Artificial Intelligence

Real business through artificial intelligence

Artificial intelligence (AI for short) is not a recent phenomenon. The first attempts at artificial intelligence were made and the first programmes written as long as 60 years ago, and since then the paradigms, algorithms and methods have been constantly developed, spurred on by the general technology boom. Above all, the rediscovery of neural networks, the incredible quantity and quality of training data (images, texts, videos, …) and the immense computing power available continue to drive the development of AI forward.

The term “artificial intelligence” is used for several aspects of machine data processing:

Rule-based systems
Big data systems
Learning systems
NLP systems (Natural Language Processing)

All these types of systems can be used in different ways, but it is of enormous importance that their use does not happen in an unplanned and random way. The hype that has driven the development of AI forward in recent years is unfortunately also responsible for a phenomenon that is reflected in a paradox: Many companies try to get quick wins from the use of AI with as little effort and, unfortunately, as little planning as possible

Selective use of artificial intelligence

Unfortunately, such quick fixes often fail because the objective / expectation of the results of the implementation is either unclear or unrealistic. Thus, many of the approaches sink into irrelevance sooner or later.

So what must be done to be able to use the – certainly outstanding – possibilities of AI in a targeted and successful way as a revenue generator?

On the one hand, you have to take some of the magic out of AI: AI is not per se the panacea it is often sold to be, it does provide an enormous range of possible applications, but this very diversity makes it necessary to identify the most suitable application for one’s own company, to provide it with clear goals and expectations and then to implement it within the framework of an AI strategy.

On the other hand, one must show forbearance and be prepared to adapt the chosen path to the new insights and experiences. In this context, AI should be seen as a new employee, which offers a lot of potential for the future, but which first has to settle into the corporate environment and its new field of activity and learn in order to be effective. No one who thinks realistically would expect 100% productivity from a new employee from day one, and so the AI must and will also gradually prove itself and establish itself. And like every new employee, it will bring new ideas, new approaches and new opportunities into the company.

Computers normally excel at tasks that process the most well-structured data possible according to clear, deterministic rules. Such tasks can be taken over 100% by the AI without further human intervention. The human factor would act rather counterproductively here, as it would cancel out the high efficiency. While such approaches offer enormous performance (measured in decisions / operations per unit of time), they naturally offer the least room for improvement / value enhancement or new insights.

This contrasts with tasks where important decisions are made based on experience-based trade-offs and a complex, heterogeneous data structure. Here, AI can advise, make recommendations and measure their success through data analysis or refine them.

So what we are looking for is the ideal combination of human decision-makers and machine assistants. And it is precisely this mix that makes the difference between an isolated solution and an AI-based strategy.

Event tip

“Real sales through AI? How can we achieve this?” is the title of David Ondracek’s lecture on Thursday, 20 May at OFK Marketing. Learn with many examples how to use AI and thereby increase turnover and profit. This lecture will give you the know-how you need. You can register here free of charge.

20 May 2021, 1:50 – 2:30 p.m.
“Real sales through AI” at the Online Focus Conference MARKETING

to the event

David Ondracek

Senior software architect and AI enthusiast at BAYOONET Service GmbH & Co. KG

David Ondracek has been working as an enterprise software architect in the finance and services segment for BAYOONET AG for more than 15 years. He shares his knowledge at lectures and events.

But how can I increase sales now?
Here are some examples where AI already supports or improves sales processes:

Understanding texts with AI (Sentiment Analysis).
Automatically and accurately capture content from emails and respond to customer questions or complaints automatically and in a timely manner
Price calculation
What discounts and special conditions today are not based on “room for manoeuvre” and “gut feeling” of the salesperson. An intelligent algorithm analyses the optimal price
Cross-selling and upselling
When is it worth offering a customer an additional product and when is an upgrade to a higher-quality product the right incentive to buy?
Lead Scoring
Generating new leads is not all that difficult. But filtering out the prospects with the greatest sales potential from the masses is a much more difficult task
Personalisation
Personalisation is about much more than just sending emails with the customer’s name in the subject line. It’s about connecting with customers at the exact moment they are looking for a product or service.
Conversational Marketing
Conversational marketing has been on everyone’s lips for a few years now. It’s about creating an ongoing dialogue with customers to get to the heart of their concerns faster and create a better customer experience.
Customer lifetime value prognoses
Customer lifetime value (CLV) or customer value is a very well-known concept from marketing management. The CLV includes the evaluation of customers according to their profitability and open potentials. And AI develops the forecasts for this.
Termination prevention
Early detection of cancellations is particularly important for contract and subscription-based business models.
Buying behaviour in marketing campaigns
Many companies invest a lot of time in improving campaigns through rules and regulations. But that only works up to a certain point. AI then gives the further push.
Customer segmentation for marketing target groups
Customer segmentation is represented by cluster algorithms

Author: Ingo Schmall, BAYOONET Service GmbH & Co. KG