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A wake up call: operationalising analytics for the post-Covid economy

(Image credit: Image Credit: Shutterstock/Sergey Nivens)

Across every sector and industry, Covid-19 has challenged old assumptions and exposed digital deficiencies. Together, the experience of lockdown and the persistent danger of infection have dramatically altered consumer behaviors almost overnight. ‘Business as usual’ will not be coming back any time soon. Brands need to react, respond and reimagine their businesses for the new normal if they are to stay afloat.

As economic hardship continues, commerce will remain digital-first – and often digital-only – for some time to come. Simultaneously, entirely new audiences and demographics have been forced online for perhaps the first time. This has opened the floodgates for unprecedented online traffic. But many organizations, confident they were well on the path to digital transformation, have been rudely awakened as their call centers buckle under the strain of customers unable to manage their queries online.

Over the last decade, brands have leveraged advanced analytics to report on customer insight and behavior in support of data driven decision-making. The foundation is there, but now brands must move onto the next stage – use of analytics to automate decisions, rather than just providing decision support. Advances in AI and machine learning should be leveraged to automate end-to-end customer journeys, removing latency and improving outcomes from the customer experience.

A new digital revolution

Customer intelligence has long been at the center of business efforts to understand consumers and deepen customer relationships. Yet the effect of coronavirus has been to reset the board.

Brands have spent years building robust models of customer behaviors and purchasing cycles, using these insights to guide their strategies and decision making. The predictive power of both traditional and machine learning models depends on the quality of their data inputs and underlying assumptions. But massive shifts in consumer behavior and circumstances have rendered many of these models all but redundant as the underlying data is now largely not applicable.

Internet usage has surged by up to 50 percent across European markets, as a new wave of digital converts switch to e-commerce for most or all of their buying needs. Mainly from older demographics, these converts engage online very differently compared to the digital natives of today. In addition to recalibrating existing customer insight models, brands will need to rapidly understand these entirely new customer segments, and tailor their online experiences to them.

The test businesses now face is how quickly they can capture and model new customer behavioral data, and get those models into production to drive accurate and relevant decisions. This could be anything from a bank testing a customer’s eligibility for a new loan, to a retailer working out the most appealing product for an individual customer.

But rather than just developing up to date insights, it’s now time for organizations to invest in the last mile of digital transformation and exploit the opportunity to automate operational decision-making.    Brands have been encouraging customers to move online for years – but now it’s all happened at once. And where services have not yet been digitally transformed, unprecedented traffic has overwhelmed call centers while spiraling customer queries over email, text and social media have slowed customer service operations to a crawl.

React, respond, reimagine

Business leaders can’t just focus on dealing with the current disruption. To survive and thrive in the long term, they need to adapt and reimagine their businesses for a very different future. The new emphasis will be around improving speed and efficiency in both front and back-end processes. Indeed, our research with The Economist Intelligence Unit has revealed a 7 percent increase in executive strategies to improve operational agility across their business and to become meaner and leaner. 

Ultimately, servicing ever-growing demand and building new customer intelligence models requires companies to operationalize analytics and automate operational decision-making, and quickly.

Companies have been making use of analytics for decades, primarily to produce insights to guide decisions and more recently for predictive intelligence. Yet, now is the time to take it a stage forward. Wherever manual decision processes exist – whether it’s including a customer in a marketing campaign, or assessing a customer application – latency is baked into the process. AI-driven analytics can automate these decisions to deliver faster and more seamless customer experiences. 

Both robotic process automation (RPA) and machine intelligence will be important in the new environment. RPA is best for automating high-volume, repetitive and rule-based human – computer interactions, such as copy-paste tasks or moving files from one location to another. In this way many organization’s have already automated many of the back-end processes involved in a customer journey. However, where data is unstructured, where there is a decision-point in a process or where systems are customer-facing and need to be smart, self-learning and adaptive, AI is a must.

Advanced analytics and machine learning allow systems to become intelligent and constantly adapt and learn from new, contextual data. It’s the best option for automating the thousands of operational decisions that are undertaken across many organization’s every day and for digitally transforming services. For example, a bank’s decision to grant a mortgage payment holiday is usually dominated by manual decision processes – including determining the customer’s eligibility and considering better alternatives for the customer. Automating such decisions would enable the bank to deliver a decision in real time to customer applications across their websites and mobile apps, significantly reducing the number of telephone and email-based queries and eliminating manual back-end processing tasks.

To survive in the post-Covid economy, companies must face up to the last mile of digital transformation, identifying services that are still paper-based or require human intervention and understanding bottlenecks in the customer journey. Automated decision management provides companies with the best approach to make better operational decisions based on customer behavior and data directly within an organization’s digital touchpoints. The use of AI at key moments of the customer journey will reduce operating costs and increase efficiency whilst improving service speed, relevance and satisfaction for consumers. Autonomous digital self-service is a crucial requirement, not only for servicing unprecedented demand in the present, but for building greater resilience for the future.

Tiffany Carpenter, Head of Customer Intelligence, SAS UK & Ireland (opens in new tab)

Tiffany Carpenter is Head of Artificial Intelligence and Machine Learning at SAS UK & Ireland.