May 2019 – Mukund Krishan – Tech Entrepreneur | Investor

Using the Digital to Drive Real World Transformation: Points to Ponder for the C-Suite

A 2017 Gartner survey reveals 42% of CEOs have already initiated digital business transformation, and 56% of them are already profiting from the initiative. What sets out these market leaders from the rest is their ability to intelligently and strategically use digital to drive real-world transformation in their business.

How are they doing it?

The Importance of Objective Research

Consider the research from ThinkJar, which places the cost of acquiring a new customer as six times more than keeping an existing one. Another research from Wunderman reveals 79% of US consumers prefer brands to demonstrate care for them before they consider a purchase.

Successful transformation efforts inevitably require strong, objective research to ascertain the genuine insights from the customer. To apply such research at the organizational level, the C-suite has to focus on digital transformation initiatives aimed at improving CX. For instance: deploy a strong AI based analytical engine to understand the mind of the customer, wants, preferences, behaviour, and everything they speak or think about the company, updated in real-time, with the context added for a better perspective.

The Criticality of Intelligent Software

The rapid growth of data flows, digitization of key systems and practically unlimited computing power has given rise to intelligent software. Artificial Intelligence suites offer a significant upgrade over conventional data analytical tools by dint of its ability to work on huge swathes of data from multiple disparate sources.

A powerful AI-based engine can offer deep personalization capabilities by understanding the nuances of each individual customer to deliver tailored promotions delivered at the right time, using the right device. Such a truly customised DT initiative would herald the transformation of the enterprise from a “process organization” to a “caring organization.”

However, the golden rule is to keep customer perspectives in mind when driving transformation initiatives, Capgemini estimates 55% of consumers preferring interactions enabled by a mix of AI and humans, meaning a fully automated and impersonal CX may actually be counter-productive. Even for AI-based solutions, the overriding preference is for human-like attributes, such as human-like voice and intellect, ability to hold a sensible conversation, respond to follow up questions, and more.

The Importance of Design Thinking

Businesses increasingly accept design thinking as an effective problem-solving and innovation tool, and as the most effective way to understand a customer’s requirements. The core of design thinking, aimed at understanding the customer, is asking questions such as ‘what they do’, ‘why they do,’ ‘how they do’ and so on, and in the process identifying solutions.

Implementing design thinking within the boundaries of an agile framework requires a methodological approach of understanding the audience, defining the requirements, and creating new ideas to formulate innovative solutions for the requirements. The best approach creates prototypes of tentative solutions, and tests these out before committing to it wholeheartedly. The prototype based approach helps to create a high-quality model of the final product seamlessly, without having to run through the complete development life cycle.

The combination of Design thinking and Agile works wonders in integrating a team with people of different skill sets, backgrounds, perspectives and creative ideas. The approach offers the freedom to create digital “how” of the transformation, having understood the innate story behind the scene. Objective research into what the customer really wants, and applying intelligent software to drive change through design thinking helps in leveraging DT as a powerful tool for real-world transformation.

How Artificial Intelligence Enhances Customer Experience

Artificial Intelligence Enables Better Data-Driven Decisions

And for good reasons. AI-powered systems process, sort and analyze copious amounts of data, extracting patterns much more quickly and deeply compared to humans or even first generation data analytic tools. AI systems also infuse an added dimension of context to the analytics, adding potency.

For instance, luxury hotel brand Dorchester Collection set a new paradigm by developing a custom AI-powered analytics system that leveraged content from the many online review sites to evaluate nearly 7,500 guest reviews from 28 hotels across 10 brands, to deliver the findings in a 30-minute video.

AI Understands Customer Needs Better

AI’s ability to consume and analyse vast amounts of data from various sources enable accurate and fast prediction of customer buying behaviour. For instance, an AI-powered system could collate weather forecasts to stock up on umbrellas and raincoats.

