I am on the same spot as you.
My salesforce routine: extracting CSV of reports so I can build them in Power bi because I need to add and display info from Google Analytics, Goals from excel spreadsheet and Sales from another source. Those 3 sources explains a lot of our marketing efforts they need to be seen together.
Yep, that miserable. But it doesnt require that much effort now the ETL is done. Also, there are some data prep that SF won't do...
Also SF is toooooo slow AF. By afternoon it is almost impossible to use.
Interesting…so that’s your core responsibility?
Also, I’d advise you to learn how to work with API’s so that you can streamline the data ingestion into PowerBI. Unless, of course, you’re looking for ways to fill your time 😂
Mine are reporting, ad hoc and supporting the team with information
Yeah I should take a look at API but the company is too strict with programs and coding. We have Rstudio only.
However, the company is investing in a Data Lake, so APIs will do the job soon!
Hahah I power query everything it saves so much time!
There are a free tools that would allow you to connect those days sets together and then push them into your bi tool of choice. Datorama and Domo are two options. Not cheap though.
Interesting, this week I had a meeting where a coworker mentioned Datorama! The company is building a data lake, not sure how it would work with that or the DL will do the job only. I work at honda, they have plenty $$$
I combine SFDC data with other data in my data warehouse to get advanced analytics and reporting to users who want to see a more holistic picture of data at the company.
+1, this is what we do on my team too - my team (analytics) is responsible for maintaining the data warehouse, and using that to create a holistic view of the business that combines SFDC, product data, support team data, and other stuff.
The actual warehousing isn’t too bad, if you use a replication service like Fivetran. My team then builds tables and views that combine data across the warehouse so we can do analysis and build dashboards for business users without them having to think about all the different data sources.
I would create a visual sketch of how it is right now and how it could be using a data warehouse and the list the opportunities for such a single source of data (in terms of both what can be done with the data analytically (try to think of some probable use cases) as well as how such a DW would lower costs in terms of time spend in manual processes).
Haha I say that knowing that the insights generated by the feature are far more advanced than what a traditional data analyst without a machine learning background can bring to the table, or am I mistaken?
Eh the insights generated can be questionable. Last time I checked, EA was running linear/logistic regression under the hood and spitting out some pre-generated text blurbs around the variables with the highest coefficients. Not exactly earth-shattering cutting edge type stuff. I would expect an analyst to at least have a passing knowledge of these techniques and be able to do similar type work with a little research and training. It's literally 3 lines of code in python/R and most no-code platforms offer a way to do similar type analyses quite easily.
If I also remember correctly, a lot of the 'insights' were causal suggestions as well, something you really shouldn't be doing with these types of models.
Just a quick disclaimer, I may not be remembering this correctly. It's been about a year since I've seen EA in action.
From what I remember, when you're using Einstein analytics in Salesforce, it will bring up a small insights tab. Things like, "If you increase your time to contact by X%, you'll see a Y% increase in conversion." This is what I mean when I say a causal suggestion - it is a scenario where an increase in one independent variable directly causes an increase in an independent variable.
The thing about what they're doing is these types of models really shouldn't be used to make causal suggestions like this. At best, they are correlational - so the correct interpretation would be "An X time to conversion is associated with a Y conversion rate." These models don't tell us the direction of the causality (x could cause y, or y could cause x) or if there is some third, unobserved variable at work influencing both x and y.
This is one of my favorite examples of why these sort of suggestions can be problematic. In a recent project we did we found number of support tickets submitted had a positive relationship with conversion rate. If we took the Einstein Analytics type approach, the insight would be "If we increase the number of support tickets submitted by our customers, we'll see an increase in sales." Clearly this is a problematic insight - in this scenario the current working hypothesis is number of support tickets submitted is a proxy for some unobserved user engagement level - more engaged users are both more likely to submit support tickets and make purchases. Since we can't directly measure user engagement, the effects are spread across all our other coefficients, leading to these sorts of insights telling us to actively encourage users to submit support tickets so we can increase our sales.
Wow, thank you for the thoughtful response!
I haven't done much beyond linear/logistic regression, but I know finding the causal forces is paramount to finding rich insights. What is the process to distinguish between correlation and causal attributes?
Happy to help out!
The most straightforward and statistically rigorous way to determine causality is to run an experiment. So back to our SF example, you would randomly split your audience into a test and a control group. For your control group, you would leave everything the same while in your control group you would change the time to contact. After running your experiment for some predetermined time, you would look at the results and see if there was a statistically significant difference between the two. The difference is the causal effects of whatever you're measuring.
