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Dec 17, 2020
There’s an embarrassment of data in customer experience (CX) feedback right now. Social media platforms have exploded alongside both passive feedback collection streams like online reviews and active channels like customer surveys. The stream of feedback data has become so strong, it has become nearly impossible for companies to effectively collect and process it into actionable insights—which means a tech-enhanced CX system is necessary.
But not all CX service providers or platforms are created equal. It’s crucial that brands know to employ best practices using different forms of collection to get customer insights at every stage of analysis—from the wide brush strokes of high-level themes, to the granularity of individual comments, and every level of complexity in between.
In fact, the right capability in a customer experience feedback solution can even address often-overlooked insights, help deliver great customer experiences, and change the trajectory of an organization.
Modern user feedback provides so much information, it has become no easy feat to manage with human hands alone. The first step is to find the right technology.
Great CX feedback is essential for modern brands to reach their objectives in a competitive market, and an effective text analytics solution that assists with data processing is now a crucial part of that strategy.
Text analytics streamlines analysis of open-ended customer experience feedback, an important part of surfacing what customers’ perceptions are and where they come from. An efficient text analytics solution is a reliable method for gleaning those insights you need to find the voice of the customer and take informed action on feedback data.
These essential text analytics-based CX functionalities and features can serve as a guide to the feedback-to-insight process you need to be successful:
With today’s heightened health concerns, operational risks present a number of significant threats to multi-unit businesses serving a high volume of customers. An AI-powered text analytics platform mines customers’ unstructured feedback and alerts you to time-sensitive comments so you can take immediate action when negative experiences arise.
A key example of this is food safety. There are few things that can damage a brand’s reputation more than foodborne illness. Real-time alerts are a great way to bring to light these high-impact, low-frequency events—identifying potential food safety issues before they lead to widespread negative feedback, hit headlines, and become systemic issues.
The ability to apply natural language processing (NLP) is the best way to capture accurate sentiment within customer feedback—both at the whole-comment level as well as granularly by phrase, product, or category. This type of analysis provides clients a breakdown with the best indication of the overall customer experience and a path to surfacing specific customer needs. But to provide the best accuracy in sentiment, these machine learning algorithms must support variables such as the language of your industry or your geographic presence that helps indicate sentiment. A great example is in how customers expressing overall positive feedback in the U.S. can vary greatly from those in the UK.
Sentiment values can also benefit in different ways from adaptive learning, informing the accuracy of machine learning algorithms to continuously fine-tune sentiment accuracy to find customers’ true opinions amid ever-evolving idioms and vernacular. A practical example: “This chicken is sick!” vs. “The chicken made me sick.”
Unlike the quantitative data you’re collecting through your CX program with different kinds of surveys and other customer experience feedback mechanisms, findings from unstructured data don’t always fit into a neat little box. More often, they’re spread across the wide array of products, services, and initiatives you’re trying to track—which means you need to find the right way to align your strategies for tracking them.
That’s where custom entity ontologies come in. Entity ontologies essentially serve as brand-specific encyclopedias—ensuring the lingo consumers use lines up with your own terminology. With customizable comment groupings, users can:
But many text analytics technologies either can’t handle the volume and complexity of the data being processed or they’re so convoluted it’s difficult to know where to start. More than ever, brands need robust, intuitive tools designed with a diverse range of team members’ user experiences mind—especially when it comes to input as complicated as the unstructured data found in customer comments.
Your experience management program requires a cross-channel approach that provides a variety of solicited + unsolicited feedback mechanisms. Here are a few text analytics must-haves:
Using different methods to collect customer experience responses is most powerful when you can combine it and take a closer look at how one source of data impacts another. With an open API architecture, your XM program can integrate related data into one spot—providing a holistic view of the customer’s perception and revealing more actionable insights.
In addition to being a source of on-demand insights, your text analytics solution should also help answer complex research questions that impact your business long-term. With customized industry libraries and text benchmarks—populated with hundreds of millions of comments—you’ll have a deeper, more contextualized understanding of how customers perceive your brand relative to competitors, providing insights like:
Text analytics helps you collect and categorize data, but without a strategy for finding and applying insights to drive business decisions, data alone isn’t very helpful. Here are some ways you can apply next steps to turn customer experience feedback into actionable insights and quantitatively document the effect different teams and actions have on customer satisfaction.
No one is closer to the customer experience than your front-line team members, and text analytics can enable the unification of employee and customer input to not only help build cross-organization visibility of feedback, but to also show how employee success leads to customer satisfaction. By pairing customer experience (CX) data with employee experience (EX) data, brands are able to show higher employee engagement equates to better performance with customers.
For the most intuitive integrated results showing correlation between the customer and employee experiences, revisit your EX touchpoints with an eye toward where and how they overlap with your customer experience feedback metrics. This is a relationship that is commonly overlooked, and the more synchronized your experience management (XM) strategy is, the easier it will be to find meaningful correlations in the open-ended data.
By applying this strategy, you’ll also receive critical insights about the customer experience from the unique perspective of your customer service teams and front-line employees. Being able to identify and address employee-customer disconnects around your customer-centric mindset will make it easier to commit support teams to improving customers’ holistic perception and focusing efforts where they’ll have the biggest impact.
While quantitative customer feedback data is invaluable, it’s often the qualitative insights from open-ended comments that help you add context to scores and answer questions you hadn’t thought to ask in building a good customer experience. Text analytics enables driving a back-channel of market research from suggestions, user experience reports, and customers’ questions collected from your website and apps that can be applied across an organization. But this information is useless unless it’s shared.
It’s an excellent practice not just to use text analytics to process complex feedback, but to appropriately distribute the insights derived across teams and departments who might not usually see data from outside their silo. In practice, this can facilitate information sharing across regions to help give best practices to areas where customer perception lags or can enable product-level insights across teams for marketing and merchandising strategies.
Current and potential customers can have different digital—and even cross-platform—journeys with wildly varying steps. To be most responsive in building the best customer experience, you need to collect smaller instances of mid-stream feedback based on customer behavior at the right time, not just through follow-up email surveys or post-transaction pop-ups. A mid-journey feedback mechanism allows you to ask specific questions based on active customer behavior on your site, and text analytics allows you make sure this data is rich.
This journey-specific feedback allows you to follow up on important milestones—like when a customer fails to complete an online transaction—at the best time: when the points of friction are happening. This can even be used as a form of customer support, providing what can feel like a personal touch to reengage with customers to identify and resolve issues.
Text analytics technology helps brands turn open-ended feedback into next-level insights—with top-tier accuracy and powerful, multi-source reporting. As you gather customer feedback, the strategies above give you a great place get to every level of insight to build high-quality customer experiences.
SMG has honed our text analytics visuals and dashboards to sort the inherent complexity of open-ended comments, find proof for suspected hypotheses in customer feedback, and surface new data and insights that were previously unknown to guide users to where they need to focus.
For more information on how text analytics + customer experience feedback can deliver next-level insights and big-time ROI, download our best practice guide: The essential guide to text analytics for CX pros.