Adapting to COVID-19 by Accelerating Analytics and Digitalization
75% of companies surveyed by Accenture stated that they saw a negative or very negative impact on their business because of COVID-19. In particular, 94% of Fortune 1000 companies are seeing supply chain disruptions attributable to the global pandemic.
Most of the Fortune 1000 companies operate at a scale where cash-reserves and systemic risk management should have helped them sustain through the pandemic. However, even these well-established enterprises have witnessed a deep impact on the overall business.
While the disruption highlights the problem, businesses have started devising a strategy to fight the challenges. McKinsey reported that businesses worldwide had adopted digital initiatives in eight weeks to the extent that would have otherwise taken half-a-decade. This highlights a critical point – while digital transformation and analytics were considered capital-intensive technology expenditures, businesses are using COVID-19-induced disruption as the motivator for adopting digital technologies and moving towards analytics-driven operations.
If your business has faced severe distress caused by the global pandemic, here is how you can start utilizing digital adoption as a bridge towards developing intelligent analytics-driven operations:
1. Safeguarding Employee Interests.
According to research, factors like physical & mental health, trust among co-workers, and fair treatment at the workplace have the biggest impact on work effectiveness. Even the CEOs understand that remote collaboration is the top concern for their firm's ongoing operations.
As most employees work from remote locations, business operators and managers will have to redesign the everyday workflow and monitoring systems to ensure employees feel safer without losing their productivity.
Enterprises should invest in analytics systems to flag employees who might be having health concerns that might be contributing to productivity / quality concerns or some other immediate impact of workplace policy changes on employee behavior. With the help of NLP embedded into online collaboration tools, this can be achieved in a non-intrusive manner.
Managers can now focus on need-based employee-availability plans. This would mean, managers can engage employees only when they are critical to the process. Such steps can create space for engaging contract-based human capital and also providing employees a well-needed break away from work. Working from home can actually a bigger challenge than originally perceived. To simplify the onboarding or productivity of such resources, automated workflows that take care of redundant and repeatable tasks can help pay only for the value generated by the individual.
For the employees not working in knowledge services, businesses have to start focusing on 'dark factories' that can operate with zero-human intervention. While this would mean renegotiating labor contracts in the short-term, it can safeguard employees for the time-being and ensure enterprise productivity. IIoT and Network Monitoring systems can be the route to get started with deploying such programs.
2. Mitigating Supply Chain Risks.
As stated in the Accenture report, close to 94% of Fortune 1000 businesses face supply chain disruptions attributable to COVID-19. While it is a speculation, it would be very hard to assume that most Fortune 1000 companies were not running intelligent tools for supply chain and demand forecasts. The problem, as this MIT Management Review research post shows, is not in the demand and supply chain forecasting processes. The central issue is in the procurement, reliability, ownership, and availability of data.
Since Fortune 1000 companies are referenced here, it is not difficult to speculate that most of these companies have enough resources to hire the best business process transformation or digitalization consultants. Yet, the supply chains proved to be quite vulnerable to a global pandemic. While the pandemic is largely a black swan event and hence not that predictable by design, it should have been a part of most of these firms' risk mitigation strategy & planning.
Most of the firms did not have supply chains optimized to survive a global disruption because most of the supply chain is focusing on the firm at the center. The MIT Management Review report stated that most of the firms the researchers approached did not have a detailed idea about the reaction in the downstream. Irrespective of how the companies create internal policies and strong forecasts, if the downstream supply chain cannot adhere to their standards, the entire operation comes to a standstill.
Primarily, the supply chain data has the following issues:
1. Data Availability: From origin to delivery, the process owners should have real-time availability of the entire supply chain and where each raw material as well as end-product is at. This can help in benchmarking routes and running stress-tests when necessary.
2. Data Transparency: The data released in public for compliance purposes has nothing to do with supply-chain data transparency. Internally, the enterprise should have an idea of the risks prevalent to its supplies, the supplies of its largest suppliers, and the ecosystem of suppliers working at the system's outsets. This can be possible only where there is transparency in data internally.
Pre-COVID contracts generally did not have clauses on supplier data transparency. Now, the firms will have to ask their suppliers about the source of procurement to identify potential exposures to volatility.
