By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.
What's inside:
Next article

Rob Olson

View more



Data is everywhere. In the “Mad Men” days, the only data you had on your customers was whatever you learned over a three-martini lunch. Today, every interaction a customer has with any aspect of your company can generate data, from how much of your video they watched before getting bored to how many times they consider that one splurge before purchasing.

Data is meaningless—unless you have a purpose for gathering it and a plan for using it.

We believe in a tactical approach to data. Ask yourself: “What’s the simplest, smallest data point I need in order to make a decision and move on?”

The wealth of data available today, at relatively low costs, provides enormous opportunities to drive continuous improvement for your company. But you can’t just collect data for data’s sake. You need a data strategy.

This guide will show you how to take advantage of your data by explaining:

  1. The 3 basic components of any data strategy
  2. Designing a data strategy
  3. The best data tech stack for startups
  4. What you really need to measure
  5. Turning positive churn into negative customer churn
  6. When to uplevel your startup's data strategy
  7. Common misconceptions about data strategy
  8. Resources we love
  9. Takeaways & next steps

The 3 basic components of any data strategy

The 3 basic components of any data strategy

Given how easy it can be to overwhelm yourself with data, it’s helpful to break down any strategy into these three buckets.

  1. Data collection and storage: What do we observe, what do we ask, and where do we store it all?
  2. Data processing and analysis: How do we aggregate and calculate metrics, and what are those metrics?
  3. Data reporting and utilization: How do we aggregate and calculate metrics, and what are those metrics?

It might sound logical to start with #1—collecting and storing data—and build from there. But that’s data for data’s sake. You’d end up with more “interesting data” than you could parse through in a lifetime.To avoid data paralysis when your team is small and resources are scarce, start with #3: the use case for your data.

Why you should start with data reporting & utilization

Start by thinking about the types of decisions you can only make with good data. It will be truly useful only when you know why you’re collecting data and what you’ll use it for.

Of course, not all decisions run on data. Sometimes data only serves to reinforce a decision you were always going to make, or to report on what you’ve already done. It’s absolutely fine to make those kinds of decisions without trying to attach a data point to it. Don’t let data paralysis stop you from making crucial calls!

Once you know how you’re going to use your data, you can build your data collection and processing systems strategically to generate the specific, actionable metrics that you need to make good decisions.

Understanding when and where data will be used will help you avoid situations where data becomes isolated and contained to side conversations. “Reviewing the data” should never be its own agenda item.


Data is not your job. Data exists to help you do your job.

Designing a data strategy

Designing a data strategy

Your goal is to extract value from your data and let it help you make decisions. That means you should:

  • Limit the breadth of data gathered to areas where you actually have a “lever”—a decision to make that can be influenced by data.
  • Know at what level of complexity you need to analyze your data.
  • Make sure all your reported and reviewed metrics are optimized for actionability.

One of the best ways to get a handle on actionability is to map out what kinds of decisions are happening, who is making them, and when, where, and how they’re made. Start with decisions made on a regular basis, and expand to categories of one-off decisions after that.

Let’s look at two examples.

Decision #1: How do I allocate next month’s marketing dollars?

First, walk through when, where, and how to decide:

Who decides: Chief Marketing Officer

When: Monthly

Where: Monthly paid marketing review meeting

How: How: Review cost per action (CPA) across all channels and try to allocate the budget primarily to the best-performing CPAs, while maintaining a distributed presence.

Now ask: “Is there any additional data that would help us make a better-informed decision?”

Possible answers include:

  • “I want to understand customer lifetime value (LTV) better so I can optimize for long-term ROI, not just acquisition.”
  • “I’m not sure how to value indirect channels like unbranded ads, content marketing, banners, etc.”
  • “I want to figure out the right message for the right channel, but I’m not sure how to test.”
  • “Do some channels deliver customers with higher costs in terms of support and returns?”

Decision #2: How much should we discount our subscription offering compared to our normal prices?

Walk through how the decision gets made:

Who decides: Chief Experience Officer (CXO) When: Once upfront, reevaluate quarterly Where: Quarterly Business Review

Review percentage of purchases opting for subscription and evaluate against target.

Now ask: “Is there any additional data that would help us make a better-informed decision?”

Possible answers include:

  • “I’d like to see the rate of re-orders for our ad-hoc purchasers versus subscription customers.”
  • “What’s the average order amount for these two groups?”
  • “Who’s buying more distinct products?”
  • “How many subscribers drop out after their first refill/billing cycle?”
  • “Do some channels deliver more ad-hoc purchasers who are willing to buy at a higher price point, versus bargain seekers who value a subscription discount?”

Once you’ve walked through several decisions this way, you’ll generate a list of additional data needs. For example, using the two decisions above, you might find you want an alternative metric than CPA to measure both customer LTV and the value of different marketing channels. You might find you want to track customer behavior in more detail for both subscribers and ad-hoc purchasers.

