Interview with Cameron Thom, Adikteev Director of Sales

We interviewed Cameron Thom, Director of Sales at Adikteev, to discuss the evolution of Adikteev and strategies for combating user churn. Cameron highlights the challenges in the market, the effectiveness of Adikteev’s Churn Prediction product, and the importance of retention strategies in today’s competitive landscape.
adicteev logo ove tech-themed background.

1- Let us introduce you and of course, Adikteev to our followers. How did Adikteev’s story begin and how has it evolved until today?

At Adikteev, we’ve been leaders in the app retargeting space for the last 10 years. After the iOS privacy changes, we saw paid user acquisition become more expensive, and retention become even more critical. We pride ourselves on our extreme emphasis on user behavior—we’ve invested heavily in-house to analyze data and to release products that empower marketers despite  changes in the performance marketing landscape.

2-Can you tell us about the areas Adikteev is currently focusing on? How do these align with the current trends and demands in the market?

The biggest challenge in the market today revolves around privacy changes. This is where app retargeting comes into play. With the right tools and strategies, you can still run campaigns profitably even in the midst of IDFA/SKAN constraints. And with Google’s Privacy Sandbox factoring in, Adikteev continues to work on shaping the retargeting landscape. We’ve developed new retention products to help mobile app marketers face these privacy challenges with a fresh mindset. One such product is our user churn prediction technology, a tool to help marketers save time and target about-to-churn users.

3-Let’s continue with the question on the minds of everyone who is new to application/game development. Why is user churn inevitable? Could you give some scenarios/examples?

Let’s say you have a user who downloads a gaming app. Upon opening the app, they discover that it’s not what they’ve expected or they realize that they don’t quite understand the gameplay. Another example is a user who has been actively using the app for a year. Suddenly, this user gets overtasked with daily life and has stopped using the said app. Or perhaps, a user has disabled their notifications because they needed to focus on other things, such as their job or their kids. These are just some of the many situations where user churn is bound to happen. With millions of apps vying for attention, the competition is fierce in the mobile app world. It’s really challenging to be an app on top of everyone’s mind. Not to mention that big companies actually spend millions on user acquisition each month. This begs the question: as an app developer or marketer, how do you compete with that?

4-The competition in the market is increasing and users’ habits are in a process of rapid change. So, how can developers and publishers predict user churn and retain the users? What strategies can they use to retain users?

Essentially, there are two strategies teams can use to tackle this: develop retention products in-house or leveraging third-party marketers like Adikteev. Developing it in-house is tricky, though. First, most companies don’t have the infrastructure to build their own. Second, CRM teams don’t traditionally talk to performance marketers for a united message and shared framework. Hence, campaigns and channels could have different messaging due to this divide in communication. In such cases, the best way forward is to seek help from outside vendors like Adikteev who are better equipped with the approaches to user behavior. 

5- As Adikteev, you help companies to build the right strategies against user churn. Churn Prediction is a very effective product in this regard. What is Churn Prediction and how does it help companies develop winning retention strategies?

User churn prediction uses machine learning models to analyze actions and behavior patterns and predict which users in danger of leaving an app. This way, marketers get a better data-driven sense of where to focus their efforts. With up to a whopping 70% industry average churn rate, leveraging churn prediction scores has a lot of benefits including boosting user LTV and lowering UA costs. 

6-What figures do we have about the success rates of Churn Prediction? And what kind of a method should be followed for a more accurate churn prediction and a more appropriate retention strategy?

We have tested our churn prediction performance using a data science metric called AUC ROC or Area Under the Curve and Receiver Operating Characteristic. You can check the entire testing process on our website. But to summarize, our model reached over 90% accuracy in predictions in all of the tests. This makes us confident in the success of this prediction technology.  

7- How Churn Prediction and retargeting work together? What’s Adikteev’s approach on this?

Often, clients have an inactivity window for retargeting because they don’t want to engage active users. However, through this predictive intelligence we have developed, we’ve discovered that there are valuable users within that window that are highly likely to churn if they’re not engaged at all. So, letting them become lapsed users is an OK strategy if you want to avoid cannibalization. However, you’re going to make it much more expensive to engage these users later on— the further lapsed a user is, the less engagement is expected and the harder it will be to get them back to the app. Given this, it’s actually much more efficient to keep them than to lose them altogether. At Adikteev, we leverage our churn prediction technology to retarget users before they churn. This means preventing the churn before it happens and targeting them at the right time.

8-Lastly, who is your Churn Prediction product suitable for? Which companies should use this product? 

It’s really applicable to anyone who has a decent level of events coming in and with high rates of engagement among active users. The more data we have, the more accurate we’re going to be. 

Given that, you might not think the best use case are apps with a once-a-year subscription— nonetheless, churn prediction is dynamic and it could be quite helpful to study the daily user engagement actions to predict who is likely not to renew their subscription. This gives the chance for marketers to segment and offer special incentives to avoid losing these users.

Another use case where churn prediction is suitable are apps with high LTV users. In this situation, these users usually bring in most of the revenue and it’s paramount to retain them. Meanwhile, for instances like in hyper casual gaming apps where monetization happens in only one or a few sessions and then churning will likely follow, it will be best to convert those users to another app in the portfolio, and keep spending on new users.

In practice, the premise is changing our landscape. Most companies are also switching from hyper to hybrid casual. Having some type of in-app purchase makes churn prediction more relevant in this scenario. 

Cameron Thom

Director of Sales, Adikteev

next: Mobidictum Interview with Philipp Gladkov of Gameram

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