In Evolving Consumer Products Space, Data Fills COVID-19 Gap

Consumer products companies are increasing their use of data analytics to gain insights that are otherwise hard to obtain in the era of COVID-19.

The global pandemic has upended what many consumer products companies understand about their businesses, customers, risks, and opportunities. As these organizations work to transition to a “next normal” state, the ability to harness data around the rapidly shifting environment has become an important differentiator. Technology to gather and analyze data can help provide insights that companies can use to pave their path to recovery and future growth.

 Data analytics and cognitive risk sensing are becoming increasingly important tools for organizations that are shifting their business models to respond to significant changes driven by the spread of COVID-19. Leveraging AI, machine learning, and natural language processing, cognitive risk sensing offers automated capabilities to gather information from hundreds of thousands of discrete sources—including news media, social media, websites, regulators, government agencies, and competitors. Sometimes called predictive intelligence or predictive analytics, this technology can extract, interpret, and analyze information across dozens of different languages to help identify emerging risks before they manifest into adverse consequences.

A recent Deloitte analysis of the top 52 U.S. consumer products companies by market capitalization suggests those organizations were already increasing their use of data analytics across many areas of the business, although they have been doing so in different ways. According to the analysis, some companies are investing more heavily in analytics to gain marketing and consumer insights or to evaluate sales and customer management trends and opportunities. Others are more focused on assessing supply chain and logistics issues, managing finance and business operations, or arriving at manufacturing decisions.

These analytics have taken on a heightened importance amid continued uncertainty. Risk assessment is an important objective for many organizations as they deploy new uses of data analytics. Risk management teams are deploying cognitive risk sensing to modernize their traditional risk assessment processes and gain increased awareness of the rapidly shifting risk landscape.

In food service, for example, the closure of many restaurants and their shift to take-out-only service significantly affected demand for a variety of food and packaging products, prompting data-driven changes in product portfolios. As many restaurants in certain parts of the country resume sit-down dining services, analytics can also provide insights that can inform further adjustments in production and logistics.

In a sector such as travel and hospitality, organizations might leverage data analytics to monitor and assess service quality across widely dispersed geographic areas. The technology also can help alert an organization to localized regulatory changes that might affect the business.

As new cases of COVID-19 are identified, government officials may issue new directives, which could lead to continuing shifts in how businesses operate. Cognitive risk sensing may provide companies with critical insights into how potential changes might affect their business, giving them actionable information that can aid compliance, strategy, and decision-making.

Based on poll responses during a recent Deloitte webcast, organizations appear most interested in leveraging cognitive risk sensing analytics to proactively detect emerging threats that may affect the company’s reputation or to develop awareness of potential financial losses and opportunities that might be tied to certain events. For example, risk sensing might help organizations navigate uncertainty related to liquidity, cash flow, and communication with critical stakeholders—all areas of significant concern.

Many consumer products companies already leverage analytics to some degree in sales and marketing efforts, but the pandemic may have opened eyes to additional possibilities, such as identifying risks related to culture, leadership, and other nonfunctional areas. Some organizations recognize that data analytics can provide useful insights across such areas, and they are forming cross-functional task forces to facilitate agile decision-making while also developing a sense of collective ownership.

As an example, cognitive risk sensing can draw on extensive information sources to help evaluate whether a company may have a developing workforce conduct issue that has yet to be identified. In organizations where workers represent multiple generations, data analytics may help assess whether some groups are fully aligned with operational strategy. These insights can inform decisions about additional training or other interventions that might be warranted before a situation escalates.

Cognitive risk sensing might also help identify early indicators that a company’s reputation may be at risk. It may also be helpful in performing due diligence assessments for a merger, acquisition, or divestiture. Across the consumer products sector, companies can benefit from improvements in risk mitigation, operational efficiencies, and customer and employee experience.

Ongoing monitoring for potential disruption can enable more nimble responses to challenges that may still be unfolding, a capability that can elevate the risk management process within organizations. Risk management leaders may find that these insights are in greater demand at higher levels and are valued as critical for making strategic decisions.

Given the potential benefits, consumer product companies might consider four critical steps to leverage analytics to a greater degree as a way of recovering from recent disruption and thriving in the future. These steps include:

Assess current methods. The organization may need to evaluate how it currently identifies and monitors emerging trends, threats, and opportunities. Current risk assessments may be based on more limited or historic information, with only a few leveraging deeper data analytics.

Layer on data. The next step might involve considering how data could enhance the current risk assessment process. The organization can evaluate where assessments might lack insights that new datasets could enhance.

Evaluate technology. To bring new or more data into risk assessments, organizations may need to invest in new hardware and software. Cognitive risk sensing analytics might require the use of data lakes or warehouses, which could require significant additional storage capacity.

Fold data into strategy. When the organization makes a commitment to gathering and analyzing new data, it also needs an action plan for putting it to use. Strategic planning may need to consider how to consolidate new and existing data into strategy to determine how best to leverage it.

Many companies that invest in cognitive risk sensing analytics are beginning to distinguish themselves from their competitors in compelling ways. Many are demonstrating improved operating metrics, such as those related to efficiency, inventory management, and supply chain agility, and are differentiating and improving their customer experience. These traits will liikely be critically important to companies as they seek competitive advantages that enable them to thrive even as the market continues to ebb and flow.

Posted October 5, 2020 by & filed under News.