How You Can 4x Your Sales In 90 Days


Our step-by-step scaling process from $250,611.59 to $1,424,866.75 with facebook ads in 90 days

Ever wondered how you could scale your ecommerce business to millions of dollars in revenue?

We wrote this case study to show you how we generated $1.42 million in revenue for one of our clients through Facebook advertising.

Keep on reading to find out the exact strategies we used to achieve this.

The Business

The client is an established ecommerce business that sells clothing and jewelry. The entirety of their revenue is generated through Facebook advertising. The breakeven ad account Return on Ad Spend (ROAS) was 1.2X.

The Problem

The client had plenty of data on their customers and target audience, but lacked the knowledge to use this data effectively. They spent more than four hours every day on managing ad campaigns, which left them with no time to work on branding, fulfillment, and other activities necessary to maintain and grow their business.

Having tried out more than a dozen different freelancers and agencies, and not being satisfied with the results, the client resolved to running their Facebook ad account themselves.

Within the first month, the client spent more than $115,372 on ads and generated around $250,611 in sales for a ROAS of 2.17x.

However, they were certain that better results could be achieved if someone with more knowledge and experience took over their advertising account. That’s when they decided to ask Alpha Empires for help.

The Solution

After examining the client’s ads and past results, we formulated a strategy that will help them get a better return on their Facebook advertising spend and allow them to generate more revenue.

We took over the entire advertising aspect of the client’s business by creating ads, managing campaigns and ad spend, as well as optimizing their website for conversions.

Our initial acquisition strategy was based on a Cost Per Acquisition (CPA) model. Our intention was to use an irresistible offer to acquire a large number of customers quickly and affordably, and then upsell them post-checkout and through retargeting campaigns.

This is something we did for the first few months to acquire large amounts of data, emails, and subscribers at a profit.

The Strategy

Our strategy consisted of four distinct steps. We’re going to cover these in detail below.

1) Increasing AOV by implementing a post-purchase upsell offer

One of the first things we wanted to do after taking on the project was to try to increase the client’s average order value. To do this, we implemented a post-purchase upsell offer.

We split-test a number of different products for the upsell offer until we found the one that had the highest conversion rate. Once we’d found our winner, we tried out different price points and discounts until we improved conversions even further.

In the end, we managed to get a 25% upsell conversion rate, which helped to increase the client’s AOV significantly.

2) Testing & optimizing

The next step involved testing and optimizing creatives and audiences in order to create high-performing ads that would help the client grow their sales and revenue.

Creative Testing

We used two campaigns to test and optimize ad creatives and also semi-automated this process with rules:

1) PPE (Page Post Engagement) Campaign – For the PPE campaign, we used a single proven audience and tested all the ads against it. The campaign was set to shut down after lifetime impressions exceed 1500/2000.

2) WC (Website Conversion) Campaign – We included all the ads in the Website Conversion campaign and set it to be optimized for landing page views. As with the PPE campaign, the WC campaign was set to shut itself down after reaching 1500/2000 impressions.

PPE is the most affordable way to test creatives 99% of the time, so that’s what we used for our initial test.

One thing you need to keep in mind is that creative testing depends on your market appeal, which means that if you’re looking to advertise a product with broad appeal, the more expensive it’s going to be to test creatives because you’ll have to test to more audiences.

Luckily, we were promoting a very specific, niche product, so we could happily use that interest as our ‘control’ for creative testing.

The main metric we were looking to optimize for here was CTR, but not overall CTR (WC – LPV campaigns are obviously far higher in CTR, as you can see from the screenshot). We were looking for a correlation between PPE CTR and winning WC – PUR creatives.

To put it simply, we were looking to find out if the highest CTR PPE creative translated into a high ROAS creative when moved over to our prospecting campaigns (WC – PUR).

However, our PPE campaign started to generate sales, so we knew that we’d found a high-converting creative.

We used a single ad set based on the main converting interest. The ad set consisted of completely new ad variations. We were running anywhere from three to five variations at the same time.

We also tested carousel ads, except we had to use the WC campaign to do it. After getting about 2000 impressions for each variation, we had enough data to determine the winning creatives.

After finding proven creatives, we moved on to testing audiences.

Audience Testing

Testing audiences requires proven ad creatives because we need to reduce the amount of variables to 1. The first audience we tested were ones we believed to be the closest to the target buyer persona.

We then started to test lookalike audiences since these were the ones that were the most likely to convert. We were looking to get a minimum of 1000 conversions before we could know for certain that a particular audience is a profitable source of traffic.

