Enhancing Customer Experience with Customer Interaction Data

Posted on : August 16th 2022

As Customer Experience (CX) leaders, we should be aware of how customers feel about our enterprise and service. In an increasingly digital world, the onus is on us to deliver digitally augmented human-centric customer experience on behalf of the enterprise. The need to offer seamless and enriched digital experiences has made embedding customer experience into products and design more important than ever before. Today, digital customer experiences drive brand equity and revenue growth. Yet, many of us limit ourselves to sending Customer Satisfaction (CSAT), and Net Promoter Score (NPS) surveys instead of analyzing customer interaction data in the form of calls, emails, texts, and chats.

The response rate for CSAT and NPS surveys varies between 7 to 9%, and it drops to roughly 5-6% for repeat customers. One of the biggest challenges of using a CSAT survey is that the satisfaction levels vary from customer to customer, making customer satisfaction per se challenging to define. Then there is the ever-present danger of missing out on dissatisfied/satisfied customers, as they might not respond to your survey.

In the case of NPS surveys, the inability to determine the underlying reasons for a response to a question in the survey and act on them is a critical disadvantage. There is a genuine need to ask supplementary questions to understand the drivers and the actions required. True, NPS helps us balance between asking too many questions and minimizing the interviewing time. However, in most instances, the extreme limit of a single question overtly loads the dice in favor of the customer, leaving the enterprise’s needs unsatisfied. This could lead to a vicious circle. If the enterprise cannot enhance customer experience due to a lack of data, the customer will ultimately be underserved and take their business elsewhere.

The key to running an effective, impactful CX program is  the continuous refinement of customer journeys and action plans. This would help align business objectives, market trends, and customer expectations. Exceptional CX programs take advantage of customer interaction data through calls, emails, texts, chats, and more. Moreover, customer interaction data should be analyzed along with additional data sources to create an effective CX program.

The next step would be to analyze the data and derive actionable insights. Significantly, it would be rewarding when the customer interaction data leads directly to an opportunity such as sticking points in the digital journey or the impact of a recent product change that prevents enterprises from delivering complete customer satisfaction.

Analyzing Customer Interaction Data

In the last few years, the volume of customer interactions has imploded and concomitantly there has been an increase in the data collected from diverse touchpoints. The importance of this data lies in the fact that it connects customers and the desire of the enterprise to provide enhanced products and enriched customer service.

The objective of analyzing all customer interactions is to correctly gauge the value the customer receives during the exchange and, similarly, what an enterprise gets out of the interaction. This is vitally important for any enterprise, irrespective of whether they regularly interact with their customers or not. These interactions offer insights that help improve experiences and establish invaluable customer relationships. At this point, enterprises need to remind themselves that for every customer who reaches out to them regarding an issue, there are hundreds if not thousands who do not. That is an enormous chunk of potentially valuable unstructured CX data they do not have.

Why bother with unstructured customer interaction data

However, most enterprises are neck deep in unstructured data gathered from customer interactions across SMS, chat, voice, email, social media, and more. In all probability, they would also not be sure how to transform the data so that it can be ingested directly into their data workflows for analysis. The trick is in creating linked data, i.e., establishing the relationships between the customer interaction data, transforming them into easily digestible formats to derive insights, and subsequently using the insights to enrich every customer interaction.

Today differentiation is the name of the game. Enterprises have to think beyond just satisfying customers. Using customer interaction data to glean insights into customer expectations can help enterprises personalize each interaction and add value, incremental or otherwise. This would also provide enterprises with the differentiation that could well be the creative, competitive advantage they are looking for.

The challenge is in processing unstructured data

As mentioned earlier, the key to an exceptional CX program is leveraging customer interaction data and analyzing them to wangle deep insights for meeting customer expectations. This data includes interactions with customer service representatives and those from self-service interactions. Food for thought—a global survey reveals that 40% of the respondents, approximately 3,000 consumers, prefer self-service, and 70% expect a website to have a self-service application.

The standard practice is to analyze structured data because unstructured data, such as customer interaction data, is challenging to process. In the case of customer interaction data, the challenge is extracting data from silos created by each channel, enriching them, and transforming and delivering data in formats that can be seamlessly integrated with data workflows. In other words, the larger question is how do we get a 360-degree view of your customer's contact as they traverse across touchpoints and channels?

