We live in a digital world. Data is available at low cost. Real-time communication is the norm. Customers expect excellent quality and instant service.
Today, the term product comprises the entire experience with a company - from the first advertisement to the cancellation of the contract.
To survive in this competition, companies must be able to act with precision and in real time. Companies must know what is happening at any moment and be able to react right away. They have to recognize which customer is about to quit and take appropriate action. They need to understand which customer is looking for a deal and place appropriate offers. There’s no room for delays and no second chances, since the competition is only a click away.
The raw material for taking action is data, both on customer behavior and on the availability of goods, market prices and at times other factors, such as the weather.
The tools for processing this data are technologies like Machine Learning, Deep Learning and Reinforcement Learning. Such algorithms actively learn and can be used to automate processes and decisions. They are no longer just using a fixed set of rules or are exclusively based on the evaluation of historical data. Instead they exploit data to develop functional rules to fulfill their mission. They learn autonomously by uncovering patterns as well as by forming and evaluating hypotheses against their own learned behaviour.
However, this does not mean that such systems are still too complex or expensive to handle. Quite the contrary. Modern algorithms can be used flexibly and with reasonable effort. While it used to be that only large technology companies and corporations could build truly self learning systems, this opportunity is now available to a much wider range of companies.
This is due to more and more standardization and the fact that self-learning systems can easily be set up alongside existing infrastructure and, if necessary, re-integrated into existing architectures.
It is for example possible to merge previously separate data sources with next to no manual effort, using smart matching algorithms - even without a shared primary ID, a connection by syntax or rules, just relying on semantics. With this approach, data silos can be quickly overcome.
In addition, advancements in Natural Language Processing make it possible to easily depersonalize free-form text. Letters, emails, etc. can be easily transformed into depersonalized, GDPR-compliant data records.
This makes it possible to use curated open source technology to build powerful artificial intelligence with minimal risk - allowing existing companies to protect their competitive edge.