When branching out into other similar audiences based on interests, we used public information available on users’ Facebook profiles to find out even more related interests that we could use to improve our targeting.

The audiences were tested by putting three proven ads into a cold campaign.

3) Scaling

Once we’ve found the highest-performing ad variants, we started to scale the winning ads through:

  • Interest-based audiences – We created new audiences based on related interests and showed them our best ads.
  • Lookalike audiences – Apart from horizontally scaling based on interests, we also created lookalike audiences based on page views, view content, add to cart, purchase, and aggregated value.
  • Geo-targeting and location grouping – Another strategy we found success with was building lookalike audiences for different countries (US, Canada, UK, and Australia) and then grouping the most profitable countries with the best creative and ad copy.
  • Broad DPA carousels – Finally, we used dynamic product carousel ads targeting broad audiences (e.g., top 20 countries by GDP, all related audiences, etc.)

4) Remarketing and retention

  • Warm – Anyone who engaged in any way but hadn’t fired the view content pixel in the last 30 days nor made a purchase in the last 180 days. This was further broken down by intent while using a 30-day time window.
  • Here are a few Warm audiences we used and a sample;
    • FB/IG Page engagers
    • Video Views
    • Page View (Excluding View Content)
  • Hot – Anyone who had viewed content or added to cart but hadn’t made a purchase in the last 180 days. This was then broken down by date;
      • View Content & Add to Cart In the last 3 days, Excluding Purchaser 180 days
      • VC & ATC In the last 7 days, Excluding; VC & ATC 3 days, PUR 180 days
      • VC & ATC In the last 14 days, Excluding; VC & ATC 7 days, PUR 180 days
      • VC & ATC In the last 30 days, Excluding; VC & ATC 14 days, PUR 180 days

It’s important to remember that at each stage the previous stage is excluded to stop any overlap. That’s why the 7 day audience would also exclude the 3 day VC & ATC etc.

Here are some dynamic carousel ad examples from our hot campaigns. As you can see there are 3 text variations and also no mention of a discount.

We used catalog sales as the campaign objective, first serving a simple reminder ad with no offer. This was ran for up to seven days.

We don’t like to give discounts when we don’t have to, and after split testing across many client accounts we’ve always found a simple reminder with no discount works very well initially.

Then we used discount offers of Free Shipping, 5% 10% and 15% to incentivize customers to make another purchase.

The final step involved running a retention campaign using a thank you ad. Our focus for this campaign was to improve customer retention and increase repeat purchases by using a Messenger bot ad offering existing customers a loyalty discount.

We optimized the campaign for reach to be able to get our ad in front of as many of our previous buyers as possible. We used a 30-day custom audience and set the frequency cap to 2 impressions per week to avoid annoying our target audience.

Our first thank you ad included a video in which the owner thanked customers for buying from his business. We ran this for three days after a purchase.

The goal for this ad was not to achieve a high ROAS, but rather to build brand loyalty and grow our Messenger subscriber list. However, we also got 97 purchases from this ad. Pretty cool, right?

The second ad we used simply asked customers if they were satisfied with their purchase and offered them a discount in exchange for a review.

The Result

We helped the client reach a positive return on ad spend within the first month, generating more than $160,000 in revenue.


Facebook Spend







May (30 Days)




June (60 Days)




July (90 Days)




Within 90 days, we helped the client generate more than $1,174,254 in revenue with a total ad spend of $461,189.

Here’s a quick summary of the entire process:

Month 1: We scaled what was already working, launched more audiences and lookalike audiences, tested creatives, and implemented upsells.

Month 2: We optimized the winning ads, got rid of the losing ones, and split-tested upsells to improve average order value.

Month 3: We continued optimizing the winning ads, started optimizing our post-purchase upsell offer in terms of both conversion rate and AOV, and also split-tested initial product price.

One of the biggest wins was that we managed to improve the client’s average order value from $13.62 to $20.12 in 60 days, achieving a 47.7% increase. Which in turn meant each customer was worth more to us. The knock on effect… we can now spend more to acquire customers!

In summary in the first 4 months of this clients business we were able to help them cross over the 1.4M mark which is a pretty awesome feeling for everyone involved!

Apart from helping the client achieve a better return on their advertising spend, we also helped them save time by not having to worry about the advertising aspect of their business.

This, in turn, allowed them to focus on building their brand and running the business itself, which has continued to grow to this day.

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