Data is the gold of modern times. However, in order to win it, companies need methods and applications of artificial intelligence (AI). Artificial Intelligence is a type of computer science that emphasizes the creation of intelligent machines that are capable of performing human-like tasks. This sounds complicated, but today even small and medium-sized companies can successfully launch into the data future with just a few steps.
Data science and AI were once more exclusively the domain of technology companies – however now, the basic applications can be implemented at relatively low cost and with moderate effort in almost every business model in which data is collected. But why should companies abandon the existing and the proven for new technologies?
From the high-tech laboratory into everyday life: Strategic advantages through AI and analytics
Simply put yourself in the position of a medium-sized mail-order company for pet food. You want to minimize your shipping time and storage costs for cat food. In order to achieve this, it is important to make accurate predictions about the behavior of cat owners. The raw material for this is all the data about customer behavior, which is collected over the already existing customer channels.
The pure data extraction is however insufficient.
Analytics applications gain qualified insights from the data’s raw material that can be used to identify and evaluate trends at an early stage. In our example, analytics is a sub-discipline of data science that attempts to understand existing patterns in customer behavior data and thus to predict future customer decisions and purchasing patterns. In the case of our mail-order company, for example, we could determine whether customers will place greater value on regional products in the future, which premium products have the best sales opportunities, or where the price pain thresholds of certain customer segments lie.
The strategic advantage is obvious: good analytics eliminate redundancies and slow-moving items and optimizes procurement and increases sales.
The company learns to predict future customer behavior from existing behavior patterns. For this purpose, machine learning methods are then available, which are assigned to artificial intelligence.
Introduction to the Data Future: First Steps to Implementation
How do you start your Data Science project? You first sift through and evaluate the data you already have. In our example, these consist of various information or characteristics of the purchasing behavior of your customers:
- Time spent on the website,
- Number of products in the shopping cart,
- The total amount of the shopping cart,
- Age of customers
- Order history of the customers
You are now considering whether, for example, you should proactively order the product “Cat Food Senior” or not.
First, you examine the data situation. You check whether you have data available in sufficient quantity and quality. Even the best data can hardly be processed directly. You remove incorrect data records (e.g. duplicates), and, if necessary you combine different information or add missing/partial information.
Once your records are up to date you can begin processing the data. For example, you can calculate a new characteristic “proportion of discounted cans of wet food”, i.e. the proportion of discounted products in the shopping basket in relation to all products in the shopping basket. Then you divide your customers into two groups (the economical and the less economical buyers). This process is usually repeated several times, to gather more accurate data patterns.
Now the analysis or analytics comes into play because you are trying to track down existing patterns and derive predictions. In our example, the following assumptions could be checked:
- Cheaper products are bought without hesitation, i.e. faster.
- Repeated visits to a product increase the likelihood of orders being placed
- Customers younger than 40 years increasingly buy cat snacks
The algorithms used, learn the decision criteria themselves, on the basis of existing data (in this case from the purchasing behavior of customers in the past). No further action is required on your part. For future customers, you can now use the decision tree to quickly derive recommendations for ordering and storing cat food.
The described procedure can of course also be extended to other areas: AI can be used to identify trends in social networks such as Facebook or Twitter (e.g. word frequencies, sentiments, hashtags or likes are counted). It is also possible to include weather and news data in your forecasts. In mechanical engineering, for example, sensor data and their prediction can also help to maintain technical systems with foresight (and thus not only reactively or preventively).
Whether it is the automatic differentiation of incoming documents such as invoices, reminders, expert opinions or complaints – data science and AI procedures, in particular, machine learning procedures, can always be used well if you are looking for previously unknown connections in large amounts of data.
Do you already have your own approaches or ideas on how to use Data Science and AI? Then contact us.