Shopify Data’s Guide To Opportunity Sizing

For every initiative that a business takes on, there is an opportunity potential and a cost—the cost of not doing something else. But how do you tangibly determine the size of an opportunity?

Opportunity sizing is a method that data scientists can use to quantify the potential impact of an initiative ahead of making the decision to invest in it. Although businesses attempt to prioritize initiatives, they rarely do the math to assess the opportunity, relying instead on intuition-driven decision making. While this type of decision making does have its place in business, it also runs the risk of being easily swayed by a number of subtle biases, such as information available, confirmation bias, or our intrinsic desire to pattern-match a new decision to our prior experience.

At Shopify, our data scientists use opportunity sizing to help our product and business leaders make sure that we’re investing our efforts in the most impactful initiatives. This method enables us to be intentional when checking and discussing the assumptions we have about where we can invest our efforts.

Here’s how we think about opportunity sizing.

How to Opportunity Size

Opportunity sizing is more than just a tool for numerical reasoning, it’s a framework businesses can use to have a principled conversation about the impact of their efforts.

An example of opportunity sizing could look like the following equation: if we build feature X, we will acquire MM (+/- delta) new active users in T timeframe under DD assumptions.

So how do we calculate this equation? Well, first things first, although the timeframe for opportunity sizing an initiative can be anything relevant to your initiative, we recommend an annualized view of the impact so you can easily compare across initiatives. This is important because when your initiative goes live, it can have a significant impact on the in-year estimated impact of your initiative.

Diving deeper into how to size an opportunity, below are a few methods we recommend for various scenarios.

Directional T-Shirt Sizing

Directional t-shirt sizing is the most common approach when opportunity sizing an existing initiative and is a method anyone (not just data scientists) can do with a bit of data to inform their intuition. This method is based on rough estimates and depends on subject matter experts to help estimate the opportunity size based on similar experiences they’ve observed in the past and numbers derived from industry standards. The estimates used in this method rely on knowing your product or service and your domain (for example, marketing, fulfillment, etc.). Usually the assumptions are generalized, assuming overall conversion rates using averages or medians, and not specific to the initiative at hand.

For example, let’s say your Growth Marketing team is trying to update an email sequence (an email to your users about a new product or feature) and is looking to assess the size of the opportunity. Using the directional t-shirt sizing method, you can use the following data to inform your equation:

1. The open rates of your top-performing content
2. The industry average of open rates

Say your top-performing content has an open rate of five percent, while the industry average is ten percent. Based on these benchmarks, you can assume that the opportunity can be doubled (from five to ten percent).

This method offers speed over accuracy, so there is a risk of embedded biases and lack of thorough reflection on the assumptions made. Directional t-shirt sizing should only be used in the stages of early ideation or sanity checking. Opportunity sizing for growth initiatives should use the next method: bottom-up.

Bottom-Up Using Comparables

Unlike directional t-shirt sizing, the bottom-up method uses the performance of a specific comparable product or system as a benchmark, and relies on the specific skills of a data scientist to make data-informed decisions. The bottom-up method is used to determine the opportunity of an existing initiative. The bottom-up method relies on observed data on similar systems, which means it tends to have a higher accuracy than directional t-shirt sizing. Here are some tips for using the bottom-up method:

1. Understand the performance of a product or system that is comparable.

To introduce any enhancements to your current product or system, you need to understand how it’s performing in the first place. You’ll want to identify, observe and understand the performance rates in a comparable product or system, including the specifics of its unique audience and process.

For example, let’s say your Growth Marketing team wants to localize a new welcome email to prospective users in Italy that will go out to 100,000 new leads per year. A comparable system could be a localized welcome email in France that the team sent out the prior year. With your comparable system identified, you’ll want to dig into some key questions and performance metrics like:

• How many people received the email?
• Is there anything unique about that audience selection?
• What is the participation rate of the email?
• What is the conversion rate of the sequence? Or in other words, of those that opened your welcome email, how many converted to customers?

