Resilient businesses use digital technologies, data and analytics 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.
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 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. From the very beginning of MAF’s journey, the COE’s task was to work together with business unit leaders to turn highly specific business challenges into a concrete use case with crystal-clear KPIs. A typical collaboration between the COE and the business units would entail data collection, field tests (A/B testing), clear KPI setting and possibly organisational changes to ensure what used to be siloed roles now work in “agile squads”. Use cases are prioritised based on the relative value to the organisation of the associated KPIs as well as their feasibility (data availability, technical capabilities and implementation potential).
This approach produced a string of dramatic successes. For example, an assortment optimisation pilot programme run through Carrefour stores in Dubai (MAF operates the French retail brand in 17 countries) increased revenue by US$10 million in the second half of 2019.
To further strengthen the data-driven momentum and collect customer data on a wider scale, MAF launched loyalty programme SHARE in 2019. By generating omnichannel customer profiles and de-siloing data across the conglomerate, it enabled deft targeting of lapses in the customer journey. For example, the analytics centre noticed a soft conversion rate through the Carrefour website for visitors responding to specific marketing campaigns. After the problem was traced to customers having forgotten their login information, the team was able to resolve the issue and raise the purchase completion rate considerably.
The company’s cinema arm, VOX Cinemas, increased the spend per head for F&B sales, a key customer profitability metric, by 3 percent in just three weeks by rolling out new snack combos based on novel insights from existing purchase data.
Overall, MAF’s use cases range from relatively simple and concrete (e.g. optimising SKU mix) to more ambitious and long-term (e.g. data-powered solutions like scan-and-go that facilitates in-store payment processing). The road to a data-driven digital transformation starts with BI (business intelligence), then proceeds to AI, to arrive at EI (extended intelligence, or decision support systems). Majid Al Futtaim’s strategy is to have humans leading and machines learning.
Continually enhancing customer value
Over time and thanks to its use case approach, MAF has become a data-driven firm whose business units are equipped with a “digital memory” that enables them to keep a log of past tests and keep on improving. Being data-led not only improves effectiveness internally and delivers more customer value, it also opens the possibility to pivot MAF’s business model beyond its traditional verticals. Because the company knows its clients inside out, it can create new data partnerships that will benefit the broader ecosystem.
The patterns revealed by the MAF case are no outliers. In fact, these best practices and transformation guidelines are also present in other successful organisations. Building digital resilience starts with an analytics transformation, which entails building first a dataset, a mindset and a skillset to build use cases. Along the way, each organisation will meet its own challenges. Still, we believe that significant growth awaits any company that successfully completes its digital journey – especially if it stays laser-focused on how data and digital tech could empower company collaborators in the service of the customer.
This article was first published in knowledge.insead.edu