Here's my post about improving the user's orientation in the Google Maps app.
Especially in cities that are big and offer a variety of shops or attractions, these features are a great advantage.
To give just a few examples, a typical situation as a tourist or a traveler experiences. In addition, imagine visiting Berlin, Tokyo or New York. Whether by train car or plane. In one, all cities are the same. They are so big and therefore confusing. The language barriers are also not to be underestimated. However, our travel time is limited, and of course you want to have the maximum experience. Thanks to Google Maps, the whole thing has become much more comfortable, of course, than a few years ago.
But in one point everything is still unclear, you zoom in from a certain place in the map and you are overwhelmed by the multitude of shops, these many small points are more irritating than helpful. After a short time it is just a hodgepodge of everything, without a clear structure. As a result, we are more concerned with searches than with the experience, although we actually just sit in a cafe and wanted to make everything work on us. Ultimately, the large selection of options leads to a dead end and one decides for something. But people do not want to experience something, it should be the way you want it.
More structure and clarity would be the development goal here.
Two big possibilities: one is personalization in combination with machine learning and algorithms.
Each of us is individual, just in what we want to see in cities and what not. Our preferences for certain kitchens, or some only boutiques, others prefer antiques shops, etc. Sports shops. Textile, expensive medium inexpensive. Bookstore chain or bookstore small as a mom and pop shop. To enumerate all would not be so exciting now.
Already on the plane we can enter and tap what we want to see and find in cities. If you visit Tokyo you can set your preferences for this city.
If you land in Tokyo and you call the app, all business that does not correspond to your interests will disappear.
With a tap in the menu, you can of course make all visible again.
Without individual personalization we can rely on machine learning. On Good Luck you move through the city and at the end of the day or at the end of the journey we see where we have been. In a statistic, there are various data available, including in which stores we were the longest.
So you get to know his behavior better, if you let yourself drift in the cities.
Technically, this is all possible, but here rather software adjustments must be carried out.
In another version Machine Learning comes into play, the software remembers what we visited and creates a profile from it. So why is that interesting? Quite simply, the next time you travel to a big city, the app can ask you if you want to see and find similar businesses again. So the app starts to get to know you better and you have it easier. After 10 trips or excursions you can see that you are more often
in places that you would not have expected.
Mystic the whole, but ok, it works the same way.
This feature is part 1 of my post.
Now let's devote part 2
Keep everything on the tongue. The Google Maps app is still open. You have just dropped out of a central station somewhere in the world. With a feature that I call the compass mode, the following happens. Take your smartphone and hold it like a compass in front of the body. Of course, you will not see anything yet, as the feature does not exist yet, but imagine you would see a long corridor with a width of 100-200m.
Slightly wider than a typical shopping street. In addition, you will find shops and attractions up to a depth of 1-2km. What would you see then? Quite simply, everything that interested you from previous cities. So personalization of his interests in other cities also plays a role here. Distance information as well as footpath time or cycling time should then also be read. Without Corridor you can see everything, with Corridor only what you want to see.
In practice, that would be so. You and your girlfriend enter the mall and you will only move in one direction. That's the classic. One follows the stream of society. With a tap, the corridor shows only sights that are in front of you or just the restaurants, such as pizzerias. One would like to have something to eat.
Feel free to think about how useful that would be and if such features add value to your everyday life.
It is because only because everything is so beautiful and feasible, only then begins the work of the programmer. That's a lot of typing and typing on development teams.
Could you follow me here? Well, there is now a bonus that came to me while writing.
Most of us live in structures and in at least partly planned days, whether it is the afternoon or the morning
is sometimes put there. Somewhere everyone finds his appointments in his calendar or in his everyday life.
Club visit, fixed meal times, going out, cooking, etc. Dog run.What is it like to go on a journey? A tour?
Do you need some structure? Everyone has to know that for themselves.
For those who need one, the next lines may be of interest.
Already on the plane you talk to your girlfriend, the travel partners, what do we actually want to do when we arrive? Depending on the time of day, that's usually what food is. So you take the Google Maps app and make a note of the things you want to do on arrival in a chronological order during the flight.
Example: 1. Bicycle rental 2. Eating out 3. Culture / Festival / Market 4. Visit bookstore
5. Museum 6. Eating ice cream 7. Visit the park to relax. Half a day is certainly over.
Because the app knows what you are looking for and where you are, it can give you a route on which you can experience all these 7th points. A digital tour guide just made by Google. If there are photographic highlights, then you can activate this and the app will send you hints. You may then turn around or stay on the way. Decisive here are also the preferences mentioned at the beginning of each, to mention here again. Personalization and machine learning as well as intelligent algorithms.
Where we use the learning effect of the apps in everyday life and increasingly intelligent systems are used, you can also ask yourself, if we already have a digital assistant with a small app, what actually happens in the big companies? The same as on the lower levels. Where the little ones learn, the big ones learn as well. Everything is bundled upwards, turned into usable data floor by floor. A data cycle of a special kind.
In detail this means that if the feature is used successfully, the software can learn which routes we prefer to take, which hints we followed when jumping up, and as a result you can then offer more routes as travelers can move through the cities in the future , Or you can easily see which type of travel has which preferences. Everything we experience in the future will feel like a tailor-made suit that only suits you. What opportunities arise here for the advertising industry, speak volumes. Here's an example. Examples are always good. In Tokyo Berlin New York since you have been in the museum and the Italian on a pizza.
On the next trip, Machine Learning, of course with the help of marketing strategists, will automatically negotiate attractive conditions for you with the local shops. They can look like this. You pass participating Italian restaurants but have not entered a daily program into the app for today.
Via Beacon the traders exchange their devices and the network and you get a coupon, if you visit certain restaurants preferred here again Italians until the end of the day or within a given time, then you get here on the total price 25%. You are surprised.
No, that's business. The same can happen if you borrow wheels or go to the cinema.
So where does the industry have to make some fine changes? In the morning navigation. This decides decisively whether we travel attractive and pleasant and whether we travel again.