3. Data Ownership, Governance, and Analytics: Once the data has been sourced within the firm, the enterprise will have to focus on cross-functional availability. For instance, if the Supply Chain Operations team has started scenario-planning based on 80% unavailability of the workforce, can the Human Resources team ensure the rest 20% would be available at that time? Or is there is a possibility to renegotiate terms with labor teams and optimize availability? Such questions can be answered only with uniform availability of data across functions. Hence, the 'enterprise owns the data' and not a particular department.
Finally, now that the data ownership issue has been fixed, the management team can focus on data governance practices followed by investments in digital process management and analytics. If any of these steps are missed, the enterprise will invest in areas that are not prepared for scaling.
3.Amplifying Customer Engagements Across Channels.
Businesses in the USA and Europe saw a major trend – consumer spending decreased by over 50% across major categories. Only three categories were able to deliver growth – groceries, entertainment, and household supplies.
With the onset of the pandemic, consumer behavior shifted not just in the preference for categories but also in the preference for channels. Since many markets witnessed a lockdown, consumer behavior did not evolve gradually from offline to hybrid to online. Many buyers had to opt for online purchases since a physical pickup was either not preferable or not possible for them.
While the pandemic also gave rise to a lot of panic-buying, if companies want to keep customers engaged, they should be taking a strategic approach:
1. Building a Compelling Online Platform: Some businesses have not resorted to online as a medium stating the complication of their product category or industry. While this worked in the pre-COVID-19 era, where the difference between online and offline was primarily convenience, it might not work for the post-COVID-19 era as customers are now concerned about their own safety. In short, even the most high-ticket and long conversion cycle categories will have to focus on online sales. Case in point – Tesla is already selling its cars online. It has brought the entire product exploration feature to a virtual realm and is providing all the information necessary for the purchase process on its website.
2. Provide Contactless Services Across the Customer Journey: While online purchases are here to stay, as the pandemic settles, there would be more space for an omnichannel experience. Your purchase and delivery touchpoints will have to be optimized for contactless delivery. Moreover, processes like customer support, pre and post-sales information dissemination, and even cross-selling will have to be delivered with intelligent mediums.
For instance – businesses that have not resorted to NLP-powered chatbots and Natural Language Generation engines can now work on such applications. With an iterative approach, these systems can help solve customer queries systematically, make the customer experience seamless, and do it all at scale.
In Conclusion
COVID-19 has impacted different businesses in different manners. While there is no one-size-fits-all solution, it is high time that management teams start focusing on their digitalization efforts. Analytics systems across all functions, transactions, and data are the future. But, the disruption caused by the pandemic can be used as the platform for building the digital infrastructure that can lead to the analytics future.
Author Profile:
Randhir Hebbar is one of the founders of Convergytics — Asia’s fastest growing and leading analytics brand. Randhir heads Digital, BI, AI and Products at Convergytics and Blik.ai is a solution that he has conceptualized, designed and built with his team of team of developers, BI specialists, data engineers and data scientists. You can read some of his other posts here.
About Blik.ai:
Blik.ai is Convergytics’ proprietary end-to-end business intelligence platform for SaaS platforms and provides one-click integrations and pre-built modern-BI dashboards for platforms like Google Analytics, Adobe Analytics, Google Adwords and Freshdesk. Many more integrations are in the works and are being added each week.
If you are looking for a quick and easy solution to aid in your data-driven decision-making journey, do get in touch with us by writing to us at [email protected] or just sign up for a trial at blik.ai to understand how you can utilise Blik to navigate these uncertain times.
About Convergytics:
Convergytics is a Microsoft Gold Partner for Data and Analytics. We focus on four core areas:
- Data Management and Data Engineering: data ingestion, validation, transformation, warehousing and OLAP
- BI and Advanced Dashboards: reporting KPIs/metrics, real-time BI, automated monitoring and alerts, ETL, executive/operational dashboards
- Digital and CRM Analytics: site optimization and A/B testing, digital audits and implementations, optimizing digital media spends, real time personalization and predictive CRM, and automated reporting
- AI and Machine Learning: forecasting, safety and risk, defect prevention and preventive maintenance use cases