Prioritization for Data Analysts



For the past two years I’ve worked as a data scientist, first on the marketing team for and now on the growth team at Help Scout. In both of these roles I’ve been the sole data analyst on my team so I tend to have more work to do than I have time for. As a result, I spend a lot of time thinking about how to prioritize what’s on my plate. My goal with this post is to share some of what I’ve learned to help other data folks who are in similar roles.

The prioritization problem

Here’s a made up scenario:

Your manager asks you for help with a medium priority analysis that will take several days. Shortly after you begin working on it, a coworker pings you for help with an urgent request that will only take a few hours. A little while later, your boss’s boss asks you for help with a low priority one-day project.

You now have three requests from people with differing seniority each with a different duration and urgency – which do you work on next and why?

Ad hoc requests vs projects

At a high level, there are two types of data requests:

Ad hoc requests are questions that can be answered fairly quickly. They may take a few minutes or a few hours and the urgency can range from “drop what you’re doing” to “no rush at all”.

Projects take longer and usually have a higher impact on the business. For me, they range from a few days to (rarely) a few weeks.

While it may be tempting to only want to work on ad hoc requests or only on projects, the reality is that they’re both important and if you’re the only analyst, you’ll need to work them both into your schedule. For example, even if you’re working on a big project, you can’t simply ignore the urgent ad hoc requests that come up during the course of a week. Telling your CEO that you’ll get to his request in three weeks when you’re done with your project is not recommended.

Therefore my advice is to set aside about two hours each day to work on ad hoc data requests and spend the rest of your time working on your highest priority project. This will give you time to work on those longer, high impact initiatives but still give you time to work on the shorter requests as well.

I wouldn’t loop your manager into the prioritization of ad hoc data requests; he or she probably has better things to do than helping you decide “spend 30 minutes on this then spend an hour on that” etc etc. Use your judgment: work on whatever is the most urgent or will have the highest impact on the business. When in doubt, base it on the seniority of whoever is asking.

For projects, I definitely would recommend getting your manager’s help prioritizing what to work on. He or she help you decide where you can be the most impactful and when questions come up about why you’re not working on some other thing, it’s not just you who made that call.

I would only work on one project at a time. I’ve tried doing like 3 hours a day on one project, 3 hours a day on another, but all that’s going to do is make both take twice as long if not longer due to the frequent context switching.

When you meet with your manager, keep him or her up to date about where you’re at with your project. Often it’s hard to predict at the start how long a project will take. Something you thought might take a week will take a day. Something you thought would take three days will take two weeks. That’s just the nature of it. Keeping your lead informed.

Sometimes priorities will change. Your expected three-day project that is now in its second week may get put on the back burner in order to work on something else. It’s easy to get frustrated when this happens, but remember that you’re there to have the biggest impact on the business, not to finish projects just for the sake of finishing them. Sometimes what’s highest priority will change so be flexible.

When priorities change and you have to change projects midway, try to estimate what impact that will have on the completion of the original project. If you estimated you would complete your original project by the end of the week, but by switching projects it will now take an additional week, communicate that to the people waiting on the result of the original analysis.

Get clarity before beginning your analysis

Regardless of whether a request is ad hoc or a project, I’d recommending asking the requestor several questions:

  • How important is this?
  • Is there a deadline?
  • How accurate do you need the answer to be?
  • What type of end result are you looking for?

If you don’t ask about importance, you risk spending a lot of time on an analysis that is not that important to the person asking about it. It’s common for someone to ask a quick hey-I-wonder-about-this question, but they only care if you can find the answer quickly.

Similarly, ask whether it needs to be done by a specific date. If the answer is holding something up, it should get higher priority than something that doesn’t.

Asking about accuracy is something that took me a while to appreciate too. I’ve run into a lot of data requests that I can give a 95% accurate solution to fairly quickly, but that would take much longer to get to 100%. A lot of times, people will be fine with a 95% quick solution. Understanding when 95% is acceptable and when 100% is necessary can save you a lot of time.

Sometimes I also ask what type of end result the person is interested in. It often helps them and you clarify what the analysis is all about.

For longer projects, I’d also recommend periodically updating the person who requested it with your progress. As they see your progress, they might want to refine the request or may be happy with the results as-is, freeing you up to work on something else.

Staying organized

I’ve seen some data teams ask people to fill out a little template (usually in a Trello card) to help people ask good data questions. I prefer to discuss the details with the person, then create a Trello card for myself with the relevant information.