Today, few customers have the time or even the inclination to painstakingly type in their requirement and search the inventory. AI’s image recognition capabilities allow the customer to simply click and upload the image of a dress she likes. The AI engine would locate a similar match from the stock inventory, or even better, work towards sourcing a similar dress from available sources anywhere in the world.

Artificial Intelligence Delivers a Human Touch to Automation

Gartner estimates that by 2020, 85% of a client’s relationship with a business will be managed without interacting with a human. Such widespread automation brings benefits in terms of accuracy, internal cost-cutting and process efficiency. However, unbridled automation comes with the risk of losing the personal touch, and with it the capability to get a nuanced understanding of the customer. Also, it is impossible to hand-program automated system with rules to handle every conceivable customer history.

Artificial Intelligence offers the best of both worlds. Chatbots and other virtual assistants leverage AI to obey commands or answer questions, understanding customer requirements through simple conversations. AI powered solutions such as Amazon Alexa, Siri, and Nest thermostat, make recommendations, automatically order goods and services, and do more, after understanding customer preferences. The Intelligent prediction and customization that AI brings into the automation mix could make customers feel as if the brand experience was tailored just for them.

AI Drives Extreme Personalization

In retail, the top 1% of customers is usually worth 18x the value of an average customer. Engaging with such high-value premium customers requires extreme personalization, which goes much beyond a customized interface.

Extreme personalization is breaking down customer segmentation to one and takes the form of customer-tailored promotions delivered at the right time to the right device, ensuring timely and relevant touch-points. The Royal Bank of Scotland delivers a good paradigm through its new AI-powered system, which flags customers who repeatedly overdrafts their account, and triggers appropriate bank personnel to contact the customer with financial advice.

Artificial Intelligence Powers Innovative Customer Service

Companies who innovate to offer a differentiated experience, while still being rooted in customer wants and preferences, enjoy a sizable advantage over their competitors.

Today, the overwhelming preference is for consistency across touch points. However, customer behaviors are chaotic. The rules of engagement are undefined and way beyond the comprehension of an ordinary data analytic tool. Customer facing executives and call center agents often falter since they have no way to understand the customer’s entire history and derive insights from it in real time.

AI-powered advanced analytic engines find patterns across an overwhelming number of data points, overcoming such hurdles, and in the process deliver a coherent experience across all enterprise touch points. For instance, AI enables the call recording system to analyse customer sentiment during calls, transcribing it, and delivering it in a text file, for easy and effective follow–up, and co-opting text analytics to gauge customer sentiment.

Also read: The Importance of Analytics in CX and DT

AI Enhances the Integrity of the System

Consider the case of online ticketing. While offering a world of convenience to customers, it also raises the prospects of cyber-criminals unleashing advanced bots. These bots are especially active during live events (sports championships!) to buy up large blocks of tickets, depleting supply, and then instantly re-selling the same for mark-ups using forums such as Craigslist. Ticket sellers now deploy AI to rewrite the rules, enabling the system to identify and block scraper bots with ease.

The path to memorable customer experience lies in how effectively the enterprise can apply emerging technologies such as AI to enhance their capabilities. AI-powered CX will enhance the quality of interactions between the enterprise and the customer, improving customer trust, loyalty, and netting repeat business.

The Importance of Analytics in CX and DT Interventions

Customer experience (CX) and Digital Transformation (DT) are the buzzwords for success in corporate corridors today. Top management has woken up to the competitive advantage on offer through CX and DT. They realize facilitating the customer and delivering an engaging experience is the route to success, considering more than half of all customers having switched providers just because of poor user experience.

Analytics – the Driving Force

The driving force behind CX and DT is data analytics. Advanced analytics allow enterprises to deliver better user experiences, leading to higher satisfaction and in turn greater customer loyalty. Today’s fast-paced nature of business leaves no room for finding out what customers want or what they prefer by actually asking the customer. Businesses have little choice but to ascertain customer wants and preferences proactively, by crunching data, and engaging with them on their terms.

