👋 Hey, it’s Sundar! Welcome to experiMENTAL: a weekly newsletter on B2C marketing & data science how-to guides, frameworks, and stories from 15 years including early Uber.

The most successful B2Cs didn’t grow virally. As Lenny Rachitsky shared in an article, virality is a myth. What they did was grow consistently with quality.

The same can be said about careers. A few have temporary virality, but on average careers are linear. If you’re a data analyst or scientist that means to have a long lasting and growing career you need to produce quality consistently.

Quality without consistency = unreliable

Consistency without quality = unpromotable

How can data analyst or scientist produce consistent and quality analyses that drive your career?

You have to P.A.C.E. yourself.

The P.A.C.E. framework

I’ve produced 100+ analyses and reflecting on what went right vs wrong, there are 4 major milestones in an analysis. Many data analyst or scientist focus on just the analysis, but the battle is actually won before you start. After you deliver an analysis, you have to continue to follow through or your analysis won’t be remembered.

Let’s dig in.

Prioritize

One of the biggest challenges data analyst or scientist have is prioritizing work. We look at analyses as problems to be solved instead of tools to improve the business.

“Why is revenue down Y%?”… ooooh that sounds like a fun problem to dive into when our first instinct should be “OH F*CK, revenue is down y%. That doesn’t sound good!”

Our education systems don’t really prepare data analyst or scientist for the reality of professional work. Nowhere in high school or college are we ever told “Here are 5 problems. Pick the 3 most important ones and you’ll be graded based on which problems you pick and your answers for them”. However, that’s exactly how it works on the job. You’re graded on which problems you solve and how well you solve them.

That’s why the first step of the framework is prioritize.

Here is how you prioritize:

1. Get in the room with your stakeholder.

   If your only interaction with a stakeholder is through a Jira ticketing system, then good luck. You’re just a data monkey to them. Instead, set up a weekly or biweekly check in. Understand their top priorities and what they’re seeing in the business.

2. Once you have a few ideas of what’s important, do a quick impact analysis. Do back of the envelope calculations to see the estimated business impact of what you can work on. “If I work on X, this could lead to $Y in revenue”. It’s okay if you’re wrong at first because over time you’ll get better at this. The exercise is a forcing function to maximizing your impact per analysis hour.

3. Estimate the effort you think it’ll take for you to deliver the analysis. I prefer the t-shirt sizing methodology (Small, Medium, Large).

   Small = a few days

   Medium = a week or 2

   Large = a month +.

   In almost all cases, throwout the ones that take a month or break them down into smaller analyses. The reason is because the business doesn’t have the patience to wait that long. If you told a stakeholder it’s going to take a month, they’ll bug you the next week and say “okay can I get it next week”.

4. Pick the analysis with the highest impact to effort ratio and add a buffer of a couple extra days to the estimated effort. Now start to analyze

Analyze

There are 5 key reasons an analysis flops:

1. the data was wrong *

2. it’s not actionable

3. it took too long

4. it says too much

5. it says too little

• this one is just a doozy. Hard to avoid until it’s too late.

I’ve fallen into all of these traps myself and coached data analyst and scientist through how to avoid them. An analysis has to be long enough to be interesting but short enough to be engaging. It has to be actionable yet timely while the business problem is still a priority.

How do you do this?

Map out the questions you’re going to answer by creating a problem plan. List out research questions, follow up questions, and questions your audience might have. In addition, write out the end goal of the analysis. This will help you tell you when to stop.

Many data analyst or scientist that I’ve discussed this with are concerned that they’re limiting their analyses and might not find the true insights. Here’s my response: Analyses are already chaotic enough that you’ll likely still stumble upon something you haven’t seen before. Also, insights aren’t going anywhere. You can always revisit them and share your new findings a bit later.

Lastly, timebox your analysis so that you know when to stop falling down the rabbit hole. You can use the information from the T-shirt sizing exercise. Don’t keep extending deadlines or you’ll preemptively ruin the trust of your stakeholder as stakeholders hate to be told to wait so creating accurate deadlines is crucial.

Finally, account for the time to create the presentation and practice it (at least a day). Which brings me to communicate.

Communicate

If a data analyst or scientist finds a great insight but nobody knows about it, then did they find a great insight? The answer is no.

I can’t count the number of times I’ve seen a great analysis get buried while a mediocre analysis get circulated. Humans are visual creatures, but more importantly we are inherent storytellers. Analyses are just stories and they should be treated and presented as one.

Every analysis MUST have a presentation. It’s a rule I implemented with all of my teams after being sent a spreadsheet with some findings. I sent the spreadsheet back and said I wasn’t going to read it.

Why?

