Overview

uBreakiFix, an entity under the Asurion umbrella, is a prominent franchise chain specializing in electronic repair services. Since its inception in 2009, the company has experienced remarkable growth, expanding its reach to over 700 locations across the United States and Canada. uBreakiFix has established a reputation for repairing a wide array of household electronics, catering to a diverse customer base with various electronic repair needs.

Challenges

Challenges with Cost-Per-Customer-Repair Metrics:
Initially, the business faced significant challenges due to a high cost-per-customer-repair. This situation adversely impacted the overall profitability of the company. The primary issue was the elevated expenses incurred in acquiring and servicing each customer, which, in turn, reduced the net revenue generated per transaction. This challenge necessitated a strategic reassessment of cost management and pricing strategies to ensure sustainable business growth.

Navigating the CPC and Conversion Rate Equilibrium:
A major hurdle encountered was in balancing the delicate equilibrium between cost-per-click (CPC) and conversion rates in digital marketing campaigns. The company struggled to optimize its online advertising expenditure, where a high CPC was not correspondingly reflected in improved conversion rates, leading to inefficient marketing spend. This required a nuanced approach in ad targeting, budget allocation, and continuous monitoring of campaign performance metrics to achieve an optimal balance that maximizes return on investment.

Adaptation to Google's Performance Max Campaigns:
‍The transition from Google's Local Campaigns to the new Performance Max Campaigns presented a unique set of challenges. This change demanded a swift and strategic adaptation to the new system, which emphasizes automation and machine learning to optimize ad placements across Google's entire suite of channels. The company needed to reevaluate its digital marketing strategies, develop new skill sets, and reconfigure campaigns to align with the Performance Max framework, ensuring continued effectiveness in reaching target audiences and achieving marketing objectives.

Platforms Leveraged

• Google Ads
• Meta Ads
• Tableau
• Google Looker Studio
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Strategies

Bid Strategy Optimization:
Shifting from conversion-based strategies to maximize clicks and manual CPC to lower cost per click and increase traffic from our campaigns.

While our agency is a strong advocate of utilizing automation across Google’s machine learning through our talks with Google and Join or Die, we believe the biggest disservice an agency can do to their clients is to be dogmatic and arrogant with their approach.

Our team had a hypothesis that the CPCs were too high, and the conversion rate did not justify that bump in cost per click, so we decided to test a different strategy. This hypothesis was soundly based on data and rigorous sequences of regression analysis, and there were indications that this was a possibility. After conducting this test, after 60 days, it was a resounding yes that this led to overall better CPA and volume numbers for our campaign.

Never Never, Never Always, is one of the mottos our team lives by, and is something that is quite relevant with this bid strategy shift.

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Focusing on Extension-Based Conversion Actions:
Prioritizing actions like 'get directions' and 'click-to-calls' which were previously underutilized.

Because of the more conversion-based bidding strategies, Google’s algorithm forced our campaigns to go after on-site conversions with more weight than extension-based actions. Google’s hypothesis was that the user who went to the site to schedule a booking was more interested in a service appointment, and we should pay a premium for that user.

However, based on the data and our analysis, it seemed the user that we were looking for in the tech repair franchise world is someone who needs their computer or phone fixed, and they need it NOW… They don't have time to fill out a form but would like to get directions or speak to a sales representative ASAP.

Adopting Google's New Campaign Type:
Navigating change to previously profitable campaigns is never easy, especially to a more automated campaign type like Performance Max. When the forced migration of Local campaigns to PMAX, the results were not good at all; CPCs were far too low, and the CPAs were skyrocketing.

Our team hypothesized that the lower CPCs were due to display and discovery placements, and we should shift our conversion actions to only optimize for get-directions to ensure that our ads will be showing on search results and local map listings. After making that shift, our CPCs did increase, and our overall CPA decreased as well; even though we were paying more per click, we were getting more of the right clicks that were turning into good repair customers.

Data-Driven Approach:
Continuously monitoring and adjusting strategies based on performance data. We created specific dashboards and data visualization reports to monitor changes and performance and recommend how to improve and optimize the campaigns further.

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Results

• Surpassed Repair Goals: Exceeding the annual repair target by 40%.
• Improved Campaign Efficiency: Achieving a 238% decrease in total cost per acquisition (CPA).
• Adaptability and Success Across Two Countries: Implementing these strategies effectively across 700 stores in the US and Canada.