Bridging the Gap to a New World of Hyper-Personalized Service
Updated August 2023
Introduction
Data science is blowing up old business models as visionary organizations put the pieces back together in innovative ways.
Whether large or small, companies can face Big Data problems without the technical knowledge and insight to put the information to work. Awash in information, businesses are 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), and customer relationship management (CRM), all in concert with mathematics, statistics, and computer engineering.
The studies mentioned above are all useful in their own way and bring about individual and unique results. As it now encompasses different methods and practices, data science is an essential 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 was formed to convert data into 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 use data science to transform the guest experience with flexibility and scalability.
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 can 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, creating possibilities for businesses entering the Experience Economy to provide lifetime memories.
Putting Data Science to Work
In a business setting, data science has three main components. Each plays its role in ensuring that data is accurate, usable, and effective:
- Data engineering: Implementing best practices to ensure collected data is clean, reliable, 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.
Daily, the World Generates 2.5 Quintillion Bytes of Data
Data science is more than just using statistics to analyze small samples of individual data points. There are software tools available that handle millions of data points for accuracy. Data scientists use statistical coding languages centered around mathematical functions; they also apply sophisticated statistical and higher mathematics to draw inferences from vast data collections. Data is readily available, while comprehension of it is much more challenging to obtain.
“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,” says Alexandrr Sirach, co-founder of YouScan.
Solution
Solving Problems
Data science is not pure analysis. It is also about building tools to increase productivity or better serve guests. It involves understanding available information, creating a hypothesis, and 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 throughout 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 that data-driven companies are 23 times more likely to acquire new guests, six times more likely to retain them, and 19 times as likely to be profitable.
Importance of Well-Engineered Data
To put data to work, it must 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 is important to ensure the behaviors being tracked are linked to the people that are supposed to be tracked.
For instance, how can a company ensure it tracks 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 is 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 takes place. It is important that data be well-engineered and formatted on the front end.
Protecting Guests and Businesses
The customer experience is more than about ease of use or personalized recommendations. It also has to encompass concerns like information security and system integrity. It is 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 do not 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 streaming quality 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 using 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 helps 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 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,” says 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?
- How can we meet their personal needs going forward?
By applying data science insights, businesses can transform the guest experience to give consumers what they want, when, and how they want.
Data is worthless without the ability to understand, analyze, and put it to use. Collecting data and creating functional production-level solutions require 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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