• Cristina Ferrero Castaño

What role will data science play in the future of Customer Experience?

In this article we list 5 areas where data science and analytics will play a role in improving the customer experience.


William Beresford

América Retail

16 January 2021

https://www.america-retail.com/shopper-experience/que-papel-desempenara-la-ciencia-de-los-datos-en-el-futuro-de-la-experiencia-de-cliente/


The volume of data available to businesses continues to grow. From 2022 onwards, the value of Data Science and Analytics will increase even further; its role in customer experience, business success and growth will become increasingly important.


Big data analytics is the art and science of harnessing huge volumes of data and uncovering valuable sources of information that a company can use to leverage its insights and support its strategic goals and ambitions by putting data to work.


Data science is important because the benefits a company can realise through the intelligent application of its big data can be far-reaching in terms of generating growth and enabling huge operational efficiencies that drive profitability.


In this sense, the power of big data helps companies to better understand customers. The better you know what your customers want, understand how and when they want to buy, and do it through a customer-pleasing experience, the more they will want to buy from you rather than your competitors, increasing their loyalty and brand advocacy.


Generating insights from big data will enable you to put customers at the heart of the business, grow the business and create efficiencies that reduce costs and increase revenues. With this in mind, it is vital to consider the following areas where the benefits of implementing big data technologies and putting data to work are typically found.


1. Identifying opportunities of growth


Due to the extensive and far-reaching nature of big data, it allows you to understand patterns in customer buying behaviours and product choices, for example, to identify where customers have "gaps" in their shopping carts.


Understanding which products customers might buy if they were available, or identifying their alternative product options, allows companies to evolve their product line and increase sales. Sales teams can use this information to enhance their outreach and promotional strategies.


Also, changes in buying patterns can be early signals that customers are switching to competing brands and the CRM team can take action with corrective actions and marketing tactics to retain customers.


2. Developing product design and innovation


Data is generated every time a customer makes a purchase, clicks on a web page, etc., and the aggregate of these data footprints can be used to generate behavioural patterns. Using additional data sources, such as product metadata, data scientists and analysts can model behaviour to help predict and identify the needs and motivations behind purchases.


An example of this could be that a customer who only buys ready meals may be classified as someone who is short on time and not interested in cooking. This information can be very useful in the process of product design and development, to keep products fresh and meet the latest customer needs.


3. Shaping the customer experience through data science


Customer data, whether it's the journey they've taken through a website before making a purchase or abandoning it, social media posts, in-store transactions or click-through rates on marketing communications, provides powerful insights into what customers enjoy about a brand and what doesn't work.


With the right big data analytics tools, we can develop alerts or triggers along the customer experience journey, which can notify the business in real time to implement strategies and tactical quick wins to effectively react to the customer and continuously improve the customer experience and brand reputation.


4. Generating operational efficiency


For many businesses, in addition to their advertising spend, the next two biggest resource and budget burdens are staff and physical shops or branches. Optimising staff schedules and opening hours offers businesses the opportunity to dramatically increase their operating margin and reduce wasted resources.


Firstly, by optimising the operational aspects of the business, companies can ensure that shops are open and adequately staffed to meet peaks and troughs in customer demand, as well as ensuring that the right skills and channel mix are targeted to the different relevant customer groups to optimise sales conversions.


5. Improving risk management


Due to the sheer volume of data available, big data is perfect for detecting anomalies in transactions or events. Finding and investigating these discrepancies in activities is an extremely effective way to detect and prevent fraud and becomes an effective tool to investigate the risk of financial crime for financial services institutions.


With large volumes of historical data we can identify historical patterns of behaviour, allowing companies to forecast and predict what the future will look like and better plan their activities to reduce risk. For example, historical sales data can be used to identify stock problems and inefficiencies based on external contributing factors, and can therefore be used to ensure appropriate stock levels are produced.


Once the benefits are understood, it is fairly easy to see how putting data to work through the use of analytics over the long term can make well-informed decisions for your business, leading to greater return on investment, opportunities to develop new revenue streams and generate cost savings, enabling companies to help grow their business and streamline activities.


The future of data analysis


As more businesses migrate to the cloud and consumer digital usage increases with a network of connected devices and the use of apps, data growth will continue to grow rapidly and the application of big data will continue to increase.


What role will data science play in the future of Customer Experience? With more data than ever before, companies will need to increase their understanding of how to implement data science and machine learning solutions to access insights and build their business strategies more effectively.




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