The E-commerce Growth Framework
The E-commerce Growth Framework is a model designed to help e-commerce businesses understand the customers’ journey through their platform and help give structure to their growth and optimization efforts.
“If your business is not growing, it’s dying there are no neutral grounds”
Table of Contents:
Part I: Introduction
- What is the Pirate Framework?
- What is the E-commerce Growth Framework?
Part II: Framework Breakdown
Part III: Conclusion
Part I: Introduction
What is the Pirate Framework?
Introduced 11 years ago, the Pirate Framework is a model that helps founders understand how their customers are interacting with their platform. It gives you a bird’s-eye view of the business by splitting up each section of the platform based on the user journey.
The framework is divided into 5 segments:
- Acquisition: How do people find my website?
- Activation: How many are experiencing the “aha” moment?
- Retention: Do they come back after X months?
- Referral: Are they telling their friends about the product?
- Revenue: Are they paying for the product?
Why is the Pirate Framework not enough?
Although the AARRR model is a globally recognized growth framework, it was built primarily for SaaS businesses and not e-commerce businesses.
From the funnel you’ll notice this differs to most e-commerce businesses where revenue comes much early in the customer journey, this is because most SaaS businesses offer a free trial where during the trial period they aim to get users to their “aha” moment as quickly as possible i.e. activate them, then hopefully users see the value of the product leading them to upgrade to the paid version and finally invite their friends to use it.
Hence the need for a modified model: The E-commerce Growth Framework.
What is the E-commerce Growth Framework?
The E-commerce Growth Framework is a model built specifically for e-commerce businesses with rapid but sustainable growth has its focus.
Unlike the Pirate framework, this model is divided into 4 segments:
Each of these segments with its own unique North-Star Metric. The north star metric is the most important metric we consider while trying to gauge the performance of that segment.
Part II: Framework Breakdown
In this section, we’ll get down into the nitty gritty of the framework. One of the things that makes this framework so effective is that each segment has its own specific North-Star Metrics; so rather than conducting uncoordinated optimization processes in random segments of the store in hopes of moving the needle, we can take a more specific approach in improving the performance of each interdependent funnel segment, ultimately boosting the overall store performance.
The acquisition segment is at the very top of the funnel. It helps you understand where exactly your store visitors are coming from at the moment and where the best places are to find customers.
There are hundreds of possible acquisition channels a business can employ to get customers but if they make the mistake of chasing too many at the same time, it’ll quickly get overwhelming. To overcome this hurdle, you need to figure out your fundamental acquisition channels.
Fundamental acquisition channels are defined as a store’s most valuable traffic sources. These sources are not necessarily the sources that deliver the most traffic to the website but the ones that deliver the highest return on investment.
There are two types of acquisition channels:
- Paid Acquisition Channels
- Evergreen Channels
Paid Acquisition Channels are traffic sources that get visitors to your store within a short period of time. It’s a “pay-to-play” system. The more money you put in, the more traffic you should be getting. Examples include Facebook ads, Google Ads, Programmatic Ads, e.t.c
Evergreen Channels are traffic sources that are built over a long period of time at low-costs but consistently brings visitors to your store. Examples include SEO, Podcasts, Video Marketing, Microtools, e.t.c.
Businesses shouldn’t rely solely on paid traffic because prices have been consistently rising (Facebook’s average price per ad increased by 13% last year) and might eat too deep into profit margins. But by introducing an evergreen channel into the mix, it can help bring down overall customer acquisition costs, and help you maintain healthy profit margins.
A relevant case study to this approach would be Airbnb’s use of media coverage from large news agencies like CNN to sell its Obama and McCain’s cereal boxes. They ended up making $30,000 in a day, which the founders later admitted was pivotal in keeping their business afloat.
Determining your store’s Fundamental Acquisition Channels
In order to determine the store’s fundamental acquisition channels, we’ll be using an e-commerce adaptation of the Bullseye Framework originally proposed by Gabriel Weinberg.
