Eulerr is a marketplace of vetted agile software development teams with the expertise in Data Science. Whether difficult cases your business has, we’ll handpick the perfect agile team for you project.
In 1987, 30 years ago, the amount of all digital data in the world was 8 gigabytes. It can be saved several times by a modern flash drive. Nowadays the amount of all digital data is about 10 zettabytes or 10 trillion gigabytes. Take into account that 1,5 Mb of information per person is creating every second in the world, the increasing all data amount over the next 5 years fourfold sounds plausible. And, if you could invest in the data just like in stocks, maybe it would have been a successful investment in your life.
At the same time, the cost of data storage fall down every year. For instance, in 1981 Apple presented hard drive with 5 megabyte with price tag at $ 3,500. It means that the cost per megabyte is $ 700. Today, the cost of storage is 35 thousand times lower – 0.02 cents per megabyte (without taking into consideration inflation).
The cost of data-processing has fallen down too. In 2002, $ 100 million was spent to decode the human genome. In 2008, the decoding of the genome cost $ 10 million. And today there are companies on the market that will completely decode your DNA for about $100, other words, cost become cheaper ago in million times than 15 years.
So, there are three main factors:
increase of the amount of available data
fall of data storage cost
development of analytical tools for data-processing
They together gave the growth of data science era. “The revolution of Big Data” brought technical advances in storage sector of data cloud computing. These things help business to compute similar patterns for future events. Today, Data Science algorithms help to predict everything from influenza outbreaks to crime-related deaths.
The main reason of boom in e-commerce is the hidden efficiency that data is contained. Every serious e-commerce company collects their data and its analysis allows company to open the road to remake the existing processes.
2014 was record year, for e-commerce retailers, in terms of using all advantages of using Big Data and Data Science as an online optimization tool and a sales strategy.
Big Data tracks the customer behavior from begin to end of the shopping process, and also classifies customers into different categories. All this allows you to understand the customer’s buying strategy better. You can use it to increase profits in the future: during the season sales, holiday sales and usual regular sales.
Moreover, analysed Big Data are also used to find out about new trends in e-commerce in the next years. According to the study, Data Science can help forecast demand and optimize prices to increase sales. This thing is essential, because a price increase of 1% leads to an average profit increase of 8.7%.
The advantages of using of Big Data and Data Science are obvious for e-commerce, but nowadays, there are factors that hamper the development of Big Data, such as the shortage of qualified specialists and an absence of implementation experience.
Personalization. You should understand, that customer can visit the same resource in a variety of way: This can be direct transitions, email-sending or transitions from social networks and so on. Various types of interaction with the resource has a great potential for creating offers in real-time mode in the form of relevant or exclusive proposals. Stores can create personalized user profiles that based on: the history with interaction with website, the involvement and activity of users. That contribute enhancement of CLV (customer lifetime value).
Dynamic pricing. Big Data has revolutionized the dynamic pricing in the personalization process based on on the user experience of e-commerce. Dynamic pricing is another segment where in the future BIG DATA will revolutionize the personalization process based on the user experience of e-commerce. This technology allows to gather history of user’s interactions, analyze them and create the relevant price of each product individually for potential buyer. Special algorithms take into consideration many things, like current prices of competitors, difference between new and old price, location factors, customer activity and so on. Having analysed these data, you can offer buyer a unique price for goods.
Personalization and dynamic pricing are two of the many implementations of Data Science technology that have made great changes to the world of e-commerce.
The abilities of of tracking and analyze individual transactions has crucial advantage over in a highly competitive e-commerce sphere. The ultimate goal of using the Big Data and Data Science is the increasing store profit as any other commercial organization.
At Eulerr our main goal is to deliver the results of work ASAP. As the results, you give the report, which will include:
You will have detail statistics of different testing hypothesis with visual addition. Here is the example of using different algorithms based on Machine Learning for best sell optimization (Picture 1 and Picture 2).
During the process, we use sprints, each two weeks a team brings results. The sprints can easily be reorganized in accordance to your needs. More important here is that you don’t need to wait long to meet the product and you can follow the progress constantly.
The first two-weeks period is trial. Only after complete satisfying with the first results, we will charge you for the time and continue with our collaboration.
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