In any industry and market organisations are witnessing a new era where our customers’ behaviours are measured, tracked and often predicted with the use of Data. What does this mean? Technologies are rapidly changing the ways people and organisations create, use and share data and knowledge.
The new paradigma is digital transformation considered as ‘the change associated with the application of digital technology in all aspects of human society’
Every business that is using digital transformation to better serve the end consumers and to take advantage of data around it.
Life Science, Retail, Financial Services, Betting and many other sectors are facing the opportunity of adding new value streams to their marketing activities with the use of dedicated technologies built by Data Scientists.
Data Science can be defined as the language or informatics tool that can be taught about businesses.
Data Science is the science of processing information. It’s about building, creating, and inventing machines that automatically process all kinds of information, from numbers to text, images, or video. This started with the calculating machine, here, an example of numbers and arithmetic operations.
For example: 346 + 78 = ?
Besides arithmetic, Data Science can analyze business trends and it can generate operational solutions with the aim to enhance the performance so that businesses can operate more effectively in the market. Embracing new technologies has become a significant facet in the development of products and services in modern times. In order to grow in the market, businesses have to be dynamic; the adoption of new tech innovations has enabled them to manage business operations very efficiently. Imagine a place where more than 278,000 skilled IT professionals roam, bringing their expertise and creativity to the table.
For activities like marketing the role of Data Scientists building Artificial Intelligence and Machine Learning solutions has become very critical as behavioural models and algos help businesses to enhance their clients engagement and optimize their budget. See our success stories
Below are some interesting use cases..
AWS Intelligence for everyone
Amazon has been a pioneer in using Machine Learning to offer personalized product recommendations. However it has been challenging for Amazon to extend these capabilities to the companies that were running their sites using Amazon Web Services.
Last June, Amazon announced the general availability of Amazon Personalize, which brings the same machine learning technology used by Amazon.com to AWS customers for use in their applications.
Starbucks Uses Predictive Analytics
Companies that identify customer needs through predictive analytics are able to increase their organic revenue with predictive analytics.Starbucks is one example of a brand that is using its loyalty card and mobile app to collect and analyze customer data.
This came as a result of the work of a team of Data Scientists who have been focusing on these analytics programmes since 2016 and creating dedicated programmes with the latest data languages (MongoDB, Pytorch and Spark).
Relevant things of Stitch Fix
Stitch Fix is an online styling service that delivers a personalized assortment of fashion products to customers each month. Managing return costs and warehouse space are key components of profitability for Stitch Fix.
The brand combines personal (human) stylists with the insight and efficiency of Artificial Intelligence. Data Scientists built models to analyze the data on style trends, body measurements, customer feedback, preferences and delivers a narrowed down set of possible recommendations to the human stylist. The stylists then use their expertise to handpick clothing and accessories for the individual customer.
Incorporating AI into their systems allows Stitch Fix to invest in merchandise with more confidence. As Eric Colson, Chief Algorithm Officer at Stitch Fix, said: “Our business is getting relevant things into the hands of our customers”.
The above use cases show how Data Science is changing organisation Go To Market strategies, how does your business take advantage of data?