Covid-19: Accelerating Digitization efforts across Industries

We are living in strange times. The pandemic has changed us profoundly – the way we work, the way we buy, the way we entertain ourselves, the way we educate ourselves – well almost everything has changed. The economic impact has been brutal on most industries. And the natural reaction has been to cut spending in everything deemed “non-core”. But the current situation has also brought out creative strategies and relative advantage to few industries and players.

Given remote working, remote purchase, and even remote consumption (entertainment & education), digital channels have naturally become the mainstay for most organizations. Digitally native businesses like app and web-based grocery, online food services and even local retail stores have geared themselves to take advantage of the new environment. Some of these aggregators have also tried to bring-in new partners on the platform. Digital technologies like image recognition, face recognition etc. are being used very extensively to digitise erstwhile physical parts of the supply chain for these organizations. Increasing number of local business owners are becoming adept with digital accounting and payment systems

Education as a sector is witnessing a new wave of digital adoption, thanks to remote classrooms, and proclivity of professionals to upskill themselves during this time of economic uncertainty. Enriching the learning experience through digitally aided interaction between learners, digital assistant to recommend learning paths, power of simulation, data driven adaptive course structuring etc. are becoming interesting tools to enrich learning experiences. Some of the psychometric tools to measure learning effectiveness are becoming part of digital learning platforms and are picking data not directly from responses but from behaviour of learners. EdTech businesses are witnessing a sea-change and a major opportunity.

Enabling remote working, remote collaboration between employees and making information available more easily to them is becoming critical for organizations to adapt to this new normal.
Customer service is an area that is witnessing a massive shift, and organizations are scrambling to integrate data across platforms and making them readily available to front-line servicing teams. Managing workforce productivity and stress is a key area where new sources of data on digital usage will probably yield measures to understand productivity levels of employees and monitor if they are overworking which might lead to stress. This is especially crucial now, because no longer can managers and leaders ‘see the body language’ of their team-mates or have a cup of coffee to understand ‘what is going on’.

Financial services in general and lending in particular is facing a tough challenge. The economic impact of the pandemic will surely create a surge in delinquencies and credit loss. But we have never seen an economic impact at this scale before. The industry is grappling on methodologies to estimate loss and create the right provisioning. Location driven customer segments that aim to separate out early delinquency, proclivity of accepting moratorium, promise to pay, etc. are approaches that everyone is trying to predict. Collection is an area that is under tremendous focus and collection managers are having sleepless nights. It will be required to build collection prediction models frequently with a very short prediction horizon to capture the dynamics of the fast-changing phenomena.

The healthcare sector is also overwhelmed managing a huge surge in care seekers in addition to facing a heightened sense of risk and fear among employees. Newer modes of healthcare delivery using apps and web-based technologies will probably be adopted very fast by most providers. Usage of platforms that will aggregate experts across locations to provide remote consultation, will become interesting business models.

The overall situation has brought to light interesting views towards usage of data driven strategies. All machine learning or statistical learning models assume that the “future will somehow be like the past”. And therefore, it is possible to learn from past data and apply the model on future data. A disruption of this scale may question that premise. Particularly when one is attempting to predict outcomes like credit default. Designing the right sample and the right target (or “Y”) will be very important (and tricky) while building models and data driven strategies in the current environment.

To conclude, while we focus on adapting to business during these challenging times, we also know that there is light at the end of the tunnel, and we all believe that “even this shall pass”. Once we start getting out of the current situation, we may have to create business models and digital strategies ready to harness the opportunities that are waiting at the other end of the tunnel.

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