Analyzing data for mobile marketing campaigns is a complex process. A wrong number leads to a misinterpretation of the entire campaign.
A number of problems arise in this context:
- This is how you get quality data for analysis
- In this way you make the analysis process as transparent as possible
- How to properly measure campaign or app issues with metrics
What is Marketing Analytics?
In general, analytics is the feedback you receive as a result of your actions. Thanks to the feedback, a person can draw rational conclusions about their behavior.
Everything works the same way in business: feedback is needed to analyze previous actions and then develop or change a strategy. To get feedback, you first need to collect data that can benefit marketing analytics.
Marketing analyzes therefore show how correct the chosen strategy and audience are for the acquisition.
There are a variety of solutions for data analysis in mobile marketing. At AdQuantum (AQ), for example, we most often use tracking platforms such as AppsFlyer, Adjust and Google Analytics.
Data analysis principles
How can you avoid pitfalls and analyze data correctly? Here are some guidelines:
- relevance is critical to understanding exactly what we are analyzing. For example, does it make sense to compare the countries we are comparing? Tier 1 countries (e.g. USA, Australia) and Tier 3 countries (e.g. Cuba, Korea), for example, have traffic of different quality, so the data in these countries can differ significantly.
- Seasonality is a common concept in mobile marketing. We need to understand how long we are comparing the data for. For example, the data for December 2019 should be compared to the data for the same month in 2020. The approach should be the same for a specific month or even a specific week: weekends should be compared to weekends, for example due to the activity of the mobile app user is much higher on weekends.
- context Partly includes seasonality and other data parameters, such as B. Types of optimization of the analyzed campaigns. Do not compare the CPI in an app installation campaign to the CPI in a campaign with ROAS optimization. These metrics can be very different. That would be like comparing an apple to an avocado: yes, both are fruit, both can be green, but otherwise they are very different. The basic analysis is based on the end goal of the product in choosing the data to analyze.
Before you begin your analysis, you should find out what data you will be working with. Comparing initially different metrics or selecting the wrong key metrics can be misleading or just useless.
A metric is a qualitative or quantitative indicator that reflects a certain characteristic and the degree of success of a product. All marketing metrics are just numbers; they make little sense without interpretation. It also doesn’t make sense to analyze all marketing metrics at the same time. Each needs to be considered for specific purposes.
Let’s divide them into different groups.
Daily, weekly, monthly active users (DAU, WAU, MAU); Revenue; Retention rate (RR); and Churn Rate (CR) … all provide a general understanding of the situation: how many users are there on the app, how do they like it, how much they pay, and how many of them leave the app.
However, these metrics do not allow us to make specific marketing decisions or measure the impact of product changes. When working on an app, the main thing is volume, not mass.
Lifetime Value (LTV), Average Revenue Per User (ARPU), Customer Acquisition Cost (CAC) and Return on Investment (ROI) are the metrics that define the financial success of the product and its value to the user.
Cost per Mille / Thousand (CPM), Cost Per Installation (CPI), Click Rate (CTR), Installation Rate (IR), Engagement Rate (ER), Cost Per Action (CPA), Conversion Rate (CR) and Return on Advertising Expenditures ( ROAS) are the key metrics for marketers.
CTR, IR and CR are especially important. They enable us to draw conclusions about the efficiency of our work on the product and to show the quality of the recorded traffic.
How to organize analytics
When starting a performance campaign analysis, it is of paramount importance to define your goal: what exactly do you want to solve with this particular analysis?
Once the goal is defined, it is time to strategize and collect all the data you need. Based on our experience, we have established rules that allow us to organize transparent and high-quality analyzes:
These are the metrics that the product already had before it worked with us. They allow us to find out how well our campaign is doing compared to the past.
For the analysis to be objective, you need to have all the details in the big picture of the data: What traffic sources are we taking metrics from? Which countries? Types of Optimization? Traffic volume?
It makes a big difference whether we consider metrics from TikTok or from Google Ads. whether the traffic is from the USA or from India; whether the campaign was optimized for installs or conversions; whether we collect data from 100 users or 1 million.
Adequacy of the data
For example, suppose a product team provides us with a set of metrics: IR, CPI, CR, and CPA. Now we have four metrics, but they don’t mean much to us unless we understand what traffic sources they are from.
For example, Facebook, TikTok, Snapchat, and Google Ads all have their own characteristics. When we run a Facebook campaign we need to consider benchmarks from that particular traffic source. It is similar with countries: it would be wrong to use global data as a benchmark when running a US campaign.
The importance of well-chosen dates
Marketing analytics start with high quality, relevant data. Much depends on the completeness and reliability of the information collected: KPIs in reports, decisions about where to head the campaign and how much money the project will ultimately make. Bad data processing is the first reason for loss of time and money – for both the customer and the marketing agency.
Not sure which marketing strategy is right for you? Do you want your product to grow? Let’s talk.