Businesses everywhere thrive on consumer data. Knowing what customers want, when they want it, and how they want it can help them deliver a better experience. Data-driven decisions can also boost sales and revenue, which means data science initiatives are invaluable for a company’s success. Data science consists of multiple processes that allow large amounts of data or "big data" to be interpreted and used for decision making. In this article, we'll explore how data science can vastly improve a business’s customer experience processes and, as a result, increase revenue.
Applications of data science in customer experience
Trends and predictions
Analyzing data on customer behavior has a number of benefits. Data analytics can help your business spot historical trends and better gauge future demand. For example, what factors affect demand for our products or services? Were there seasonal spikes for certain products? How did pricing changes affect sales? Transactional businesses often have very nuanced seasonal dependencies and it is increasingly difficult to forecast demand across all stores and SKUs. With access to this historical data, you can better plan for the future while optimizing for consumer demand at any level of granularity of your business. Check out these demand planning and pricing analytics dashboards to see this in action.
Personalization
Customer data can also be used to deliver more personalized experiences. Personal, engagement, behavioral, and attitudinal data are all vital for gathering useful insights about your consumers. Recommendation services are a prime example of a personalized customer experience, such as Netflix’s recommendations page. With the user’s viewing history, the streaming platform puts together a list of movie and TV show suggestions they may like. Recommendations that are tailored to each user’s preferences, maybe even before they know it, can help retain business and boost sales. Loyalty programs are another way of personalizing the experience in order to get customers back into stores more frequently, or to incentivize them to purchase higher margin products by offering the right discount. Data is invaluable for developing this strategy; too steep of a discount and you eat into your profit margins, too little and you don’t incentivize enough.
Inventory optimization
For businesses running brick-and-mortar stores, data analytics can help with pinpointing what the local market is most interested in. This eliminates the guesswork when it comes to which products to sell in which locations. Careful market research paired with data science can optimize store arrangements too. For example, fashion label Rebecca Minkoff uses sensors in their store so they can track how their clients move about. The data they gather from this helps them optimize their store layouts and create more attractive window displays.
How to get started with data science
The growing amount of data increases the ability to optimize customer-related operations, but these benefits are hindered by a lack of data literacy and data management. According to a survey by Accenture, a whopping 74% of workers feel overwhelmed when they have to deal with data, and 14% admitted that they avoid data-driven tasks altogether. Many organizations have a wealth of data that could be used for data science initiatives to generate real business value, but they don’t know how to get started. If you lack the organizational and strategic initiatives to make these processes come to life, it can seem impossible to leverage data-driven insights to promote growth and revenue. What are the steps you need to take to get from where you are now to where you want to be with your data? Download our white paper Practical Applications of Predictive Analytics to get started on your data science journey.