Data science is nothing new. As early as 1962, John W. Tukey, famed mathematician, and inventor of the term “bit”, kicked-off the discussion on the potential of data analysis in the era of computing. But it was the breakthroughs in computing speeds and hyper networking in the late 1990s that enabled businesses to rapidly collect vast quantities of data to improve everything from marketing and promotions to supply chain operations.
Customer experience (CX) has been one of the areas that witnessed the earliest adopted use cases for data analysis. From the early era of qualitative and transactional data analysis to today’s granular digital impressions – every “bit” of data today is directed towards enhancing customer experience, whether it’s in the personalized search engine results that cater to our preferences or online shopping portals which provide personalized purchase recommendations. And it has only gotten better through the years.
Data Science in Customer Experience
Nearly 20 years ago, just as I was about to start my Freshman year in college, I received a letter from a certain bank offering me a credit card tailored perfectly to my needs. With a low APR, cash back rewards, zero annual fee and discount at local bookstores, it gave me the flexibility and access I needed on campus. Of course, this was a surprise to me and my parents.
The turn of the millennium was a time when most credit card issuers were in a competitive hunt for customers. But somehow this particular bank knew that having a personalized credit card was exactly the experience a naive 17-year-old desired. Little did I know that this decision was driven by the earliest uses of data-driven demographic profiling – a rapidly emerging trend that has since evolved into data science.
Today, businesses are harnessing the power of data science, analytics, and cultural insights to generate highly effective and actionable business intelligence. Thanks to advances in computer vision, machine learning and artificial intelligence, organizations can leverage insights from structured and unstructured data unlike ever before. Data science is driving everything from predictive maintenance in manufacturing and real-time recommendations in media to semantic relationship mapping in finance and demand forecasting in healthcare. In fact, data science-driven customer experience mapping has been a decisive factor in the rise of autonomous vehicles and digital voice assistants, which are on the road to becoming ubiquitous.
Crafting Meaningful Customer Experiences with Data Science
As digital becomes an essential part of people’s life, every interaction between customers and businesses will become an opportunity for better customer experience mapping. But despite the meteoric rise in data volumes and analytic capabilities, many businesses continue to struggle in their attempts to scale their CX management solutions. In fact, a McKinsey study discovered that only 8 percent of firms are engaging in the core practices needed to support widespread adoption of data science within the organization.
So what can companies really do to offer their customers unparalleled customer experience powered by data science? Here are a few examples of how we’ve done it at HCL:
- Overcome Organizational Barriers
The digital transformation journey can be unforgiving. More often than not organizations can have the right technology, the right data, and the right analytic models, and still fail. In such cases, the failure of digital transformation is usually found in organizational structures and leadership. The first step towards winning the customer experience game is winning over internal stakeholders. Organizations need to be trained from the top down to propagate the value of data science and the value it brings to the business. And while traditionalists might still be hesitant, data leaders need to show that black-box decision making is a thing of the past as data-driven insights can enhance the contributions from across every level of the organization. This is a key step if next-generation leaders wish to fully adopt data-driven automation and make the most of their investments.
- Invest in Forecasting and Predictive Analytics
Digital customers are accustomed to instant fulfilment. And, what else can be more fulfilling than instant predictive analytics recommendations for items you actually want? HCL’s Digital and Analytics team worked with a major American household product manufacturer to achieve this. By analyzing customer demographics, product information, and customer purchase histories, we were able to design and deliver a custom decision support service for our client. Our solution maximized inventory forecasting accuracy and improved the “make-to-stock” metrics, ensuring that the right products were available at the right time, at the right stores and at the right price.
- Focus On Hyper-Personalization and Real-Time Feedback
E-commerce is the home of hyper-personalization, but can brick-and-mortar stores achieve the same? By partnering with HCL, a major Australian supermarket chain was able to do exactly that. We helped them by designing an object detection and image recognition solution that was created to extract meaningful information from optical photo and video footage. This tool allowed the supermarket to collect real-world data that could help them improve customer experience. We used real-time data gathering to analyse the customer journey inside our client’s stores. This analysis of customer journey included monitoring various indicators such as products viewed by customers, their dwell time, brand preferences, customer demographics, traffic flow, repeat visits and other factors which generate value for the businesses.
With a focus on hyper-personalization, our client was able to make critical changes to their store layout to improve customer flow for more comfort and ease. They were also able to optimize their inventory and shelf management to offer customer-centric placement and greater appeal and enhance their customer loyalty by implementing customized promotional activities. The immediate results included higher footfalls, greater sales, and an increase in customer loyalty. All of this was possible by ensuring that consumers had a more seamless and positive customer experience while the client continued to improve their own operational efficiency.
- Enhance Product Quality at Reduced Costs
Nothing wins customers over more than getting exactly what they want. In the fast-food industry, this can often be a disappointment. With HCL’s help, a major American restaurant franchise was able to surpass their customer’s expectations. The client wanted to automate their food grading process to ensure high quality standards. Using our Convolutional Neural Network technology developed using Google Tensorflow, we used pre-trained analytics models with transfer learning to improve the quality of produced goods. Once implemented, our client was not only able to enhance their overall production quality but also improved the cost to performance ratio in production to offer their customers a winning food experience every time.
The Future of Customer Experience
Nearly 20 ago, in December 1999, as I was reviewing personalized credit card offers, Professor Jacob Zahavi, a pioneer in data science, was giving a talk at Wharton Business School. He was discussing the potential future of the field and how the biggest challenges were those of achieving scalability in analytics. Even as he pondered those words, a fledgling 5-year-old bank was leveraging IT-driven analytics to offer me a delightful and personalized customer experience that appealed to my financial situation as a young student, through a zero annual fee card that allowed me to avail discounts on purchases made at local bookstores -- which formed a significant fraction of my typical monthly expenditure.
This bank did so successfully across the nation, with college students everywhere, achieving unprecedented success by eliciting a 70% greater response than other offers targeted to the same student demographic. The bank not only identified college students as an untapped market, but also offered differentiated products to match their needs. The bank in my story was Capital One, which is now the 10th largest bank in the United States by assets.
In just 20 years, we’ve gone from targeted mailing lists to cash-free retail stores. And through all this evolution, technology has played a key role in paving the way forward. But while technology is the engine that drives us, our compass is entirely focused on people and their needs. I expect the next 20 years of this evolution to be as surprising as the last 20 years have been. As data science continues to improve, its capacity for connecting people and institutions to solutions and services is limitless. The only question that remains is – who will take the lead in building these new bridges to the undiscovered country of the future.