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Large FinTech Startup - Universal App, Google Ads, Apple Search Ads

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Scaled up spend 68% while decreasing average CAC 11%

Paid Media Channels

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Apple Search logo Apple Search
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illustration of case study: Scaled up spend 68% while decreasing average CAC 11%
41%
Average CVR increase
50%
Average decrease in CAC

The story

Client Overview: The fin-tech client was in need of an agency to manage their Google Universal App Campaigns (UACs) and Apple Search Ads (ASA) while they looked to source these responsibilities internally. Galactic Fed was brought on to bridge this gap with the goal of decreasing CAC while scaling spend.

In addition, the agency also took on the responsibility of launching their Google Ads Search & Display Campaign with the release of their new web application. While the relationship was expected to be temporary and last for 1-2 months, the team at Galactic Fed continually exceeded performance expectations, and the project ran close to a total of 9-months before being handed off to their internal team members.

Strategy:

Universal App Campaigns (UACs)

With very few levers to pull to increase performance in UACs, our strategy focused heavily on cycling in creative assets (text, images, videos, HTML5) while also adjusting Target CPA bid in minor increments. Being a Machine Learning campaign type, it was essential to avoid making several small changes to the campaigns as this would make the campaign recalibrate and enter into a brief learning phase, hindering performance. With this insight, we created testing campaigns to avoid changes in our main Evergreen campaign.

Google Search + Display

Our primary focus was to launch Search + Display Campaigns with the release of the client’s web application. We leveraged several resources such as competitor research, past search campaigns, and other paid channel insights to understand specifics to avoid and areas to further exploit. Due to an initial six-figure first month budget, we focused on casting a wide net to accelerate learnings of what works / what doesn’t work.

Apple Search Ads (ASA)

Having a significant overlap with Google Search Campaigns, we were able to utilize the majority of our insights from those to refine the current ASA Campaigns. Our strategy included simplifying the account structure to allow for more control over the keyword CPT bids, accelerate testing new keywords, and creative set testing - while saving a lot of time in the high-touch point interface.

Execution:

Universal App Campaigns (UACs)

Heavily focused on creative, we devised a dual-threat campaign testing system to accelerate creative learnings and rotate the highest performing creative into our main Evergreen campaigns. Additionally, we leveraged our network of design resources to produce several high-quality videos and images that passed compliance approval.

As for evaluating creative asset performance, we were fortunate to have a direct Google UAC fin-tech rep that requested the Google measurement team to build a Google Data Studio dashboard to showcase creative performance. With the help of this dashboard and a pivot table, we were able to discover creative assets that produced low impressions, declining CTR, and reduced spend. Subsequently removing them from the campaign and add in new creative, or vice versa, taking the high-performing creative from the testing campaign and moving to the main Evergreen campaign.

Furthermore, we systematized our creative learnings in a spreadsheet as we rotated creative to keep track of the assets’ qualitative features. This system was essential in performing monthly qualitative analyses of our learnings that were shared with the client.

Apart from cycling creative, we also carefully adjusted Target Bid (tCPA) in minor increments to avoid drastic performance shifts and to help decrease CAC.

Google Search + Display

After scouring through several resources (competitor spy tools, past campaigns, other ad channels), we cast a wide net of attack with 30+ search campaigns (Brand, Competitor, Category) to drive traffic to the new web application. Our search campaign structure consisted of SKAGs (BMM + Exact) followed by two ETAs and one RSA per ad group to test the several compliant approved headlines and descriptions.

Early on, we focused on reducing our wide cast net (i.e., finding what didn’t work) as we paused keywords, added negative keywords, ad copy, ad extensions, and landing pages. This strategy helped accelerate the learning process as it was a priority to reasonably scale spend as fast as possible. It took us about four weeks for our initial major tests to surface clear winners / losers. We then double-downed on winning campaigns by rotating through automated bidding strategies and winning keywords by driving up search impression share. Within six weeks of launching, we were able to scale spend to six-figures per month.

