Data Integration to Improve Customer Experience

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In the world of big data, a major goal for most enterprises is to maximize the value of its customer data in order to gain a 360 degree view of the customer. Most customer data, however, are housed in separate data silos. While each data silo contains important pieces of information about your customers, if you don’t connect the data into a single view, you’re only seeing portions of the customer equation.

A primary barrier to these goals is the well-known reality that often up to 80% of the effort involves data transformation–working to get the data in the right shape and quality to analyze. Poor data quality will undermine and undo many of the advantages integration provides. Rather than enacting an integration strategy that unintentionally furthers this damage, businesses should first focus on getting the data right. It is particularly difficult when you have both structured and unstructured data coming in from different sources. If you combine internal data with external data you increase the view of the customer, but how can you effectively mung the data? The first hurdle is realizing such data integration is needed, and many companies don’t.

Siloed data sets prevent enterprise managers from a complete view of their customers, and analytics can only be conducted within one data silo at a time, restricting the set of variables that can be used to describe a given phenomenon. This means predictive models are likely underspecified because they’re not using a complete set of useful predictors, thus decreasing your model’s predictive power while increasing your model’s error. Ultimately you can’t make the best prediction about your customers because you don’t have all the necessary information about them.

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The integration of disparate customer data silos helps your analytics team identify the relationships between the different elements of customer information. Integrating information about your customers allows you to see how the the variables work together and are related, driving deeper customer insight about why some customers churn, why customers recommend your business to others, and why other customers buy more from your business.

Once you have integrated all your data silos, the next step is to use predictive modeling to identify the variables that are predictive of your outcome variable, which is often related to customer loyalty. Because these integrated data sets are so large, data scientists are simply unable to quickly sift through the sheer volume of data manually. Instead, to identify key variables and create predictive models, businesses can now rely on the power of machine learning to quickly and accurately uncover the patterns in their data.

Machine learning algorithms are iterative in nature and as such they continually learn from data. The more data they ingest, the more accurate they get. Based on mathematics, statistics and probability theory, algorithms find connections among variables that help optimize important organizational outcomes, in this case, customer loyalty. Coupled with the compute resources available today, these algorithms can provide insight quickly to improve marketing, sales and service functions.

Your customer insights are limited by the variables used in your modeling and your analysis of those data. Because each data silo contains only a small part of what defines your customers, analyzing silos separately leads to sub-optimal models. Moreover, machine learning when coupled with proactive data integration, allows you to interrogate your data more effectively. By knowing more about your customers in terms of the variables and how they are related to each other, you can build better, more comprehensive, predictive models which ultimately allow you to have a deeper understanding of your customers. It is this understanding that provide you with strategic insights on ways to improve the customer experience to increase customer loyalty.

About the Author

This article was written by the staff of Clarity Insights, a big data and data science consultancy. It is the largest 100% onshore big data and data science consultancy in the United States. Clarity Insights helps companies unleash their insights by creating data strategy, building data platforms, and finding actionable insights that build processes & culture.



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