Irrespective of whether the underlying data comes from e-shop customers, your clients, small businesses or both large profit and non-profit organizations, market segmentation analysis always brings valuable insights and helps you to leverage otherwise hidden information in your favor, for example greater sales. Therefore, it is vitally important to utilize an efficient analytical pipeline, which would not only help you understand your customer base, but also further serve you during planning of your tailored offers, advertising, promos or strategy. Let us play with some advanced analytics in order to provide a simple example of efficiency improvement when using segmentation techniques, namely clustering, projection pursuit and t-SNE.
As your goal might be improving your sales through tailored customer contact, you need to discover homogeneous groups of people. The different groups of customers behave and respond differently, therefore it is only natural to treat them in a different way. The idea is to get greater profit in each segment separately, through diverse strategy. Thus, we need to accomplish two fundamental tasks:
- identify homogeneous market segments (i.e. which people are in which group)
- identify important features (i.e. what is decisive for customer behavior)
In this post, I am focusing on the first problem from the technical point of view, using some advanced analytic methods. For the sake of brief demonstration, I will work with simple dataset, describing the annual spending of clients of a wholesale distributor on diverse product categories. Following the figure below, it would be difficult to detect some well separated clusters of clients at the first sight.
In the last few years the obvious fact that for successful marketing you need to “contact the right customers with the right offer through the right channel at the right time” has become something of a mantra. While there is nothing to disagree here, it is a pity that for most part the saying stays in words and only gets realized in rare cases. The issue is that while many can repeat the mantra, only few actually know what is needed to put it in practice. In this post, I am going to talk about the first part – how to target the right customers for your marketing actions?
There are many approaches to solving this great puzzle. One of the extreme solutions is having a team of marketing experts who rely solely on their gut feeling, projecting their opinions on customers, without any proof, not even evaluating or testing the campaigns. Because that’s what they did in their previous job. It might sound ridiculous in today’s digital era, but surprisingly it is often the case.
The other extreme is building complex AI engines and let them make all the decisions. This is typically a proposition by some geeky start-up run by fresh PhD holders. This approach is in my opinion also wrong. First, you have absolutely no assurance that the data available truly reflect the reality, that the algorithm works flawlessly or simply that the randomness in the world is not too strong to predict. After all, even companies running algorithmic trading have human dealers overseeing their algorithms, who focus on addressing weaknesses of the algorithms and generally on preventing internal disasters.
As always, I think that the solution lies somewhere in between. An experienced marketer, whose opinion is backed by information extracted from the data available, can truly hit it. Imagine that you have to run a campaign to increase sales of a saving account (or a road bike, new robot, a holiday in Caribbean…). The long proven data extraction technique one should consider is called propensity to buy (or to purchase or to use).
After introducing R capabilities in Tableau 8.1, the new Tableau 10.1 now comes also with support for Python. This is a great news especially for data scientists, who use the reports to visualize results of some more sophisticated analytical processes. Such reports can now bring the analytics much closer to the end users, while preserving the given level of user-friendliness.
In this post I am using a simple modelling example to describe how exactly the integration of Tableau and Python works.
Apart from being a data scientist, I also spend a lot of time on my bike. It is therefore no surprise that I am a huge fan of all kinds of wearable devices. Lots of the times though, I get quite frustrated with the data processing and data visualization software that major providers of wearable devices offer. That’s why I have been trying to take things to my own hands. Recently I have started to play around with plotting my bike route from Python using Google Maps API. My novice’s guide to all this follows in the post.