A seamless app or a portal is no longer a big deal. Rather, what impresses the customer is an interface which recognizes their interests or preferences, preferably leveraging Artificial Intelligence to make choices for the customer. Netflix, Spotify and Amazon have already adopted the art of such personalization to a high degree of perfection. Personalization is not possible without crunching data of customer preferences, wants, needs, and sentiments.

A direct corollary to creating better CX is DT. The best DT initiatives stem from customer preferences, or making things better for the customer. Enterprises create new value for their customers by leveraging new technologies such as IoT and Machine Learning to disrupt existing models. Analytics connects these technologies to the enterprise platform, enabling insights which allow strategists to make smarter decisions, keeping pace with the speed of changes in the external environment.

Measuring CX

A fallout of data dominance is the need to quantify decisions and actions. It is not enough if someone at the top “knows” CX and DT will enhance customer engagement, and deliver rich returns. Today’s highly challenging and competitive environment places a premium on every investment dollar, and assurance of a positive ROI, made explicit in quantifiable terms, is imperative.

A methodological approach to measure CX, as expounded by research major Forrester includes prioritizing customer segments most important to the business as the first step. The enterprise next selects the level of experiences – discrete customer journeys, or individual interactions, again depending on what is most important for the business, to measure: overall relationship. Next, the enterprise defines CX metrics for the selected experiences, in terms of customer perceptions, what actually happened, and the business outcomes connected with each experience. The enterprise then collects data for the selected CX metrics.

Effective data analytics and comparing results with an internal benchmark for each metric would not just set performance targets, but also motivate both internal stakeholders and external partners to work towards improving CX in a mythological way. The insights a good analytics engine offers enable the enterprise to identify problems of individual customers, collate it prioritize broad-scale improvement opportunities.

End-to-end interaction metrics

The importance of data can never be understated. About 80% of data remains dark, or never actually used to improve CX. Many enterprises burn themselves out collecting data that they are not able to do the critical step of putting such data to analysis.

Again, the type and nature of data matter just as the quantity of data matters. For all the talk on the importance of live and real-time data in the scheme of things, only about 23% of companies are actually able to integrate customer insights in real-time, as a SAS study reveals. However, at the same time, enterprises caught on the real-time data trap run the risk of losing sight of the bigger picture.

What truly sets apart an enterprise is the ability to use both active and passive data to gauge customer sentiment not just at any one point of time, including current time, but to get an end-to-end understanding of CX data.

An end-to-end understanding of CX data not only reveals what the customer did but also the rationale of why he did so. For instance, live analytics would make explicit a customer who is making a “high effort.” Business managers usually indulge in fire-fighting to facilitate such customers and resolve their wants. However, it could be the customer, having visited the website and not finding what he sought, turned to online chat and ended up dissatisfied with the partial or vague answers provided by the chat agent, ended up making two or more phone calls to indicate the “high effort,” and finally reached resolution three days later. Considering the end-to-end interaction metrics enables the business to address the root cause of the customer’s frustration, and make the necessary CX and DT changes, rather than merely offer a remedy the symptom.

Across the business landscape of today, the common thread running across market leaders is them having integrated analytics into everything, right from everyday discussions to formal contracts. They align appropriate internal resources with analytical skills and ensure each relevant business area feeds into the larger data management strategy.

A successful end-to-end data management strategy co-opts not just structured data but also various unstructured data even outside the provider’s systems. Twitter feeds, Facebook updates or any external source where customers indicate their mood, like or dislike for the product or service is useful in making course corrections and upgrades. Smart businesses adapt to procedures based on customer preferences rather than expect the customer to adjust to their internal process efficiency requirements, applying DT initiatives to cater to what their customers want.

Data improve the customer journey dramatically, but only for companies willing to be led to where the data leads them. Enterprises entrapped in the sentiments of legacy structures or incumbent products will find their CX and DT interventions stifled and pay a heavy price in terms of missed opportunities.