In every product category, retailers face a common challenge: demand for your business fluctuates. Your sales performance (and operational strain) changes with the time, day, season, and more. Perhaps you have a morning rush as people grab food on their way to work. A mid-morning lull before people run errands during lunch. A surge right after school gets out. A final peak in demand as people finish up work.
The rhythm of your operations may be relatively consistent and predictable, but it’s also difficult to manage and staff for. And while historical sales data can help you recognize and plan for patterns in demand, seasonal trends and localized commerce activity can make it difficult to reliably predict these patterns before you’ve committed to a location.
Demand is always changing, and those changes have a significant impact on your daily operations, expenses, sales, and the customer experience. Thankfully, today’s retailers have more tools at their disposal to prepare for the problem of variable demand—before even selecting a location—and more options to respond to consumer behavior.
Predicting variable demand before and after choosing a location
Some retailers have detailed sales data that enables them to understand the days and times where they see the greatest volume of sales. With the right analytics tools, they can aggregate this information and then schedule staff, prepare inventory, and plan services around the peaks and dips in sales. But not every retailer has granular sales data or the tools they need to analyze it in meaningful ways. And sales data alone can only give you a limited picture of demand for your business at a given location.
Most retailers use some form of site selection software to find and prioritize the best locations. But more advanced solutions, like Tango Predictive Analytics, can help create a picture of demand over time at a given location.
Combining intuitive GIS mapping capabilities with mobile movement data from Near Intelligence and traffic flow data from INRIX, Tango lets you examine how foot and vehicle traffic changes throughout the day, week, or month, giving you more precise insights into how and when demand moves within your trade area. Add geofencing to the mix, and you can track anonymized information about the people who pass within a specific radius of your location.
You can do this analysis before you even commit to a location. So you’ll know if a location is wildly popular in the summer and dead in the winter. And you’ll be far better equipped to evaluate the scale and opportunity of the morning rush—especially when you work with our experts to create accurate site models and sales forecasts.
With established locations, Tango Predictive Analytics can help you see whether your location is adequately equipped to tap into the nearby demand. You don’t just want to prepare for times when sales are low or high—you want to see what you might be missing, too. Maybe there’s not enough parking, or navigating your lot is difficult, so most of the relevant demand simply passes you by. Perhaps alternative pickup options could improve accessibility, making your sales peaks higher and valleys lower. Using site models based on your portfolio, you can also explore how changes to your site (like adding a new drive-thru lane or expanding a department) will affect your ability to tap into nearby demand.
In recent years, retailers have also learned that historical patterns in demand can shift quickly. Major changes to a trade area or shifts in consumer behavior can render older data irrelevant. COVID-19 restrictions and public health concerns created major disruptions across the globe, but even much smaller, localized disruptions—like a nearby construction site—could create a “seasonal shift” in the flow of demand. In these situations, recent historical data becomes far more relevant than the patterns you’ve seen in years prior.
But recognizing patterns in variable demand is also only part of the challenge. Retailers also need a strategy for responding to the ups and downs of daily commerce activity. Some might argue that retailers should simply increase their supply with more employees, better pay (to attract better workers), or better equipment and facilities. But supply isn’t a simple valve you can infinitely crank open. There will always be an upper limit. And most retailers are still exploring how they want to address that.
Redistributing demand with disincentives
While some businesses (most famously Uber) have aimed to control demand by driving up prices during peak hours, that strategy may not have the same effect for retailers, who tend to have more competition and a different relationship with consumers.
Some have argued that strategies like surge pricing help redistribute demand to different times of day (when prices are “normal”), thus allowing the supply to keep up. But when consumers don’t need a good or service and can make other choices (like choosing a competitor, using alternative transportation, or making meals at home), practices like surge pricing could essentially train customers to simply make those other choices on a regular basis. It’s taking the gamble that the friction of making those alternative choices will be greater than the friction of paying the surge price. For some people, it will be. And for others, it won’t. Whether that pans out on a given day depends on the brand and the demographics they cater to.
That surge pricing can also change consumer perception of your brand. To the consumer, surge pricing is just a euphemism for price gouging. And if your surge price is the price they regularly encounter (because they’re only in the market for your category at times when demand is high), it effectively becomes the price of your products. Some of these customers will simply perceive your product as too expensive for them, which could be pretty problematic for businesses that:
- Depend on repeat customers
- Have a large number of competitors
- Sell relatively commoditized products
- Are expected to have low priced goods
Unless surge pricing becomes the norm for your entire industry, it’s a risky play. But if you’re considering it, then it’s crucial that you test it in the right locations, first. Analyze each store’s surrounding demographics, purchasing behavior, movement data, and concentration of competitors—so you can redistribute demand without harming overall performance.
A different response to variable demand
Consumers already experience greater friction during peak demand—service is slower and more disruptive, and some products may be unavailable—which disincentivizes purchases. Rather than further disincentivizing transactions during peak demand by raising prices, retailers could use the opposite demand redistribution strategy: incentivizing purchases during off-demand hours. This could come in the form of discounts, add-ons, bundles, or special items or services that are only available when demand is low.
A strong enough incentive with significant awareness can entice customers to visit during off-peak hours, helping you redistribute demand without losing transactions. Consumers are encouraged to purchase what they want at another time, rather than discouraged from purchasing at the time they prefer.
Variable demand isn’t going away, and every retailer should consider how they can implement a strategy to promote buying behavior during hours where demand is low. As you experiment with various incentives, you can increase or decrease them until the demand becomes more manageable.
Understand and react to variable demand with Tango
In order to effectively, you need the ability to predict patterns in demand at your locations, not only with sales data, but also local consumer behavior. And that’s where we come in. Retailers all over the world use our site selection software, Tango Predictive Analytics, to analyze variable demand using datasets like vehicle traffic, foot traffic, and sales figures.
This analysis helps retailers choose locations with the greatest relevant demand, and prepare to meet that demand. Want to see what Tango can do for you?
Request a demo today.