First, you consider all the possible traction channels and hypothesize which set of channels are best suited for your business. Choose at least 4 channels you think your target audience is most active on.
A way to determine this is by analyzing the current traffic channels your competitors are using. You can do this by using SimilarWeb. But you should know, the biggest opportunities often lie in traffic channels currently generally being underutilized by your competitors.
After deciding what channels you think might be most viable for your store, you’ll need to conduct tests on each channel. In order to do this, you’ll need to allocate a specific budget and timeframe to your testing efforts. Your budget typically shouldn’t be more than $1000 per channel (depending on the scale of your business) and should typically last for 21-30 days (also depending out the size of your business).
During this period focus on testing one paid acquisition channel at a time, this is to ensure you’re focused on maximizing results from each channel.
Although paid acquisition channels might yield immediate viable results, low-cost channels might take a longer period to produce results, so multiple evergreen channels can be tested at the same time. So I’d recommend testing such channels over a longer period while you test individual paid channels too.
This is the final step in the acquisition channel selection process. I recommend focusing on a single paid and evergreen channel at a time. This is so that you can double down and focus on getting the most value from each channel rather than having mediocre returns from multiple channels.
Suppose after running tests on 8 channels over the course of 6 months here were my results:
As you can see from the spreadsheet, out of the 8 channels, 6 were profitable while 2 were not. According to the Bullseye framework, your next move should be to put all other channels on hold and invest in Blog Sponsorships and search engine optimization (SEO).
If your major paid channel becomes too expensive to maintain or starts proving problematic, you can put the channel on hold and focus on the second most effective channel, in this case, Facebook Ads.
Note: In order to ensure you get the most accurate results from your tests, no conversion optimization experiments should be carried out on your store during the acquisition channel tests.
OMTM in Acquisition
Based on the results of our tests, we’ve selected the acquisition channels that provided us the highest return per dollar spent. But in order to increase our acquisition performance as our store grows, we’ll need a key metric to guide our optimization efforts. The most important metric to track in the Acquisition funnel is CPC (Cost Per Click).
The reason why CPC should be used as our north metric in the acquisition funnel is that it is a function completely dependent on the acquisition channel and not interdependent on any factors relating to the store e.g. conversion rate. This way we can isolate the performance of the acquisition funnel when trying to optimize it.
In order to optimize the CPC, your primary focus should be on reducing the cost. Because we’ve already gone through our channel testing phase, we know how much we should be expecting to make if 1000 visitors came from each channel. Hence, if we can drive down the costs of acquiring the same number of visitors, we’ll inevitably generate more in revenue, provided all store variables remain constant.
So we’ve determined that our most viable traffic sources are blog sponsorships and SEO, the next thing we need to figure out is how we are going to get those visitors to convert when they come to our store. That’s the focus of this segment of the funnel, to make as much revenue as we can from the visitors coming to our store.
There are two major metrics that determine how much revenue a business will derive from the traffic coming to its store:
- Conversion Rate
- Average Order Value (AOV)
Both are extremely valuable metrics that’ll help you gauge the health of your store.
The conversion rate is the percentage of orders that were placed on the store relative to the total number of sessions on the store during the same time frame.
Conversion Rate: (Number of Orders/Total Number of Sessions) x 100%
For example, assuming a Merchandise Store recorded 60,117 sessions on the website in the last 30 days out of which 168 orders were completed. This means that the conversion rate is (168/60,117) x 100% = 0.28%.
Growing Conversion Rate
One model I really recommend when you’re thinking about how to increase your store’s conversion rate is the Fogg Behavioural model developed by BJ Fogg, a behavioral scientist at Stanford.
According to the model, for a visitor to perform a target activity, in our case that’s place an order from our website, there are three factors that must be adequately satisfied:
- Motivation: The visitor must be motivated enough to carry out that action. The fundamental goal for your store should be to amplify the pain the visitor is feeling, then prove you’re a perfect solution to that pain.
