Much more than a buzzword in today’s business world, data science is redefining the way companies interact with their customers.
No matter the sector or industry (retail, insurance, manufacturing, banking, travel), every large company has its own way of handling data science. They have to. Data is everywhere. It’s the new gold, and mining that data is critical to the success or failure of any business.
Data gives access to the kind of information that separates competitors. Data-driven companies serve their customers better and make better decisions, all because those decisions are backed by data.
Data science is the next evolution in the business world, and those who do not adapt to this new reality will cease to exist. The alternative is extinction.
That was the fate faced by a European fashion and clothing retail chain. Founded in the early 1980s, it built a legacy of a unique, customer-focused in-person shopping experience. The advent and proliferation of online retailers dealt a severe blow to his business. When its brick-and-mortar stores began to struggle, the store could have accepted its fate and moved into the dustbin of history.
Instead, it embraced digitization.
Keeping a positive customer experience as its focus, the company planned an omnichannel digital transformation to manage customers, collect data, and provide products and services sought after by its customers.
It started with launching an eCommerce channel and building a CRM system to manage customers and collect data through a loyalty program. In keeping with their customer-focused entrepreneurial ethos, they focused on building a dedicated innovation capability to ensure they were providing consumers with the products and services they wanted. Ultimately, they moved on to digitize customer processes and optimize the customer journey.
Today, the fashion retailer maintains its physical stores to allow shoppers to see and experience the collections it offers. The online store is used as a communication channel to interact with a subset of your customers and understand what they need and want.
To maintain this new approach, eight digital teams were created and everything that can be measured is measured. This digital transformation has allowed the company to trace 90% of its revenue back to the end customer.
building a team
For companies that have yet to jump into the data science game, or are taking their first steps into the space, the first and most important piece of advice is to be humble, recognize that this is not something you can do on your own, and work together. a team of professionals.
Data science is a complex field, and to be put to good use, it needs engineers, scientists, and analysts to develop the AI platforms that will identify, collect, evaluate, and use data to its maximum advantage. They can develop the strategy that identifies the type of data that is needed, the best methods to collect that data, the systems needed to collect the information, and how to ensure that the data is clean and usable so that it can be monetized.
This team can also develop the necessary infrastructure to support the data capture and collection, including the artificial intelligence or machine learning platform and a cloud platform for large computing storage capacity.
The cloud platform is key. It enables rapid data deployment and drastically reduces the time it takes to gain valuable insights about a business and its customers. Analytics engineers can build trusted data pipelines that enable self-service reporting and visualization.
But looking at millions of touch points and trying to figure out how to extract meaningful information from them can be a daunting task. Being data-driven means more than just unlocking data, storing it, and giving everyone access. It is about extracting insights from the information collected to predict future insights, advise where to invest in the short, medium and long term, reduce customer turnover, predict demand, optimize the logistics chain or automate business processes.
When it’s most useful, data science extracts non-obvious patterns from a large data set, such as purchases, reservations, claims, or bank transactions, to help a business make better decisions.
Procurement data mining
Knowing your customer is a basic principle for any business, and historical data on customer buying patterns is not only the most common and easily accessible data set, it is also among the most important. It enables predictions of future wants and needs and provides valuable information to influence future consumer choices.
A customer relationship management (CRM) system is a good starting point for using data science effectively. Retailers can use this data to identify groups of customers who have similar behaviors and tastes, and also develop a better understanding of products that are frequently purchased together.
One of North America’s leading apparel manufacturers has a proud 150-year history, and over the years has increased its production capacity, expanded its sales network, and invested in marketing. But perhaps its most important initiative today is its data science analytics. The data science division reports directly to the CEO and works with an ocean of data on a Google platform to engage customers more effectively.
During the COVID pandemic, as the number of online clothing shoppers increased, the company’s data science division flourished, enhancing the company’s digital footprint to collect as much consumer data as possible: who buys online vs. who buys in-store, what they’re checking out online, how much they spend, how they pay for their purchases, what they end up buying and using all of this information to build profiles and track patterns.
The data was then monetized through marketing campaigns directly targeting consumers who matched those profiles.
User data mining
As data science advances, customer interactions become much more personalized. Instead of creating broad profiles on groups, specific markets or regions, the focus is becoming more and more individual.
Streaming services use data to improve the user experience. They offer viewers recommended titles that their algorithm has determined the individual may enjoy. The easy assumption is that this is simply based on what the viewer may have previously seen. For example, because you enjoyed this action movie starring Tom Cruise, you might enjoy this other action movie starring Tom Cruise.
However, it is much more complex than that. The streamer would start with archetype profiles created by analyzing mountains of user data from around the world. It will then take the individual’s viewing patterns (titles, genres, actors, seasonality), interweave them with others within that profile from around the world, and what they’re viewing, to generate its recommendations.
Travel Data Mining
The travel and hospitality industry is relying on data science to help it recover from the pandemic.
Few companies were spared from the negative impacts of the pandemic, but the travel industry was decimated. Before the pandemic, the global market for airport operations was worth an estimated $221 billion. After the pandemic forced border closures and nearly canceled recreational air travel, that figure plummeted to $94.6 billion. There was a slight improvement in 2021 to $130.2 billion, but it’s still a long way from where they want to be.
The challenge is to develop and implement data-driven solutions that renew revenue streams, prioritize public health, improve customer experience, and support sustainability initiatives.
Focusing on the customer experience while improving operational efficiencies is more crucial than ever, and is expected to be done within the parameters of unchanged financial goals.
One of the world’s largest airlines is using data science to forecast costs related to complaints and claims for delays and cancellations. This has helped the airline resolve operational disruptions and improve customer satisfaction. He was also able to develop and implement new solutions to improve online payment methods, initiate a performance alert system, and optimize the use of maintenance capital.
From customer service to cargo shipments, the airline now has processes for collecting and analyzing information and developing new ideas, with greater insight into internal data analysis.
Only the beginning
We are standing at the tip of the data science iceberg. Data science is already a lifeblood of a successful business, and its use will grow a hundredfold. It won’t be long before all transaction systems (purchasing, booking, banking) have AI built into the workflow. Data analytics will be implemented in all applications of all businesses. Without it, no organization will survive against the competition that invests heavily in data analysis.
Vipul Baijal is the General Manager of the Americas at Xebia. Ram Narasimhan is the global head of artificial intelligence and cognitive services at Xebia. Headquartered in Atlanta, Xebia is a global leader in IT and digital technology consulting.
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