The data speaks for itself. Check out how we found space to increase PPC revenue by 200%

Ladymakeup.com | Case Study
Growth Navigator

Mateusz Mikołajczyk
Team Leader mateusz.mikolajczyk@mta.digital

About the client

The customer (Ladymakeup) is an e-commerce company in the cosmetics industry that primarily focuses on two markets, but ships products to over 50 countries worldwide.

 

From this case study, you will learn how:

  • We found the space to potentially increase revenue by 200% within a year?
  • We calculated that the customer’s Lifetime Value (LTV) from Country A is over 460% higher than the customer’s LTV from Country B?
  • Can we increase the Average Order Value (AOV) by 10-20%?

Problems and Challenges

The main goal was to create a strategy for scaling the business.

The profit generated in e-commerce in the FMCG industry can be mathematically simplified to:

We don’t have control over the margin, so through data analysis on the accounts, we looked for specific opportunities to:

  • Increase Average Order Value (AOV)
  • Increase purchase frequency and thus Lifetime Value (LTV)
  • Grow the number of customers

To provide strategic advice and build a plan, we collected data, combined and analyzed it. Based on these analyses, we formulated conclusions, hypotheses, and the direction for the client’s business development.

What were our most important recommendations?

1. To increase Average Order Value (AOV): 

Analyzing the CRM, we noticed that the average purchase value in Country A is 324% higher than in Country B, while having a lower New Customer Acquisition Cost (nCAC). 

  • With this knowledge, we understood significant differences that can occur in consumer behavior between different markets/countries.
  • RECOMMENDATION: Focus marketing efforts on Country A, maximize revenue from this market, and then test other markets that could be equally (or even more) profitable. 

To increase AOV, sections such as “Others Also Viewed” or “Bestsellers” can be helpful, which, in our assessment, can increase AOV by 10-20% where it was lacking. Additionally, by analyzing the products purchased in Country A and Country B, we noticed that these users buy different types of products.

  • RECOMMENDATION: Add product bundles to the offers and include a Bestsellers section at the cart level, tailored to the user’s country of origin.
Example of the Best Sellers section

We noticed that promotional campaigns didn’t always have a clearly defined duration displayed on the website. 

  • RECOMMENDATION: Implement a sense of urgency in promotions, for example, by including a countdown timer indicating the remaining time for the promotion.

 

2. To increase purchase frequency and LTV:

Continuing the analysis of CRM data, we examined how often users make repeat purchases and at what value. It turned out that users from Country A make repeat purchases more frequently than in Country B, resulting in a 464% higher revenue per user from Country A compared to Country B within a year. At the same time, the full potential of PPC remarketing campaigns was not utilized.

  • RECOMMENDATION: Launch dynamic remarketing (product-based) campaigns. 

Promotional activities were not communicated in PPC campaigns, particularly for remarketing groups. 

  • RECOMMENDATION: Increase the reach of promotional communications by restructuring advertising account structures and creating dedicated campaigns (including remarketing campaigns) to inform about promotions. 

The analysis of CRM and Google Analytics data revealed a correlation between email marketing and the number of purchases (70%). However, email marketing targeting existing customers was used sporadically.

  • RECOMMENDATION: Implement regular email marketing campaigns informing about promotional activities.

 

3. To increase the number of customers:

In order to determine how to increase the number of customers, we first needed to find out:
Which traffic acquisition channels generate the most revenue? 

We compared CRM data with Google Analytics data and calculated correlations between channels. This allowed us to see how the number of orders and the share of specific traffic sources in the total traffic changes over time.

Significant correlations were found between purchases and:

  • Paid Search traffic
  • Organic Search traffic
  • Email Marketing

Furthermore, our channel analysis revealed the potential to increase revenue from Paid Search by 200%.

RECOMMENDATIONS:

  • Focus extensively on Paid Search, Organic Search, and email marketing as primary sources.
  • Test new customer acquisition channels after maximizing efforts on the aforementioned channels.
  • Expand SEO efforts for non-brand queries.

What’s next?

In the following months, we will focus on implementing our recommendations. We see room for doubling the growth of PPC efforts. On top of that, we plan to expand into additional markets because we recognize that finding the right market is crucial for business development and increasing sales. 

We continuously monitor the impact of our recommendations and implemented changes on the client’s business, and we will present the analyzed data in our next case study.

 

For the curious ones | More information about our approach

What were the stages of our process?

Operations Analysis – This involved analyzing individual channels and providing recommendations for their optimization. 

Media Efficiency Analysis – This is a strategic analysis that seeks to answer the question “Do your marketing channels work together?” It identifies areas with the highest scaling potential. It relies on data analysis from various channels to draw common conclusions for the business. The workflow can be presented as follows: 

  • Data collection 
  • Data aggregation and organization 
  • Data analysis 
  • Conclusions 
  • Implementation plan

1. Data collection 

  • from Google Ads 
  • from Meta Ads 
  • from Google Search Console 
  • from Google Analytics 
  • from CRM

Advertising platforms (Google Ads, Meta Ads) or Google Search Console only show results from one source. Google Analytics, on the other hand, combines this data, but it is often modeled and may not provide a complete picture depending on the attribution model used. Additionally, in the modern world, data privacy poses an increasing challenge in attributing events to individual users.

Therefore, we adopted data from the CRM (exported as .csv) as the source of truth, as it accurately represents sales data.

 

2. Data aggregation and organization 

When analyzing marketing results, there is often a temptation to focus solely on the Return on Advertising Spend (ROAS) on a specific platform. However, it is necessary to take a step back and ask: 

Is the marketing data accurate? How can we connect it all?

A necessary question to ask to have a broader understanding of the entire business and the relationship between marketing and sales.

Data organization is a tedious and less glamorous process, but it is necessary to draw accurate conclusions. Making decisions based on incorrect data can lead to errors in strategy and potentially worse sales results.

 

3. Data analysis 

After properly organizing the data, it was time to analyze it. It is crucial not to analyze data from just one platform, as this is a common mistake in strategic analysis of marketing activities.

During this stage, we search for dependencies, anomalies, and correlations.

The main objective: To understand which markets and channels generate sales and determine their scaling potential.

In the case of Ladymakeup: 

  • We observed significant differences in Average Order Value (AOV) and Lifetime Value (LTV) between countries. 
  • We identified the channels that contribute the most to sales. 
  • We estimated the extent to which these channels can be scaled.

4. Conclusions 

Every business is unique – based on the results of the analysis, appropriate assumptions and conclusions are made to help generate an implementation plan.

By analyzing: 

  • 6 channels, 
  • 8 data sources, 
  • and over 200 factors influencing their performance,

we obtained a complete picture of how online channels contribute to generating profit.

 

5. Implementation plan 

With such a large amount of data and after extensive analysis, numerous recommendations arise. However, to implement them effectively, it is necessary to assess priorities and create an implementation plan. This plan allows us to focus on the recommendations that can bring the highest revenue growth at the lowest cost. 

We have prepared two sections:

  • TOP 10 recommendations
  • Organized all 57 recommendations in a Kanban table 

This ensures clarity and provides a clear picture of the next steps.

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