Analytics, artificial intelligence and machine learning are key digital accelerators that will help businesses recover quickly in the new normal
From increasing sales to reducing costs to driving efficiency, data- and analytics-led insights will be more important than ever in decision-making
Companies can build operational resilience leveraging data and analytics
The COVID-19 crisis takes me back to the 2008 economic recession. At that time, a leading airline, like many other businesses struggling to recover, was looking to cut costs across the organization. There was, however, one area the airline firmly refused to compromise — getting data-driven insights for decision-making. It rightly placed its bets on boosting its analytics program, especially during the crisis.
We are now faced with a crisis of greater magnitude. More than ever, I strongly believe that analytics is the game-changer that will enable organizations to not just recover, but thrive. It is a pivotal digital accelerator that can help businesses increase revenues and reduce costs simultaneously — what we call the ‘positive jaws’ ratio.
Decision-making is the most crucial business imperative for rebooting. The questions uppermost on the minds of leaders are:
How can I engage / re-engage with customers in order to increase sales and revenues?
How can I drive efficiencies for cost optimization?
How can I accurately forecast trends in supply and demand with leading indicators?
Let me elaborate on how analytics is the answer to each of these questions.
A 360-degree view of evolving customer behavior is key as consumer demand shifts from offline to online, and supply chains get re-configured. Applying Artificial Intelligence (AI), Machine Learning (ML), deep learning and cognitive technologies to customer data from traditional and non-traditional frameworks will provide insights for intelligent customer segmentation and personalized engagement — leading to curated customer acquisitions, and high customer confidence and loyalty. In the face of increasing cost and revenue pressures, data and analytics can also carve out new revenue streams through data monetization.
Data and analytics engines spanning the collection and synthesizing of relevant data, and analyzing of customer sentiments, behaviors, interactions and experiences across channels will further drive revenues. For example, a leading CPG company leveraged an AI-infused tool to understand trends, sentiments and behavioral patterns of consumers to restart market research and revive its product deployment strategy in the wake of COVID-19. For more interesting insights on how companies can leverage customer data, and apply customer analytics and loyalty analytics to restart, revive and reinvent in the new normal, read my colleague Brian Burchfield’s point of view here.
There is no doubt that COVID-19 has significantly dented the cashflow of many companies. As we know, data and analytics, integrated with intelligent automation, can identify underperforming departments and programs, and determine where to reduce expenses while correlating operations, performance management and financial analysis.
In the new normal, companies will want insights to be an even more integral part of decision-making, and disseminate these insights across the hierarchical organization structure. Hence, reporting capabilities and insight generation will have be scaled to self-serve and automated formats more quickly, to provide insights at scale, at a fraction of the existing effort, time and costs.
For an industry like insurance, the outbreak has resulted in opportunities cropping up in various sub-segments. However, false claims and processing humongous volumes of claims in the aftermath of the crisis may offset these gains. Analytical interventions across the value chain can bring in the cost optimization required. The latest WNS DecisionPointTM report delves into the role of analytics in designing policies for the new normal.
The banking and financial services sector, constrained by changing regulations, stressed assets and short-term lending, will require analytics for portfolio optimization apart from effective decision-making. As lending priorities change, banks and financial institutions will have to boost their processing capacity to leverage data- and analytics-driven credit modeling to accurately determine the risks associated with new borrowers.
For cash-strapped utility and telecom companies, smart analytical solutions can identify defaulting customers who are most likely to pay, when contacted. Such solutions create a sizeable impact for companies operating with skeletal staff in their collections’ processes.
The magnitude of uncertainty that COVID-19 has created should be tackled with new data and forecasting models. From accurately identifying pockets of demand and supply opportunities to maximizing marketing return on investment to minimizing supply chain costs, data-driven predictions will help drive the outcomes businesses need in the new normal. In one of my recent articles, I have elaborated on how companies can build efficient supply chains by leveraging data and analytics to predict fluctuating baselines.
In digital-led recovery, data and analytics will be the star performers to meet new customer needs, bolster decision-support systems, and reinvent business models to operate at speed, scale and efficiency. As businesses move forward in the altered landscape, data and analytics could well sift the leaders from the laggards.
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14 February 2022
09 February 2022
21 December 2020