Retail analytics provides insights into sales, customers and products to help merchants see more ways of increasing profits and growing their business.

Retail analytics can identify your best and worst customers. It can identify your best- and worst-selling products. It dissects transactions to show where sales lag and how they can be improved.

There are two important types of Retail Performance Metrics:

• Retail Sales Metrics: Measure the details of sales revenues and transactions.
• Retail Customer Metrics: Measure characteristics of customers, such as profile, value and segments.

Retail Sales Metrics

Retail Sales Metrics describe the revenues and transactions of sales. They help you understand past sales performance and predict future sales.

There are three important Retail Sales Metrics:

1. Retail Sales Transaction Metrics:

There are six metrics that describe a complete sales transaction:

• Channels: The sales channel where the transaction originated can include: store, e-commerce, dealer, country, region, as fits your business.
• Customers: The customer or account who ordered the transaction. Customers can be named or anonymous.
• Transactions: The sale of goods to the purchaser.
• Units: The specific product model and configuration, such as size and color. Often called Stock Keeping Unit (SKU).
• Quantity: The number of units sold in transactions.
• Unit Price: The sale price of each unit in transactions.

Multiplying the metrics together produces the amount (dollars) of the transaction.

It is important to monitor all six metrics to understand which ones contributed to an increase in sales revenue and which ones contributed to a decrease in revenue. If you do not look at all of them, you will not see the complete picture of what metrics contributed to your sales and what changes in the factors contributed to changes in your sales.

For example, maybe your sales revenue increased, but was caused by higher prices due to inflation. Or, although sales increased, the number of customers declined.

2. Retail Sales Revenue Ratios:

These revenue ratios are the total period sales revenues divided by each of the above Retail Sales Metrics. They show how each metric contributes to total sales revenue:

• Revenue per Channel: Shows how much revenue is collected through each channel (store, e-commerce, dealer, country, region). Use it to improve sales from the weaker channels. They are calculated for a specified time period such as week, month or year.
• Revenue per Customer: Shows how much revenue is generated by each customer. Usually shows about 80% of sales come from about 20% of customers. Use it to retain customers and grow smaller customers into bigger ones.
• Revenue per Transaction: Shows how much revenue is accounted on each transaction. Use this to develop marketing and sales strategies for incenting customers to purchase more products or higher priced products. Also called average purchase value.
• Revenue per Unit: Shows how much revenue is generated by each product (SKU). Use it to track response to marketing and product promotion activities. Shows whether a revenue increase is due to selling more expensive products or selling more products (see [Units/Transaction] below).
• Trend (Change) in Revenue Ratios: The total revenue for the period changes due to various factors. This change can be explained by changes in the Revenue Ratios. For example, revenue might be up. But the increase was due to increased prices when the number of transactions decreased or the volume of units sold decreased. Changes in the details of the Revenue Ratios show where each member affects sales . For example, show the changes in sales revenue from the top-10 customers or top-10 products.

3. Retail Sales Transaction Ratios:

There are six transaction ratios that multiply together to produce the total sales revenue for a specified period.

The fundamental retail sales revenue equation is:

Sales Revenue = Channels x Customers x Transactions x Units x Quantity x Price

The transaction ratio parts of the revenue equation are:

[Sales Revenue Amount] = [Channels] x [Customers per Channel] x [Transactions per Customer] x [Units per Transaction] x [Quantity per Unit] x [Amount per Quantity]

Where:

• Channels: The number of sales channels (store, e-commerce, dealer, country, region). Important to know where sales come from in today’s omni-channel market.
• Customers per Channel: The number of customers per channel. Shows the effectiveness of the sales funnel in each channel.
• Transactions per Customer: The number of purchases per customer. Shows how active the customers are as buyers. Often combined with [Units/Transaction] to show [Units/Customer].
• Units per Transaction: The number of products (SKUs) purchased per transaction. Indicates the effectiveness at point-of-sale of filling the shopping basket and moving products. Shows whether a revenue increase is due to selling more products or selling more expensive products (see [Revenue per Unit] above). Often combined with [Transactions/Customer] to show [Units/Customer], which measures the basket size per customer. Also called items per sale.
• Quantity per Unit: The number sold of unique products (SKUs). It is a broad measure of inventory movement.
• Amount per Quantity: How much revenue per total number of units sold. Shows what inventory is generating more or less revenue. It is as a weighted average “unit price” for all the transaction line items.

