Customer awareness, customer engagement, monetization — here is the base which the sales funnel is built on. But this is just a general plan. If you develop and promote a subscription app, then we are talking about seven or more stages. Let’s figure out how to create a funnel for this kind of mobile service and how to evaluate its effectiveness at each stage.

What Does the Funnel Look Like?

Typical sales funnel for mobile app

To create the funnel, imagine the user path of your potential subscriber.

First of all, the user sees the advertisement of your app and starts to be aware of it. That’s why it is important to…

When launching an iOS-app, it is important for a marketer to foresee all the details: from methods of interaction with different segments of the target audience to the cost of actions within the service. Sometimes it seems that everything is calculated and works well, but subscribers still leave. We figured out what mistakes marketers could make. Some details are very easy to overlook.

Problems With the Sales Funnel

A ready-to-use solution

NLP is one of the main directions of our work at Poteha Labs. We do text analysis, chatbot development and information retrieval. Therefore, we regularly use Flair, Natasha, TensorFlow and Pytorch, NLTK, sometimes encountering languages other than English. Existing solutions are not always suitable for every problem we face: some are difficult to launch, others are too complicated and heavy.

Consequently, we’ve compiled our own Docker image with all the convenient frameworks for NLP, including deep learning. It suits almost 80% of our tasks and saves time for installing the frameworks.

Current approaches

Generally, the majority of data science solutions are now…

Homepage is

Developing 1d/2d data container and transformers for data analysis


Each problem in data science is based on data. Before analysing any data this data usually needs to be preprocessed. Preprocessing can be a
simple filtering or something more complex such as transformation as well as
feature extraction.

For Python programming language the most popular library for working with 1d/2d data sets is Pandas. For 1d data such as a sequence of numbers pandas.Series object is very appropriate. For 2d data such object is called pandas.DataFrame. Pandas objects are great for storing and transforming in-memory data both for quantitive and categorical…

This tutorial presents how to run a GPU machine with Docker for Deep Learning on Azure

If you ever faced a Deep Learning problem you have probably already heard about the GPU (graphics processing unit). It’s mostly used for computational graphics (rendering) but not limited to. When NVidia first released CUDNN, a high level API (called CUDNN) for it’s cards, Deep Learning training speed dramatically increased and the Deep Learning Industry saw considerable growth!

What’s the difference between a GPU and CPU?

Vitaly Davydov

CEO at Adapty; Rock climber&mountaineer

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