Posted on: April 28, 2019 Posted by: QUALQEM Comments: 0

Data science and machine learning applications for business success

In the last years technologies such as data science, artificial intelligence and predictive analytics have become increasingly popular. The academic field behind these technologies is called machine learning. The core idea is that the computer (the machine) learns autonomously from data and generates business insights and leads to data-driven decision making. In this article we will discuss where and how these techniques are applied successfully in industry.

Advertising, Sales & Customer Experience

There are a lot of machine learning applications for customer data. The key idea is to gain some insights about your customer so that you know when to contact whom, when to offer what kind of discount, or when to suggest which product. If other companies have similar or the same customers we could even think of insights as the product.

Targeted Marketing

The most popular technique in advertising probably is targeted marketing. Every individual is targeted differently so that the probability of action such as a click on your advertisement is maximized. In order for the machine to learn, it needs positive and negative examples in the data. A positive example would be data from people who clicked on advertisement in the past and negative examples are where advertisement did not work. In online advertising click rates are recorded automatically, which makes it optimally suitable for machine learning. But also in direct (e-mailed) advertisement it is possible to record response rates and let the machine learn from the data.

Targeted marketing is famously used by Facebook and Google. The content of the advertisements is optimally chosen from your past click behaviour. The advertiser has the advantage to only approach people where the effort is most effective, and the users only get advertisements that might be interesting for them.

Churn Prediction

Very often it can be forecast that a customer will cease their custom or switch to a competitor. Possible predictors are decreasing use of your service, increasing complaints, or something less obvious like using a different vocabulary in their written communication with you. If you have enough examples of churn in your client data, machine learning can autonomously find patterns and predict the churn event before it happens. With these warning signs, you can try to convince the customer of remaining with you with, for example, special offers.

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