Retail Customer Metrics

Retail Customer Metrics describe the customers as a whole, in groups, and individually. Maybe you know all customers by name, maybe customers are anonymous.

Customer segments are based on transactional behavior, product preferences, value and other customer characteristics.

Each customer segment can be given a “persona” that is defined by some common characteristics. Marketing campaigns can target the needs of the persona of each segment.

Calculating a profile of your customers is important to understand their past behavior and predict their future behavior. Customer profiles are based on product sales preferences, and also on customer demographics and behaviors.

1. Customer Lifetime Value (CLV)

Customer Lifetime Value (CLV) measures how valuable a customer is to your company in a specified (or unlimited) time period.

There is no universally accepted formula for calculating CLV. The complexity of the CLV model increases for larger companies. For example, are customer losses and acquisitions included in the time period? Will you predict the lifespan of each customer? What are the costs to service the customers? Do all your products stay the same or change? How certain are those estimates?

There are two basic methods of calculating CLV:

• Based on Revenue: Sales revenue minus costs of products sold, and compared against customer acquisition cost to estimate the profit per customer. It is based on average values, so prone to inaccuracy for each customer. Suitable for smaller and simpler businesses.
• Based on Profit: Calculates a more complete measure of customer profit where activity-based costs are allocated to the activities related to customers to estimate the profit per customer. It balances complexity with accuracy. Suitable for larger and more complex businesses.

2. Recency, Frequency, Monetary Value

Recency, Frequency, Monetary Value (RFM) is a fairly simple metric for calculating CLV:

• Recency: How recently a customer made a purchase.
• Frequency: How often a customer made a purchase.
• Monetary Value: The total amount of money a customer spends on purchases actually in the past and estimated in the future.

RFM analysis groups each RFM metric into three to five categories and scores each customer in each metric. The overall RFM score for each customer is a blend of their three RFM scores. The analysis ranks the customers according to their score.

RFM analysis identifies the best, the middle, and the worst customers in terms of value to the company. Marketing can plan how to move customers in the lower ranks of each metric into higher ranks. Namely, make customers buy sooner, buy more often, and spend more each time. Next month, run the RFM analysis and see the changes in customer scores and their migrations.

The advantages of RFM are it can be simple, fast, and easy to use.

The disadvantages of RFM are it can be complex and require special skills to use and interpret. The averages and groupings can be very misleading.

We advise to start with a simple RFM model and process. See how well it helps you to increase your customers, profits and growth. Adjust your RFM model to improve its usefulness and your results.

How to Use Retail Analytics and the Retail Sales Metrics

You can use the above metrics to better understand your sales and customers for improving your profits and growth. We recommend the following basic plan for using retail analytics:

• Set a clear business objective: Do you want to increase profits or growth? Do you want better customers or more customers? Trying for one is much easier than trying for both at the same time. Use your business competitive advantage to support your objective. Deploy your resources accordingly to marketing, sales, operations, etc.
• Use data and analytics to augment your staff and decisions: Analytics should not replace human intuition. It should support your decision process by giving you new ideas, clarifying your plans, and reducing your risks.
• Monitor your progress: Retail analytics is a continuous process, not a one-time activity. Run the analytics again to check your progress and make changes as necessary.
• Learn from your problems and mistakes: Business is complicated, with many unknowns and much uncertainty. Understand why the outcomes did not follow your plans, and how to make better decisions, plans and actions in the future.