The key is that you’re approaching data strategically—you’re using it as a tool to make better decisions, not as an end in itself.

The best data tech stack for startups

The best data tech stack for startups

We recommend three tools as the gold standard for new data capture and storage. We’ll summarize them below; for more detail, check out Intercom’s overview of the ultimate marketing technology stack for 2021.

1. Google Analytics

image alt

What: There are many reasons that an estimated 30-50 million websites use Google Analytics. It’s reliable, and it can tell you how people found your website, where they’re coming from, what they looked at, and how long they lingered on different areas of your site. These are the key data points you need to generate buyer personas and start to optimize your marketing.

How they did it: Google Analytics is even more powerful in combination with a tool like Intercom, which allows you to track all customer interactions with your help and chat functions. With those tools combined, you’ve got all the data you could need on who your customers are and what they want from your website.

2. Looker

image alt

What: Business intelligence software is an essential tool for any modern company. It gives you a comprehensive view of what your customers are doing at every point on your sales funnel. The best products combine data from multiple sources, so you don’t have to check on your AdWords campaign separately from your other marketing efforts—it’s all right there in one dashboard.

How they did it: We like Looker for its top-notch data modeling. It makes connecting disparate datasets very easy, and the end user experience is intuitive and clean.

3. Segment

image alt

What: With oceans of data coming at you from all sides, you need a way to pull all that information together into one useful application. Segment is a customer data platform that collects data from multiple sources, including your customer relationship management (CRM) software and your web and mobile apps, and creates a unified view of your customer.

How they did it: We like Segment because it integrates well with the other tools we recommend, and it makes data accessible and usable for every team in your organization.

What to measure for subscription-based models

What to measure for subscription-based models

Now that you’ve got data flowing into these excellent tools, what should you try to measure with it? Here are some of the most crucial KPIs your early-stage company needs to track.

Some experts have argued that every business will be subscription-based in the future. We’re not sure subscriptions are the way forward for every business, but there are good reasons for this model’s increasing popularity. Namely, subscription customers are almost higher value (justifying a higher cost per acquisition).

Early-stage companies should be experimenting with different pricing options, and subscriptions and bundles should definitely be part of that mix. This article by forEntrepreneurs is an excellent read about KPIs for cloud software companies, now nearly always sold on a subscription basis. Check out some of the key points below.

For subscription models, you need to be tracking:

The subscription cash flow trough: Acquiring customers costs money up front—that creates your trough.

As those customers start generating revenue, the trough turns into a cresting hill. But that’s often the moment when you should step on the gas and spend more on customer acquisition. That deepens your trough again—hopefully temporarily.

See below for a visual look at your cumulative cash flow:

Customer Acq Chart Journey copy 2 v2021

Unit economics: Ultimately, your customers need to generate more revenue than it costs you to acquire them.

You should keep an eye on two questions: 1. Is your customer lifetime value (LTV) at least three times your customer acquisition cost (CAC)? and 2. Does it take you 12 months or less to recover your CAC with revenues? The answer to both questions should be yes.

Churn rate: This measures how many existing customers/revenue you lose versus how many new customers/revenue you acquire.

Some churn is inevitable, but too much churn will limit your ability to grow.

Cohorts: You need the ability to track customers in groups based on the month they signed up.

That gives you visibility into how effective your churn-reduction tactics are, the value of customers who come in through different marketing campaigns, etc.

MRR movements: It's important to understand the underlying mechanics of a net change in MRR.

Changes in your monthly recurring revenue (MRR) due to reactivations, expansions, new business, and churn.

Average sale price: This establishes the first dollar value for a new customer.

It’s crucial to track this figure because if you only look at a measure like average revenue per account, you’ll get a view skewed by customers who’ve upgraded over time. You need to know what your new customers bring in on day one.

Shifting from positive churn into negative net churn

Basically, you need expanding revenue from your existing customers to exceed the lost revenue from churning customers. There are two ways to do this:

  1. Build some variability into your pricing scheme, so that when customers use your product more, they pay more—for additional seats used, leads tracked, etc.
  2. Upsell customers into more robust versions of your product, or cross-sell them additional modules.

For more on metrics that subscription businesses should be tracking, check out this cheat sheet from ChartMogul.

What to measure for ad-hoc purchasing models

What to measure for ad-hoc purchasing models

Whether you’re all ad-hoc purchases or a mix of subscription and ad-hoc, you’ll need to track some additional KPIs specific to individual purchases, too.

If you’re a DTC business, you need to be tracking:

Average Order Value (AOV): Total revenue/number of orders. Driving up AOV will improve your margins.

