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Building Digital Resilience Around the Customer

Digital resilience starts with an analytics transformation: data set, mindset, and skill set

Published: Thursday, July 30, 2020 - 12:02

Since Covid-19’s arrival, 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 pandemic’s 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 companywide change in mindset, the development and integration of analytical skill sets, and the adoption of a use-case methodology across test-and-learn outsets.

Digital leadership

Chief experience officers (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 organizational 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 center of excellence (COE) for advanced analytics that bridged all relevant silos and hierarchical levels—a kind of open analytics practice that would act as a data broker across the group. This center quickly became the “nervous system” for the company’s transformation.

Data and analytics foundation

To effectively collect data and turn them into insights, MAF recruited four different types of tech talents, who 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 visualization. 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 but, 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 behavior). 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 key performance indicators (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 organizational changes to ensure what used to be siloed roles now work in “agile squads.” Use cases are prioritized based on the relative value to the organization of the associated KPIs as well as their feasibility (e.g., data availability, technical capabilities, and implementation potential).

This approach produced a string of dramatic successes. For example, an assortment optimization pilot program run through Carrefour stores in Dubai (MAF operates the French retail brand in 17 countries) increased revenue by $10 million during the second half of 2019.

To further strengthen the data-driven momentum and collect customer data on a wider scale, MAF launched the loyalty program 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 center 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 food and beverage 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., optimizing the SKU mix) to more ambitious and long-term (e.g., data-powered solutions like scan-and-go that facilitate in-store payment processing). The road to a data-driven digital transformation starts with business intelligence (BI), then proceeds to AI, to arrive at extended intelligence (EI), 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 MAF 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 not outliers. In fact, these best practices and transformation guidelines are also present in other successful organizations. Building digital resilience starts with an analytics transformation, which entails creating first a data set, a mindset, and a skill set to build use cases. Along the way, each organization 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.

First published July 3, 2020, on INSEAD’s Knowledge blog.

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About The Author

Joerg Niessing’s picture

Joerg Niessing

Joerg Niessing is an affiliate professor of marketing and excels in bridging academia and the business world. He holds a PhD in marketing from the University of Muenster and has more than 13 years of consulting experience. His background includes marketing and branding strategy, marketing analytics, digital & social media marketing, and customer relationship management across many industries. At INSEAD, Joerg focuses on marketing analytics, brand management, and big data analytics, and develops and teaches courses in these areas. He is executive director of INSEAD’s eLab, that focuses on the intersection of data analytics, customer insights, and new technology