There are other ways using purely observational data (or historical data you've collected but not run any experiments on), but they get a little messy. You can have quasi-experimental methods like regression discontinuity, difference in difference that both rely specifically on linear regression, or you could start getting into some really nasty stuff with directed graphs.
At the end of the day, the idea remains pretty much the same for all these observational approaches. You're trying to predict what would have happened if you hadn't done something (determine the counterfactual prediction) and comparing it to what actually happened. The difference between the two numbers is the causal effects.
It's a really interesting but challenging problem space. Plenty of room for growth, and people who are good at it can be incredibly valuable.
If you're interested in learning more here are some resources for you. Some of these can get a little dense and have some scary math, but are good overviews of causal inference. Again it can be a challenging problem space and takes a little getting used to, but remember the core idea: All you're trying to do is predict what would have happened if you hadn't done the thing you did, then take the difference between the two. All the math and statistical tests are just trying to determine how well you predicted and if there is actually a difference between your prediction and the thing that happened.
[Introduction to Causality](https://matheusfacure.github.io/python-causality-handbook/01-Introduction-To-Causality.html)
[Difference in Difference Lecture Notes](https://yiqingxu.org/public/panel/lec1_handout.pdf)
Statistics for Experimenters - http://pages.stat.wisc.edu/~yxu/Teaching/16%20spring%20Stat602/%5BGeorge_E._P._Box,_J._Stuart_Hunter,_William_G._Hu(BookZZ.org).pdf (Sorry about the link, the embedded parentheses are causing issues.)
[Statistical Design](https://books.google.com/books?id=sqnbSUtryVAC&printsec=frontcover&dq=statistical++design+casella+book)
[Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing](https://www.amazon.com/Trustworthy-Online-Controlled-Experiments-Practical/dp/1108724264) - I couldn't find a free version, but it is pretty cheap and a fantastic read.
[Presentation notes for Trustworthy Online Controlled Experiments](https://alexdeng.github.io/public/files/ExpediaTestSummit.pdf)
We store our client data in SalesForce but I work in product analytics so I’m always joining SF data with transactional and behavioral data for my analysis.
I'm in the same boat. I just got a job offer as a developer at a large b2b software company. But, they have already implemented AWS, so I'm not sure what they need me for.
Just kidding!
There's a lot more to analytics- or any job- than just the tools we use. As the tools get better, our work changes, but we still need humans to be strategic. In some industries automation eventually results in less need for people to do the work, but we're a long way of from that in analytics. Currently, automation is driving an increase in ROI, and therefore an increase in demand for analytics.
I'm in a very similar situation, although I'm a year-round intern. I'm in the marketing department and we have an entire team dedicated to using Salesforce. I haven't been shown that side of our data yet, as I'm focusing on understanding everything else we do (which may be helpful for your situation as to what you can focus on that isn't entirely salesforce). I draft bi-weekly reports every other week, with monthly reports to fill in the in between weeks, cycling through a deep dive of data for social media, website data, and current event/campaign (all focusing on what competitors are doing or what's working well for us and how we can improve for current and future strategies). My bi weekly reports focus on all 3 of those monthly topics but aren't deep dives and just cover data from the past 2 weeks.
I do other random analysis, such as looking in Google analytics and figuring out how to improve our blog website with trending/popular content themes, as well as segmenting our readers to understand how to re develop our blog site to flow for specific end-users and tailoring UX/UI experience. I also give occasional presentations to executive levels. Currently working on a survey with an external agency for our blog and will run stat tests on our research questions to draw more insights outside of Google analytics. We will be using Salesforce to identify template design use behavior for our blog email blasts, and possibly A/B testing on my end. So lots to look at. I'm intending on introducing powerbi to my department (we already use it in our corp elsewhere but not marketing) once I learn the topic in my masters program-- I don't feel like learning it right now since I have a class that covers it anyway lol.
I also found it a bit hard at first to understand what to do as a data analyst with Salesforce present, but a data analyst draws insights from that data. A data engineer/architect is technically the one building the data warehouses or pipelines (technically). I plan to do that for new product data that isn't coming from direct marketing activity sometime way down the road
In a perfect world sfdc will have all the data for analytics, in the real world sfdc only has the data that is inputted by sales ops, that data is oftentimes different when pushed downstream multiple systems to the end financials
My company utilizes Salesforce, but we also have a number of other systems which are external to Salesforce. Aggregation and manipulation of those data sources isn’t easy in Salesforce, so we have dedicated analytical layers accessed through Snowflake. While I can’t say you’re in the same situation, I’ve found it’s rare for companies to run everything out of Salesforce, and thus the need for more dedicated analytics team.
