Understanding Customer Analytics and Data Analysis


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In the world of customer experience, we often hear people discussing the various uses and advantages of customer data analytics. The term generally refers to the process of using data and information surrounding customer behavior to make business decisions and it usually involves techniques that include predictive modeling, data visualization, and information management. According to Gartner, “Customer analytics is the use of data to understand the composition, needs and satisfaction of the customer. Also, the enabling technology used to segment buyers into groupings based on behavior, to determine general trends, or to develop targeted marketing and sales activities.”

Organizations rely on customer analytics in order to gain a deep understanding of their prospects and customers. The information gathered enables businesses to deliver relevant information and strengthen relationships with existing customers, as well as identify key drivers of buying behavior to better target prospects and nurture leads through the sales funnel. Ultimately, customer analytics and data analysis can lead to an increase in loyalty and customer lifetime value as well as greater efficiency in customer acquisition.

The Challenges of Customer Data Analysis

Companies may struggle with the vast amounts of customer data available today. In fact, companies only get value out of their Big Data when they know how to translate raw data into actionable insights. To gain real business value from raw data, today’s enterprises turn to customer analytics to identify patterns, trends, behavior, and other relationships within the raw data that can help to drive decision-making.

Managing customer data from multiple sources, quickly uncovering key relationships between metrics, and determining how to best utilize the information gleaned to move the business forward is a challenge for organizations of nearly every size. However, data management platforms, customer analytics and data analysis platforms, and other software solutions make it possible for companies of all sizes to benefit from customer analytics.

The Goals of Customer Data Analytics

According to TechTarget, the over-arching goal of customer analytics is to “create a single, accurate view of the customer for the group to work with and make decisions about how best to acquire and retain customers, identify high-value customers, and proactively interact with them.” As such, customer data analytics give organizations the insights they need to gain a complete picture of their customers.

With a 360-degree view of every individual customer, organizations are able to improve customer experience and build brand loyalty. Likewise, data relating to an organization’s customers as a whole, as well as data relating to segmented customer groups, can provide valuable and actionable insights when analyzed and interpreted.

Companies using tools and applications that gather and analyze data in real time are better positioned to deliver more relevant offers and messaging to their prospects and customers. Solutions that automate the process and learn from the success or failure of those interactions make it possible to directly impact the company’s bottom line, simply by making better use of the data they’ve compiled.

Customer Analytics and Self-Learning Knowledge Bases

Customer analytics is not just useful for selling to customers, but also for customer service post-sale. Self- learning knowledge bases and self-service software take advantage of customer data to deliver more relevant, personalized service to customers and rapidly addresses needs and answers questions. More importantly, it does this before customers have to take action and reach out to service departments by phone or email.

Leading self-learning knowledge bases integrate with digital self-service solutions and improve with every customer interaction. Using information gleaned from individual sessions, the solutions combine to provide more in-depth resolutions and knowledge to give customers precisely what they’re looking for at the moment they need it. Thanks to the machine-learning algorithms in advanced knowledge base systems, companies are able to evaluate and process the vast amounts of data that results from customer interactions.

Benefits of Customer Data Analysis and Data Analytics

The benefits of having customer data analytics with self-learning knowledge bases are numerous. Companies that implement advanced solutions are better equipped to deliver timely, relevant knowledge to address pain points throughout the buying journey and post-sale. This continues to nurture customer relationships long after the sale, increasing brand loyalty and even cultivating brand ambassadors. From reducing shopping cart and site abandonment to identifying and closing knowledge gaps in the customer journey, these solutions enable businesses to improve the overall customer experience in a variety of ways.

Customers today expect faster customer service, and they expect the technology to meet their demands for instant gratification. Customers are beginning to expect self-service options because they are used to having information at their fingertips via the internet. Add to that the rising usage rates of mobile devices like smartphones and tablets, and that faster service must also be ubiquitous and have around the clock accessibility. Fast Company found that 39% of modern consumers expect a reply within four hours, while that number was 55% for Facebook and Twitter users.

Self-service is the best option for meeting customer expectations, and 70% of consumers now expect a self-service option for commercial questions and complaints. It is becoming clear that companies need to have as much information about their customers to make data-driven decisions and to best serve customers’ needs. Self-learning knowledge bases and self-service solutions that employ customer data analytics within their capabilities are the answer for today’s customer-centric world.

Yaniv Reznik
Yaniv brings a 25 year track record in international leadership positions. While holding Senior VP positions at Amdocs, a world leader in customer experience, Yaniv led AT&T's Innovation Program, managed P&L and customer success for North America, in addition to other duties. He is a results driven leader with a passion for driving business results via technology. Currently, he is the CPO & SVP Customer Success, at nanorep and aims to bring the company to its full potential.


  1. Hi Yaniv: thanks for posting this article. I agree with many of your points about the insights that analytics can provide about customers.

    I’m less sanguine on the ability of data analytics to gain a complete picture of customers. The variables that influence buying decisions are many, and we are nascent in our understandings about how they relate. Further complicating our predictive capabilities is the rapidly changing environment. What we know to be true today will inevitably shift based on forces and new conditions we have yet to include in our calculations. We’re never quite done. That’s a good thing if you sell predictive analytics.

    I caution executives not to become content that their measurements and dashboards approach a full description of a consumer, or that we can explain everything that customers do (or will do) based on analysis their copious digital exhaust.

    We still have a long way to go in understanding customers, and we need qualitative information, too. For now, I wouldn’t give up that focus group. Not yet, anyway.

  2. Andrew, I certainly agree with much of what you said and I too believe that analytics systems alone can’t really provide a complete picture of the customer (at least not the systems currently being offered).

    With that said, I also believe that it would be extremely difficult to have as complete a picture as we can today without such a system.

    There are also incredible advancements every year in the field of analytics and systems can be combined with other solutions like Voice of the customer technologies to form a more comprehensive view of the customer.

    I agree with you that we’re not there yet, but I don’t think we’re that far off either.


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