After receiving confirmation that UACs did not compete with mobile-web search campaigns, we had a significant breakthrough in performance on mobile-web campaigns as they produced half the CAC of the computer device search campaigns. Serving on mobile-web allowed us to increase spend even more as we hit a six-figure spend in just one week. Apart from this, we executed several weekly and bi-weekly analyses such as mining the search terms report, device / location / ad schedule / keyword bid adjustments based on performance, and new ad copy / ad extensions.

As for Display, we launched a remarketing campaign, segmenting individuals based on website recency and viewed content. After hitting enough conversions from Display Remarketing, we launched a Smart Display Campaign that ended up producing a similar CPL to our Category Search Campaigns.

Apple Search Ads (ASA)

Our first order of business was to restructure the campaigns into ASA’s best practice campaign structure (Brand, Competitor, Category, Discovery). Inside these campaigns, we used SKAGs with the primary intent to set creative testing at a keyword level and control targeting options within ad group settings.

One limitation of ASA is that it only shows cost-per-install (CPI), not down-funnel KPIs (e.g., CAC). We needed a solution to this problem. With the help of exported MMP data, exported ASA data, and internal search volume data, provided by our two ASA reps, we were able to create a framework to join all three data sets. Subsequently, this allowed us to determine CAC at the keyword level. This newly devised bidding strategy allowed us to increase our spend on ideal CPA keywords and pause / lower CPT bids on poor performing keywords.

Apart from weekly CPT bid adjustments, the other primary driver of performance in the account was adding new keywords. We derived new keywords from mining the Search Terms report in our broad-match Discovery Campaign, as well as brought over high-performing keywords from Google Search Campaigns to our Discovery Campaign. Pending the performance of the keyword in the Discovery Campaign, we either moved it to the appropriate exact match campaigns and added as a negative in our Discovery Campaign, or paused the broad-match keyword altogether.

Performance Results:

UACs

Before partnering together, the client was spending close to six-figures per week on UACs, in-which averaged out over the 9-months, we were able to help scale up spend 68% while decreasing average CAC by 11% and driving an increase in conversion rate (CVR) by 42%.

Engagement Date Range: 5/27/19 - 1/5/20

Previous Date Range: 2/25/19 - 5/26/19

68% Average Spend Increase.

  • $151k/week from the average spend per week during our engagement.
  • $90k/week average spend for the previous three months.
  • (151k - 90k) / 90k = 67.8%

11% Average Decrease in CAC

  • $59 Overall (iOS + Android - Evergreen + Testing) during our engagement.
  • $66 Overall (iOS + Android - Evergreen) three months prior our engagement.
  • ($59 - $66) / $66 = -10.6%

42% Average CVR Increase.

  • 23.74% average CVR during our engagement.
  • 16.77% average CVR for the previous three months.
  • (23.74% - 16.77%) / 16.77% = 41.6%

Google Search + Display

Due to not having CAC performance to benchmark from, our main highlight was proving out that Google Search + Display Campaigns are a long-term growth channel for the client as we had a few weeks with over six-figure spend. Furthermore, in search campaigns, we were able to achieve a 15.56% CVR (click to conversions), a 7.17% CTR, and a lower CAC compared to other profitable channels. Lastly, it was recently discovered that these two campaign types are bringing in very high LTV users - another sign of success.

Apple Search Ads (ASA)

Prior to our partnership, the client was spending close to six-figures per week on UACs, in-which averaged out over the 5-months, we were able to help scale up spend 52% while maintaining CPA. Also, we were able to have a significant break-through in CAC performance by using a LAT-ON install calculator to find a new audience segment that produced a CAC 50% less than the average of all the other campaigns.

Engagement Date Range: 5/27/19 - 10/13/19

Previous Date Range: 2/25/19 - 5/26/19

52% Average Spend Increase.

  • $25.2k/week from the average spend per week during our engagement.
  • $16.6k/week average spend for the previous three months.
  • (25.2k - 16.6k) / 16.6k = 51.8%

50% Average Decrease in CAC

  • $27 CAC for LAT-ON Campaigns during our engagement.
  • $54 CAC for Non-LAT-ON Campaigns during our engagement.
  • ($27 - $54) / $54 = -50%
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