On Sept 9th, Apple announced the new models of its iPhone with two sizes: iPhone 6 (4.7") and iPhone 6 Plus (5.5"); the Apple Watch, the new smart watch with a lot of features with two sizes and three versions (Apple Watch, Apple Watch Sport and Apple Watch Edition); and the most critical announcement (at least from my perspective) was Apple Pay, a totally new mobile payments platform using Apple-based devices. But, Why is Apple entering in this particular sector with strong competitors like PayPal/Braintree, Stripe, Square, Amazon, Google? The answer is simple: they used a Data-Driven approach to calculate the right time to launch it. Yes my friend, the answer is in their own data.
First, some stats and facts
First, I want to put you some stats and facts to help you to understand why Apple released Pay right now.
Everyday flows more than 12,000 million USD in U.SThere are just 220,000 NFC-enabled merchants in U.S today, out of the roughly nine million total merchants in the country…But here’s the twist. As of October 2015, any merchants that do not support EMV credit cards — smart cards with integrated circuits that enable point of sale authentication and help prevent fraud — will be liable for the fraudulent use of counterfeit, lost and stolen cards… For all these reasons, every merchant should install new card-reader hardware units, which are compatible with NFC (Near Field Communications), the technology behind Apple Pay.
Launching Apple Pay with the massive switchover in the US to new card readers in 2015 to support chip in card is unbelievable timing.— Adam Nash (@adamnash) septiembre 11, 2014
While EMV is the norm around the world, only 14 percent of U.S merchants support this technology today and very few consumers own credit cards incorporating these chips
“In the case of Apple Pay, Apple announced integrations with Macy’s, MacDonalds’s, Walgreens, Staples, Disney, and several majors retailers; not to mention its back-end partnerships with VISA, MasterCard, American Express, and six of the largest issuing banks that it says collectively represent 83 percent of all credit card volume”
Apple has 1 Billion of iOS users and 800 million of iTunes accounts activated
, Tim Cook, Apple’s CEO
The first pillar: Data Science
So, you can see that Apple’s data is rich and ready to be used in several Analytics projects focused to accelerate the adoption of Apple Pay by its users. Ideas? Many of them come to my mind.
For example, they could use its iTunes payments data to analyze which users are more propense to buy products and apps; studying the frequency of payments by each of these users. Based in this information, they could generate the Top 100,000 iTunes accounts who spend more in the platform.
Another thing that they should be working in the seamless integration between BeatsMusic service with iTunes and make them an unique platform, revamping the recommendation system for music discovery and playing; based in personal tastes and homophily (a tendency of people to associate with others like them) using Data Mining techniques, where they could build an accurated predictive model to see which of the users of both services are more available to use Apple Pay (a very good example of it you can find it in KDNuggets) or simple to make an advanced customer segmentation using clustering techniques based in these variables (music frequency plays, preferred genre, user’s geolocation), and use these targeted users to build integrated Marketing campaigns to spread the word about the service using referrals a-la-Dropbox (yes, word of mouth works !!!):
Bring your peer to Apple Pay, and receive 100 credits from …(Apple, you can fill the blank)
Like you should know, my dear reader, Data Science and Big Data Analytics are redefining the way how companies and organizations make decisions, and Apple could learn from this today. Two recent studies have came out about how Big Data has become in a critical piece of every company. One is from Accenture, and another is from PwC called “Gut and Gigabytes: Capitalising on the art and science in decision making”. Some of the key findings in the first report are:
- Executives see tangible business outcomes from big data in finding new sources of revenue (56%), enhancing the customer experience (51%), new product and service development (50%), and winning and keeping customers (47%)
- Challenges in implementing big data are security (51%); budget (47%); lack of big data implementation talent (41%); lack of talent for big data and analytics on an ongoing basis (37%); and integration with existing systems (35%).
- “Many companies have different definitions of big data.” Indeed, in answer to the question “Which of the following do you consider part of big data?” responses varied from “Large data files” (65%), to “Advanced analytics or analysis” (60%) to “Data from visualization tools (50%).”
and in the second report from PwC, the key findings are:
- Intuition has got a bad rap in the age of big data and the most interesting finding of this report is that 30% of executives admit that intuition is what they most relied on when they made their last big decision. An additional 28% relied on other people’s intuition (“advice or experience of others internally”). Only 30% said that “data and analysis (internal or external)” is what they relied on for their last big decision and another 9% relied on “financial indicators.”
- Still, 64% of the executives surveyed said that big data has changed decision-making in their organizations and 25% expect it will do so over the next two years. And 49% of executives agree that data analysis is undermining the credibility of intuition or experience, compared with 21% who disagree. Says the report: “In reality, however, experience and intuition, and data and analysis, are not mutually exclusive. The challenge for business is how best to marry the two. A ‘gut instinct’ nowadays is likely to be based on increasingly large amounts of data, while even the largest data set cannot be relied upon to make an effective big decision without human involvement.”
To read more about this, you can visit Gil Press’s blog here.
But, OK, Apple got it the message, but how they could all this to the company? There are good companies that could help to embrace Data Science today:
- MapR, the well known company behind the most powerful Apache Hadoop platform, the de-facto framework for Big Data Analytics based in MapReduce
- DataStax, the company behind Apache Cassandra, a very good platform for Real-Time Analytics (with Spark), which works very well with Apache Hadoop too (if you want Batch-based analytics)
- Revolution Analytics, the best company for R support and training, with great features for Hadoop/R integration and Data Visualization
The second pillar: Cloud Computing
Apple has embraced Cloud Computing already, but it would even better if they could build an internal Cloud-based Analytics platform to work faster to obtain more accurated results. One company that could be a great partner here would be Red Hat, that with the last release of Red Hat Enterprise Linux 7; they are dominating the industry with this strong foundation and of course with its Enterprise-ready Red Hat’s OpenStack product; Apple could build this quickly with the best Total Cost of Ownership (TCO).
The third pillar: Security
With the recent hacks from iCloud, and this new system; Apple should invest heavily in Security, so one idea could be a partnership with Lookout to combine datasets from both companies and make joint analytics; and the another idea would be to use Palo Alto Networks’s hardware-based firewalls to protect from massive DDoS attacks and if Apple wants to go beyond; they should consider to improve its networking infrastructure using the amazing high-speed switchs from Arista Networks.
So, the question that comes to my mind is simple: Is Apple ready for this? For me: a big YES. Apple can do all this and they could be building the foundation of a Cloud-based Analytics platform for the next wave: Internet of Things; so the efforts that they could make here, it will be paid off in the long term. So, embrace these pillars, Tim and work hard; you will be happier in the next years of Apple.