How to Compare Stores KPIs?
Retailers have 3 Key Performance Indicators (KPIs) to compare physical stores – Comp Sales, Sales per Square Foot, and Sales Conversion.
Here is a step-by-step process of how to compare stores KPIs:
What’s Key Performance Indicators (KPIs)?
Key Performance Indicator (KPI) measures the ability to achieve a key business objective. Retailers use a variety of KPIs to measure store performance, inventory turnover, and financial success.
To compare performance of physical stores, we use 3 KPIs:
1. Comp Sales
Comp Sales refers to Year-Over-Year Sales of the same store. Omnichannel and Unified Commerce strategies create a challenge in evaluating stores.
With programs such as Click & Pick, physical stores became distribution centers. As a result, COMP SALES is not a clear metric of performance.
2. Sales for Square Foot
Sales Per Square Foot (SPF) is the average revenue the store generates for ever square foot of sales floor. SPF is a core KPI for InStore Analytic. But it is not always represent performance of the store as a single holistic entity.
3. Sales Conversion
Sales Conversion is the ration between buyers and browsers. It represents the ability of the store to translate sales opportunities into revenue. With Average Order Value (AOV), Sales Conversion is the best KPI to quantify store performance.
But store performance starts with demand. In other words, for the physical store to perform first it needs traffic.
Therefore, our first step is to know how many people visited the store.
Location Analytics: Why Care About Footfall Traffic?
Footfall Traffic is the metric that measures sales opportunities. It counts the number of people entering the store, per period of time.
Quantifying traffic is more complex than it sounds. The quality of data starts with the People Tracking Technology. And the way the analytics works.
Learn more in FREE Master Course “How to Increase ROI from People Tracking”.
Here’s a brief summary of why you must count traffic:
Location Analytics evaluates demand.
Mall Analytics reflect the relationship between Shopping Centers and Retail Stores.
Sales Conversion should not be set as an arbitrary target by Headquarters.
Static Targets do not reflect local conditions.
But comparing store performance is a must for corporate managers.
Here’s how to identify the Target Traffic & Sale Conversion:
9 Step-by-Step Formula to Compare Stores KPI
In this step-by-step process, we segment physical stores. We identify laggards who require change. And we pinpoint best practices of winning physical stores.
1. Define Business Objectives
Many retailers use KPIs to calculated compensation and bonuses for store managers. A common practice ranks stores from top-to-bottom. But those lists tend to ignore local context.
A bad habit is using Averages as Target Rates.
Average Sales Conversion may sound great, but it is not practical. Nor does it capture the distribution of behaviors.
A better way is to segment stores and target outliers. The business objectives answer the questions – how do we define a “winning” store? And how do we align staff incentives to performance?
2. Select Criteria by KPI
Comp Sales is the most often used KPI. Other metrics include Inventory Turnover, Average Order Value, and Sales Conversion.
But these metrics do not stand alone; they should be defined in context.
Traffic and Sales Conversion tend to have an inverse correlation.
When Traffic increases, Sales Conversion goes down, and vice versa.
The reasons come from Behavioral Economics. When Service Intensity is low (salespeople to customers ration), the better customer service gets more revenue. At the same time, there is a diminishing margin to adding salespeople.
The short version –
To compare Sales Conversion, you must control for traffic levels.
3. Categorize by Demand
What’s Demand? How to quantify the Sales Opportunity for Physical Stores?
The number of people entering the store provides a clear count of demand. A store averaging 3,000 weekly visitors does not operate the same as a store with 500 visitors.
Retailers often have multiple brand formats and customer segments. By creating Demand Bands, we negate the impact of Traffic on Sales Conversion.
With Demand Bands, we clarify the line between store operations and corporate marketing.
Marketing’s responsibility is to bring visitors to the store.
Store Operation is tasked with transforming visitors to buyers.
4. Classify by Demographics
Demographics play a core factor in segmenting stores.
Urban, suburban or rural environments have different group behaviors. Income levels impact the sensitivity to price and premium brands. A store can be surrounded by competitors or standalone.
With Store Location and Demographics, our store segments will have feasible KPIs. A better Target KPI improves the alignment between staff compensation and store performance.
