The Retail and Consumer Landscape may Change, but our Purpose as Retailers Does not

By: Sean MacCarthy, Executive Director Global Analytics and Store Segmentation, Claire's Inc

Sean MacCarthy, Executive Director Global Analytics and Store Segmentation, Claire's Inc

Over the past few months, the COVID-19 pandemic has had an immense impact across the global economy; no industry sector has been untouched for better or worse. In Retail, it has accelerated the need not only to be digitally available to our customers but also to detect changes in customer behavior more quickly than many in the industry were used to. The underlying technology, data, and organizational and people skillsets necessary to “tune” our companies to the rapidly evolving consumer behaviors that are a result of the pandemic, are actually the same as we ought to have been pursuing anyway.

As our customers’ lives have become further centered around their digital devices and assets, they have become much more purposeful in where they spend their time and attention, and the brands that are not quick to pick up and ultimately predict where this is will only meet their customers like a broken clock meets the correct time twice a day.

Across the technology, data, and skillsets, I will stick to more general platitudes as the underlying tools, languages, and platforms are constantly evolving.

For technology, the most important piece is to have a platform that is flexible and robust enough to handle all of your important 1st and 3rd party data in a way that will allow your tools and data scientists to take advantage of the innumerable relationships between all of the features these data sets may have. It must be flexible in that it can scale with your companies needs from a pure storage and ingestion capability. It must be robust enough to handle computational needs, varied ingestion sources, and, with the increased regulatory need and ethical duty to properly handle consumer privacy, provide clear data lineages. Any attempt to build such a platform needs to understand minimally: current needs of the business, planned future needs, and at least an idea of future “dreams.” What I mean by future “dreams” is to ask your data science leaders, your strategy teams, your innovation groups, “Despite not having any formal plans or budget dollars assigned at this point, over the next 5 years, what are some projects you are not doing that you think would be beneficial to the company based on where you currently see our customers headed and what would we want to ensure we have in our platform to address these as we built out/expand?”

"Our world has rapidly changed in the last six months, but Retail, at its core, has not"

For data, the point is concise: clean, governed data. The actual creation and maintenance of healthy datasets is the least glamorous but most important part of any ability to digitally understand and serve our customers where they want to be. That means robust processes internally to maintain and support 1st and 3rd party data. And further, maintenance of the metadata layers, dictionaries, and maps that help us understand when those data sets may or may not be applicable to various systems, models, analyses, etc. being utilized to help better serve and understand our customers. The investment in process, culture, and time is not to be underestimated here. These things need to be a part of a continuous process that lives beyond any initial setups of tools and systems.

For skillsets, there are two levels: organizational and individual. Organizational skillsets are born of the company culture and ought to encompass: curiosity, love of customer, competency of KPI’s and their influencers relative to operational ownership, and shared/shareable knowledge and insight. All of these can be hired for, but for the latter two, it is important for the analytics/insights functions to provide the education to ensure peers and colleagues understand the why and how of the analytics/insights teams’ work. And then, further, for the analytics/insights functions to work with and understand the operational worlds well enough to codify and automate the knowledge and insights that well-honed experience naturally identifies. Employee churn is inevitable, and this last point ensures the business itself does not suffer an extreme loss of productivity or capability as these natural churn cycles occur.

On the individual level of skillsets, especially as it concerns those who fulfill the data and analytic functions, from the engineers to the data scientists, the first two above still absolutely apply: curiosity and love of the customer. Specific technical skills depend on your platform choices. What I would add, and it leans technical and specific, is finding people who can or can quickly learn toscript. People who have a good grounding in either formal logic or mathematics, have played around in languages like Python, allow for a flexibility to accomplish things, especially when it comes to going out and exploring the vast oceans of public, third party, social media data that are out there. This is where I believe we will be able to better marry what we know thru 1st party data of our customers, which is where she is and sometimes where she is going, to where she aspires to be, and thus where we can guide our strategies to meet her. This same skillset also enables two other capabilities: to peekin on what our competition may be doing and where they may be going, and to start wading into the water of Robotic Process Automation without necessarily the cost of third-party tools.

Our world has rapidly changed in the last six months, but Retail, at its core, has not. Our jobs are to ensure that when she is out there expressing a need that we are ready to meet her, as best as possible, on her terms, where she is or wants to be. The tools to do this are the same now as they were before as well, but the speed with which companies need to mature with those tools and their related processes and culture has no doubt increased.