Customer Experience Improvements Require Data Context

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Solidifying an omnichannel, data-driven approach can accelerate a company’s digital transformation journey and drive business success.

Digital-first organizations understand that leveraging technology to enhance business processes, improve customer experience (CX), and increase efficiency is merely good business practice. In this day and age, you’d be hard-pressed to find someone who believes otherwise. In fact, according to Deloitte Insights’ 2020 digital transformation survey, companies that are more digitally mature tend to outperform their industry peers financially.

But despite their best efforts, some companies aren’t achieving the full potential of digital transformation and the impact it can have on the customer experience. They may have replaced paper with digital processes, automated their workflows, embraced cloud services, and developed an omnichannel marketing strategy – but they lack a concerted effort across one major front: data.

See also: Servitization Takes Customer Experience to a New Level

First, there is the issue of data relevance. Many organizations rely on data that is proscribed by the IT department or use whatever is most easily accessible because it’s ‘clean’ and already formatted correctly. This is especially true when it comes to listening to the voice of the customer (VoC) and understanding their experiences. Many companies rely heavily on online surveys to provide them with this insight – but that is simply a tool; it can’t offer a holistic view of the customer experience.

So, unless feedback from forums, chats, contact center calls, app reviews, product reviews, surveys, and many other sources is considered in aggregate, a company won’t be able to extract key attributes from recurring topics to gain more comprehensive insights. That’s because the data lacks context.

The key to finding context is knowing what data to analyze, how to acquire that data, and interpret the findings based on the real-world context. While artificial intelligence (AI) makes it faster and easier to parse data and correlate trends and outliers, augmented intelligence empowers business users to draw insightful conclusions by giving them the tools to easily interpret the data.

In this real-life example, a manufacturer released a new version of a popular product to market. Thanks mainly to strong early sales, the manufacturer deemed the launch to be a success. But in fact, the numbers flew in the face of actual customer sentiment, which showed that consumers were unlikely to purchase this product again. The manufacturer averted a significant spend by using an AI platform to collect and analyze omnichannel customer feedback in real-time.

In another example, a Fortune 500 auto manufacturer pinpointed the exact location where a manufacturing error (missing resonator caps) occurred, which prompted customers to complain of a burning smell. It was also able to isolate the affected model and class, enabling the company to recall only those specific vehicles rather than issuing a mass recall. This insight saved the company millions of dollars.

Using omnichannel data, organizations end up with information that is far more relevant and impactful, improving their ability to make informed, real-time business decisions that impact their bottom line.  

A seismic shift in how data is disseminated

Once companies understand how powerful the combined forces of artificial and augmented intelligence can be, they need to enable a shift in how data is shared across departments, business functions, and lines of business.

Most organizations collect and store vast amounts of customer data – ranging from customer profiles, call center transcripts, and financial information, to survey results and statistics – as granular as how long visitors spend on each web page. All of this information is used to understand the existing customer experience and make it even better by leveraging real-time insights.

There is no shortage of data. However, what’s in short supply is a holistic and collaborative approach to bringing organizational-wide data together to maximize value and impact across a customer’s journey with an organization.

Many companies have very distinct data silos, where data collected from multiple channels and business functions is stored but not shared. These silos can result from numerous factors: legacy systems and technology, employee turnover and transitions, lack of cross-departmental communications, and market dynamics.

As a result, companies may never really know how information collected by the social team can impact the work of the web team or how their data insights can inform the marketing team’s overall strategy – or the company’s overall goals and objectives. And in the retail example above, it might ultimately make the difference between moving more or fewer products off the shelf.

Customer experience professionals use the term ‘data-rich but insight poor’ to describe this very scenario, which plays out across businesses in various industries every day. And it’s a situation backed by research: 45% of customer experience professionals cited lack of cooperation across organizations as the biggest obstacle to transforming the customer experience according to Forrester Research report CX Teams In 2019: What They Do, Where They Report, And Their Size And Budget.

Companies, especially in uncertain economic times, simply can’t afford to miss out on the insights that unshared and untapped data can provide. The results can manifest themselves in enhanced employee productivity, increased revenues, reduced costs, and happier customers.

A customer-first approach, driven by data

It’s clear how people, process, and technology overlap to empower this shift towards a flat data delivery model that provides useful and relevant insights to those who consume it. But creating a customer-first approach to every aspect of the business is dependent on an organization’s ability and willingness to tear down these silos.

Companies can make it a little easier for themselves to reach the next stage of digital transformation if they have the right tools in place. This includes an AI platform that can analyze, categorize, and visualize omnichannel customer feedback in real-time. This can save business users weeks and months of time. 

A platform that ingests omnichannel, structured and unstructured data means business users are never limited to seeing insights from only a portion of data. Coupled with capabilities such as unsupervised machine learning, natural language understanding, topic modeling, and predictive intelligence, companies gain insight from comprehensive datasets to help them make sound business decisions.

Solidifying an omnichannel, data-driven approach that is meaningful and impactful can accelerate a company’s digital transformation journey – and drive business success.

Kurt Trauth

About Kurt Trauth

Kurt Trauth is SVP of strategy for Stratifyd, a data analytics company that uses AI to uncover and predict human experience. Previously, he was director of worldwide customer experience for Lenovo and director of voice of the customer and CX analytics at USAA.

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