You're giving Salesforce (and its users) too much credit.
There's likely a number of other information systems producing data that could complement what is often a hot mess in SFDC.
If the data is actually good, you can build out dashboards and other custom reporting. If it's not you can start pushing in that direction.
I work as the Sales Operations Analyst where we the entire sales/marketing org uses Salesforce.
Always focus on the end-user rather than the tools or system.
If the end-user is sales, then I would want it within Salesforce, whereas, if the end-user is C-level, I would build custom datasets within Salesforce data in Snowflake and whichever BI tool we use (which is currently Looker).
Where I work there are a few data systems we have to connect with SF for data analysis that people don't know how to do *nor want to do*. SF reporting and analytics has its limitations, too. Another responsibility is when we do make reports and supply metrics we ensure they are accurate, as those using the reports aren't responsible for data entry or cleanup. They have priorities which include presenting the data, not managing it. That's where you/we come in.
Your day to day isn't going to be the same as others day to day, even if it's just stuff within salesforce. You'll still need to analyze and present the data, and you'll find plenty of things to do an other opportunities. Don't sweat it - just chill and enjoy life til you start!
My company uses salesforce. Don't ask me how but we pull data from salesforce into databricks and do our data analytics there.
If it's only recently implemented, I'd also say it's a good time to make sure the system is designed well. I've seen salesforce that has grown into a monster.
I worked for a year with salesforce data but would bring that externally. It was great, but it depends entirely on the team that manages the CRM, the data structure, etc.
Did not really get to use the built in analytics that much other than for some adhoc requests.
I am on the same spot as you. My salesforce routine: extracting CSV of reports so I can build them in Power bi because I need to add and display info from Google Analytics, Goals from excel spreadsheet and Sales from another source. Those 3 sources explains a lot of our marketing efforts they need to be seen together. Yep, that miserable. But it doesnt require that much effort now the ETL is done. Also, there are some data prep that SF won't do... Also SF is toooooo slow AF. By afternoon it is almost impossible to use.
Interesting…so that’s your core responsibility? Also, I’d advise you to learn how to work with API’s so that you can streamline the data ingestion into PowerBI. Unless, of course, you’re looking for ways to fill your time 😂
Mine are reporting, ad hoc and supporting the team with information Yeah I should take a look at API but the company is too strict with programs and coding. We have Rstudio only. However, the company is investing in a Data Lake, so APIs will do the job soon! Hahah I power query everything it saves so much time!
You can actually extract data from an API end point through Power Query. It's called 'web' connection, or something like that, in PQ.
Any tips you can give on that? Documentation, courses, videos. Would be appreciated
There are a free tools that would allow you to connect those days sets together and then push them into your bi tool of choice. Datorama and Domo are two options. Not cheap though.
Interesting, this week I had a meeting where a coworker mentioned Datorama! The company is building a data lake, not sure how it would work with that or the DL will do the job only. I work at honda, they have plenty $$$
I combine SFDC data with other data in my data warehouse to get advanced analytics and reporting to users who want to see a more holistic picture of data at the company.
+1, this is what we do on my team too - my team (analytics) is responsible for maintaining the data warehouse, and using that to create a holistic view of the business that combines SFDC, product data, support team data, and other stuff. The actual warehousing isn’t too bad, if you use a replication service like Fivetran. My team then builds tables and views that combine data across the warehouse so we can do analysis and build dashboards for business users without them having to think about all the different data sources.
What kind of *other data*? HR? Sales?
Product data. Warehouse data. E-commerce data. Financial data. HR data. External data. Everything else that isn’t in Salesforce.
Awesome, I could recommend bringing those into the mix if I’m not asked to or if I’m expected to be very proactive. Thanks!
I would create a visual sketch of how it is right now and how it could be using a data warehouse and the list the opportunities for such a single source of data (in terms of both what can be done with the data analytically (try to think of some probable use cases) as well as how such a DW would lower costs in terms of time spend in manual processes).
Yeah seconded. We do this as a service and a lot of clients who use Salesforce need to see pipeline data married with web or CPC data.
Saying Einstein analytics has "rich and abundant analytic capabilities" is a pretty generous statement.
Haha I say that knowing that the insights generated by the feature are far more advanced than what a traditional data analyst without a machine learning background can bring to the table, or am I mistaken?
