Berk Cakar Oct 1, 2019
I work as Junior Digital Product Manager at Garanti BBVA Payment Systems. I have been dealing with data since the first day of my job, mostly on reporting. Lately, I am trying to use the data as a foresight for activation campaigns and behaviour of our customers. For now, I am not able to build machine learning models but I am trying to do my best with my current knowledge. As I learn more about the data and the data models, I will try to get more insights about our customers with their data.
The Spatial Data & Maps video by Timothée Giraud is about an R package called SF (Simple Features) which is a standardized way to encode spatial vector data. The package has three main features; +Symbologies –> It helps us to create maps with basic (typology or choropleth) and complex (flows, discontinuities) representation. +Transformation –> The package helps us to transform the data like from points to links or from polygons to borders. +Map Layout –> With this package, you are able to create good presentations that includes a real map and legend. Also Timothée offers alternative solutions like ggplot2 and tmap.
If you are interested about the topic you can follow the link to watch the video.
The market segmentation is dividing customers into groups according to their characteristics and we assume the customers in the same group tend to respond similarly to your offer or to your product. It is really important in product management to offer the best product or offer to your customers. With a right segmentation, you may lower your expenses and raise your revenues. You can read more about market segmentation and how to use R for segmantation.
Spatial data is crucial for marketing analysis. If you are able to know where your customer is, you can offer better financial solutions for them in the banking sector. The customers are not into your offers while they are at work. But if we are able to know where they are, we can offer better products and services. If you are interested about using and analyzing spatial data, you can read more here.
Machine learning provides systems the ability of learning and improving from experience without being explicitly programmed. In financial sector machine learning is a hot topic and the most of the companies are trying to use it. One of the my main goals in my job is to increase the number of customers that use the BonusFlas. One of the best way to do it is preventing churns. With a machine learning model, it can be easy to analyze the activity of the customer and find the best time and offer to keep them using the application. You can read more about the machine learning here, and if you want to start to build your machine learning model today, I recommend you to read the following article.