I would recommend keeping a list of all potential projects and ad hoc data requests. That is, don’t just try to keep it all in your head. I do this in Trello with four lists:

  • Multi-week Projects
  • Multi-day Projects
  • Multi-hour Projects
  • Quick Requests

Whenever someone asks a question, I get the details from them then create a new card and add it to the appropriate list.

You could also have a Completed list where you drag cards as you complete them to keep a record of it. I’ve also seen some teams have a list for each month or quarter (January 2018, February 2018, etc) and drag completed cards to the relevant list when it’s complete. I did that in the past, but tend to just archive the cards now to avoid cluttering up the Trello board.

Invest in a proper BI tool like Looker

One other big win has been switching from manual data analysis (using MySQL queries and R) to Looker, the premier Business Intelligence (BI) tool on the market today. I could go on for hours about how amazing Looker is, but at a high level it lets you:

  • Create dashboards that are automatically updated as the underlying data changes.
  • Use LookML, Looker’s modeling language, to teach Looker how our data fits together. With that in place, I rarely have to write queries by hand anymore. I can use Looker’s interface to quickly ask and answer questions.
  • This also means that other people at the company who may not have querying skills can answer their own questions without asking me every time.

I’d highly recommend checking out Looker if you still find yourself wrangling data a slow manual way.

If this post resonates with you, I’d love to connect

If you’re a data analyst, especially if you’re the only one at your company or part of a small team, I’d love to chat to learn more about what you’re working on, how you prioritize, etc. Drop me a note: Cheers!

Help Scout’s New Partnership with Fivetran

Today at Help Scout we’re excited to announce that we’re partnering with Fivetran, a fantastic service that makes it easy to centralize all of your data in a data warehouse. You can read my blog post about the partnership here: Performing Advanced Analytics on Your Help Scout Data with Fivetran.

The short version is that if you’re a Help Scout customer, you can sign up for Fivetran (for free!) and use it to get all of your mailbox data out of Help Scout and into a data warehouse like BigQuery. From there you can use SQL to analyze the data (on its own or combining it with your data from other services) or hook it up to a Business Intelligence tool for easy analysis and charting.

If this sounds interesting to you, check out the blog post and don’t hesitate to reach out if you have any questions about any of it:

Quantity and Quality Metrics

Something I learned from Help Scout’s former Growth Lead, Suneet Bhatt, is that for every quantity metric you should also strive to have an associated quality metric.

For example, one of the metrics we look at is the number of New Customers. We also track Initial Average New Users Per Customer. Together these help us understand not just how many new customers we’re bringing in, but how large they are.

  • Quantity Metric: New Customers
  • Quality Metric: Initial Average New Users Per Customer

New Trials is a quantity metric. A quality metric for it might be the % of trials that convert into customers. I’ve found with my own products that that it’s easy to have either the quantity or quality metric go up, but hard to have both. For example, with Preceden, my timeline maker tool, New Trials tends to plummet during the summer months when students are not in school (a lot of students use Preceden to create timelines for class projects). When this happens, the trial to paid conversion rate goes way up because without a large number of students signing up (who rarely convert into paying customers), the conversion rate for the remaining trials winds up being pretty good. Then in the Fall when school resumes, trials increase as students begin signing up again, but trial to paid conversion rates fall. If I could find a way to increase conversion rates as more students sign up, it would be huge – but way easier said than done.

  • Quantity Metric: New Trials
  • Quality Metric: Trial to Paid Conversion Rate

Another example from Help Scout: we track how many new employees we hire, but also measure what % leave within 1 year. If our hiring process works well, the % that leave within a year should be fairly low; if not, it will be high.

  • Quanity Metric: New Employees
  • Quality Metric: 1-Year New Employee Churn Rate

When you’re thinking about your own business’s metrics, try to come up with quality metrics for most quantity metrics. You’ll gain a deeper understanding of the health of the business which allows you to make better decisions about its future.


Identifying the Primary Keys for Deleted MySQL Records

At Help Scout today I had to perform an analysis that required me to identify the primary keys of deleted MySQL records.

For example, these results contain primary keys for records 1-5 but not 6-10:

The question is how to identify records 2, 3, 5, 7, and 8.