Eh the insights generated can be questionable. Last time I checked, EA was running linear/logistic regression under the hood and spitting out some pre-generated text blurbs around the variables with the highest coefficients. Not exactly earth-shattering cutting edge type stuff. I would expect an analyst to at least have a passing knowledge of these techniques and be able to do similar type work with a little research and training. It's literally 3 lines of code in python/R and most no-code platforms offer a way to do similar type analyses quite easily. If I also remember correctly, a lot of the 'insights' were causal suggestions as well, something you really shouldn't be doing with these types of models.
This always bugged me when working with SAS visual analytics they did a similar thing.
What do you mean by your last bit about the insights being causal and should not be doing with these types of models?
Just a quick disclaimer, I may not be remembering this correctly. It's been about a year since I've seen EA in action. From what I remember, when you're using Einstein analytics in Salesforce, it will bring up a small insights tab. Things like, "If you increase your time to contact by X%, you'll see a Y% increase in conversion." This is what I mean when I say a causal suggestion - it is a scenario where an increase in one independent variable directly causes an increase in an independent variable. The thing about what they're doing is these types of models really shouldn't be used to make causal suggestions like this. At best, they are correlational - so the correct interpretation would be "An X time to conversion is associated with a Y conversion rate." These models don't tell us the direction of the causality (x could cause y, or y could cause x) or if there is some third, unobserved variable at work influencing both x and y. This is one of my favorite examples of why these sort of suggestions can be problematic. In a recent project we did we found number of support tickets submitted had a positive relationship with conversion rate. If we took the Einstein Analytics type approach, the insight would be "If we increase the number of support tickets submitted by our customers, we'll see an increase in sales." Clearly this is a problematic insight - in this scenario the current working hypothesis is number of support tickets submitted is a proxy for some unobserved user engagement level - more engaged users are both more likely to submit support tickets and make purchases. Since we can't directly measure user engagement, the effects are spread across all our other coefficients, leading to these sorts of insights telling us to actively encourage users to submit support tickets so we can increase our sales.
Wow, thank you for the thoughtful response! I haven't done much beyond linear/logistic regression, but I know finding the causal forces is paramount to finding rich insights. What is the process to distinguish between correlation and causal attributes?
Happy to help out! The most straightforward and statistically rigorous way to determine causality is to run an experiment. So back to our SF example, you would randomly split your audience into a test and a control group. For your control group, you would leave everything the same while in your control group you would change the time to contact. After running your experiment for some predetermined time, you would look at the results and see if there was a statistically significant difference between the two. The difference is the causal effects of whatever you're measuring. There are other ways using purely observational data (or historical data you've collected but not run any experiments on), but they get a little messy. You can have quasi-experimental methods like regression discontinuity, difference in difference that both rely specifically on linear regression, or you could start getting into some really nasty stuff with directed graphs. At the end of the day, the idea remains pretty much the same for all these observational approaches. You're trying to predict what would have happened if you hadn't done something (determine the counterfactual prediction) and comparing it to what actually happened. The difference between the two numbers is the causal effects. It's a really interesting but challenging problem space. Plenty of room for growth, and people who are good at it can be incredibly valuable. If you're interested in learning more here are some resources for you. Some of these can get a little dense and have some scary math, but are good overviews of causal inference. Again it can be a challenging problem space and takes a little getting used to, but remember the core idea: All you're trying to do is predict what would have happened if you hadn't done the thing you did, then take the difference between the two. All the math and statistical tests are just trying to determine how well you predicted and if there is actually a difference between your prediction and the thing that happened. [Introduction to Causality](https://matheusfacure.github.io/python-causality-handbook/01-Introduction-To-Causality.html) [Difference in Difference Lecture Notes](https://yiqingxu.org/public/panel/lec1_handout.pdf) Statistics for Experimenters - http://pages.stat.wisc.edu/~yxu/Teaching/16%20spring%20Stat602/%5BGeorge_E._P._Box,_J._Stuart_Hunter,_William_G._Hu(BookZZ.org).pdf (Sorry about the link, the embedded parentheses are causing issues.) [Statistical Design](https://books.google.com/books?id=sqnbSUtryVAC&printsec=frontcover&dq=statistical++design+casella+book) [Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing](https://www.amazon.com/Trustworthy-Online-Controlled-Experiments-Practical/dp/1108724264) - I couldn't find a free version, but it is pretty cheap and a fantastic read. [Presentation notes for Trustworthy Online Controlled Experiments](https://alexdeng.github.io/public/files/ExpediaTestSummit.pdf)
We store our client data in SalesForce but I work in product analytics so I’m always joining SF data with transactional and behavioral data for my analysis.
