Who owns big data - people, government or corporations? Part 2.

A person by nature is endowed with the right to own - his body, his actions, his thoughts, finally - the products of his intellectual work and the things that he was able to acquire in exchange for action by his body and mind.

Personal data are an integral part of the human body and its biological indicators. User’s data is a part of “digital DNA”. This unique information must physically and legally belong to the person who produces it. No state or service provider, no one should have the right to full and lifelong access to a person's “digital DNA” without his or her informed consent.

A lot of people don’t understand the value of their data. Only a few understand that this is not just an analog of gold, the data is a new single universal currency, new oil, the only thing that is valuable. If the data will belong to anyone else, except for the people themselves, the system (Goolge, or government) can’t effectively protect these data, but the main thing - it can use data in the selfish interests of the minority - the analysis of big data will control the growth, development and behavior of the masses - the dream of any dictatorship perfectly described in the novel "1984" by George Orwell.

In the US there is no overall law about the protection of private user’s information. According to Albert Jidari, director of the privacy division of the Stanford Internet and Society Center, different approaches to the problem may exist in different states, industries and even individual companies. In society, however, there is no particular concern. "People are ready to sacrifice part of their privacy for the sake of convenience," Jidari states.

"We all are afraid of the government but people are most closely watched by the corporation. Big data processing requires tools and technologies. First of all, the money will be invested by those who will be able to quickly sell the analysis of personal information: Google, Facebook, Apple, Microsoft and similar large international market players that are interested in marketing research of consumer behavior. The game Pokemon Go was launched based on the already calculated model of the behavior of the population."

Part of the big data only the government has. For example, generalized flows on transport or from cellular operators. By signals Wi-Fi they can track the movement of a particular smartphone and person. Special services and police can combine all the data from surveillance cameras into a single system and analyze people's faces in real time on the scale of a city or region. They can combine passive information collection: sound sensors in cities in aggregate will indicate the exact place of the shot or car crashes for example.

So far no one has the answers to the questions "What to do with big data?" and "how to protect this data?" Legislative regulation is fraught with abuses by the authorities in questions of pressure on freedom of speech, and there is also a great risk of using big data for illegal surveillance of citizens, but the process of self-regulation of this industry is not yet visible. In addition, it's not a fact that IT companies that have data about their users won’t take advantage of the temptation to use these data for selfish and not always legitimate purposes. Perhaps, mankind has faced another global problem, the solution of which is possible only with open discussion of the whole "virtual community".

 http://lifehacker.com/you-dont-own-your-data-1556088120

http://fortune.com/2016/04/06/who-owns-the-data/

You can learn more about business analytics here: https://analyticsinbusinessworld.blogspot.com/2017/05/facebook-future-strategies-for-ad-agency.html

Who owns big data - people, government or corporations? Part 1.

Experts have said repeatedly about the fact what to do with big data. Questions how to regulate the work with companies which accumulate information about the user's downloads of applications, purchases by credit cards, participation in social networks and receiving letters via e-mail and other "big data", has also been voiced a lot of suggestions.

In my opinion, traces of user activity after a certain time after their collection, processing and accumulation companies should simply destroy, because the accumulated arrays of information about our stay on the Internet, made purchases, the exchange of files with friends can tell too much about us, and thus endanger our lives, because these data can fall into the hands of hackers and be resold several times. In addition, companies that collect information about their users don’t always take a responsible approach to cleaning these data, reselling or exchanging them with other firms and their partners.

Hedge funds analysts argue that vendors selling such data sets don’t always clear data from all sorts of confidential details that can identify the personality of the user. "The sellers insist that personal information is removed from the data sets, but we have repeatedly found telephone numbers, postcodes, etc.," says Matthew Granade, director of marketing at Point72 Asset Management. The head of another fund notes that even in the event of the removal of personal details from data sets, it is sometimes quite easy to restore these details. That is why if after certain amount of time this data would be destroy, problems about its storage and protection could be partly solved.

To be continued. Find out answer to this question in the next post.

