5 Implementation Challenges of Customer Data Analysis

Information is one thing, insight is another. And extracting the latter from the former is one of the major challenges businesses face in the Big Data era.

Done effectively, customer data analysis can provide organizations with essential information about their customers and prospects, and light the way to improved processes that enhance the customer experience. That in itself is a primary goal for any business looking to forge strong bonds and maximize lifetime value in a hyper-competitive environment where customer experience counts for everything. That goal can be met in a number of ways, including building on relationships with current customers and more precisely and economically targeting and forming connections with prospects.

And, thanks to cutting-edge software solutions such as customer analytics and data management platforms, the tools for sophisticated analysis are within reach for large and small companies alike, with a growing majority of organizations ready to embrace the solution, as reported in CIO Insight. The study has shown that there are numerous barriers businesses regularly face when they move to implement a customer data analysis program. Yaniv Reznik, CPO at nanorep, a leader in guiding the digital experience, addressed the fact that companies of any size can struggle with turning Big Data into actionable insights without the proper tools for comprehensive customer data analytics. According to him, there are a number of barriers they typically encounter, notably these:

Too much of a good thing

The sheer volume of data and information businesses amass these days, whether it’s about customer demographics, sales, fulfillment, or other operational facets, can be overwhelming. Sifting through it all to mine it for meaningful insights is made possible by the advanced software solutions now available, but analytics alone still don’t obviate the need for carefully drawn parameters and nuanced interpretations. If you don’t know what you’re looking for, there’s a strong possibility you won’t recognize it when you find it. Indeed, the CIO Insight article indicated that 39 percent of businesses surveyed found it difficult to integrate data, and 37 percent had trouble determining how to turn the data into insights they could act on. Doing that requires a comprehensive plan that fully aligns data, analytics, operational processes, and company objectives, with an eye toward defining, extracting, and analyzing the relevant data.

Siloed operations and information

Without shared access to, and analysis of, data and information, not even the most sophisticated analytics will prove transformative. For a large portion of businesses, the siloed departments and functions that are the products of traditional organizing principles can be a major barrier to garnering the actionable insights customer analytics can offer. When the goal is providing and maintaining an exceptional customer experience, as it must be for any business looking to differentiate itself and prosper these days, organizations need to rethink that siloing and rally all players around the central goal in order to get maximum value from customer data analysis initiatives.

Analyzing data from different sources

The digital age has opened up previously undreamed of opportunities for organizations to interact with their customers and increase the value and lifespan of those relationships. These channels and devices continue to proliferate at an amazing rate. But all that good news doesn’t come without posing some significant challenges as well. Chief among those is that most businesses have found themselves effectively isolating each new channel and device as well, drawing on the old structures rather than envisioning new, integrated approaches. That separation results in disparate information coming in from disparate channels, and that can be hard to assimilate with other data. The solution – and it is one that has a broader, beneficial impact on the customer experience itself – is to treat each device and channel as part of the whole, providing a consistent experience throughout and generating consistent data.

Avoid self-limiting solutions

It almost goes without saying, but both technology and your company are forever in motion, and that means that analytics solutions must be adaptable and scalable. When they are effectively selected and deployed, customer data analysis systems give you a 360-degree view of the customer, rather than snapshots; they provide real-time, historical, and predictive insights all at once, and are critically important guideposts. Today’s software options include those that have the versatility to integrate data and information from various sources, scale and adjust to changing requirements, and are designed to be self-learning in order to continually improve.

Be careful what you learn from data

It may or may not be surprising, but one pitfall businesses frequently cite when considering the effective use of data is that it can be easy to “learn” lessons that really aren’t there. This goes back to the idea that there can be too much information – if it clouds insights rather than make them visible. The preventative (or remedial) course of action to avoid that is, as above, to precisely and strategically define both the touchpoints you need to analyze and the insights you hope they will illuminate.

Without a clear understanding of those factors, it can be far too easy to mistake the correlations you might find within a large body of data with causality, and that can lead you down the wrong path altogether.