With the help of Stack Overflow, I found a neat solution that involves creating a temporary table of all possible primary keys, then left joining it on the original table to identify the missing ones. Here’s what it looks like, customized for my situation:

This solution does require that the keys be auto-incrementing, but that should be the case most of the time. Also, it assumes the last record is not deleted.

Happy querying!

Using Fivetran’s Google Sheets Integration to Get Monthly Unique Visitors Into BigQuery and Looker


At Help Scout we use Fivetran to get data from various data sources (like our internal MySQL database, HubSpot, and Salesforce) into BigQuery so that we can analyze it all using Looker.

One of the challenges I’ve run into is that even though Fivetran supports Google Analytics, the data is limited to what’s available via the Google Analytics API. One of the main metrics we want – Unique Visitors – is available, but only on a 1-day, 7-day, 14-day, or 30-day basis:

Screen Shot 2017-12-05 at 9.41.27 AM.png

This means that if we want to know how many Unique Visitors we had each month, there’s no way to get that out of the Google Analytics via its API, which means Fivetran can’t grab it and automatically put it into BigQuery, which means we can’t have it in Looker.

Enter Fivetran’s Google Sheets integration

Fortunately, there is a hacky way to do it by taking advantage of Fivetran’s Google Sheets integration. Big picture, we’re going to manually export monthly Unique Visitors from the Google Analytics UI, put that data into a Google Sheet, then configure Fivetran to pull that data out of the Google Sheet and put it into BigQuery so we can analyze it with Looker. Here’s how the setup works:

In our Google Sheet, we select the range of data we want then go to Data > Named ranges… to give it a name:

Screen Shot 2017-12-05 at 9.48.53 AM.png

We give it a name that begins with fivetran_ such as fivetran_monthly_unique_visitors_test:

Screen Shot 2017-12-05 at 9.51.44 AM.png

Next, we grab the Google Sheet ID from the URL:

Screen Shot 2017-12-05 at 9.54.01 AM.png

And paste that into Fivetran:

Screen Shot 2017-12-05 at 9.54.52 AM.png

Authorize Fivetran to access Google Sheets:

Screen Shot 2017-12-05 at 9.55.39 AM.png

Fivetran will then kick off the initial sync:

Screen Shot 2017-12-05 at 9.56.14 AM.png

And then shoot you an email when it’s done:

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Now over in BigQuery, we can check to verify the table is synced:

Screen Shot 2017-12-05 at 10.05.29 AM.png

Step 1, complete.

Next, lets create this chart in Looker

We’ll tell Looker we want to create a view from a table:

Screen Shot 2017-12-05 at 10.07.07 AM.png

I had to click the Refresh button to get the Google Sheets table to appear:

Screen Shot 2017-12-05 at 10.07.56 AM.png

Before tinkering with the view, here’s what the Google Sheet model might look like:

And here’s the view:

There is some hacky stuff going on here so let me explain what’s what:

  • In the Google Sheet, our dates are formatted like “1-Jan-16″. Looker has no idea what to do with that out of the box, so we need to use Standard SQL’s PARSE_DATE function to tell it how to parse it. The dimension expects a timestamp so we then need to cast the date to a timestamp. And because we’re only ever going to be using months here, we can get rid of the other date/time options leaving just months.
  • Similarly, the spreadsheet has numbers like “743,353″. This string isn’t a valid number, so we use REPLACE to eliminate the commas before casting it to an integer.
  • You might be wondering about the measure: in order to use this value in a chart, it has to be a measure, not a dimension. A hacky way to do that is just to use the measure type of max (though min or average or others will do too) which will just grab the maximum value for that date range – and since this value is the only value in the date range, it just returns the number of monthly uniques.

Back in Looker we can now create the chart:

Screen Shot 2017-12-05 at 10.24.28 AM.png

The main downside to this is of course that you have to manually update the Google Sheet if you want the chart to update, but for something like Monthly Unique Visitors, updating it once a month isn’t a big deal. Though if you can think of an automated way to do it, I’d be interested to learn how.


Counting in MySQL When Joins are Involved

There’s a MySQL pattern that I use fairly frequently that I want to share, both to help anyone else who might find it useful and also to find out if there’s a beter way.

Here’s an example: you have one table for users, another for posts, another for pages:

And we want to know how many posts and pages each user has:

Attempt 1: COUNT with JOIN

For the moment, lets focus on just getting the post count by user.

We might be tempted to try JOINing the two tables and using COUNT:

The problem is that when we JOIN the two tables, the results will only include users who have posts. In this case, there’s no result for Jen (user id 3) because she doesn’t have any records in the posts table.

