By: Nikola Hopkinson, Content Manager, ADMA
In 2017, most businesses have jumped onto the data and analytics bandwagon, realising its importance in developing quality products and meaningful customer experiences.
In fact, a recent global study of over 3,200 thought leaders found that data has become the central pillar of marketing, and 2016 is now better known as “the year of measurement”.
Although businesses have begun to embrace data and analytics, they are struggling to make sense of the sheer amount available to companies. They realise that the fast pace of technological change has put customers in the driver’s seat and that data now plays a vital role in developing outstanding customer experience.
According to Mohammad Shokoohi-Yekta, Data Scientist at Apple, the first step to building useful products and satisfying customer experiences is through data.
“Data plays a main role in understanding and predicting users’ needs. A [quote] from Steve Jobs also emphasises the point: ‘You’ve got to start with the customer experience and work back toward the technology – not the other way around.’”
Shokoohi-Yekta has seen, at first hand, the effects of data and analytics on businesses.
“In recent years, customer data, obviously collected anonymously and with users’ permission, has become a game-changer for high tech companies. This data is used to verify the quality of products and measure customers’ satisfaction.”
Also a Lecturer at Stanford University, Shokoohi-Yekta is a true doctor of data: he has a PhD in Computer Science and has worked with some of the biggest tech companies including, Apple, GE and Samsung.
In the lead up to Data Day 2017, where he will be a keynote speaker, we picked Shokoohi-Yekta’s brains on four hot topics in the data world.
1. Internet of Things (IoT)
“According to the International Data Corporation (IDC), by 2020, more than 4 billion people will be connected over 50 billion devices and will produce over 50 trillion gigabytes of data.
“Unstructured data generated from IoT devices makes the analysis very challenging. The variety of format and modality of data will be more than ever. The rate of data production will also not let current algorithms to analyze correlations of streaming data vs offline data.”
2. Real-time analytics
“Real-time analytics has become a huge buzzword, much like ‘Big Data’.
“Predicting the next move of customers is based on real-time analytics. For example, some big corporates have already harnessed intelligence into automated phone calls to analyze customers’ behavior and temper over the phone and make the best decision in real time. For instance, if a customer sounds angry, the automated system will forward their call to a promotion specialist to in order to satisfy their needs.
“Imagine a store where a customer puts an item in their cart, but before leaving, decides not to buy it. Real-time analytics would suggest emailing a coupon for the same item to corresponding customers so that they get persuaded to purchase the items of their interest.
“Real-time analytics has become a game changer in digital marketing as well. That is why Google monitors mouse pointers moving along the screen. So, that the logged data will suggest the most interesting parts/sections on the screen for each user.
This data may also lead to predicting users’ interest.”
3. Executive buy-in
“When it comes to innovation, many tech giants may not easily take the risk to accept changes, since they are already making billions of dollars and do not necessarily see the use of analytics.
"On the other hand, every company understands that if they do not take advantage of innovations created by data science and machine learning, they may not be on the competing edge anymore. Eventually, convincing executives to apply innovative data science is not impossible but it takes a lot of effort and analysis to show very strong, reliable and consistent results in your prediction/classification results.”
4. Artificial Intelligence (AI)
“AI will be the top disruptor in technology very soon. Many giant companies in the last decade are now out of the market because they did not embrace AI. As computational costs go down and AI methods become more advanced, the disruption of AI will accelerate.
“Marketers in general should be able to augment intelligence into their technology in order to predict their customers’ needs ahead of time and make personalized recommendations. For reaching such a goal, they may need to train their employees on AI or take advantage of data analytics experts to show them how AI can be applied to their platform.
“Data scientists should be able to understand companies’ needs and data at the same time, in order to harness opportunities from AI. Since advancement of methods and technologies in AI is accelerating, keeping up with the latest technology would be a key point.”