K-Factor: Virality beyond attribution marketing metrics

12 October

In the dynamic world of Growth Marketing, metrics and data are the beacon that guides our strategies. Among these metrics, there is one that is often overlooked, when in fact it is very important. We are talking about the K-Factor.

Often, in our eagerness to understand the performance of our advertising campaigns and attribute each sale or installation to a specific source, we overlook the magnitude of the impact that we cannot measure directly. This impact, often referred to as “organic”, is the result of an effective user acquisition strategy that spreads like a virus, through, for example, user referrals.

In this article, we will explore in detail the concept of K-Factor in marketing and how it can help us go beyond conventional attribution metrics.

What is the K-Factor?

This metric, which originated from the medical terminology used to evaluate the speed at which a virus spreads, has become a fundamental indicator in the world of digital marketing.

But, let’s start from the basics, what is the K-factor.

The K-Factor is used to take into account those conversions that cannot be directly attributed to paid advertising, but have their origin and cause in advertising.

What usually happens in advertising is that a user sees a digital advertisement, (he may click on it or not), and then when he goes to the If the Apps Store is searched for such an app, the MMP will detect that user as someone who was previously impacted by an ad and therefore assigns that conversion to that ad campaign.

However, a first-time user may download an app after seeing an ad. He then recommends this application to his group of friends and two of them decide to download the application in question. An MMP would say that the advertising generated one user, when in fact it generated three. If the first user had not been impacted by the advertising and downloaded the app, he would not have recommended it to his friends, nor would they have downloaded it.

This is one of the two main impacts of advertising not measurable by an MMP and is what we commonly call “word of mouth” in which lies a large part of the viralization effect of a digital product.

On the other hand, there is another type of impact not directly measurable by an MMP, which refers to the behavior of the algorithms that control the prioritization of the different applications. Whether in the Google Play Store or the App Store there are certain rules that tend to give more or less visibility to certain Apps, in the same way that Google Search tends to give higher priority to certain websites before certain searches, the same thing happens in the world of mobile applications.

And what does this depend on?

For the purposes of this article, the important thing to note is that the greater the number of downloads, the greater the reliability of an application for the algorithm, and therefore the greater the degree of visibility it will have in highly relevant contexts. Whether they are searches related to what the application solves, or users that match an appropriate profile to be benefited by the application.

For this reason, if we activate advertising campaigns for a mobile application, we will be indirectly influencing the degree of “organic” visibility of that application. And therefore we will have a second impact not measurable by the MMP.

This is the type of measurement, which undervalues Paid Media efforts, that the K-factor can solve and, for this reason, whenever there is a digital marketing campaign, it is advisable to take this factor into account.

What is the importance of the K-Factor?

You may wonder why it is important to measure K-Factor when we already have attribution metrics that track the exact source of each user. The answer lies in the fact that the real world is more complex than it seems. Organic user propagation is difficult to measure, as it is sometimes hard to determine why an organic user installed your app, whether it was because of a recommendation from a friend, whether it was because they saw an ad, or whether it was a combination of both.

Advertising campaigns are an essential part of user acquisition and, while they are not the whole story, we must give them the weight they deserve. Likewise, word of mouth, recommendations from friends and positive user experiences can generate a steady stream of new users that cannot always be directly attributed to a specific source. This is where the K-Factor comes into play. It helps us understand how our paid campaigns affect organic acquisition.

How the K-Factor is calculated

Calculating the K-Factor is not an overly complex task, but it requires consideration of two key variables: the number of application invitations sent per customer (i) and the average conversion rate of each invitation (c). The basic formula is K = i * c, where “K” is the K-Factor.

For example, if you have a
Fintech
that has its application and rewards its users for inviting friends, and each user invites two friends (i = 2), and every fifth invitee becomes a new user (c = 0.2), your K-Factor would be 40%.

How to improve your K-Factor

Do you want to improve your K-Factor and achieve that exponential growth? Here are some key strategies that can boost your K-Factor:

App Store Optimization (ASO)

The

effective app store optimization

is essential for app developers who want more organic downloads. Download volume is one of the most important ranking factors in ASO. When app stores see steady traffic and a growing number of active users, their rankings improve. This creates a virtuous circle in which Paid Media efforts impact organic, leading to a higher K-Factor.

Experimentation and continuous optimization

It is important that you explore different advertising channels and creative

creative strategies

and seek out the source of the most valuable users. In order to understand the effectiveness of advertising campaigns it is necessary to make accurate measurements and proper analysis and thus know how to optimize your campaigns to maximize your return on advertising investment.

Shareable applications

The K-Factor benefits greatly if your application is designed with sharing capability in mind. Encouraging users to share their experience with friends and acquaintances can turn a paid installation into three or four additional organic users. Ideally, this strategy should be planned from the beginning of your application development.

Are the data provided by MMPs accurate?

First of all, an MMP is a

Mobile Attribution Partner

. It provides advertisers with information on how their ads are performing. Your data is precious and thanks to it we can measure the conversions generated by each advertising piece and optimize our campaigns based on that.

Now, while the data provided by MMPs is crucial, understanding how paid campaigns affect organic acquisition is essential to allocating your campaign budgets wisely.

Perhaps it will be clearer if we give an example. Let’s say you have a gaming app, you are running ads and the MMP says that you have generated 1000 users thanks to your Paid Media strategy. The truth is that you have most likely generated more than 1000 users with this strategy, thanks to the K-factor. You may have gotten 1200 or 1300 users, either because someone who already downloaded the app recommended it and got more downloads or because someone who saw an ad ended up downloading the app “organically”.

And what’s the problem if you don’t calculate the K-factor?

That, when you want to make calculations such as cost per user or the

ROAS

you may find that the results are less positive than they really are. If we do not include the K-factor, the cost per user will probably be higher, while the ROaS will be lower.

Ultimately, the K-factor calculation provides a clearer picture of what the real results of Paid Media’s efforts are.

Conclusions on the K-factor

In digital marketing, metrics are essential, but we must not let attribution metrics blind us to the importance of the K-Factor. This metric reminds us that there is a world of organic users heavily influenced by our user acquisition strategies.

With the right tool, we can make more informed decisions and take our marketing strategies to the next level. This is what we always seek to provide in

Boomit – Growth Marketing

designing Data Analytics and Attribution processes tailored to the needs of each client and considering the unique characteristics of its users.