Resilient businesses use digital technologies, data and analytics as a process to create long-term customer value.
The ongoing crisis has both brought digital resilience to the forefront and broadened its meaning. This idea originally emerged in the area of cybersecurity and reflected the need to upgrade and maintain IT capabilities to resist cyber-attacks.
Since the Covid-19 crisis, digital resilience increasingly refers to the strategic use of digital technologies in delivering customer value and business growth despite adversities. Indeed, some industries – such as hospitality, higher education or traditional retail – were hit more than others because they did not embed digital technologies and analytics early or strongly enough.
In building resilience, the customer-centric perspective is critical. Only companies that leverage digital technologies and data to engage with customers more effectively, enrich customer experiences or offer innovative customer-centric business models will create long-term growth.
INSEAD’s upcoming case study on Majid Al Futtaim (MAF), the Middle East’s leading shopping mall, retail and leisure pioneer, explores this issue further. Despite Covid-19’s impact on many of MAF’s industries, like shopping malls, entertainment and grocery retail, the conglomerate’s digital readiness, which had been ramping up for years prior to the pandemic, significantly limited the negative effects.
But how did a company whose business model is based on brick-and-mortar activities tied to leisure and lifestyle plan its transformation? The secret sauce includes three ingredients: a company-wide change in mindset, the development and integration of analytical skillsets and the adoption of a use case methodology across test-and-learn outsets.
Customer focus requires digital leadership
CxOs often report that their digital transformation efforts fail. Of the 1,350 senior executives Accenture interviewed for its digital transformation study in 2019, 78 percent failed to exceed their return on digital investment goals. The primary reason is that digital efforts tend to be embraced at either the top or the bottom of the organisational hierarchy, without coordination and often within silos.
In MAF’s case, one of the authors (Bejjani) actively became the top-down champion of the initiative. He provided a clear upfront commitment with defined objectives, while emerging tech talents were given crucial seats at the table to help drive change. Together, they created a Centre of Excellence (COE) for Advanced Analytics bridging all relevant silos and hierarchical levels – a kind of open analytics practice that would act as a data broker across the group. This centre quickly became the “nervous system” for the company’s transformation.
Data & analytics foundation to create customer value
To effectively collect data and turn them into insights, MAF recruited four different types of tech talents that started working together and in coordination with the business units.
The first type – data engineers – are responsible for collecting, processing and cleaning data to make them available for downstream analytics, in real time when possible. Next, business intelligence experts are focusing on making sense of data from a business perspective (“What does this mean for our market?”). Business intelligence experts are seasoned data analysts who also turn to asking “why” questions, testing assumptions and enabling data visualisation. Further, data scientists take on more advanced investigation and prediction responsibilities, design A/B testing and formulate recommendations. The last critical role is that of the business partner. Acting as a “translator” or connector, that person helps to transform business pain points into technical solutions.
These four talents interact in an end-to-end process that translates raw data into learnings that are at first descriptive and, through further refinement and iteration, become prescriptive. Together, tech talents leverage AI to trace correlation (what variables – from search to sales – covary together) and causation (what factors cause a change in attitude or behaviour). For example, correlation-based algorithms can reveal patterns in customers’ digital shopping habits, which prescriptive models can use to measure the profitability of various product combinations in physical or virtual retail environments.
Digital use case approach
Effective transformation takes place through the successful spread and adoption of data-driven use cases that generate actual customer value.
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