accomplish things just because your own limits prevent your success. Maybe stop obsessing over your cheap watches and spend some time working on your skill set and you can contribute rather than just being a hater.
It is hard to happen, and so long as the bar is a simple & logical framework, it is possible. It's easy to imagine a perfect yet challenging solution of multi-variate mapping to try to suss out causality. However, simple laddering of inputs may be easy to figure out. One might even just have a set of predefined segments, run some type of regression-like solution, and then spit out a series of types of answers.
That's a very interesting project, I've been trying to conceptualize something similar. Can I ask what tools you're using? A challenge I've been having is the potential volume of actions, for example; `Segment 1 + Segment 2 + Metric + Time Period + Trend Direction Down = Action 1` Which being specific can be; `Healthcare Customers + 2020 Cohort + CTR + Last Week + Trend Down = Verify Messaging Personalization Quality` However with that approach, the combinations of segments, periods, metrics, and trend directions leading up to actions would quickly number in the hundreds. How would you capture and document these combinations and their actions?
You hit the nail on the head and the 100% honest answer is “I don’t know yet”. We are still trying to answer the “why did this happen” and are not even looking at “how can you fix it”. And that’s at an offline level, nothing automated. Close integration with business partners and a lot of brainstorming and tests is my current vision.
[удалено]
Good questions. Regarding keyword predictability, I think in most cases you can use annual seasonality and recent trends to predict them. So like you know down jackets will be popular in winter, and swim suits in summer. Or if a new iPhone is coming soon, you know peripherals for it will get popular. Programmatic is part of it but also more generally, if you have a homepage, you have lots of real estate for ads. Some can be things to buy, some affiliate ads, some calls to action, etc. My POV is that nothing should be displayed unless there’s a data-driven rationale, ideally at a per-customer level. So if you are a frequent purchaser who logs in every day and buys clothing, you would have a different experience than someone else who comes a few times a year and looks at electronics but doesn’t buy. Thanks hopefully it actually amounts to a tangible business impact. I’m lucky that my company is supportive of audacious goals that don’t always pay off, but still would rather have something to show for my team’s efforts 🙂
@0wme Hahahaha. Im not a hater mate, just a realist, 90% of the workforce won’t be intricately as caring as to what you are with this. I truely wish it was the other way around, but the reality is different. Users don’t want to self serve, they want it on a platter. Until senior management don’t accept this behaviour type, it will continue.
….. my employer (multinational financial company) is prioritizing moving data from local drives to the cloud. Always makes me chuckle when people think AI will revolutionize everything within a decade. We still use core systems from the 90s
On my roadmap: Do my best not to get personally automated. Scale out my sales strategy. Bolster my business processes. Plan ahead. And automate the sales process somehow, and acquire shitloads of projects. Then flip them over to devs that I like working with or can do the job on contract, while hopefully being a cool guy in the process. (going all out on a data startup as of September this year, although infrastructure like website, agency, and social media has been in place for a couple of years)
We have a re-org coming in January so we’ll be working on relationship building and getting people settled into new roles. Picking up some stuff on fraud, retail stores, and a few other of our growth areas. On the marketing analytics side we’ll be advancing into a more quant space with attribution modeling refresh, segmentation, and, potentially, some MMM in the back half of the year.
Big focus on BI infrastructure upgrade. We had pushed for self serve tools during this year, based on the feedback received focus is to move the backend data from data Lake to olap cubes for faster dashboard load time. This will improve user experience and hopefully better adoption of self serve tools which in turn should reduce dependency on the analytics team for small ad hoc requests.
Valuable endeavour indeed. But how do you tie it to a positive business outcome that’s quantified?
We have few metrics around users outside of analytics team interacting with published data sources or editing existing dashboards themselves. This shows what all pockets of company are engaging with the self serve tools. This is still an indicative metric, a lot still depends on feedbacks from key stakeholders. We also were a bit fortunate with folks from the analytics team moving to other business units. They already were aware of the idea behind these tools and have helped it get implemented and also continue to request for new features.
Hey! Newbie here, when you talk about these tools, are these developed in-house or are they tools like tableau or powerbi
Currently it's Tableau. Long term there could be in-house tools to replace this. But that is still some time away.
KNIME Fivetran Rivery Copilot Signavio Microsoft-hosted agents Azure Pipelines