 More analytics insights are here: http://d-analysis.blogspot.com/2017/05/big-data-vs-data-analytics-vs-data.html

How Airbnb created a referral program to increase sales by 300% per day

Case studies give a great opportunity to learn someone’s experience and try to adapt it for your needs. Today we’ll find out how Airbnb created a referral program to increase sales by 300% per day.

Referral programs are often used to recruit audiences, but in Airbnb the outdated system was not in demand by users and worked poorly. It was difficult to find the program on the site, and on mobile applications it was not at all. Word of mouth is an excellent source of growth in Airbnb, also because the experience of using Airbnb is very personal. People use Airbnb to experience incredible sensations - trips with friends, cultural exchanges and even such unique events as a honeymoon. Airbnb’s referral program encourages for the invitation of friends to get $25 by the person who invites and the person who is invited, when the invited user pays the first booking through Airbnb. The idea was simple - to increase the number of people who tell friends about Airbnb, and to strengthen this effect.

The company decided to restart the referral program on all three platforms: on the site, iOS and Andoird applications. The referral program is a great project, because it is the embodiment of growth. It is easy to measure and scale. It is necessary to recognize the right moment when the user is ready to send an invitation, and to strengthen this desire.

Step 1.

For the referral program, company defined success by choosing several metrics.

The number of users per month who sent invitations,

The number of invitations sent per person,

Conversion to logged-in users,

Conversion to those who rented a house,

Conversion to those who rented out a house.

For each metric, they made three different predictions:

good,

better,

the best.

Company built forecasts by looking at other successful referral programs, like Dropbox and Voxer.

Step 2.

Airbnb developed mechanisms to track the progress and build reports. There is logging platform, called air_events. With it, it is possible to call the same method from any platforms and log events in a centralized repository. Special libraries were created: Ruby, JS, Objective C (for iOS) and Java (for Android). For the referral program company identified more than 20 events that occur during user invitations and subsequent registration. With this tracking, they can track the whole process of the invitation - starting with the number of page opening invitations to the number of users who have booked apartment.

Step 3.

Personal referral codes and links make letters more personal. When the user starts the application after installation, he goes on special lending of the referral program.

One of company’s developers, Jimmy Tang, was co-founder of Yoz.io, where he developed analytical tools for mobile devices. Yoz.io has created a tool that accurately monitors which link the user clicked before installing the application. Thus, it is possible to determine that the user installed the application using a referral link and open a certain page for him. When a user clicks on a link, Yoz.io remembers it. Then, when the user downloads and starts the application, it again determines the user and associates it with the one who clicked on the link. Next, it throws information into the application, so the certain page can be displayed.

Step 4.

It possible to improve some metrics by:

Increasing the number of invitations by offering to import the list of e-mails from the notebook or mail services,

Increasing the number of active users who send invitations, improving the visibility of the referral program in services,

Increasing the conversion rate for new guests by sending a reminder to those to whom the invitation was sent and to those who registered, but did not use the $25 gift.

Step 5.

Company started advertising the referral program at the time when the user is most likely to invite friends, for example, after booking or leaving a positive response.

Company is also in the process of A/B testing of promotional letters. In one letter, they stressed that you can earn $25 for inviting a friend (personal interest). In another letter, they stressed that you will give $25 to your friend (altruism). As a result, altruistic letters worked better.

Also how the program works depends on the culture. For example, a referral program is incredibly popular in South Korea. Company segments the results of A/B tests for different countries to find out which messages in which culture work better.

This case study is a good example of how Airbnb works on projects: set a measurable goal, define metrics, create a product, measure the result and repeat again. Other companies can adopt some mechanism and change it according to its needs.

https://medium.com/airbnb-engineering/hacking-word-of-mouth-making-referrals-work-for-airbnb-46468e7790a6 
  
 More analytics posts you can find here: http://haneenalansari.blogspot.com/2017/05/web-analytics-implementation.html

Monetization methods of mobile applications

1. Freemium strategy.

A good option for a free application, but with the possibility of built-in purchases or additional fees for the use of certain features.

