Welcome to Part 3 in our collection on using location data for holiday shopping!
In Part 1, we demonstrated how location data can be used to predict and evaluate visitation patterns during peak weeks of the season.
In Part 2, we looked at how customer segmentation and trade area data enable brands to get highly targeted in how and where they get holiday customers to their store.
Now, in Part 3, we’ll look into how to evaluate performance at the conclusion of the holiday shopping season.
In this article, we’ll cover:
- Comparing actual visitation patterns to expected visitation
- Cross-analyzing sales data with visitation numbers to assess conversion
- Zooming out to market-wide trends and visitation market share changes
Comparing Actual to Expected Visitation
After the holiday rush, there’s an opportunity to assess the foot traffic performance of each of your locations.
The key questions to ask here are:
- How close did the expected numbers track to the actual?
- If there are differences, what is the root cause of the difference?
In Part 1, we showed how businesses can use past years’ data to anticipate visitation volumes for this year.
This results in an expected visitation range compared to benchmarks, for example “We expect foot traffic to average 25% above benchmark levels from November 1-December 30th.”
At the conclusion of the season, this assumption can be analyzed by looking at the data in the Unacast Insights platform.
Let’s look at an example below.
Let’s say we want to evaluate 2022 performance against expectations for a given Target location. Based on analyzing past seasons (covered in Part 1 of this series), we might expect to average 25-35% above benchmarks.
However, in 2022 the number was closer to 11%.
This leads us to the second key question - based on this, what are the possible explanations for this deviation?
One explanation is that foot traffic leading into the season was higher - about 8-9% higher - which means the location may have actually done a better job acquiring customers throughout the year. The performance above benchmarks wasn’t as high because the starting point was already higher.
The team could also look at the effectiveness of its holiday strategies and marketing.
- Were there changes to the marketing budget?
- Was there an assumption or decision made that missed the mark?
While factors like broader economic health and industry trends factor into foot traffic, this simple tactic to compare actual to expected, then analyze the results provides a starting point for benchmarking season performance.
By understanding what works and what doesn’t, teams can continue to refine and build upon their strategies heading into the next holiday season.
Cross-Analyzing Sales Data with Visitation
Many companies will rightly judge their holiday performance by the revenue generated at a location. This is effective but it doesn’t tell the full story.
One other important metric to track post-holidays is conversion from foot traffic to sales.
Was that revenue generated on lower, similar, or higher foot traffic than previous years?
Depending on the answer to this question, there are different scenarios for the business to discuss:
- If the business generated higher revenue than prior years on lower or similar foot traffic, that means it had either higher conversion (percentage of visitors that made a purchase) or larger average ticket sizes.
- If the business generated lower or similar revenue to previous years with higher foot traffic, that means it had either lower conversion or smaller ticket sizes.
For the purpose of this article, let’s assume the ticket size (average spend per transaction) is similar from prior years to the current year.
The implications for business teams is thus:
- Scenario 1 above means that efforts to get customers in the door didn’t produce the expected results. Why did visitation not meet expectations? Was it economic conditions? Competitor share gain?
- Scenario 2 above means that there was revenue left on the table as a result of lower conversion - if it converted at prior year levels, revenue would have been higher given the increase in foot traffic. Why was conversion lower this year? Did we have the right promotions to convert customers, not just get them in the door?
Both scenarios represent an opportunity cost of lost revenue.
Location data provides the additional layer on top of revenue to understand how well a location did at capturing its total revenue opportunity, not just the revenue number itself.
Zooming Out to Market-Wide Trends
Internal data like holiday season revenue provides a measure for an individual store’s performance. Location data provides a measure for how the industry and competitors performed.
This unlocks insights to two key questions:
- How did your location’s holiday performance compare to the broader market?
- Did your location gain, maintain, or lose visitation market share during the season?
A location’s performance in the context of the market sheds light on whether a change in foot traffic is a result of broader industry trends or actions influenced by the location.
Let’s continue to use Target as an example. Among other groups, Target competes with big box retailers like Walmart, Dick’s Sporting Goods, Costco, and BJs.
Let’s say we’re working for Target’s market research team and are interested in the performance of this competitive set to benchmark our own performance during the holiday season.
Our analysis of Unacast visitation data for these brands in a specific market - Wake County, NC (home to Raleigh) - shows that total visitation to all locations for these brands decreased from 2021 to 2022.
This provides the backdrop for an analysis of Target’s performance in this market. Target locations in this county increased between 2021 and 2022.
This suggests that Target’s increase in visitation was not the result of a market-wide trend of more visitation to big box retailers. It was the result of Target outperforming their peer set in holiday customer acquisition.
The increase in foot traffic led to a noticeable increase in visitation market share within the big box retailer competitive set.
Looking again specifically at the holiday season - which we’ve defined as October 1 to December 31 - Target gained 3 percentage points of visitation share between 2021 and 2022.
This is an anomaly from past years where there was limited share shift between these competitors.
With Target generally attracting higher income shoppers, perhaps a healthy economy and low unemployment during that period meant more people found Target’s holiday value proposition compelling.
Zooming out to see the broader industry picture helps brands understand their performance in the context of industry performance while also maintaining visibility into the activity of key competitors.
All brands will measure their own sales post-holiday season as an indicator of success, but one of the key advantages of location data is the ability to see beyond a business’ location(s) and into their broader market.
How did a location perform in the context of their industry or competitive set? How did a location perform relative to the total revenue it could have generated given the customer visitation counts?
With these insights, brands can most effectively judge a location’s holiday performance and uncover previously hidden industry trends.