Does Krissy have an existing separate dataset related to churn/attrition? – Yes, cancellation dashboard in PowerBI – is this the right version @Martial Fodouop?
We enumerated states of product/customer relationship.
As product or ops, the diffs between these sets are interesting (client needs fulfillment or needs support):
1. No relationship
2. Entitled
3. Fulfilled
4. Functioning
5. Cancelled (reason)
6. It's Complicated
Action: Doug to review and propose refinements to current SFDC product taxonomy
Output: separately from how SKUs shook out, here’s how product managers think and talk about products and hierarchy (this human-readable schema should be applied across user experiences (e.g. Salesforce vs. PowerBI use cases))
Is there a distinction between “sub-group” (SFDC) and “sub-category” (NetSuite)?
Action: gather current state of conversions and leads across company
Doug email going out 2/2 to collect this info
Action: Let's create an Analytics portal/table of contents thing (needs an owner) – we have overlapping data being consumed (APIs, databases, data lake, data warehouse, excel, google sheets, punched cards, rope core memory, braille) and presented in multiple output channels (SFDC, PowerBI, NinjaCat, probably others)
There should be some kind of ANALYTICS STOREFRONT thing where you can
Find the data thing you need or
Ask for it
Includes a list of links to reports about various topics (@aishwarya has a proto-version of this already to share)
Should include some kind of "dictionary" (I am 90% sure that "dictionary" is the wrong term but) that describes where the stuff comes from and how it's derived
This is a good idea
I think I volunteered to own it but I am glancing over at you @Brian Gildea
Are there "duplicated" datasets between lake & warehouse for product sales data? – Charlotte asked @Martial Fodouop to sort this out because there was some angst somewhere about it
Drew’s presentation is intended to be closer to “installed” base vs. “entitled” set from SFDC
Ideally Drew would like to be pulling data for Layered App Penetration from…? (Drew can you elaborate?)
Right now based on manual data recorded by Drew’s team
Drew and Martial to have coffee or something
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February 8
Review: Reina/Aishwarya to compare notes on MRR / Core Product KPIs dash
Aishwarya: Lance can use the Products KPI dashboard instead of Sales Dashboard created by Reina. Lance has a copy of report based on his requirement. Once that’s approved by him we can discontinue using Reina’s dashboard. Lance Copy of Core Product KPIs | Salesforce – Created by Aishwarya
Discussion: Ownership of the complete picture
Robert: The owner of the complete picture should be a product person not an engineering person. Given the scope of that complete picture, seems like it should be a product person on the analytics team.
Discussion: Agreeing about how to evaluate churn
Aishwarya: There are few cancels reports created by Reina which will eventually be replaced by the cancellation report we are creating (Martial Fodouop and Abbey Middleton from Internal Systems team).
Is there anything to be done here; should we try to make sure that products are enabled to make all of these distinctions?
As product or ops, the diffs between these sets are interesting (client needs fulfillment or needs support):
1. No relationship
2. Entitled
3. Fulfilled
4. Functioning
5. Cancelled (reason)
6. It's Complicated
Review: Doug Chase (Deactivated) to review and propose refinements to current SFDC product taxonomy
Output = separately from how SKUs shook out, here’s how product managers think and talk about products and hierarchy (this human readable schema should be applied across user experiences (e.g. Salesforce vs. PowerBI use cases))
Is there a distinction between “sub-group” (SFDC) and “sub-category” (NetSuite)?
Martial: These are not currently consistent. We have sub-group in NS as well. It is replacing sub-category. And that's where SF is pulling from. So new field "sub-group" in NS and SF are consistent (the same).
Discussion: Other inputs to CX assessment and attribution
Things like page speed, time on site, customer flows through pages
These sorts of metrics ultimately impact conversion rates. They are leading indicators of future conversion impacts.
If we roll out an inventory detail page redesign and conversions are unaffected or even go up currently we say "yay! that's a win!," but if we look at other metrics we might find that conversions on those specific pages went down and leads were redistributed.
Or we might see that the page speed time went up on average 0.1 seconds, or that bounce rate went up 2%, or that we are seeing more rage clicking.
Or we might see a redistribution of leads to other conversion types.
All of this while conversions remain stable. But if you roll out 2, 3, 5, 10 updates with similar impacts you have meaningfully negatively impacted conversions. Death by a thousand cuts.
It's easier to see the cumulative future negative impact by looking at these other metrics with each release.
Review results and define next steps. How do we get consistent across the whole company?
Action: (Needs owner) Let's create an Analytics portal/table of contents thing – we have overlapping data being consumed (APIs, databases, data lake, data warehouse, excel, google sheets, punched cards, rope core memory, braille) and presented in multiple output channels (SFDC, PowerBI, NinjaCat, probably others)
There should be some kind of ANALYTICS STOREFRONT concept where you can reliably:
Find the data, visualization, or widget you’re looking for
Ask for one to be created
Find a list of links to reports about various topics (@aishwarya has a proto-version of this already to share)
Learn where the data for a given visualization comes from, and how it's derived
Question: As someone sending data to the Lake, how do I monitor and assess the quality of my data there?
Review: Are there duplicated datasets between lake & warehouse for product sales data?
Charlotte had asked @Martial to sort this out because there was some angst somewhere about it
Drew: Charlotte and I had a good conversation this week about our current state. We understand that subsets of some data is copied to the lake, and that it is for different purposes/use cases than the BI that is coming from the enterprise team.
In other words, we're not sending data to the lake with intention to duplicate insights that can come directly out of SF or through BI coming from her team.
That said, we do want to document definitions for common types of metrics for two reasons:
1) the SF experts can have visibility and input into calculations made using SF data and
2) in a case where the same metric does exist in product and enterprise reporting, we can ensure we're using the same method to calculate them.
Aishwarya Ghumekar and Martial Fodouop, I'm looking forward to working with you two on documenting these definitions. I figured we could use some of our next Power BI meeting to start that discussion.
Drew’s sales data is intended to be closer to “installed” base vs. “entitled” set from SFDC
Ideally Drew would like to be pulling data for Layered App Penetration from…
We would ideally pull this from historical data stored in the lake. The main thing is to get away from manually recording it since you can't modify the filtering for past data and it requires human entry. That said, there are some challenges to pulling point in time for all the datasets... so I don't have any timeframes around tackling this.
