Analytics- Redefining Customer Experience for Digital Customers
We live in a technology-fueled, hyper-connected and hyper-competitive world. As a result, it is becoming increasingly difficult for businesses to acquire new customers and retain the existing ones. The time between innovators and challengers is reducing at a much greater rate than ever before. Just look at the number of challengers Uber has spawned in each country they landed in. A Verint digital tipping research revealed that digital customers are most prone to attrition. Analytics is helping organisations to uncover issues and comprehend their magnitude so that organisations can address those pain points which are most impactful for their customers and businesses. Companies are also channelizing customer analytics reports to enhance their employees’ productivity. For example, multichannel insights for Firstsource drove an 18% reduction in cost to serve the customer. https://www.youtube.com/watch?v=cYSbcz3YEgA One of the definitions of Customer Experience (CX) is that it is net sum experience of a customer at the sensory, emotional, rational and physical level based on the engagement with an organization/business/brand over the entire duration of being “customer”. By this definition of CX, it would be right to say that most organizations provide customer experience in some manner. This experience might range from experience in a store, bank branch, website, phone app, call, email, chat, an IoT device at home or maybe all of them, depending on the type of business. Many believe CX actually starts from the time the customer was not yet a customer and was not even evaluating your product. Customer’s experience of your organization started when they heard your marketing messaging played important role in converting them into prospects. As the prospects continued to evaluate your product offerings, this messaging built on and set their expectation of customer experience when conducting business with you. Industries like no-frill airlines have effectively used this to set expectations of low customer service parameters around their offering which the customers now accept. Conversely, it is easy to set over expectation which can be expensive, if at all possible to deliver. Big Data analytics has been an active approach for many organizations for almost a decade now. It is increasingly being used to understand the critical phase of Customer Journey which results in customer acquisition. Analytics can help in understanding which messaging and what mechanism, in this digital era, is most appealing to the customer to consider a product, narrow down to a specific brand for that product at the time of purchase. For example, analytics can suggest the best way to create and reinforce confirmatory bias in prospects evaluation. Amazon and its clones use consumer purchase pattern and preferences to present offers which have given positive results to them. Compare this to your local app-based taxi which spams you with an offer every morning without analyzing your route, time of usual travel or your typical bill amount. It is easy to be caught up in our vision about our products and services and how we will serve around our offerings, while the customer expectation might be different or might change over a period.  Traditional organizations have developed their customer service processes to fit around their internal systems and procedures, such as accounting, order fulfilment, and delivery. These systems and processes are convenient for the organization, but not necessarily for the customer. Unless organizations have a means of collecting, analyzing, sharing, and acting on statistically valid customer sentiment, they run the risk of alienating customers without even realizing that a problem exists somewhere within their service delivery chain. Listening to the voice of the customer is arguably the most important step in creating the great customer experience. While organizations have used a proactive mechanism of collecting customer feedback they are also sitting on a rich set of interaction and transactional data. Analysis of contextual feedback by customer across all channels of service can provide the most comprehensive view of the customer on what they think about our service and point to the gaps that need to be fixed. Customer surveys are the most commonly employed method for understanding the customers’ needs and their views on our processes, products and people. The data gathered in this manner can bring direct actionable points for an organization. While this approach is popular, it is often employed as a blanket approach which can negatively impact the analysis and provide grossly wrong results. For example, organizations might miss out on critical elements because they failed to pose relevant survey questions. Luckily customers are constantly interacting with us and are conveying, hinting, suggesting and often demanding specific services. In the process, they could be talking about a better product, deal or service from the competitor. Or they could be sharing what disappointed them or pleased them with our offering.  