What happens when the organisations experience an exodus of employees and remain clueless “why”? Or, when they’re unable to improve the sales for a key category in a very important region despite every effort? Or, how do they understand which customer complaint has the potential to go viral in social media and cause an appalling load of brand damage?
A company, during its life cycle, faces many challenges. Almost every challenge is prone to become a full-blown crisis. An effective crisis management strategy has become imperative for an organisation’s sustainability and long-term success. Since these crises are unexpected and sudden (in some cases), organisations can harness an analytics-driven approach to devise a befitting crisis response mechanism.
Preparation is the key
The Harvard Business Review conducted a study published in their 2010 article titled ‘Roaring Out of Recession’. This research was on the recessions of 1980, 1990, and 2000, which found that 17 per cent of the 4,700 surveyed companies went bankrupt, were acquired or reverted to private status. On the other hand, 9 per cent of the organisations did not just bounce back; they were also able to outperform competitors by 10 per cent in sales growth. Preparation was found to be the biggest differentiator. Analytics can prove to be a great tool to effectively manage risks and mitigate crisis-like situations.
A survey by Deloitte found that 55 per cent of the organisations surveyed think that analysis improves their competitive position and 96 per cent believe that it will become more important in the future. Analytics can be embedded in the entire crisis management lifecycle – from identification to assessment to mitigation.
Analytics and the new era in crisis management
Crisis can originate from both internal and external environments. Irrespective of its origin, they can hinder an organisation’s processes and stop it from achieving crucial targets. The first step in preparing an analytics-driven crisis management approach is to have a strategy for collecting and preparing relevant data quickly and accurately. The data can be obtained from different sources like emergency guidelines, government restrictions, internal & external sources and up-to-date information on the current and prediction of future industry trends. Rapidly progressing cloud technologies may aid in storing and analysing diverse and large scales of data.
The second important step is to be proactive. We may not prevent every crisis that hits us but can at least brace ourselves to be prepared for a range of crisis-like situations. The organisations must have key data elements that help them understand the nature, stage and gravity of a crisis at a bare minimum. The depth and breadth of data science and artificial intelligence can prove useful to diagnose & predict crises and to prescribe forward measures. For example, if the sales and marketing team can have a priori knowledge on the new products launched that are prone to failure, an action plan can be chalked out in advance either to make them successful or to limit their market introduction.
Thirdly, plan for an optimal response strategy. This will be different from the traditional crisis response model that is often reactive. A typical crisis cycle includes four stages – preparing, preventing, coping and recovering; an analytics-driven crisis response model focuses more on the preparation stage and has the potential to combat the later phases with ease when deployed efficiently. The evolution of modern-day analytics made it easier to integrate the cutting-edge tools and technology in the crisis response toolkit. Tools such as Natural Language Processing and Generation can make quick sense of unstructured data, distributed search and analytics engines can bring together disparate data from various sources to generate faster insights, automated machine learning capabilities and rapid dashboarding can play a pivotal role in transforming the ways the crisis management is professed today.
Pandemic’s effect on crisis management
The COVID-19 pandemic challenged businesses around the world. It became the most defining feature of the decade. With a relatively rocky beginning, companies are slowly trying to gain solid ground by increased investment in decision analytics. For example, decision analytics is being actively employed to measure the damage caused by the crisis, assess the effectiveness of the initial response, and apply the lessons learned from the experience to be better prepared should any similar crisis happen again.
Healthcare as a sector needed to be doubly prepared against the pandemic’s outburst, and decision analytics helped a lot in this case. Researchers used the enormous amount of data generated during the pandemic to analyse trends, monitor patients, and deal with the challenges in the healthcare industry. They increasingly turned to predictive models to understand resource allocation, patients at risk, and where the disease is likely to spike next.
The views, thoughts, and opinions expressed in this article belong solely to the author and does not reflect the views and opinion of the author’s employer, any other organizations, committee or other group or individual. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.