- Ability: How easy is it to place an order on your store. Some factors that decrease ability are slow loading times, long checkout processes, e.t.c.
- Trigger: Triggers act has notifications that urge visitors to perform a certain task on your store. In our case, that could be an exit intent popup offering the visitor free shipping or abandoned cart emails.
If you’re looking to find out more about how you can use the model to increase conversion rates on your store, below is a video from Isaac Rudansky, that explains just that:
Average Order Value (AOV)
Average Order Value is the average amount each customer spends on your store per transaction. It’s calculated by dividing total revenue by the total number of orders over the same time frame.
Average Order Value= Total Revenue/Total Number of Orders
By knowing your AOV, you’ll be able to make accurate predictions of how much you can be expecting from X number of orders on your store, hence how many orders you need to reach your revenue targets.
OMTM for Revenue
Although both Conversion Rate and Average Order Value (AOV) are important, independently they don’t paint an accurate picture of the health of a store’s website. Imagine, I solely focused my attention on my store’s conversion rate and after optimizing it for 3 months I was able to increase it from 0.28% to 0.5%, I’d be so ecstatic because I’ve almost doubled conversions.
But little did I know, in the same time period, my AOV had dropped from $60 to $40, which sucks because it negates the improvements I made to my conversion rate taking me back to where I started in the first place.
So what’s the one metric to keep your eye on in the Revenue segment? The answer: Revenue Per Visitor (RPV). The reason why this metric works really well is because it takes into account both the average order value and conversion rate in defining how well the store is doing.
Revenue Per Visitor (RPV)= AOV x Conversion Rate
From the 3 months of optimization, I did on the store if I was also tracking RPV rather than only conversion rate, I would have seen that my store’s health wasn’t getting better rather worse.
How do we optimize for Revenue Per Visitor?
Given the fact that the Revenue per Visitor is fundamentally constituted of two core elements: AOV and Conversion Rate, in order to optimize the RPU, either one (or both) of these metrics have to improve. Although we would be tracking the RPU to gauge the performance of our store, we would actually be focused on improving both of its unit components.
So in my last example, the problem itself wasn’t in my execution (my conversion rate improved substantially) but rather in my lack of regard for its equally important sister metric: AOV.
In summary, don’t focus on optimizing RPU has a whole, rather focus on optimizing any of its constituent metrics but when looking to gauge the impact of your optimization efforts refer to the RPU has your guide.
Referral is the 3rd segment of the e-commerce framework funnel and has to do with the number of customers that your business acquires as a result of recommendations made by previous customers.
Referrals in SaaS vs Referrals in E-commerce
In most SaaS businesses, users get to use the platform before they pay for it, this way they’re more likely to recommend the product to a friend because they’ve already experienced the value in it. This is why the Referral segment is before the Revenue segment in the Pirates Framework.
But with e-commerce stores, customers have to order a product first before they can derive value from it, and consider recommending it to their friends. And this is why the Referral segment comes after the Revenue segment in the E-commerce Framework.
If an e-commerce business can effectively embed a viral element into its business it’s well on its way to becoming a runaway success because now the store is essentially just acquiring customers at a zero cost acquisition rate.
OMTM for the Referral Segment
The one metric that matters in the Referral segment is the Viral Coefficient. The Viral Coefficient is a metric that monitors the number of new customers each present customer adds to your business.
Viral Coefficient= [Total number of referrals x Conversion rate]/[Total Number of Customers]
- Total number of referrals: The total number of all the referrals or invites present customers sent to their friends.
- Conversion Rate: The percentage of friends that actually clicked the link and came to our store to order a product.
- Total number of customers: The total number of customers that we told to share a referral link.
Take for example, on a store, 100 customers complete an order today. 5 days after each one of them has received their order, we send them an email asking them to recommend the product to some of their friends via email if they liked it. This led the customers to send out a total of 300 invites, and eventually resulted in 40 new customers for our business.
