Hola!

After finisihing one long series about Blockchain the other day, it happens that the respond from all readers is quite good, and lots of them reach out to me through whatsapp or instagram (yep in Indonesia we use Instagram to ask serious question actually :P) to learn more about the nitty gritty of this technology.

Even though the question is still mainly revolving around investment (is it now too late to buy Bitcoin? Are Bitcoin buyers really become Billionares? When Moon?) but I take this as a sign of increasing public awareness of Bitcoin and Blockchain Technology.

me, whenever someone asking about Blockchain

For this essay, i will try to elaborate on of the exciting project that build on top of a Blockchain Technology.

My hope is by elaborating and try to describe an ongoing project, will give a clearer picture to my reader about what is the real use case of Blockchain Technology, what is exactly the benefit for user, and what is the differentiation between an existing centralized system with the decentralized one.

Project that i will review here is SWIPECrypto (https://www.swipecrypto.com/) or SWIPE in short.

There’s a lot of things to be unpack, so let’s dig deep one by one.

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First thing first.

What is SWIPE?

Developer Apps that enable both apps developer and users to do a fair and transparent data monetization.

Developer Tools in a simple definition is a software that used by Apps Developer to improving capability of their application product and services.

SWIPE providing SDK (Software Development Kit) that can be installed instantly by Apps Developer that will grant them full access to SWIPE Ecosystem.

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Second.

Who are the parties that got involved in the SWIPE Ecosystem? For who is exactly SWIPE created?

APPS DEVELOPER

Someone that create Mobile Application

2. APPS USER

Someone that using Mobile Application

3. DATA BUYER

Party who buys data from Apps Developer.

This can be a marketing agency, a research company, a FMCG Company, or big corporation who needs the data insight of a Application User to improve the effectiveness and efficiency of their marketing activity.

The Data that mention here is a user berhavior data or what kind of activity that the User do in the application, and not (hopefully) user personal data.

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Third.

How exactly the current sytem of data monetization works and why it needed a significant improvement?

from SWIPE Website

note : to make it easy for reader to undertand the process, we will user personification (name giving) for every party that got involved on the process.

a) the Application that we’ll discussed is called KOMPOS, Mobile Apps News — Reading Application that enable user to get the latest news for both local and international.

b) Apps Developer is named John, a smart Mobile Apps Developer from Stanferd University.

c) Apps user is named Alice, a young women who have a job as a cashier in local mini market store called Indomart, she is keen on reading political and showbiz news.

d) The Data Buyer is MARKON, a marketing research company that focusing on serving political consultant as their client.

The current data monetization process, from application building until the usage of the data, goes more or less like this : (note -there will be a simplification to enable all readers understand this)

John create KOMPOS Mobile Application Alice, while updating the existing application on her smartphone, see that KOMPOS is now trending on Google Play Store, then decided to download it. The application get into Alice’s phone.

This is the first phase of Data Giveaway, where Alice giving her Smartphone Device ID to John.

Device ID is an unique identity that owned by each smartphone.

There’s no two or more smartphone with the same Device ID. Alice required to fill her Private Data for KOMPOS Sign Up.

Because she is reluctant to fill from the start, she decided to use Sign In using Facebook Account.

This is the second phase of Data Giveaway, where Alice giving her full name, email, and birth date. Alice using KOMPOS to read news about politics, economy, sports, and various topics. Her favorite is politics and showbiz.

This is the third phase of Data Giveaway, where Alice, without knowing, giving John the knowledge to her preference, from her favorite politician (reflected by how often Alice clicking on news that involving certain politician), what kind of news format does she enjoy best (long or short), what kind of topics (politics and showbiz), and what time she prefer to read the news and how long is the duration of each reading session. John create a user persona of Alice which is a compilation of personal data and Alice’s in-apps user behavior. Beside Alice, John has millions of other users using KOMPOS, so John have a big enough data bank that he can sell to the willing Data Buyer. MARKPLAS, which is an active data buyer comes and offer some money to buy KOMPOS Users data that has been through anonymity processto protect User Personal Data.

The only personal data that John doesn’t hide is Device ID. MARKPLAS then do hyper-detailed user persona through combining data that has been acquired from KOMPOS with other application data that has also been acquired by MARKPLAS.

Because Alice not only using KOMPOS in her phone, so there’s a lot of other user behavior which belong to Alice that in possession of others apps developers.

For the sake of simplicity, let’s say MARKPLAS has acquired the data from Mobile Legenda (mobile game), Go-Jok (on-demand motor taxi services), three of the top e commerce application : Tokekpedia, Shopo, and Bukalapuk, and also two of the most well-known music streaming services : Spotifa and Joki.

Same with John, every other Apps Developer giving the data that has been through anonymity process, except for the Device ID

Why is that Device ID is an ideal data anchor for apps user behavior data?

Below is a very comprehensive explanation from adjust.com of what is Device ID and why it is important.

After we understand how important is our Smartphone Device ID, let’s examine an illustrated spreadsheet that owned by MARKPLAS as an active data buyer of several mobile application.

(note : all example here is using Indonesia Context and also the amount of money is in IDR, Indonesia Native Currency)