Customer data and customer experience are closely intertwined. Responsible customer data management has never been more important.
Access to data about customers' purchase histories, preferences and prior interactions is critical to improving customer satisfaction. But to get the most out of customer data, businesses need a secure framework to store it and the tools to unlock it. Even if the data lets you create a more personalized experience, failure to protect that data could seriously undermine it—and could send your customers running to your competitors and also create potential legal liability.
Big data and customer experience: establishing a framework
A data storage framework is more or less what it sounds like—how data is stored, whether it's in the cloud or in on-site servers. The choices a business makes about its data storage framework will depend on the data it's storing. Business decision-makers and IT will need to consider how much data they need to store, how sensitive the information is, whether the data will ever be in transit and what kinds of cyber risks they face.
Though it might seem like an invisible part of business management, data security is a critical part of the customer experience. Interest in the intersection of big data and customer experience has been matched by a corresponding interest in data warehousing, a market that Mordor Intelligence expects will grow nearly 12% annually through 2025.
Tools of the trade
One key use of machine learning is analyzing customer data to find patterns that improve customer service and provide a more personalized customer experience. Utilizing the cloud as a central part of your data storage framework means that machine learning tools can more efficiently access customer data for analysis.
This isn't the only way to apply data analytics to improve the customer experience, either. A cloud data warehouse is a good place to start; options such as Snowflake, Amazon's RedShift or Google's BigQuery have their own strengths. Snowflake, for example, is easy to use and excels at sharing data; BigQuery, meanwhile, lets machine learning algorithms work directly on warehouse data without the need for transformation.
The data analytics tool sets that can work with warehouse data are myriad. Business users will get the most value from friendly business intelligence tools such as Tableau, Microstrategy and Power BI. Data scientists favor the Python programming language, analytics frameworks like Apache Spark and Apache Storm and machine learning libraries like TensorFlow.
Customer data is an important part of improving the individual experience, but businesses should be wary of using it without strict controls. That starts with a review of privacy laws and information governance.
Tightening privacy regulations
As the tools that can unlock the promise of big data and customer experience have grown stronger, so, too, have privacy regulations. According to the United Nations Conference on Trade and Development, 128 countries have enacted legislation to secure data protection and privacy. The European Union's General Data Protection Regulation enforces tight privacy controls on personal data of EU data subjects. In November 2020, voters approved the California Privacy Rights and Enforcement Act of 2020, which is set to go into effect in January 2023, adding to the provisions of the recently enacted California Consumer Protection Act and expands privacy rights for California citizens and introduces new protections for sensitive personal information. The new act also imposes a stiff penalty on businesses that fail to comply with its rules—$2,500 per violation, triple that if a minor's data is involved.