What role will data science play in shaping the future of CX?

The volume of data available to businesses continues to grow. In 2022 and beyond and we will see the value of data science and analytics increase further; its role in contributing to the customer experience, business success and growth becoming ever more important. 

Big data analytics is the art and science of harnessing huge volumes of data and uncovering valuable nuggets of information that a business can use to empower insights and support their strategic objectives and ambitions by putting data to work.

Data science is important because the benefits that a business can make through the smart application of its big data can be wide-reaching in terms of generating growth and enabling massive operational efficiencies that drive up profitability.

Central to this is the power of big data to help businesses better understand their customers. The better you know what your customers want, understand how and when they want to buy, and do this through an experience the customer loves, then the more the customer will want to shop with you over your competitors, increasing their loyalty and brand advocacy. 

Generating insights from your big data enables you to put customers at the heart of what you do, grow the business and create efficiencies that drive costs down and increase revenue. 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 will typically be found.

1. Identifying Opportunities for Growth

Due to the far-reaching, extensive nature of big data it allows you to understand patterns in customers’ purchase behaviors and product choices, for example to identify where customers have ‘holes’ in their shopping baskets.

By understanding what products customers might buy if they became available or identifying their alternative product choices, enables businesses to evolve their product line and up-sell. Commercial teams can use these insights to supercharge their ranging and promotional strategies.

Likewise, changes in purchase patterns can be early signals of customers switching to competitor brands and the CRM team can swing into action with remedial 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 together these data footprints can be used to generate patterns of behavior.  Using additional data sources, such as product metadata, data scientists and analysts can model behavior to help predict and identify the needs and motivations behind purchases.  ​

An example of this might be that a customer that only ever buys ready meals may be classified as someone who is time-poor and not interested in cooking.  These insights can be powerful in developing product design and development process, to keep your products fresh and meeting the latest needs of your customers.

3. Shaping the customer experience

Customer data, be it the route they have taken through a website before they make a purchase or drop-off, their social media posts, in-store transactions, or their click-through rates on marketing communications, offer powerful insights into what customers enjoy about a brand and what is not working.

Know what your customers want, understand how and when they want to buy, and do this through an experience the customer loves

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

4. Generating operational efficiencies 

For many businesses, other than their advertising spend, the next two strains on resources and budgets are staff and physical stores or branches.  Optimizing staff scheduling and opening times offers businesses the opportunity to dramatically increase their operating margin and reduce wasted resources.  

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

5. Enhancing risk management

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

With large volumes of historic data we can identify historical patterns of behavior, which enables businesses to forecast and predict what the future might look like and better plan their activities to reduce risk. For example, historic sales data can be used to identify stock issues and inefficiencies based on contributing external factors, therefore can be used to ensure the right stock levels to be produced. 

Once the benefits are understood, it’s fairly easy to see how putting data to work by using analytics long-term can make well-informed decisions for your business, leading to greater ROI, opportunities to develop new revenue streams and generate cost savings, that enable businesses to help grow their company and streamline activities.

The future of data analytics

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

With more data than ever before, businesses will need to increase their understanding of how to implement data science solutions and machine learning to access insights and craft their business strategies more effectively.  

Businesses will need to ensure they have the expertise to apply these technologies throughout their businesses to aid automation, while utilising data science experts to get the most valuable insights out of their machine learning solutions. 

This reliance on machine learning and data science technologies continues to put pressure on the data science and analytics industry, with the exponential growth in demand exceeding the expert resource available.  Businesses will need to build up their own in-house expertise to manage the technological growth, but must also recognize the demand for outsourced data specialists that take a less generalist view on the business’ data.

With changes in regulation, for example GDPR, and the growth in internet use and commerce, governance, security, privacy, and fraud becomes increasingly important and data scientists, analysts and engineers alike, will need to become ever more sophisticated to tackle the growing issues of cybercrime.