How good is Ireland for computer vision
Automatic processing of emails with NLP
Lots of customers write lots of emails. To answer these, there are entire support departments or call centers that deal with the concerns or complaints of their customers all day long. Since these e-mails are often formulated as free text, this task was considered to be impossible to automate for a long time. This is changing right now.
For a long time it has been possible to use classic machine learning methods to differentiate emails into “spam” or “non-spam”. NLP (Natural Language Processing) and deep learning also open up new opportunities to understand the content of e-mails. Machines are learning to read more and more reliably individual so-called intents from texts. Intents could be, for example, that a customer wants to notify a change of address, a termination or a complaint. If such a recognition is reliable enough, it can also be processed and answered automatically at the same time.
We are already using this technology in several projects and see that the results are getting better and better.
Interactive advertising displays
Imagine an optician in a busy pedestrian street. Thousands of people walk by. There is a large display in the shop window that interacts with the customer. If the customer looks in this "mirror", he sees himself and automatically gets the latest sunglasses rendered in his face. The system recognizes whether the person is male or female, old or young, and derives product recommendations from this. The person stops and looks at himself in the “mirror”.
She can interact with the shop window using gestures. The system can display discount campaigns and integrate social networks in order to connect the online world with the pedestrian zone. In addition, the system provides statistics on how many customers stop, which ads work best and how many people then entered the store through the shop window.
The same thing works with a travel agency, where the passer-by suddenly walks across a sandy beach. With a shoe or hat store. Or in the toy department of a department store, where the latest action figure can be seen in the display, which imitates exactly the same movements as the child who stands in front of it with wide eyes.
As already mentioned at the beginning, there are even AI applications in the hairdressing salon. A mirror (or screen) that shows exactly how a man or woman will look after applying a certain tint.
(The video was created with an app from https://modiface.com/.)
There are similar applications in the field of cosmetics, where various make-up recommendations can be displayed in seconds, or in the field of cosmetic surgery, where you can see beforehand what you will look like after the operation. Of course, such applications also run at home - simply in the browser. In connection with augmented reality, this can also be applied to interior design / furniture or bathroom design.
There will certainly be many interesting innovations in this area in the near future. Possibly. do you want to promote such a development?
Predictive (business) analytics
Under predictive (business) analytics, we summarize techniques and methods from the fields of big data, machine learning and data science that generate forecast models from business data. The goals of such models are usually to optimize business processes, increase profits or save costs.
For example, a supermarket can use predictive models to predict which products will be sold when and how often. This enables purchasing and inventory to be optimized. If information is also available about which products are often bought together by whom (e.g. via Payback), new marketing strategies can be derived from this. For marketing campaigns, the amounts of data that are available in almost all industries today can already be used very effectively to predict which type of marketing will generate a particularly large number of leads in a target group at what time. This has become particularly relevant for social media marketing.
In other cases, information gathered about customer behavior is used to predict which customers may terminate in the near future. These so-called customer churn models can then help to target such customers so as not to lose them, e.g. with discount campaigns.
But there are also exciting use cases for data-driven recruiting: algorithms can pre-select suitable applicants for positions or automate the matching of open positions with (online) job profiles. This gives recruiters more time to concentrate on the essentials.
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