To classify stores by segments, use Multiple Target KPIs.
5. Sort by Store Type
Stores should also be evaluated against similar stores. A core concept is Destination Store. Another is Mall Stores. This feature represents the Customer’s Intent to Buy.
In general, there are 4 types of stores:
Standalone Destination Stores: big stores are usually Destination Stores. Examples include Walmart, Costco, and Home Depot. In such stores, a customer visits the store with high intent to buy.
Standalone High Street Stores: stores in high street or in plazas are usually standalone. Most are boutique style. Standalone stores tend to have the highest Sales Conversion Rates in the chain.
Mall stores with 1 entrance: most mall stores do not have the destination factor. Thus they are subjected to the rigors of sales conversion and location analytics.
Mall stores with 2 or more entrances: When a mall store has at least one door to the parking lot, it impacts traffic. The footfall counts include people who crossed-over from the parking lot to the mall. Those people have no intent to visit the store and are passing by. This behavior lowers the conversion rate. It is also harder for us to judge the value of marketing on demand.
Retailers can have a wide variety of Conversion Rates. Standalones are at 50% to 60% Conversions. Mall Stores with single entrance are around 30%. And Anchor Mall Stores may have 10% to 20% Conversion due to parking lots traffic.
6. Adjust by Time Period
Data consistency is critical in comparisons. Moreover, Time Period create context to the analytics.
In general, store reporting compares week-over-week. But in physical stores, each day of the week has its own quirks. And yet, store managers require daily updates. Best practices, therefore, is a daily report that compares activities also to the previous week.
In Year-Over-Year reports, Time Period should be taken in context.
It may be straight forward to compare Week #29 to the previous year Week #29, but holidays and special events tend to bias data.
In Self-Service Analytics, end users can align time periods by as needed. This is welcomed. But analytics for the sake of corporate and workforce comparison, requires attention to comparison consistency.
7. Adapt for Seasonality
Holidays are a retail fantasy. Shopping becomes a frenzy affair of culture and marketing. To exclude the bias of holidays, you need context of dates. The best practice is Year-Over-Year Comparison.
Typical errors occur when holidays shift dates.
In 2008, Easter Sunday was on March 23. In 2019, the holiday was celebrated three weeks later on April 12.
And a holiday can impact 2 to 6 weeks of revenue before the actual date. Also, much depends on timing and scope of marketing campaigns. To compare holidays, start at the end date and work backwards.
Holidays have different weights of importance. Some retailers generate half of their revenue during Christmas Season. Jewelry stores thrive during Mother’s Day. Summer is especially important for Garden Products. And Back to School is important for retailers selling school supplies and socks.
And to complicate store comparison even more… we must speak about the weather.
8. Exclude Outlier Events
Remember Easter Sunday… the holiday can come during a snow storm or a heat wave.
In retail, weather plays a significant role; think selling green plants vs. cold medicines. Weather should also be taken in context. Spring in Florida is not spring in Chicago.
In the big blizzard of January 2011, stores in Northeast lost 40% to 50% of traffic. Some stayed close for days. A strict comparison distorts Year-Over-Year KPIs.
In such cases, retailers can do nothing. They can also change the reporting data to traffic trends. Either way, the data consistency process should be transparent.
9. Judge a Store against Itself
Better Analytics requires learning. In the first year after deploying People Tracking Solution, the focus is the baseline. For location analytics to be productive, store managers need to be familiar with traffic and sales conversion data.
To improve Sales Conversion, retailers can look to InStore Analytics.
To identify winners and laggards stores, we define baselines by segments. Sometime, just by identifying a laggard, we can see a quick Return on Investment.
Once we looked at the local store, we can focus on Peers Comparison. The last stage is to analyze the retail as a whole.
By judging a store against itself, we avoid Big Brother Syndrome. As a VP Stores once said to me, “the way you introduce a technology into the store is just as important as the technology itself”.
Bringing It All Together
Location Analytics allows retailers to better segment stores. We can clarify the action lines between Marketing and Sales Operations. And we can create Multiple Target KPIs.
Better store analytics create aligned compensation incentives. And happier frontline associates generate more sales.