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The Tip of the Data Science Iceberg

Data science is already a vital element of a successful business. Before long it will be part of every application, and AI will be embedded in every transaction workflow.

Far more than a trendy buzzword in the business world today, data science is redefining how companies interact with their customers.

No matter the sector or industry—retail, insurance, manufacturing, banking, travel—every large enterprise has its way of dealing with 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 provide better service to their customers and make better decisions—all because those decisions are backed by data.

Data science is the next evolution in the business world, and those that fail to adapt to this new reality will cease to exist. The alternative is extinction.

That was the fate facing a European fashion and clothing retail chain. Founded in the early 1980s, it built a legacy on a client-focused, upscale in-person shopping experience. The advent and proliferation of online retailers dealt a big blow to its business. When its brick-and-mortar stores started struggling, the store could have accepted its fate and moved into the dustbin of history. Instead, it embraced digitization. Maintaining 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 by its customers.

It started with the launch of an e-commerce channel and the building of a CRM system to manage customers and collect data through a loyalty program. To maintain its customer-centric business ethos, it focused on developing a dedicated innovation capability to ensure it was providing consumers with the products and services they wanted. Finally, they moved to digitizing client processes and optimizing the customer journey.

Today, the fashion retailer maintains its brick-and-mortar 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 its customers and build an understanding of 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 enabled the business to trace 90% of its revenue back to the end client.

Building a team

For companies that have yet to jump into the data science game, or are in their first steps in the space, the first and biggest piece of advice is to be humble, acknowledge this is not something you can do on your own, and pull together a team of professionals.

Data science is a complex field, and to be used properly it needs engineers, scientists, and analysts to develop AI platforms that will identify, collect, assess, and utilize the data to its maximum advantage. They can develop a strategy that identifies the kind of data that is needed, the best methods to collect that data, the systems needed to gather the information, and how to ensure the data is clean and usable so that it can be monetized.

This team can also develop the infrastructure required to support data capture and collection, including the AI or machine learning platform and a cloud platform for large computer storage capacity.

The cloud platform is key. It enables quick deployment of data and drastically cuts the time required to gain valuable insights into a business and its customers. Analytics engineers can build reliable 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 simply unlocking data, storing it, and giving everyone access. It’s about pulling insights from the information gathered to predict future insights, advise where to invest in the short term, mid-term, and long term, reduce customer churn, predict demand, optimize the logistics chain, or automate business processes.

When most useful, data science extracts non-obvious patterns from a large data set, such as purchases, reservation bookings, claims, or banking transactions, to help a business make better decisions.

Mining purchasing data

Knowing your customer is a basic principle for any business. The historical data of customer buying patterns is not only the most common and easily accessible data set, but it is also among the most important. It enables predictions of future wants and needs and provides valuable insight to influence consumer choices.

A customer relationship management (CRM) system is a good starting point for effectively using data science. Retailers can use this data to identify groups of customers who have similar behaviors and tastes, and also build 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 built up its production capacity, expanded its sales network, and invested in marketing. But perhaps its most important initiative today is its data science analysis. 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 more clothing shoppers were pushed online, the company’s data science division flourished, improving the company’s digital footprint to collect as much consumer data as possible—who is buying online versus who is shopping in-store, what they are 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 create profiles and track patterns.

The data was then monetized by marketing campaigns that directly targeted the consumers that fit within those profiles.

Mining user data

As data science progresses, customer interactions are becoming much more personalized. Rather than building broad profiles about groups, specific markets, or regions, the focus becomes increasingly 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 watched. For example, because you enjoyed this action movie starring Tom Cruise, maybe you will enjoy this other action movie starring Tom Cruise.

However, it is much more complex than that. The streamer would start with archetype profiles built by analyzing mountains of user data from around the world. Then it will take the individual’s viewing patterns (titles, genres, actors, seasonality), weave them in with others within that profile from around the world, and what they are watching, to come up with its recommendations.

Mining travel data

The travel and hospitality sector relies on data science to help it recover from the pandemic.

Few businesses were spared negative impacts from the pandemic, but the travel sector was decimated. Before the pandemic, the global airport operations market was worth an estimated $221 billion. After the pandemic forced the closure of borders and all but shut down recreational air travel, that figure plummeted to $94.6 billion. There was a slight improvement in 2021 to $130.2 billion, but it is still far from where they want to be.

The challenge is to develop and implement data-driven solutions that will renew revenue streams, prioritize public health, enhance the customer experience, and support sustainability initiatives.

Focusing on the customer experience while improving operational efficiency is more crucial than ever, and it is expected to be done within the parameters of financial targets that have not shifted.

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 solve operational disruptions and improve customer satisfaction. It was also able to develop and roll out new solutions for improving online payment methods, initiating a performance-alerting system, and optimizing the use of maintenance capital.

From customer service to cargo shipments, the airline now has processes in place to collect and analyze information and develop new ideas, with a greater understanding of internal data analytics.

Only the beginning

We are standing on just the tip of the data science iceberg. Data science is already a vital element of a successful business, and its use is going to multiply a hundredfold. It will not be long before all transaction systems—purchases, reservations, banking—will have AI embedded in the workflow. Data analytics will be deployed across every application of every business. Without it, no organization will survive against competition that is heavily invested in data analysis.

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