We often stressed the critical need to improve customer experience (CX) to our readers and partners, but we can’t stop! In this post, we address the sub-topic of CX analytics. Learn what it means, why data is the key to success, challenges, customer experience analytics use cases, and FAQs.
Harvard Business Review defines the customer experience as:
The internal and subjective response customers have to any direct or indirect contact with a company.
Customer experience analytics thus refers to a tool or program that helps businesses collect and analyze data about their customers. For example, comments that customers leave on your business’s social media pages. Also, the number of times they’ve bought something from your website.
Customer experience analytics allows companies to better understand their shoppers or clients by studying hundreds to thousands of raw customer facts. Then, depending on the specific tool, the data is translated into powerful knowledge. Managers can leverage it and apply it in practice. Let it be, to improve product design and development, call center operations, marketing campaigns, and much more.
Ultimately, your team can make better informed decisions based on the voice of the customer (VoC). Meanwhile, you can discover customers’ pain points along the way to ensure they occur less in the future. And yes, even no matter how perfect you think your product or service is.
Additionally, CX analytics has all the right metrics in a single dashboard. For example, customer lifetime value (CLV) and customer effort score (CES). Plus, the net promoter score (NPS), customer satisfaction score (CSAT), and time to resolution (TTR).
Many companies today still aren’t getting the “customer experience” concept exactly down. Many believe that they are doing either, let’s say, fine or great in terms of customer service quality.
In fact, Bain & Company surveyed 362 brands and their customers. 8% of buyers called their experience with a brand “superior.” In contrast, 80% of surveyed companies reported that they indeed provided a superior customer experience. What a gap, huh?
So, the need for improvement in this realm is huge and urgent. The key to supporting a company’s digital maturity is to leverage data-driven solutions that utilize customer data. Yet, the problem is there’s been a slow adoption of CX analytics. There are several reasons for this, some of which are:
Silos exist in many contact centers. Even though these call centers generate a ridiculous amount of (potentially) profitable data, everything is all over the place. There’s still hardly a systematic process in place to pull all the scattered data into one platform. Thus, forming a convenient single source of truth.
What are many companies doing instead of leveraging a unified customer experience analytics solution? They’re more or less investing in different individualized resources and tools to solve each problem separately.
Additionally, not many organizational departments interact with one another as frequently as they should. Sometimes they lack the same kind of product and brand knowledge.
Some businesses simply collect and analyze customer data, but they fail to act on it correctly. Even some organizations start to make a move but fail to follow through on their strategies fully.
As a typical example, more and more customer care centers are adopting call center analytics (VoC analytics). They use the data to improve their first-call resolution (FCR) rate or CSAT. However, many of these customer service operations stop mid-way. They more or less lack a deep understanding of turning customer feedback data into impactful decisions and actions.
Here are just some CX analytics use cases from financial services to logistics, telecom, and more. These companies succeeded in satisfying customers, thanks to their knack for using data-driven tactics.
Best known for its athletic shoes and lifestyle products, Puma wanted to provide shoppers with a personalized online shopping experience. For instance, targeted ads suggest specific products based on the buyer’s past searches and selected settings.
So, the fashion leader leveraged advanced analytics software driven by artificial intelligence (AI). It collected thousands of product records from dozens of online channels. It then extracted only the most relevant customer insights to analyze and present in one dashboard.
In less than two years, PUMA achieved significant ROI growth. It truly understood its target demographics and held a 360-degree view of the entire fashion industry, including competitor insights.
Meet Kelly Services, a global recruitment agency and one that serves both businesses seeking to hire and individuals seeking a job. Kelly Services extracted a wealth of information. For instance, job ads, cover letters, resumes, emails, and video interviews have a customer journey analytics platform.
Consequently, they could better match the right employers with the right job seekers. This further enhanced the (already daunting) process of hiring for companies and the experience of job hunting for individuals.
Lavazza is a renowned global manufacturer of high-quality coffee products. They generate tons of consumer reviews across the web, many of which are in different languages. They needed advanced support to understand their consumers’ needs better.
Thus, the FMCG leader used an AI-based unified text analytics tool to mine their consumer reviews and ratings. They uncovered valuable knowledge about its, for instance, espresso machines and coffee grounds. By understanding what buyers preferred, enjoyed, disliked, and so on, Lavazza transformed into a more customer centric brand. They’ve continued to achieve high revenue, product star ratings, retention rates, and more.
Here are some frequently asked questions (FAQs) regarding customer experience analytics.
Although everyone benefits, mainly the executives, CMOs, and call center agents benefit from CX analytics. For instance, some CX metrics can help pinpoint underlying specific causes behind customer churn. Executives can use these insights to develop highly effective strategies.
Customer experience management (CEM) and customer relationship management (CRM) are crucial to helping brands meet customer demands. The difference is that CRM allows you to manage what you already know about a customer. In contrast, typical CEM enables you to gain a deeper understanding of them.
To optimize marketing, drive sales, and improve customer support, CRM collects data about how you interact with customers. Data from CEM helps reveal how customers behave across all transactions and interactions. This includes sales chats, service calls, channel experiences, and so on.
Customer service is only one element of the whole customer experience. Customer experience is how a customer perceives everything after interacting with your brand.
In contrast, customer service refers specifically to specific touchpoints within the customer journey experience.
Wonderflow empowers businesses with quick and impactful decision-making because it helps automate and deliver in-depth consumer and competitor insights. All within one place, results are simplified for professionals across any high-UGC organization, and department to access, understand, and share easily. Compared to hiring more analysts, Wonderflow’s AI eliminates the need for human-led setup and analysis, resulting in thousands of structured and unstructured reviews analyzed within a matter of weeks and with up to 50% or more accurate data. The system sources relevant private and public consumer feedback from over 200 channels, including emails, forums, call center logs, chat rooms, social media, and e-commerce. What’s most unique is that its AI is the first ever to help recommend personalized business actions and predict the impact of those actions on key outcomes. Wonderflow is leveraged by high-grade customers like Philips, DHL, Beko, Lavazza, Colgate-Palmolive, GSK, Delonghi, and more.
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