Let’s say we identified that our current non-localized email in Italy has a click through rate (CTR) of three percent, while our localized email in France has a CTR of five percent over one year. Based on the metrics of your comparable system, you can identify a base metric and make assumptions of how your new initiative will perform.

2. Be clear and document your assumptions.

As you think about your initiative, be clear and document your assumptions about its potential impact and the why behind each assumption. Using the performance metrics of your comparable system, you can generate an assumed base metric and the potential impact your initiative will have on that metric. With your base metric in hand, you’ll want to consider the positive and negative impacts your initiative may have, so quantify your estimate in ranges with an upper and lower bound.

Returning to our localized welcome email example, based on the CTR metrics from our comparable system we can assume the impact of our Italy localization initiative: if we send out a localized welcome email to 100,000 new leads in Italy, we will obtain a CTR between three and five percent (+/- delta) in one year. This is based on our assumptions that localized content will perform better than non-localized content, as seen in the performance metrics of our localized welcome email in France.

Now that you have your opportunity sizing estimate for your initiative, the next question that comes to mind is “what does that mean for the rest of your business goals?”. To answer this, you’ll want to estimate the impact on your top-line metric. This enables you to compare different initiatives with an apples-to-apples lens, while also avoiding the tendency to bias to larger numbers when making comparisons and assessing impact. For example, a one percent change in the number of sessions can look much bigger than a three percent change in the number of customers which is further down the funnel.

Returning to our localized welcome email example, we should ask ourselves how an increase in CTR impacts our topline metric of active user count? Let’s say that when we localized the email in France, we saw an increase of five percent in CTR that translated to a three percent increase in active users per year. Accordingly, if we localize the welcome email in Italy, we may expect to get a three percent increase which would translate to 3,000 more active users per year.

Second order thinking is a great asset here. It’s beneficial for you to consider potential modifiers and their impact. For instance, perhaps getting more people to click on our welcome email will reduce our funnel performance because we have lower intent people clicking through. Or perhaps it will improve funnel performance because people are better oriented to the offer. What are the ranges of potential impact? What evidence do we have to support these ranges? From this thinking, our proposal may change: we may not be able to just change our welcome email, we may also have to change landing pages, audience selection, or other upstream or downstream aspects.

Top-Down

The top-down method should be used when opportunity sizing a new initiative. This method is more nuanced as you’re not optimizing something that exists. With the top-down method, you’ll start by using a larger set of vague information, which you’ll then attempt to narrow down into a more accurate estimation based on assumptions and observations. 2

Here are a few tips on how to implement the top-down method:

Unlike the bottom-up method, you won’t have a comparable system to establish a base metric. Instead, seek as much information on your new initiative as you can from internal or external sources.

For example, let’s say you’re looking to size the opportunity of expanding your product or service to a new market. In this case, you might want to get help from your product research team to gain more information on the size of the market, number of potential users in that market, competitors, etc.

2. Be clear and document your assumptions.

Just like the bottom-up method, you’ll want to clearly identify your estimates and what evidence you have to support them. For new initiatives, typically assumptions are going to lean towards being more optimistic than existing initiatives because we’re biased to believe that our initiatives will have a positive impact. This means you need to be rigorous in testing your assumptions as part of this sizing process. Some ways to test your assumptions include:

• Using the range of improvement of previous initiative launches to give you a sense of what's possible.
• Bringing the business case to senior stakeholders and arguing your case. Often this makes you have to think twice about your assumptions.

You should be conservative in your initial estimates to account for this lack of precision in your understanding of the potential.

Looking at our example of opportunity sizing a new market, we’ll want to document some assumptions about:

• The size of the market: What is the size of the existing market versus the new market size. You can gather this information from external datasets. In the absence of data on a market or audience, you can also make assumptions based on similar audiences or regions elsewhere.
• The rate at which you think you can reach and engage this market: This includes the assumed conversion rates of new users. The conversion rates may be assumed to be similar to past performance when a new channel or audience was introduced. You can use the tips identified in the bottom-up method.