Retargeting for App Revenue
Retargeting for App Revenue
Retargeting for App Revenue
In a world where user attention is fleeting, and competition is fierce, the key for increased revenue lies in effective retargeting. It’s typically the top 20% of users that drive 80%of your revenue. If only there was a way to figure out your top 20%, you ask?
Data science is how you unlock this. DS models let you tap into the insights hidden within your data, boost user lifetime value, and skyrocket your revenue.
This blog features insights from Pranesh Sharma, our product director, as shared during the RevX workshop at the App Marketing Conference, 2023.
Read on for actionable insights into leveraging data science and app retargeting for long-term profitability.
From First Purchase to Lifelong Loyalty: The Art and Science of Retargeting
Many advertisers overlook a key truth about their users: they have short attention spans and are easily swayed by competitors. A first-time purchase is only the beginning of the customer journey. For example, a user who has added-to-cart might not remember your product as well as you think they will. If you are not actively retargeting such high-intent users, you are leaving money on the table!
That’s why retargeting can be so effective – it keeps your product top of mind for users who are already interested. You don’t need to reinvent the wheel – sometimes, a gentle nudge is all it takes to keep your advertising efforts running smoothly for long-term sustainability.
Maximizing Partner Data for Better Programmatic Campaigns
The world of mobile advertising is an expansive and interconnected ecosystem. Publishers, mobile measurement partners, and ad exchanges all generate a wealth of valuable data. Here’s how to utilize it for your user acquisition and retargeting campaigns.
Boost App Installs with Inventory Data
Programmatic advertising is a gateway to an endless supply of ad inventory and reach. But to harness it effectively, an advertiser needs a deeper understanding of their data.
Questions like what kind of apps or ad inventories drive installs and which apps have a similar user base, etc., are critical in this process.
Supply data or inventory data can be your guiding lamp! It helps you strategically map out where your ads should appear, ensuring maximum visibility to your app and relevance to your target audience. The value lies in its ability to guide you toward high-performing app environments and placements.
You can identify the most effective
– app categories with a similar user base
– demographics
– user behaviors
– creative/inventory types that drive app installs and engagement
Essentially, it empowers you to make informed decisions to drive high-quality installs.
Boost Conversions with MMP Data
Unlocking the true potential of your app takes a multi-faceted approach. While inventory data helps boost installs, the Mobile Measurement Partner(MMP) data can boost your conversions.
MMP data empowers you with actionable intelligence, revealing valuable insights into your users. With this data, you can:
- Identify high-intent users ready to convert
- Optimize timing for maximum impact
- Discover the creatives that drive conversions
- Fine-tune targeting frequency for optimal results.
- Track user engagement and critical activities like add-to-cart
- Overall, get really good at retargeting with precision.
Essentially, MMP data is a goldmine of actionable insights for app marketers.
Not only will this boost your performance and conversions, but it will also establish stronger connections with users and foster long-term profitability.
And you don’t need a programmatic partner to run this! With direct access to this valuable data, you can segment users based on their behavior, preferences, and engagement levels.
What follows is every marketer’s dream–highly personalized retargeting efforts.
You can easily use this data and leverage Firebase and push notifications instead of solely relying on a programmatic DSP.
Data Science and Audience Segmentation: The Path to Precision
Two things are important for your retargeting campaign’s success.
– Decoding data science models: It’s essential to grasp how your models work, how they predict user behavior, and how they can be leveraged to optimize your retargeting efforts.
– Nailing your audience segmentation: This is where the magic happens. Divide your audience into meaningful segments and retarget based on each group’s unique traits and affinities. There’s no need to hire an army of data scientists; collaborate with your tech team to build powerful models that drive results.
Consider factors like user behavior, engagement levels, and past interactions to create actionable segments. Here’s one such framework for audience segmentation.
RFM Analysis for Powerful Segmentation
RFM analysis unlocks powerful segmentation based on three key in-app behaviors: recency, frequency, and monetary value. It allows you to build predictive models that gauge conversion and spending likelihood.
Consider the following scenario: You have two users—one with high frequency but visited 10 days ago, and the other with medium frequency but visited just 2 days ago.
Which of the user has a higher probability of conversion? It’s the user with medium frequency who visited two days ago.
The key takeaway? Recent visits correlate with higher conversion chances.
Similarly, the monetary value will shed light on the users who drive long-term revenue.
You can examine the add-to-cart data, gauge the value of products being added, and retarget users accordingly.
Based on RFM analysis, you can categorize your user segment as below:
High-Value Shoppers: Users who consistently make purchases and contribute significantly to revenue. They may not shop frequently, but when they do, they spend a significant amount.
Loyal Repeat Customers: Users who show consistent loyalty with frequent purchases.
Recent Big Spenders: Users who have made significant purchases recently. They may not have a long shopping history, but their recent high-value transactions mean a higher likelihood of conversion.
Churning Customers: Users who would purchase frequently but have shown a decline in engagement and spending. They may require targeted re-engagement efforts to prevent churn.
Bargain Hunters: Users who frequently seek out discounted products and engage in promotional offers. While their spending behavior varies, they are price-sensitive and can be targeted with specific deals and promotions.
While the example is relevant to eCommerce, this can be effectively extended to your app category.
Ultimately, this helps you focus on users who can drive substantial revenue over time.
The Need for Strategic Budgeting
Our eCommerce data shows that
- 7% of Installers are repeat purchasers
- 3% of Installers make at least one purchase
- 30% of Installers are window shoppers who might purchase
- 60% of Installers install the app only to forget
Now, here’s the catch: Many advertisers make the mistake of allocating equal budgets across all these categories. A more strategic approach is in order.
Ideally, we must consider allocating around
– 30-40% of the budget toward repeat purchasers: These loyal customers are more likely to convert again, so investing in their continued engagement makes perfect sense.
– 20% of the budget toward first-time purchasers: This will help capture their attention and drive conversion.
– The remaining 40% of the budget should be dedicated to the other two categories.
These are users who haven’t made any purchases yet but show high intent. It’s important to tap into their potential and guide them toward making that crucial first purchase.
Closing Remarks
John Wanamaker, a marketing pioneer, famously said in the 20th century, ‘Half the money I spend on advertising is wasted; the trouble is I don’t know which half.
Fast forward to today, and many app marketers still struggle to measure the true impact of their advertising efforts.
Fortunately, with data science, app marketers can now bridge the gap between advertising spending and actual revenue results.
Platforms like RevX can help you increase this 50% certainty to 75% and even more.
Learn more: revx.io
Lauren Heineck
