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.
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.
