Post-pandemic Retail is emerging to be a noisy place.
As companies cautiously emerge from lockdowns and crisis mode, they face a business landscape in flux, and a marketplace full of questions. Running a business during COVID meant scrambling to function in an environment that had no historical data. Slammed by changes in every aspect of daily life, consumers responded with huge swings in their buying patterns and preferences and Retail raced to keep up.
Now, as societies re-open, Retail faces yet another era of uncertainty: Will consumers return to stores consistently or will e-commerce continue to dominate? Will seasonal shopping patterns resume or have they been permanently disrupted? Can we take the behaviors of the past year and forecast for the future? What will the post-pandemic customer want? From fashion to pharmacy, the challenge is clear: in a turbulent marketplace, product relevancy has never been more critical – or more difficult to achieve. Retail is an industry in rapid evolution.
How can retailers manage the upheaval? Merchandising leaders say the answer lies in data.
We surveyed North American merchandising executives as they emerged from their lockdown experience and looked ahead to the future. While they embrace data as a critical element of Retail success, they also find it a challenging space to navigate. Accessing data and transforming it into usable insights is not easy. What’s more, successful Retail has long been a combination of art and science – gut instinct and data – with leaders often matching what they see in the marketplace with their own well-honed experience. Adding new streams of data into the process is complicated and companies vary widely in their experiences.
We call this the Data Maturity Curve, with some retailers or companies well ahead and others working hard to connect emerging data principles to the company’s legacy systems and art-plus-science processes. All agree moving up the maturity curve is critical. But many acknowledge the challenges.
One challenge is a lack of systems.
We are not using data as we should or could,” said one merchant. “We are not yet using customer insights in a way that moves the needle. The challenge is on how to incorporate that data into the merchandising and brand decisions.
Another – and often under-appreciated challenge – is a lack of data-oriented talent who could potentially move the company along the Data Maturity Curve even while better systems are under construction.
Moving up the Data Maturity Curve is a matter not just of raw data but also having the processes and talent in place to transform data into insights and action. Acknowledging all of the challenges, we submit that steps can be taken to advance Data Maturity now.
To get started:
Feed the Data-Driven Flywheel
Merchants can embrace the flywheel concept of continuous data collection, testing and learning. Instead of a single giant step into more robust data usage, set in motion a virtuous cycle: collect available data, experiment with select new data points to discern trends, analyze the trends to harvest insights and allow the insights to drive business decisions and actions. Then, collect the data generated by those business decisions back into the flywheel process for another spin. The circular effort shows that moving up the Data Maturity curve isn’t about flipping a switch and making a big change. Instead, the data-driven flywheel makes smaller changes possible by taking data and letting it influence the busines process, bit by bit.
Embrace Data-Supporting Competencies
Talent is a key lever to move your company up the Data Maturity Curve. You may need to add or develop talent on your team that bring a different level of analytical capability in order to “connect the dots” allowing your data to positively influence the business process. You also need leaders who can embrace collaboration at a different level to engage with, for example, marketing teams earlier and in a more integrated and iterative way. Evaluate your current team for that evolved level of strategic capabilities and collaboration. This diagnostic will highlight where you stand, then allow you to see the gaps to be filled either by a new hire or an effort to develop the competency gaps in-house.
Integrate and Adapt
The final part of the process is integration. As you embrace more data and both train and hire more data-fluent talent, be sure that these elements are woven into the fabric of the existing business and that you redefine what success in the role looks like. Since Data Maturity is an evolution, the organization and organizational behavior must transform as well. How do you help new executives integrate and feel connected? How do you as a leader create forums that allow for open dialogue and creative abrasion to fuel forward momentum? How do you collaborate and connect with cross-functional partners to create a large funnel of data points to feed the flywheel? How do you integrate this new way of thinking into the culture and the operating model? Integration of these concepts will be critical to success. Remember that culture it is all about behaviors – what behaviors have to evolve in order to create permission to operate differently?
Some companies are already taking these steps forward. We partnered with an executive of a big box retailer to help him better understand the competencies of his existing team as individuals and thematically. We found the group overall scored low on strategic thinking and collaboration. Having spent much of their careers in the category, they tended to rely on their experience over analytics. They were hesitant to work cross functionally. Many blamed lack of systems or data availability. But when we evaluated the team, we could see that they overall weren’t thinking about their categories in a strategic way.
Given the competency gaps, we supported the SVP in hiring a new archetype of merchant who came from management consulting. She was new to the category and had no gut instinct to rely on. But she was very analytical and comfortable navigating ambiguity. She has since become the voice around the merchandising table to challenge the status quo and teach her peers to analyse their categories in new ways that involve working more closely with other teams like ecommerce, supply chain and marketing. The SVP is hoping to hire 1-2 more of this type of merchant to continue to move the company up the Data Maturity Curve.
We are in an age when all skills must be brought to the table in order to serve the new wave of customers. As the new world of retailing takes shape, merchants will find their mandate is a blend of old and new, art and science, gut and data. In each matchup, neither will eclipse the other. Nor can one be successful alone. The future of Retail will be a team effort. Is your team ready to play?