A Case for Using Shopper Traffic in Apparel Stores
Apparel stores are a key retail segment. Over the past several years, apparel stores have been losing sales to online retailers such as Zappos, venus.com, boohoo.com, rosegal.com, and others. Standard key performance indicators (KPI’s) that apparel stores use includes: sales (in dollars), transactions, average ticket (total sales/ total transactions), units per sale (UPS) and comparisons to current budget and last year’s performance on the KPI’s.
These KPI’s are based on “closed” sales or actual transactions. The KPI’s do not provide information on potential sales from shoppers that entered the store and did not make a purchase. Shoppers who enter the store and do not buy are missed opportunities. The shopper may not have purchased for various reasons, such as:
- Style or size of blouse not present in the store (out of stock or poor merchandising)
- Clothes do not fit well (manufacturing or sizing problem)
- Can’t find the items sought (poor signage or in-store merchandising)
- No one to answer questions or direct customer to item sought (poor in-store service)
- Check out time too long (poor in-store service, lack of staff)
A potential customer (shopper) who does not make a sale is a non-performing “asset.” The shopper came to the apparel store to shop and make a purchase, but she decided not to buy. The store missed the opportunity to convert a shopper into a sale ($).
Shopper traffic is a key metric that provides insight into the actual number of shoppers entering and exiting an apparel store. Shopper counting devices or sensors placed above the doors of an apparel store provide the actual counts of shoppers – 24/7/365. CountBOX provides shopper counting devices for apparel stores. The shopper counts are time stamped. Therefore, a distribution of the number of shoppers by time period can be developed for the apparel store. A store manager may determine the number of shoppers entering the store by half hour, hour, “4-hour shift” or the total day. Shopper traffic measurement allows for the determination of “peak traffic hours” within a week. With a 72 “open” hour week for the average mall apparel store, each “open” hour in the week accounts for 1.4% of the total traffic for the week. A “peak traffic hour” could be defined as an hour that has 1.8% or more of the total traffic within a week. More staff must be scheduled to “cover” this increased shopping demand.
Peak Shopping Hour = % of weekly traffic >= 1.8% in a 72 hour week
When the shopper traffic data is merged with point of sale (POS) data, other key metrics are calculated. One key metric is conversion rate or total transactions divided by shopper traffic:
Conversion Rate = Total Transactions
Conversion rate provides the percentage of shoppers that actually make a sale. Conversely, the percentage of customers that do not buy is (1 – conversion rate). These are the customers that the apparel store needs to convert to improve its sales. For a specialty apparel store, the conversion rate ranges from 15% to 30%. Therefore, between 70% and 85% of shoppers in an apparel store do not make a purchase. Knowing the conversion rate allows the apparel chain the opportunity to do root cause analyses into why a large proportion shoppers do not purchase. Alleviating the causes of not purchasing will increase conversion and consequently sales.
In apparel stores, staffing levels have a direct impact on sales. Conversion rate tends to vary based on the amount of traffic in the store and the number of staff members that are present to serve them. Another key traffic-based metric can be calculated – the shoppers to staff ratio:
Shoppers to Staff = Number of shoppers in the store
Staff present to serve shoppers
The shoppers to staff ratio may be calculated for different time periods, but the most useful measure is at an hourly level. In general, the lower the shopper to staff ratio, the more likely that shoppers will be converted to customers (sales) as the staff will be more “present” to the customers.
Measuring shopper traffic allows the apparel store to determine what the value of each shopper is to the store by calculating the dollars per shopper metric.
Dollars per Shopper = Total Sales
Total Shopper Traffic
The dollars per shopper metric is calculated on a time period such as a day, week, month or year. High “dollars per shopper” reflects high conversion or high average ticket, while low “dollars per shopper” reflects low conversion and/or low average ticket.
The apparel store manager has the ability to increase sales by training store staff to be attentive to customers to increase conversion rates. In addition, training may be accomplished to increase the average ticket by increasing the number of items per transaction (increase UPS) or by upselling customers to higher priced merchandise to drive up the average ticket (dollars per transaction). Such training should also increase the dollars per shopper metric for the store. Apparel chain management may also choose to provide incentives to managers and store staff members to reach goals based on the shopper traffic based metrics.
Using the shopper traffic distribution for the week, peak traffic hours and the shoppers per staff ratio, the apparel store manager is able to efficiently schedule associates to meet the demand of shoppers. Historically, Tuesdays and Wednesdays are low traffic days. Therefore, the store manager needs only to schedule minimal staff to cover the traffic. Weekends (Friday, Saturday and Sunday) tend to be the largest traffic days for mall apparel stores. Apparel store managers should schedule more staff on these days. Saturday and Sunday tend to have the most “peak shopping hours” for apparel stores within the week. Apparel store managers may need to schedule additional staff to cover these peaks in shopper traffic and potential sales.
These shopper traffic metrics can be extended to different areas or departments in the apparel store by using WIFI store traffic solutions. Shoppers can be “tracked” throughout the store using WIFI signals sent by their cell phones and other WIFI devices that they may have with them in the store. This enhanced tracking provides the apparel store with additional opportunities to create more refined metrics to improve store performance. For instance, area shopper traffic, peak shopping hours and conversion rate could be calculated for a specific area or department such as “shoes,” or “children’s apparel.” Heat maps can also be produced to show high and low traffic areas within the store.
A further extension is using demographic profiling analysis to determine demographic profiles of the shoppers within apparel stores. The demographic profile data is obtained by strategically placing sensors within apparel stores to capture shoppers’ faces. Then using facial recognition software and aggregating across shoppers a demographic profile is created. The profile would include gender, age, race and mood. The profile data could be used by the apparel chain to tailor merchandise assortments, “sales,” coupons or other marketing promotions to drive traffic and increase conversion in the store.
The CountBOX reporting dashboard provides reports for the standard traffic metrics used by apparel store managers and their superiors. These metrics help the store managers and their leadership to improve store operations and create additional revenue at the stores. CountBOX provides consulting services to clients to help them in obtaining the most value from the CountBOX retail intelligence and analytics solution.