Although COVID-19 impacted U.S. businesses across every sector imaginable in some way, retail was undoubtedly one of the most affected industries. The effects of COVID-19 have shown what we already knew: Retailers need to be prepared for anything if they want to survive.
Last year, and even well into 2021, retailers suddenly saw their global supply chains disrupted — ultimately affecting the products they were able to stock. Many shoppers utilized e-commerce channels more than visiting brick-and-mortar stores, too. Order fulfillment was similarly affected, with many more consumers needing at-home delivery while complying with health orders.
Furthermore, customer demand suddenly shifted in many ways, reflecting buyers’ lifestyle changes and their possible economic uncertainty in the wake of closures and job loss.
All these factors, plus fluctuations in sales figures throughout the pandemic, demonstrate the need for enterprises to be harnessing data effectively. This means using it to drive sound decision-making, whether it’s a fairly routine day or during a period of major disruption.
Here’s more on the true power of retail analytics in understanding performance and driving desired outcomes, however chaotic the sales landscape becomes.
Using Data Analytics to Anticipate Demand
Forecasting is one of the leading ways retailers are leaning on data analytics to understand not only what customers have bought and are buying, but to predict what they will buy in the near future.
According to one expert for Towards Data Science, there are a few ways data analytics can help improve demand forecasting:
- Managing inventory: Ordering too much stock is a costly mistake in terms of storage expenses and potentially unsold merchandise. Ordering too little stock leads to shortages that can erode customer relationships and hamstring sales revenue. Using retail analytics, companies can explore product performance and customer behavior from all angles to drive more informed inventory decisions.
- Optimizing cash flow: Managing inventory well results in less cash being tied up in products sitting in warehouses, which enables investments toward more useful purposes.
- Getting prices right: Product pricing is the deciding factor for many customers. Getting this wrong can lead to missed conversions and missed opportunities to forge valuable customer relationships. As well as helping companies understand the nuances of demand, analytics fuels a data-driven product pricing strategy.
- Improving customer service: Every customer service team’s nightmare is dealing with a slew of customers unhappy about pricing or product availability, right? Having instant access to demand-related insights can empower customer service teams to better understand and streamline the customer experience before problems occur.
Harnessing Data Analytics to Boost Customer Engagement
Customer analytics are also central to retailers today as they try to attract new customers and retain their existing ones in the face of mounting competition throughout the industry.
There are many metrics available pertaining to customer demographics and behavior, but here are a few ways retailers are applying analytics in this area:
- Customer segmentation: How are highly specific subsets of your customer base behaving? Which marketing communications, product offers and rewards motivate each subset of shoppers?
- Wallet share: Which customers are mostly likely to spend more and on which products?
- Customer churn: Which customers are leaving your store? At what rates are they leaving? What is the reason for this exodus? How can your company intervene to keep these customers?
The true power of retail analytics lies in being able to anticipate demand and understand customer behaviors at a granular level. These two broad uses for analytics tools can help retailers make smarter decisions pertaining to products and people alike — something more crucial than ever in our often uncertain market.
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