In their effort to provide prompt service, organizations overlook this goldmine of data and tend to focus on efficiency of resolving the interaction without actually dwelling on what the customer is conveying about their experience of doing business with them. Analytics can be used to assess true customer sentiment, the root cause of delight or distress, issues with product/services etc.   which realigned for positive customer experience. This conceptually simple approach can quickly turn overwhelming and complex because of the sheer number and types of customer interactions across the multitude of channels. Capturing the data isn’t the problem; analyzing it and using it to make decisions poses the difficulty. However, with the right tools organizations can transform the speed at which they make informed decisions by providing a new level of visibility into customer service processes and experience. Speech and text capture engines coupled with the increasingly capable AI components are making Analytics easier to manage. Social media, while being one of the channels of interactions needs special attention and care because organizations have the least control over this channel. This channel is truly owned by everyone and, as we have seen from the US election, has great influence on what becomes popular opinion or “truth”. It is therefore important to monitor and analyse this channel in real or near-real time for traffic around sensitive sentiments Consider this example of an international financial services company specializing in credit card processing for very large companies.  They decided to look at the accounts they lost in the last three months and analyze interactions with those accounts. They did this by studying the differences between interactions with accounts that were lost and the ones that weren’t. There wasn’t necessarily somebody saying that they were ‘going to cancel their account’, but there were other subtle indicators expressing dissatisfaction with the pain they had in accessing and managing their account information over the digital channels (app, web access). This financial services organization created a core team to reach out proactively to these customers and managed to retain 86% of these accounts. Eventually, they overhauled their digital channels based on this feedback leading to better and consistent CX Traditionally the focus of customer experience has been in the contact centre or the store/branch because this was where most of the business interactions were conducted between customer and companies. As new channels were added, with their own set of interaction nuances, the complications of consistent experience grew. Most organizations have added measures to track CX success at each touch points but only a few have managed to join the dots of these touchpoints to make a bigger picture for their customers. Simplified measurement of CX based around NPS at individual touchpoints is two-dimensional measurement of multi-dimensional function, especially with Digital touch points. Ensuring great CX/customer experience over an ever increasing number of possible touchpoints has kept the organizations busy as they try to create suitable customer journeys. Speech and Text analytics approach can be taken a few notches up to Customer Engagement Analytics wherein we can map current customer journeys and their outcomes, as customer interacts with your organization. Herein we should not just be monitoring explicit communication as in analytics approach above, but also look at behavioural journeys. This includes understanding where are the customers interacting with your organization, what actions they are taking and what were the outcomes. A comprehensive understanding of what creates successful outcomes for a customer for each type of business objectives can give great insight into what touch points customer prefer, how much is the effort required on each of the journey paths, the percentage of success and most importantly what is the customer satisfaction on those journey paths. This can be a simple process to assess the current state of customer journeys and can go a long way in designing, redesigning or prioritizing the customer journey paths for best CX. With the massive count of smart devices in India, Digital is the natural platform for customer engagement. Every major business establishment has the omnichannel/Digital presence. BFSI and Telecom are extremely competitive industries and are here leading the way in adoption of Digital and Automation to raise their service quality without substantially increasing operational costs. It is also interesting to see how in telecom even the low-cost carriers have a high focus on digitization. While technology acts as an important enabler, a customer-centric culture plays an important role for an organization to offer truly great customer experience. Data and Analytics are only as good at providing results as the decisions organizations take around the analysis. One of the leading telecom players introduced a process of greeting their walk-in customer within 10 seconds and 10 steps into their store. This simple approach increased the customer experience rating of their stores. Many businesses still have a large set of customer-facing employees that need to be enabled, cultured, empowered and engaged to have customer-centric execution through analytics-driven digital mindset.
We live in a technology-fueled, hyper-connected and hyper-competitive world. As a result, it is becoming increasingly difficult for businesses to acquire new customers and retain the existing ones. The time between innovators and challengers is reducing at a much greater rate than ever before. Just look at the number of challengers Uber has spawned in […]
While technology acts as an important enabler, a customer-centric culture plays an important role for an organization to offer truly great customer experience.
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["big data", "Go", "programming_languages:R", "AI", "Git", "automation", "ViT", "analytics", "GAN", "R"]
["AI", "analytics", "R", "Go", "Git", "big data", "GAN", "ViT", "automation", "programming_languages:R"]
Sunil Aryan, comes with close to a decade of experience in the field of customer experience strategies, automation and workforce optimisation. Sunil is keenly interested in demystifying the possibilities of RPA and AI.