1. Own the story → Don’t let stakeholders form their own story with the data. You’re the one that did the work so tell your story of the numbers.

2. Respect their time → Having someone dig through a spreadsheet and connect the dots is lazy on your part. No stakeholder wants to do that.

Here are a few ways to put together a better presentation:

1. Put the key takeaways at the beginning. Make sure the audience understands what’s at stake.

2. If you read nothing but the titles of each slide, it should tell a complete and cohesive story.

3. Each slide should only have one key point.

4. Slides should look visually consistent (this was a product of working with ex consultants for most of my life).

Example

Here is an example of a bad slide:

Issues:

• There is simply too much going on

• The title of the slide tells me nothing

• The first bullet point says “the chart shows”… so why are you adding text to tell me what the chart shows

• Too many arrows on text boxes on the chart that are distracting

Here’s that same slide simplified by me:

Ignoring the fact that I have no idea what the original slide actually meant, I think we can all agree my slide does the following:

1. More inviting → you actually want to read it vs skip it

2. Less cognitive load → you understand right off the bat what the slide is about

3. Better narrative → The bullets are succinct thoughts that build on each other

Now, that was for one slide. Imagine the impact when you do that for all of your slides / presentations!

But, wait… that was just putting the presentation together. Now you have to deliver it.

Presenting (in front of a live audience or otherwise) is a skill. It’s not an inherited trait. Please don’t fall into that trip of thinking it is. Every time I present, I still get nervous AF and have butterflies in my stomach. What I’ve trained myself to do is suppress those butterflies .

Here are a few ways to become a better presenter:

1. Practice, practice, practice. Practice with a teammate. Practice with your manager. Practice with your partner. Even if it’s for a 2 minute presentation… practice. If you suck at running 1 mile, you’ll suck at running 26.2. If you suck at running 1 mile when no one is watching, you’ll be even worse when people are.

2. Memorize key data and talking points on each slide. As a data analyst or scientist, most of you are gifted with a brain that can remember patterns. Use this to your advantage. Don’t memorize EXACTLY what you’re going to say. What if someone asks a question in the middle or there’s a noise that throws you off? Memorize the key points and the data on each slide

3. Go into the room you’re going to present beforehand. If you’re at home then make sure everything is set up the way you want and if you’re presenting in a conference room then go make the space your own before

Execute

The analysis doesn’t end when you’re done presenting. You have to execute your analysis and see to it that the recommendations are followed through. It doesn’t have to be every time, but you need some success rate to prove the impact you’ve created.

If all you do is write analyses and no one does anything about them then the analyses were either 1) not actionable or 2 ) not a priority. Given the P.A.C.E framework, those 2 issues should be nullified. Even though you might not control the levers, you have to own that you do have influence

Here are a few ways to better execute and follow through:

1. Write a crisp follow up email (example below) with a summary or tl;dr at the top

2. Schedule a follow up meeting a week or 2 later to understand actions taken

   If there’s a pattern of no actions taken by your stakeholders then it means stakeholders are just using the analyses to cover their butt but not doing anything about it. This is where you have to be politically savvy and find a way to express that you’re concerned that your analyses aren’t having impact and that you may need to begin deprioritizing their work. It’s easy for me to sit here and write this than to implement, but I’ve done this before and it’s lit a fire under my stakeholder. You need to express (again in a politically savvy way) that you want your work to be impactful.

3. Share experiment / test results broadly especially with your manager and skip level. You’ll notice that many leaders begin implementing weekly, monthly newsletters for their team. That’s because they need to surface their team's work and prove impact to other teams constantly. It’s just the reality of working in a resource constrained environment. When you share results broadly you also begin engaging with other teams interested in your work and the connective tissue between teams begins forming while breaking down silos. It’s a win win.

EMAIL DRAFT

tl;dr Revenue was down 8% because of XYZ. I recommend taking action A and B and exploring C more. That should recover Y%. 

Hey XXX,

I'm following up on the analysis I shared with some next steps. 

As discussed, we noticed a 8% drop in revenue because of X,Y,Z.

Thinking through next steps, it would be good to test A,B, and C.

I estimate it'll bring back Y%. Read the full analysis here and let me know if there's anything else I can help with.

That’s it for today. Remember, to have a long and successful data career, you need to P.A.C.E yourself.

New experiment

Hey fellow marketers and founders, I’m trying something new next week where I answer questions from you.

I’ve spent 10+ years in Marketing Data Science measuring the incrementality of every channel you can think of (Digital, Offline, Owned, Paid).

What questions do you have on measuring Marketing incrementality? Reply directly to me and I’ll answer them in an upcoming newsletter. The questions will be anonymous.

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