Data Platforms are the answer

Data platforms such as the Straive Data Platform (SDP) have the capability to acquire and extract 100 percent of the siloed customer interaction data in your enterprise. These platforms allow the processing of unstructured data at scale. As a result, CX teams or data analytics teams have enriched data for their predictive models to make more accurate predictions. In practice, SDP helps to develop a broader picture of your customers and their actions. CX teams can use this to their advantage by making the much-needed connections with customers by knowing what they require next and identifying the new trends as they arise. Furthermore, important actions could be taken at the right time.

Moreover, by enabling CX teams to analyze multiple dimensions of each interaction, data platforms ensure successful CX outcomes. Not only that, the powerful insights from customer interaction data could be used to enhance training, staffing, and customer experience management. Moreover, unstructured data combined with the customer journey elements could be used to create a feedback loop by capturing prediction errors. Furthermore, the insights derived from analyzing customer interaction data across all customer support channels could offer pointers to improve the resolution rates of self-service applications.

Keeping your customers happy

Insights gathered from customer interaction data provide valuable insight for making your enterprise relevant to your existing and potential customers. These insights help you understand what your customer wants and how his needs might evolve. Need we say again that it is still true that data-driven organizations are more likely to acquire customers, but they're also likely to retain customers and be profitable.

As CX leaders, we have witnessed, or are seeing, what was predicted before the pandemic, customer experience triumphing price and product as the key differentiator. Many of us will agree that CX is not only a critical differentiator but also a competitive differentiator. Despite all these affirmations, why are we not maximizing the CX potential of customer interaction data?

One of the prime reasons would be the fragmentation of tools and techniques used for analyzing unstructured data and the repositories of channel-specific and department-specific customer data. This fragmentation clearly brings forth the need for an end-to-end data management platform focused on unstructured data solutions. A cloud-native microservices-based architecture would only simplify the customer experience delivery process by adding the much-needed technical agility. The open and portable applications deployable on any cloud would improve time to market and speed up the response to our customers.

For example, with the help of its cloud-native and microservices-based architecture, SDP extracts and enriches data from any unstructured source and enables enterprises to harness the power of customer interaction data. On the ground, CX programs can benefit from SDP’s capability to offer a faster time to market, high quality and consistent data coverage, and a scalable and distinctive capability to work with unstructured data. Furthermore, data platforms can enhance the flagging of negative reactions in near real-time when combined with intelligent data solutions such as the Straive Data Solution.

Delivering a memorable customer experience has never been easy as customer expectations evolve rapidly. Post pandemic, there has been a quantum shift as customers are increasingly reaching out through digital channels. The increasing emphasis on hyper-individualization means that enterprises have to engage with data at a more granular level so that enterprises can give customers what they want, be it product, information, or service, the moment they want it. That is why the reliance on CSAT and NPS surveys won't do. Not only are CSAT and NPS surveys outdated, but their results are typically delayed by two weeks or more, impeding remediation and the enrichment process.

At the same time, enterprises need to gather the required velocity of change to shift to analyzing unstructured customer interaction data. They must also factor in typical IT cycles to develop mechanisms to accelerate their transition to using unstructured data. In addition, take advantage of its potential to improve customer experience without effecting a wholescale change to their data pipelines and significant investment in analytical tools that may or may not meet your enterprises’ end-to-end customer interaction data processing needs.

Ultimately, it boils down to how prepared an enterprise is to take advantage of the customer data explosion and its transformative impact on customer experience.

Straive's digitally powered customer experience capabilities like consulting, artificial intelligence (AI) and analytics support, design thinking prowess, omnichannel support, cloud-based services, and intelligent automation have helped us make a mark in the ISG Provider Lens™ report.

With 2,300+ support agents specialized across various disciplines and providing multi-channel support with 24 x 7 coverage, we drive customer engagement efficiently across the information industry segments, including publishing (scientific/professional), education, legal & tax, media, insurance, and financial information among others.

If you want to optimize business operations and collaboratively bring faster customer resolution with a blend of subject matter expertise and the latest technologies - AI, ML, and Analytics click https://www.straive.com/customer-support