If you work with data, you’ve probably noticed the phrase “data science” being thrown around a lot more frequently as of late. While terms like machine learning and artificial intelligence (AI) may feel like they’re bordering on buzzword territory, the hype is warranted. A recent study by Deloitte revealed that 88% of companies surveyed plan to increase spending on cognitive technologies in the coming year.
What’s driving this widespread adoption is the practicality and breadth of data science applications, specifically when it comes to automating routine tasks—which frees up teams for more strategic work with value-add potential. To illustrate this, we’ll break down how SMG is using data science techniques to drive text analytics innovations that help clients get more from their open-ended comments.
For customer experience (CX) professionals, the biggest challenge with qualitative feedback is that it comes in such massive volumes, it’s impossible to comb through each comment for insight. But at the same time, each comment can help answer questions you might not have thought to ask. To solve this, text analytics engines work to automatically pull out common themes and qualify whether the comment bears positive or negative sentiment.
While those out-of-box engines help save time, they’re rigid enough that they often require manual manipulation to work accurately. But with machine learning algorithms, SMG’s data science team can train the engine using hundreds of millions of historical comments from industry-specific text benchmarks. As the sentiment accuracy is continuously fine-tuned by new input, teams can spend less time monitoring the conversation and more time searching the relevant data for insight.
Another major hurdle with open-ended feedback is the broad range in phrasing used by respondents. Consider a retailer that has thousands of SKUs categorized into different product types and spread across multiple departments. Beyond having to painstakingly add these terms to build out a library of entities unique to the brand, the effectiveness of that library is entirely dependent on whether customers use those specific terms.
Fortunately, deep learning—a more sophisticated type of machine learning—is optimized for complex problems because it uses frameworks of multiple, layered algorithms working in concert. And by adding structure through linguistic hierarchies, all mentions are categorized appropriately (i.e., variations of product mentions are coded to the appropriate categories/departments for drill-down and roll-up analyses). Most importantly, by helping brands capture more product mentions with less manual effort, CX teams can turn their attention to evolving product strategies by:
Cognitive technologies offer enormous potential for CX professionals
There’s no question leading organizations are investing heavily in data science—and for good reason. By working tirelessly to put actionable data in the hands of those who can take action, data science techniques are dramatically increasing speed-to-insight capabilities and enabling organizations to be more agile in adapting to opportunities as they arise.
To learn how SMG is using cognitive technologies to help clients inspire smart changes across their enterprise, download our white paper: 3 ways data science is reshaping how brands approach CX data.
Chief Product + Technology Officer