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Data-driven solutions enable the ongoing large-scale transformation of software-intensive industries. Our research and innovation expertise helps businesses take advantage of those technologies, applications, and processes to benefit from data.
MacPaw, a product-focused software development company, specializing in utility software for MacOS, one of the largest and well-known in this product segment.
The model, which is able to predict the purchasing probability, was built and trained. The highest accuracy of the prediction (95.8%) was achieved in the case of historical data availability within a two weeks period. The achieved results enable to build a personalized online discount system based on trial version usage analysis and dynamically calculated purchasing probability.
The main goal of the project is to identify hidden (or unknown for the Customer so far) patterns of the trial version usage by users leading to an increase of a probability of purchasing the full version of the product.
MacPaw, a product-focused software development company, specializing in utility software for MacOS, one of the largest and well-known in this product segment. There are several complementary products that provide tweaks and optimizations for MacOS laptops and desktops.
Customer’s main product is a utility software that conducts optimisation of MacOS-based PC. There is trial a version (full-featured time-limited, then turns into batch-size limited) which can be purchased and turned into full, unlimited version. There’s also a possibility to purchase a version from an array of distribution channels, including company website. Customer’s Sales and Marketing Team had a hypothesis that there are certain patterns in a user behavior in main trial version of main product, in other products and on site which indicate higher or lower probability of purchase.
The analysis of collected user behavior data and product purchases data confirmed the validity of the hypothesis and identified several non-obvious trial version usage patterns.
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