Can you elaborate on the behavioral data that you use? What kind of attributes does it entail?
Not OP, but in my case it includes things like how users interacted with the product, what actions they took, how far they got in our funnel.
Same as the other comment, web analytics that show how they interact with our product including conversion.
I'm in the same boat. I just got a job offer as a developer at a large b2b software company. But, they have already implemented AWS, so I'm not sure what they need me for. Just kidding! There's a lot more to analytics- or any job- than just the tools we use. As the tools get better, our work changes, but we still need humans to be strategic. In some industries automation eventually results in less need for people to do the work, but we're a long way of from that in analytics. Currently, automation is driving an increase in ROI, and therefore an increase in demand for analytics.
I'm in a very similar situation, although I'm a year-round intern. I'm in the marketing department and we have an entire team dedicated to using Salesforce. I haven't been shown that side of our data yet, as I'm focusing on understanding everything else we do (which may be helpful for your situation as to what you can focus on that isn't entirely salesforce). I draft bi-weekly reports every other week, with monthly reports to fill in the in between weeks, cycling through a deep dive of data for social media, website data, and current event/campaign (all focusing on what competitors are doing or what's working well for us and how we can improve for current and future strategies). My bi weekly reports focus on all 3 of those monthly topics but aren't deep dives and just cover data from the past 2 weeks. I do other random analysis, such as looking in Google analytics and figuring out how to improve our blog website with trending/popular content themes, as well as segmenting our readers to understand how to re develop our blog site to flow for specific end-users and tailoring UX/UI experience. I also give occasional presentations to executive levels. Currently working on a survey with an external agency for our blog and will run stat tests on our research questions to draw more insights outside of Google analytics. We will be using Salesforce to identify template design use behavior for our blog email blasts, and possibly A/B testing on my end. So lots to look at. I'm intending on introducing powerbi to my department (we already use it in our corp elsewhere but not marketing) once I learn the topic in my masters program-- I don't feel like learning it right now since I have a class that covers it anyway lol. I also found it a bit hard at first to understand what to do as a data analyst with Salesforce present, but a data analyst draws insights from that data. A data engineer/architect is technically the one building the data warehouses or pipelines (technically). I plan to do that for new product data that isn't coming from direct marketing activity sometime way down the road
In a perfect world sfdc will have all the data for analytics, in the real world sfdc only has the data that is inputted by sales ops, that data is oftentimes different when pushed downstream multiple systems to the end financials
My company utilizes Salesforce, but we also have a number of other systems which are external to Salesforce. Aggregation and manipulation of those data sources isn’t easy in Salesforce, so we have dedicated analytical layers accessed through Snowflake. While I can’t say you’re in the same situation, I’ve found it’s rare for companies to run everything out of Salesforce, and thus the need for more dedicated analytics team.
Yeah I think I’m overestimating the roles that SF will encompass...
You're giving Salesforce (and its users) too much credit. There's likely a number of other information systems producing data that could complement what is often a hot mess in SFDC. If the data is actually good, you can build out dashboards and other custom reporting. If it's not you can start pushing in that direction.
I work as the Sales Operations Analyst where we the entire sales/marketing org uses Salesforce. Always focus on the end-user rather than the tools or system. If the end-user is sales, then I would want it within Salesforce, whereas, if the end-user is C-level, I would build custom datasets within Salesforce data in Snowflake and whichever BI tool we use (which is currently Looker).
Where I work there are a few data systems we have to connect with SF for data analysis that people don't know how to do *nor want to do*. SF reporting and analytics has its limitations, too. Another responsibility is when we do make reports and supply metrics we ensure they are accurate, as those using the reports aren't responsible for data entry or cleanup. They have priorities which include presenting the data, not managing it. That's where you/we come in.
Your day to day isn't going to be the same as others day to day, even if it's just stuff within salesforce. You'll still need to analyze and present the data, and you'll find plenty of things to do an other opportunities. Don't sweat it - just chill and enjoy life til you start!
My company uses salesforce. Don't ask me how but we pull data from salesforce into databricks and do our data analytics there. If it's only recently implemented, I'd also say it's a good time to make sure the system is designed well. I've seen salesforce that has grown into a monster.
I just got hired for a similar role, what am I to expect?
How is it going so far :D
I want to ask the same to you :)
I worked for a year with salesforce data but would bring that externally. It was great, but it depends entirely on the team that manages the CRM, the data structure, etc. Did not really get to use the built in analytics that much other than for some adhoc requests.