Retailers face the ongoing challenge of “rolling with the punches” during the best of times — but when you add in something like a global pandemic, the ability to predict and adapt becomes even more make-or-break in terms of performance. And, how are retailers equipping themselves with the relevant, up-to-date information they need to make confident decisions in a notoriously fast-paced sales landscape? The answer lies in their approach to retail analytics.
Here’s more on why retail analytics really matter for brands of all sizes, channels and segments.
Understanding Every Stage of the Customer Journey
At the heart of retail success is understanding the customer. The upside of more data being available — especially outside point-of-sale systems alone — is that there are more insights available about customer preferences and behaviors. The challenge becomes, then, turning all this available data into insights that companies can act on to boost conversions and strengthen relationships.
As Harvard Business Review notes, gaining an in-depth understanding of buyer behavior means delving into anything that provides insight into how people relate to your company and its product offerings. A few key examples (among many) include:
- Which marketing messages are reaching and driving desired actions among which specific customer segments?
- How are customers’ expectations changing in regard to delivery and fulfillment?
- How are shoppers’ buying patterns changing, how can your brand adjust accordingly?
Consider all potential (ethical) data collection points throughout the entire funnel and which questions can be answered by analyzing said data — then which decisions you can enact as a result.
Making Data-Driven Merchandising and Product Decisions
Making self-service, AI-driven retail analytics available to employees throughout the company provides unprecedented insight into all things product: inventory, cost, recalls/returns, sales, etc. On a broader level, this helps workers understand patterns in production, storage, fulfillment and sales. On a more specific level, workers can drill down to glean even SKU-level insights.
Commerce giant Walmart is one company currently using retail analytics platform Thought Spot to give a wide range of decision-makers — including executives, finance specialists, merchandisers and ecommerce team members — access to tens of billions of rows of data about product performance with the goal of improving dynamic pricing, markdowns and inventory movement.
It’s worth asking what product data is available for analysis, as well as which users have access — and how long it takes them to get the information they need to drive decisions. Legacy analytics systems based on report generation by centralized data teams fall short in terms of democratizing data, or making it widely accessible across an org.
Some key factors for retailers to consider on the path to democratizing data include:
- How to make data tools accessible and data insights understandable for those in non-specialized roles.
- How to foster a data-savvy company culture.
- How to mitigate risks through strong governance of data.
- How to turn increased data accessibility into measurable business outcomes via decision-making.
Adapting to an Uncertain Retail Landscape
Look no farther than the global coronavirus pandemic and its effect on economies to find an example of an uncertain retail landscape.
Particularly where global supply chains are concerned, disruption is a distinct possibility. According to research from Gartner, more than three-fourths (76 percent) of supply chain execs say their companies face more disruptions today than they did even three years ago. This makes real-time analytics a must for understand the present landscape as well as making data-driven predictions into the short and long terms.
Retail analytics matter because they hold the key to understanding people, products and external circumstances — and getting actionable data into the hands of more decision-makers who can drive performance with these findings.