Let’s build a new world— AUTOMATED MACHINE LEARNING
Create smart ecosystems based on clever algorithms
As if we were in a Hollywood blockbuster, some people already see intelligent robots taking over the world. But the most common misconception about artificial intelligence nowadays is that our current machines and smart digital systems are anything but truly intelligent.
Another fear is that intelligent machines will take away people’s jobs. But have you ever realized how much work it takes to teach a machine to be smart enough to do the work of a man? It will need lots of new jobs — more demanding, less flat and probably much better paid jobs — to direct and control factory machinery instead of doing monotonous production line work by hand.
It is important to distinguish between natural human intelligence, which is characterized by emotions, and artificial human-made computer intelligence based on mathematical algorithms.
But let’s take a step back and look at the assumed intelligence of machines. One may think that if machines are able to “learn” something, they have to be somehow intelligent. But Machine Learning has nothing to do at all with natural intelligence. It’s all based on clever and complex mathematics and logical algorithms. This can be quite difficult to understand — not to mention the setup and execution of machine learning itself… So Automated Learning allows non-experts to make use of machine learning models and techniques without needing to become an expert in the field first.
What do you need this for?
As explained in our articles on phygital experiences and predictive services, gathering data and behavioral data analytics is becoming more and more relevant today and into the future for creating distinguishable and desirable Ultimate Customer Experiences. So the need for smart technology-based solutions is obvious.
How do you start?
Many companies see themselves in front of a huge and unsurmountable hurdle as soon as it comes to Machine Learning topics regarding their business. Where to start? What to do at all? And is it worth it? Experts in this field are still quite rare, and so good advice is as well.
Companies like Amazon AWS offer ready-to-use services (e.g. Sagemaker), supporting businesses with lots of tools in the fields of Machine Learning, Robotics, Internet of Things and such.
What most companies don’t realize is how important the quality of data is. So as mentioned in our previous articles on behavioral analytics and predictive services, it is of the highest priority to get the right data in the right amount and format at the right time to extract the relevant information from it — and to turn those insights into the right actions. In order to establish the Ultimate Customer Experience, as many data collection points as possible must be evaluated in real-life and real-life situations to be able to offer the user / customer prompt, situationally relevant products and services.
The more situationally relevant your offered services are, the more positive the customer experience will be — and so will be the economic success of your company.
Let’s go into a bit more detail.
In order to cope with the required dynamics and agility, learning algorithms are used. They are an indispensable part of a smart CRM ecosystem. The basis of such an algorithm-controlled CRM ecosystem is the so-called SRI scorecard. The SRI — Situational Relevance Index — represents the further development of the so-called Customer Satisfaction Index in digitization. The SRI scorecard is of essential importance for the development of every algorithmically learning CRM system.
The first version of the scorecard serves as the basis for training the respective machine learning system. For this purpose, synthesized customer data “clones” from the past are used to train the AI system in compliance with data protection regulations and to gradually refine it. As with every athlete, training is of significant importance for the fitness of the system — little or no training also means little prospect of sustainable success. Already-existing algorithmic learning modules can be used so that the systems do not have to start from scratch when learning and can be set up effectively and efficiently.
In a somewhat modular manner, neural networks, cognitive computing or reinforced learning methods etc. are connected until the desired machine learning result has reached its desired threshold value. This learning and refinement process, which can take a few weeks or months — depending on the amount and quality of the data available — can be compared with a marketing or business administration study of the algorithm. The algorithmic “synapses” must be connected in such a way that they are process-efficient and capable of relevant actions.
The art of predictability
The ultimate economic competence of these AI systems naturally lies in their forward-looking assumptions and anticipatory actions. Knowing when a customer * will buy something before he orders it himself and delivering the product or service to him exactly when it is needed — in other words, to offer a product or service that is relevant to the situation — according to the new customer experience motto “Don’t make me care — make me happy!”. This predictive and anticipatory competence forms the USP of every data-driven business system and results in the best customer brand experience.
Want to learn more about the Situative Relevance Index (SRI) Scorecard?
Stay tuned to our coming up articles or have a look at our previous articles on
Customer Centricity Transformation and Flywheel Business Growth Model, Behavioral Analytics, Phygital Experiences etc… ;)
Get in contact:
Eve Cecon | Business Innovation Strategist @ Sensory-Minds
e.cecon@sensory-minds.com