Buying tips, replenishing energy, buying a shield are examples of built-in purchases in games. The user determines to spend on passing the level a few days or buy a hint. The life cycle of freemium-games is within one year. Therefore, developers have to constantly look for ways to keep users in the game. In addition, built-in purchases in games today reduce the developer's profit, and this is a worrying sign.

Access for a fee to additional application features. Skype is the best example of a freemium model in instant messengers. The user receives a free high-quality product, but only 8% of users are willing to buy and use another service and save on phone calls. On a similar principle running Evernote and Dropbox. One application and two services. The question is, will the user prefer a second product?

2. Trial.

A successful strategy that allows to use the application for free for a limited amount of time. The trial time is set by the developer, usually from 7 to 60 days. After the application will be available only for payment.

A great marketing technique is to give the user the opportunity to use the application, show what it is great, and then announce the price for further use. Some developers abandon the freemium model in favor of the trial version of the application.

3. Advertising.

Advertising in mobile applications is actively gaining momentum. Users are sometimes not ready to pay for a complete lack of advertising, so they have to deal with it even in the applications of their smartphone or tablet.

This fact allows developers to receive tangible profits from advertising in mobile applications. The condition for the success of this strategy is a mass audience and a huge market.

4. Subscription (Subscribe, SaaS).

Regular payment for the use of the service and access for the time of subscription to the resources of the application: music services, movies, e-books, news resources and other content. If the content is interesting, subscription can work, the main thing is to choose the optimal and satisfying subscriber cost of the service.

5. Premium paid application

User pay for the installation of the application a certain amount in the hope of solving all their problems. They expect to receive a very high-quality product, which in a few clicks will bring clarity and simplicity. According to statistics, 10% of profit falls on paid installations. Examples are paid games, Pro versions of some free applications (calendars, watches) or e-books (fairy tales by Disney).

Which monetization strategy to choose

Most mobile applications do not bring profit to their developers, according to forecasts in 2018 only 0.01% of mobile applications will be profitable. Many applications simply are not created for earnings. According to Juniper Research, revenue from applications in 2019 will be $ 100 billion. It is believed that the revenue from games in 2017 should be 40.8 billion dollars. According to the research company Swrve to buy game content in games, only 1.5% of users are ready.

There are not so many users who want to pay for application. Most of them are concentrated in certain countries and are owners of specific devices. Also much depends on the type of mobile application being developed: game, tracker, messenger, book or application for business. If the book is better to sell, then mass play will be in a free game.

The most payable population are citizens of the United States, Japan and Britain. A large number of users in the world are not willing to pay for using the mobile application, for example, the statistics of downloads for India or Brazil is great, but the profit from users from these countries is small. The paying audience is so small that developers will have to segment the user of their application clearly.

For freemium products, users do not make any special demands, but from the products they pay for, they expect to get a good design, beautiful picture, user-friendly interface and decent functionality.

Games and entertainment, perhaps the most popular category of mobile applications. For applications in the category "Business" users are ready to pay if the application has a high quality and helps to conduct business.

The trial-time model is well suited for business applications or for applications that require a subscription. Subscription can be offered only targeting a payable population. Experts strongly recommend to think and look for the "golden" ratio of the free and paid part of the application and not to anger the user with the requirements to pay for each action.

It is possible to get revenue with the freemium strategy if the application is designed for a mass audience. Monetizing an application using advertising will also bring results when the audience of the application is a million: the Latin American market, Russia or the Asia-Pacific market.

The user and the application are the main criteria for the developer to choose the monetization strategy. Brainstorming, discussion of case studies, analysis of recent research results and testing by users are also very helpful.

Sources:

https://buildfire.com/ways-monetize-mobile-app/

http://info.localytics.com/blog/app-monetization-6-bankable-business-models-that-help-mobile-apps-make-money

More analytics insights are here: http://haneenalansari.blogspot.com/2017/05/data-monetization-strategy-in-business.html