Strategies to increase AOV include: *Making shipping free slightly above your AOV *Offering discounts at different total thresholds *Offering a gift with purchase

Cart abandonment: 1 - total number of complete purchases/total number of shopping carts created

The #1 reason customers abandon items in their cart is a lack of free shipping. Of course, sometimes people are just researching or browsing with no real intention to buy. Strategies to combat this problem include:

  • Showing limited inventory to create urgency
  • Automating abandoned cart emails and retargeting campaigns

Gross margin: (Revenue - cost of goods sold)/revenue

This is typically higher for DTC businesses than for B2B or subscription businesses. But it’s just as important to track…

Contribution margin: (Revenue - variable costs)/revenue

This number includes things like shipping costs, payment processing fees, and fulfillment costs, which tend to impact DTC businesses more.

Conversion rate (CR): Number of conversions/number of sessions

This measures the percentage of people who made a purchase (or signed up for your newsletter or otherwise entered your funnel) after visiting your website. Conversion rate offers a window into both how effective your website is and how attractive your value proposition is overall.

For even better results, measure: CR x AOV. This basically gives you the dollar value of a session on your website. You can evaluate this number against how much it costs to drive a session through marketing. These numbers are key for optimizing your website and, ultimately, creating a test-and-learn culture that’s obsessed with the customer journey from first touchpoint to final purchase.

For more on metrics that DTC businesses should be tracking, check out this article from AllyCommerce.

When to uplevel your startup's data strategy

When to uplevel your startup's data strategy

So how do you know when you’re ready to go beyond the basics to uplevel your data game? Here are three signs it’s time to expand and improve your data strategy:

  1. Your company has 20-30 employees who all need data access on a daily basis to make decisions.
  2. Data becomes integral to how your business functions, as with Netflix and its recommendation engine. Few businesses reach this level of data-intensive needs, but those that do rely on very specific tools. More on this in a minute.
  3. You’re ready for a Series B fundraise.

Let’s take a closer look at the data needs of companies at the Series B stage and beyond. SeatGeek has a great deep dive into how they built out their data pipeline. We’ll highlight a few key points here.

Most of the data problems companies face at this stage fall into the following buckets:

  • The data isn’t there.
  • The data is there, but it isn’t formatted well.
  • The data is there, but it isn’t organized well.
  • The data is too accessible (and we’ve got too many people poking around in it).

If you’ve built your data tech stack on the three gold-standard tools we’ve recommended—Google Analytics, Looker, and Segment—you’ll avoid problem #1, and you can focus on problems #2-#4, which inevitably creep in as a company grows.

SeatGeek has some more tech stack recommendations for this stage, which we endorse:

  • Luigi for data processing
  • Redshift for data storage
  • Looker for data visualization and access

At this stage, you need to separate out those three pillars as distinct responsibilities for your data engineers (or whoever’s responsible for your data quality and availability). Once you’ve reached the point that your business is thinking about its own data ETL (extract>transform>load), you’ve progressed beyond what off-the-shelf data platforms can offer, and you’re ready to take more ownership of your data in-house.

If data becomes integral to your business, as it is for companies like Netflix, Pinterest, Zulily, and Spotify, your needs will be much more intensive. Looker has a great article on the considerations these companies face as they build out world-class data tech stacks to drive original business solutions.

Common misconceptions about data strategy

Common misconceptions about data strategy

M13 works with a lot of founders. Here are some of the most common misconceptions we see when it comes to working with data:

“If I’m not looking at all the data, all the time, I’m missing something.” We often ask founders to tell us about the last five decisions they made—and how data could’ve helped them make a better decision. And yes, sometimes this exercise surfaces a data point that’s worth capturing and analyzing for future use. But a lot of times, the decisions they mention didn’t depend on data. You don’t have to look at data for every decision. Sometimes it’s okay to look at less data.

“I have to automate all my data.” If you’ve selected the right KPIs to focus on, you usually don’t need to automate anything. Solid data analysis can be pretty low-fi. You can make most decisions with math you can do on a napkin.

“Every department needs its own custom data stack.” For raw data capture, you need a single source of truth. Don’t build your data stack with silos. You want to avoid a situation where your CFO claims you acquired X customers last month, but your CMO’s data gives a different figure. The raw data stack we’ve recommended allows you to capture everything, so you can always build analysis on top of it or go hunting for a specific answer. But our data philosophy is capture everything, focus on almost nothing (at first).


Data is meaningless on its own. Data analysis shouldn’t be seen as a super-wonky dive into the weeds. Rather, it should be a guided fact-finding mission. You’re going looking for a specific data point that will help you answer a specific question or make a specific decision.

If you don’t know why you need a piece of data, then you don’t need it. It’s so easy to capture data today that it’s easy to fall into the trap of overloading yourself with information. That’s why you need a data strategy that guides the design of your data tech stack.

Data exists to drive better decisions. Period.

Resources we love

Resources we love

Your data strategy will keep evolving as your startup grows. Dig deeper on metrics and more with these helpful articles:


Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.