To calculate the viral coefficient:
From our calculations, we can see that the Viral Coefficient is 0.399 which is considered good by e-commerce standards. Usually, the only platforms that achieve a viral coefficient greater than 1, are social media platforms and hyper-growth SaaS platforms.
Retention is the final segment of the funnel. Customer Retention is defined as the percentage of customers your business has been able to keep active over a given time frame.
Unlike in SaaS businesses, where usually users are required to pay for the service on a recurring monthly/yearly basis, apart from the subscription-box model, the average e-commerce businesses are based on one-off transactions and don’t require customers to make fixed frequency purchases.
In a SaaS business, a user might pay $10 consistently each month, subscribe as a customer for 6 months and then discontinue the service. But on an e-commerce store, that same customer might order merchandise worth $30 dollars in January, come back in May to order goods worth $5 and finally make a purchase worth $25 during the Christmas season. As you can see in both scenarios each individual business makes the same amount of revenue from the customer, but the only difference is in the recurrence of the transactions.
Therefore, if we are to track and optimize retention properly for e-commerce businesses we would need to think about transactions in an aperiodic manner.
OMTM of Retention
For SaaS, retention is often measured using Churn rate i.e. the number of customers who deactivate/unsubscribe for a service during a given period of time.
But with e-commerce businesses, the churn rate isn’t especially suited for the business model because customers aren’t locked into a recurring payments model, as explained previously.
Hence, retention should be tracked using Customer Retention Rate (CRR). Customer Retention Rate is the percentage of orders got from existing customers relative to the total number of orders made in a specific time frame.
CRR= (Orders made by Return Customers/Total Number of Orders) x 100%
Assuming, I own a store which has generated 5,980 orders since its inception 3 years ago till date. And 761 of the orders were placed by returning customers.
Illustratively, assuming I own a store that started 3 years ago and the spreadsheet provides customer order details for each respective year.
From the spreadsheet above the store generated a total of 5,980 orders during the full 3 year period and 761 of those orders were placed by returning customers. Therefore, the overall store CRR is 12.73%.
Although other e-commerce specialists might recommend using Customer Lifetime Value (CLTV) to gauge retention, this will not be the best-suited metric for the e-commerce framework, which focuses on helping store owners optimize each segment of the funnel independently and the CLTV is dependent on functions which cut across multiple segments of the funnel. Hence, CLTV is best suited for measuring the overall health of the store, which we will discuss in the next section.
Overall Store Optimization
As I mentioned at the beginning of this article, the major benefit of the e-commerce framework is that it breaks down your store into multiple interdependent funnels, which allows you to identify the key segments that are holding back the overall performance of your store.
In order to apply this framework in your daily optimization efforts, you might assign manpower ratio to each segment of the funnel. These ratios help you define what segments you believe are most important to achieve your current business objectives.
For instance, assume a store generates $700,000 yearly, as served over 8,000 customers in the past 3 years and is currently employing 5 people. This year they intend to enter into a new market presently being dominated by a legacy brand. They decided that in order to capture market share, we’ll need to increase our customer acquisition efforts and also double-down on the use of social media to generate buzz around our offering.
So implementing the framework, I assign the following manpower ratios to each section:
After which, I use this ratio to allocate staff and resources to the optimization of each segment, which is best suited for the business’ present needs.
Overarching North Star Metrics
Finally, one very important metric to note while gauging the overall performance of your store is the Customer Lifetime Value (CLTV) to Cost per Acquisition (CPA) ratio.
Consider an e-commerce store like a production engine, that takes in an input of raw materials to create an output of finished goods. Just like how the overall efficiency of the engine is defined by how much output can be produced from a specific amount of input, the efficiency of a store can be defined by how much revenue store is able to generate from each customer (output) relative to how much is invested to acquire one customer (input).
At a glance, this metric gives business owners and investors a rough estimate on how much ROI to expect for each dollar invested into the company. The higher the ratio, the higher the gross profitability of that store.