Priority use cases for Analytics Automation in Retail

“We have seen real lumpiness from the global supply chain that has led to some sort of shortages, and more so just unevenness. It’s been difficult to plan inventory flow with much precision. We do not expect those conditions to change anytime soon, so it’s really on us to find ways of mitigating that.”

This experience described by Nordstrom CEO Erik Nordstrom in August of 2021 encapsulates the ongoing planning and execution challenges facing all retailers today. As he implies, a return to normal, or pre-pandemic conditions is unlikely, necessitating action now to adapt.

Before the pandemic, the retail apocalypse had many brick and mortar retailers closing stores at escalating rates as they struggled to compete for omnichannel shoppers. Pure play digital natives and large market leaders had the upper hand in terms of growth.

As COVID and lockdowns emerged in early 2020, panic buying overtook consumers, making it almost impossible to get an accurate read on demand. This was followed immediately by a shift in consumer preferences for zero touch shopping via pick up, curbside delivery and e-commerce, leaving many retailers unable to pivot. I saw this up close in my role at Salesforce with some retailers forced into furloughing workers and bankruptcy.

Since then, no retailer has been spared challenges. Even as activist investors promote the spin off of e-commerce from traditional retailers in the spirit of higher valuations, ongoing supply disruptions combined with the high cost of shipping puts a damper on any retailer’s growth and margin objectives.

Recall that Walmart, Target, The Home Depot and others famously commandeered their own fleets of ships to mitigate supply chain disruption. Likely, these companies would rather not have taken such drastic and expensive actions, which very few retailers can afford to mirror.

Prioritization is challenging, especially when lacking resources to compete toe-to-toe with market leaders. For a good portion of retailers Deloitte deemed “laggards” in their 2022 retail industry outlook survey, this is apparent:

The left hand chart demonstrates an inability to invest consistently with strategic priorities. The retailers in this chart Deloitte deems laggards, who you expect lack the resources of those considered leaders, shown on the right. In contrast, leaders report able to fully align their investment plans with strategy.

No matter the cohort, retailers of all varieties struggle with this problem. Market leaders have the experience, expertise, and resources to advance on many fronts. Most do not and are forced to make tradeoffs. Both face immediate opportunities to improve business outcomes with advanced analytics applied at scale. The question is how to proceed given the skills and experience of your analysts, data scientists and data-driven managers - essentially, the maturity of analytics in your company.

Results like these reported by McKinsey in October of 2021 are directionally accurate for retailers in any segment. Those that achieve these outcomes, however, are the exception. Advanced analytics like AI, applied at scale, requires uncommon levels of expertise, resourcing, technology architecture and most importantly, executive leadership: “only 24% of the retail and brand leaders we surveyed would define their company as data-led,” according to Forrester’s Top Retail Tech Initiatives for 2021 survey results.

This is apparent when looking at the forms of analytic methods applied to a variety of use cases reported by retailers in the survey results shown below. Generally, the more predictive and prescriptive, the greater value potential.

This data reported by Consumer Goods Technology as part of an annual survey, demonstrates that most retailers are not yet applying analytic methods like AI to use cases that also happen to align with many of the key priorities reported by retail executives in Deloitte’s survey.

Namely the 2 percent margin improvement potential reported by McKinsey for demand forecasting and inventory optimization, that each supports priorities such as modernizing the supply chain and resetting physical stores for omnichannel.

Considering that Deloitte also found it alarming “that nearly half of executives expect a shortage in skilled workers for IT and analytics positions—needed roles that require greater investment and will likely be the foundation of digitally enabled retail,” there should be a lot of demand for solutions that do more with less and upskills workers of varying backgrounds to participate in data driven improvement. To that end, the Alteryx Analytics Cloud presents a path to investing in supporting strategic priorities, even with fewer resources than market leaders.

For your business, consider the outcomes possible with use cases that support your strategic priorities with Analytics Automation. Offered in the Cloud and covering the full range of steps and roles involved in taking a use case from inception to production, the Alteryx Analytics Cloud supports any retail analytic use case that benefits from improved time to insight, access to external data sources, or more advanced analytic methods like predictions and prescriptions.