Bridging the Gap to a New World of Hyper-Personalized Service

Introduction

Data science in the guest experience is blowing up old models of doing business as visionary organizations put the pieces back together in innovative ways.

Whether large or small, companies can face Big Data problems without the technical know-how and insight to put the information to work. Awash in information, businesses find themselves floundering as they seek to understand and use data to predict customer behavior and personalize their services.

As technological advances continue, the concept of data science has grown into a large umbrella that covers several types of data-related efforts. This can include machine learning (ML), artificial intelligence (AI), business intelligence, and more.

The above-mentioned studies are all useful in their own way and they all bring about individual and unique results. Data science, as it now encompasses different methods and practices, is an important interdisciplinary field that is valuable and necessary in today’s technologically-driven business world.

Data Science History

1974: Peter Naur first uses the term “data science”

1977: International Association of Statistical Computing formed to convert data to knowledge

1994: Business Week reveals companies started gathering personal data

2001: William S. Cleveland created an action plan for data science

2002: Data Science Journal publication created

2008: Data Scientist becomes a buzzword

Background

The most successful companies in the modern economy use data science to fundamentally transform the guest experience.

Data analysis can help companies fine-tune every touchpoint in the customer journey. Through the analysis of massive amounts of data collected about the preferences and interactions of individual guests, companies can hyper-personalize the consumer experience. This could be as simple as useful product recommendations or keeping track of a guest’s favored services or products. It could also be used on a far more ambitious scale, opening new possibilities for businesses entering the Experience Economy to provide memorable lifetime experiences.

“As AI and ML become mainstream, a new breed of security data scientists will emerge in 2019: AI and ML techniques are data dependent. Preparing, processing, and interpreting data require data scientists to be polymath. They need to know computer science, data science, and above all, need to have domain expertise to be able to tell bad data from good data and bad results from good results.” – Setu Kulkarni, VP of Corporate Strategy at Whitehat

Putting Data Science to Work

In a business setting, data science has three main components. Each plays its own role in ensuring that data is accurate, usable, and effective.

• Data engineering: Implementing best practices to ensure collected data is clean, reliable, and useful and stored in a format that is easy to process.
• Data analysis: Providing business insights by categorizing and analyzing existing data.
• Data science: Creating software tools and algorithms to predict guest behavior based on past interactions.

2,500,000,000,000,000,000 bytes of data are generated daily

“This technology brings a whole new level of marketing insights to consumer brands and agencies. It helps them to understand better their consumers’ tastes… It is an excellent way for marketers and social media specialists to learn more about product consumption situations, and uncover valuable consumer insights.” – Alexandr Sirach, co-founder of YouScan

Solution

Solving Problems

Data science isn’t pure analysis. It’s also about building tools to increase productivity or better serve guests. It involves understanding available information, using it to create a hypothesis, and then testing and refining it until a production-level solution can be reached.
Data scientists can automate A/B testing of promotions and marketing efforts for new products and services. Instead of just evaluating the reception of one advertisement versus another, how about testing 10 or 20 different versions over the course of a guest’s interaction? Data science can provide large-scale testing and analysis beyond the scope of traditional methods.

In addition, data scientists can provide statistical analysis of business trends and analysis of the effect of potential price changes on consumer demand. As artificial intelligence becomes increasingly important in business operations, data scientists build the machine learning models needed to provide AI with the necessary knowledge base to personalize the user experience.

McKinsey Global Institute indicated data-driven companies are 23x more likely to acquire new guests, 6x to retain them, and 19x as likely to be profitable.

Importance of Well-Engineered Data

In order to put data to work, it has to be analyzed. Before it can be analyzed, there are many steps to ensure its usability. Is the data being collected in the right way? It’s important to ensure the behaviors being tracked are actually linked to the people that are supposed to be tracked.

For instance, how can a company make sure it is tracking the activity of a person who logged in earlier versus their child who might be using the computer now? Collecting the right data means being able to provide the right suggestions and recommendations to the guests.

“Dirty” data is the bane of businesses interested in analyzing guest behavior. Data is useless if it’s not labeled properly or is hard to understand or process. Many companies find themselves dealing with the added complexities of trying to process and clean massive amounts of data before the first analysis take place. It’s important that data be well-engineered and formatted on the front end.

Protecting Guests and Businesses

The customer experience is more than just about the ease of use or personalized recommendations. It also has to encompass concerns like information security and system integrity. It’s vitally important for businesses to secure and properly monitor data and online operations.

Data scientists have a crucial role to play in helping to protect guest data and business services. They can build software tools to track problems in real time by detecting anomalies in traffic or usage patterns, which can indicate anything from financial fraud to denial of service attacks on websites.

Understanding Guest Behavior

Data scientists don’t just characterize how guests are behaving. They investigate the why of the behavior — digging deeper to fully understand consumer decisions. Data science can allow businesses to study and target anomalous guest behavior, using software tools to drill down and capture information about consumers whose habits fall outside the norm of particular demographic groups. It is also predictive, allowing companies to make inferences for the future to guide their decision making.

Brands Doing It Right

Netflix

Streaming giant Netflix is well known for its data-science-driven approach to all its operations. Data analysis and machine learning are used to optimize everything from the quality of streaming to customer viewing recommendations. Its reach even extends into Netflix’s investment in the creation of viewing content, bringing Silicon Valley into Hollywood by optimizing the complicated process of creating original programming.

“Using data science, analytics, machine learning, and optimization, we can support content creators’ decisions from pre-production through principal photography and editing, VFX, sound mixing, as well as localization and quality control. Then we use data to help teams [choose from] good options, rather than defining solutions [to operational challenges] from scratch.” – Jen Walraven, Netflix manager of Science and Analytics2.

Amazon

The benchmark for the successful use of data science to transform guest experiences and business operations is online retailer Amazon. Data science powers everything from customer recommendations to delivery logistics. Quantitative analysis of delivery times and transportation costs help determine the location of the company’s fulfillment centers.

“Many of the important decisions we make at Amazon.com can be made with data. There is a right answer or a wrong answer, a better answer or a worse answer, and math tells us which is which. These are our favorite kinds of decisions.” – Jeff Bezos3, Amazon Founder and CEO

Starbucks

Starbucks, one of the largest and best-known companies in the world, harnesses Big Data and an analytical approach specifically for store placement, menu design and optimization, and personalized attention through the mobile app. The company uses software and predictive analytics to learn more about each individual customer, boosting sales in the process.

“With about 90 million transactions a week, we know a lot about what people are buying, where they’re buying, how they’re buying. And if we combine this information with other data, like weather, promotions, inventory, insights into local events, we can actually deliver better personalized service to other customers.” – Gerri Martin-Flickinger, the Chief Technology Officer and EVP at Starbucks

Conclusion

Data science gives companies the ability to answer three important questions: Who are my guests? What are their current desires on an individual level? And how can we meet their personal needs going forward? By applying the insights of data science, businesses can transform the guest experience to give consumers what they want, when they want, and how they want.
Without the ability to understand, analyze, and actually put it to use, data is worthless. Properly collecting data and being able to create functional production-level solutions require a level of expertise and technical skill that a company may not have.

The demand for qualified data scientists is increasing throughout the business community, just as the demand for personalized experiences is increasing in the experience industry. Data has become an invaluable asset to big and small companies. At the end of the day, the size of a company or the amount of data does not matter. What does matter is how each brand uses it to improve the guest experience.

Data Science Encompasses:

• Machine Learning
• Artificial Intelligence
• Regression
• Classification
• Scale Computerization
• Data Analysis
• Decision Science
• Business Intelligence
• Data Application
• Data Movement

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