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Over 2 million apps are available on the Google Play Store and App Store. The average number of apps used daily is 9 and monthly is 30.1
So the question is:
Does your app figure in the top 9 – or at least the top 30 – apps among the millions of apps available?
Is your primary focus on just acquisition or are you working on retention?
It will come as no surprise to any product manager or marketer that improving the customer experience will lead to: better customer satisfaction rates, increased customer retention, higher revenues, and lower costs.
As per a report by American Express, 7 in 10 Americans are willing to spend 13% more on better customer experience.2 Further, a research by Temkin group claims that a moderate increase in customer experience could lead to an increase of $775 million for a company with $1 billion revenue.
If your app offers hundreds, or even thousands, of products, and you want to improve the customer experience, you face some monumental challenges, namely:
This is where personalized recommendations based on user interaction can become immensely useful to improve customer experience.
Here are a few examples of the impact that recommendations can have on business KPIs:
So how do these companies power their recommendations?
Key weapons in the armory of these companies are powerful machine learning algorithms that understand user behavior and suggest appropriate personalized products.
And how do these algorithms work?
There are traditionally two types of algorithms:
There are also hybrid methods that combine content and collaborative filtering to arrive at recommendations.
At CleverTap, we wanted our recommenders to be relevant and also lead to much better discovery of products. This would lead to better diversity in product sales/views in the long run.
We use collaborative filtering to power our own recommendation system. Though there are many CF algorithms, we chose item2vec, which is based on a popular algorithm called word2vec used for Natural Language Processing (NLP).
Check out these posts on neural network and item2vec, if you aren’t familiar with the underlying algorithms.
Before we started running trials (proof of concept/POC) with our clients across industries, we had four goals in mind for our recommender system:
Validating recommender systems based on the above goals can be tricky since back-testing the efficacy of recommendations is fraught with danger.
Backtesting evaluates the efficacy of the model by discovering how it would play out using historical data.
The problem with using historical data is that the recommendations generated have never been served. Hence, relying solely on evaluation metrics of models from backtesting can be risky.
The best option was to try it live in production environment to measure the impact. And we weren’t wrong. We started to observe immediate impact on engagement and conversion statistics. This behavior was observed in multiple industries that used our system. This gave us sufficient confidence to use the algorithm in production. Initial results do indicate the performance in production is either better or equal to the performance observed in the POC, which confirmed that our choice of algorithm would achieve the goals we wanted.
In this post, we discussed the need for a recommendation system to enhance the customer experience leading to better retention and engagement, and also described how we used collaborative filtering and specifically item2vec to achieve our goal.
At CleverTap, we aim to constantly innovate and improve our capabilities. In keeping with this spirit, we plan to use hybrid recommenders in future iterations to combine the meta information of items and/users and make the recommendation even stronger.
Just as the brain constitutes just 2% of the body weight, similarly the machine learning algorithm that powers the recommendation constitutes a small portion of the architecture.
If you’re interested to know how CleverTap’s recommender system is engineered to give a seamless experience from the marketer to the app user, stay tuned for our next post.