Attempt 2: COUNT with LEFT JOIN

Ok, so if JOIN doesn’t work, lets try LEFT JOIN:

Now Jen has a count, but it’s reflecting 1 post, not 0 like we expect.

We can see why it’s broken by looking at the LEFT JOIN results without aggregating them:

Jen doesn’t have any posts, but because we’re LEFT JOINing, her users record is still included. When we then aggregate the results with GROUP BY and COUNT, MySQL sees that the results have one record so returns a count of 1.

A better way to do this (as suggested by Tom Davies) is instead of counting all records, only count post ids:

Attempt 3: SUM/IF, and LEFT JOIN

Another way we can achieve what we want (for a single table join) is to use SUM/IF:

Here we’re saying “When the post id is null, give it a 0, otherwise a 1, then sum the results” which gives us the correct count. This is actually I trick I learned from the real Jen while at Automattic :).

The problem arises when we need to join multiple tables:

Now instead of Simon (user id 2) having 2 posts, he has 4 – what happened? Again, we can look at the un-aggregated results:

The problem is that each post gets joined with each page result. Simon has 2 posts, each of which gets joined with the 2 pages, so when we use COUNT it sees 4 results and returns that amount.

The solution: Subqueries and COALESCE

Here’s how I would solve it:

To understand how it works, lets focus on counting posts. The first subquery counts how many posts each user has if they have any posts:

We can then LEFT JOIN users on this derived table:

For the users with posts, the result has the post count. For the users without posts, the result is NULL. This is where COALESCE comes into play. COALESCE takes any number of arguments and returns the first non-NULL result:

So we’re saying “If the user has a posts count, use that, otherwise use 0”.

We can then LEFT JOIN again on the pages table and do the same thing with the pages count to get the posts and pages count per user. Because each subquery only returns a max of one result per user, we don’t run into the issue we did earlier where posts get joined with pages to return the incorrect count.

Somewhat complicated, but the only way I know how to do it. If you know of a better way, please drop a comment below. Thanks!


This simpler method also works:

By counting distinct post ids and page ids, we avoid counting NULLs and also avoid counting duplicates due to joining on posts and pages.

Using Data to Forecast the Impact of a Pricing Change

Back in April of this year Help Scout announced we would be raising prices for customers on some of our old legacy plans in six months time (in October). I recently helped with an analysis to estimate what the impact would be on our monthly recurring revenue (MRR). We performed a similar analysis prior to the announcement, but severals months had passed so it was time for fresh forecast.

At a high level, we performed the analysis as follows:

1. Identify the variables that impact the forecast

For us, this meant variables such as:

  • Of of the customers who we announced the price increase for in April, how many have churned between then and now? And how many can we expect to churn between now and October? And how many can we expect to churn after the price increase?
  • How many can we expect to upgrade or downgrade after the price increase?
  • How many can we expect to change from monthly payments to discounted annual payments?
  • Because customers pay per user, what % of users can we expect to lose after the price increase?
  • And so on.

2. Create a spreadsheet that lets you adjust the variables to see what impact they have on the forecast

For example (and simplifying a lot), if we had W customers originally and X have churned between the announcement and now, and we expect another Y to churn between now and the price increase, and we expect Z% to churn after the price increase, paying $N/month on average, we’ll wind up with a future MRR of (W – X – Y) * (1 – Z) * N.

Our actual spreadsheet wound up become quite complex to take into account all of the variables, but in the end we had something that took into account all of the key factors that impact will the outcome.

3. Come up with a range of estimates for each of variable

Using historic trends and educated guesses, we estimated the range for each of the key variables we identified. With those in hand, we create several forecasts (pessmistic, realistic, optimistic) showing what the outcome looks like for each of those situations:

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My original instinct was to come back with a single number: “The forecast is $X” but my lead wisely suggested calculating several outcomes to account for the range of possibilities.

This was a fascinating exercise because it forced us understand on a deep level what the inputs are (churn rate, etc) and what impact they have on our bottom line (MRR).

If you’re interested in trying this for your business, try to create a simple spreadsheet model that takes into account various metrics (number of unique visitors, trial sign up rate, trial to paid rate, etc) that comes close to predicting your historic results, then see how it well it does going forward. You’ll likely learn a lot in the process about which metrics you need to change and by how much to achieve your growth goals.