Why Unifying Big Data Matters for Customer Experience

 
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Increasingly, companies are looking to leverage their data from any and every source to better understand their customer base and provide the best possible customer experience. The marketplace has grown so competitive, with multiple companies filling a variety of sectors with every service and product imaginable.

For businesses to take advantage of customer feedback data, survey analytics, or product management metrics, they must look to unify their structured and unstructured data through data analysis tools. These two very different types of data answer completely different questions about the customer and together, provide the best possible picture of overall customer experience from beginning to end.

Structured data answers the question of what. The information that comes in on the front end of customer interaction can tell you what kind of customers bought something. It can provide information about what items they purchased, and it can inform businesses about which products are selling best.

The structured data is generally internal, very organized information about transactions. This particular person bought product X on this day for this amount. This is the type of information that fits neatly into little, sortable boxes for analysis. Even though companies have been studying this kind of data for years, it is still incredibly important in the overall CX strategy.

Unstructured data on the other hand is the new ace in the hole for customer experience professionals. This data is generally mostly text based and found in surveys, reviews, social media, or other places where customers can provide feedback. It answers the question of why. Why did a buyer purchase something in the first place? Why did a customer buy that specific thing, and why do they prefer one interaction channel over another? Importantly as well, it can answer the question of why someone defected to the competition.

Unstructured data has historically been very hard to gather and analyze because legacy tools and strategies for analysis relied on sampling, taxonomies, or analytical models that consumed vast amounts of time while still only looking at a piece of the puzzle rather than the whole thing. New advances in AI and data analysis allow companies to collect unstructured data in massive quantities and automatically analyze it for sentiment, relevance, or understanding.

In order to put these two very different data sets together, businesses must utilize these new data analytics tools to unify the structured and unstructured. Analyzing the various forms of data puts the information in context, and helps  decision makers execute on the results. By putting data together with powerful data analysis platforms, especially those driven by artificial intelligence and machine learning, businesses can foresee arising issues in real time. This speed to insight allows companies to adjust more quickly to change and better provide the excellent customer experience that consumers demand.