retail metrics

Today, retailers have vast amounts of customer and business data, which they can use to drive more targeted, effective business decisions. But having data is one thing and knowing how to filter and use that data is a whole other ability. 

In many of our earlier blogs on ecommerce and retail analytics, we have spoken about the key best practices any business needs to follow to build a data-driven business; a centralised data source, clearly laid out business objectives, data visualisations, data security, and of course consent to use all this data. 

In fact, there are several best practices that the industry follows to make the most optimal and effective use of terabytes of customer data. One among them is retail KPI analytics. 

What is Retail KPI Analysis? 

Simply put, retail KPI (Key Performance Indicator) analytics is the process of collating and using data to quantitatively evaluate the various aspects of a retail business, like inventory levels, PoS transactions, online traffic, consumer demographics and so on.  

Key KPIs that retail businesses typically measure or track are inventory turnover, sales per square foot, conversion rate, customer acquisition cost, and such. 

Specifically in the UK and Europe, there has been a steady increase in the adoption of KPI analytics. A report by Retail Week and Fujitsu shows that 66% of UK retailers use data analytics in day-to-day business, and 57% of UK retailers are investing in predictive analytics to boost sales and improve customer engagement.  

In Europe, a McKinsey report shows that retailers are increasingly using data analytics to drive better promotions, manage inventory, and optimise pricing.  

A Merit expert says, “One could argue that the demand for data analytics in the UK and Europe has risen for two reasons; the need to compete with retail giants like Amazon, which use data analytics to deliver highly personalised consumer experiences and, the ease of data usage that comes with the adoption of technologies like AI, machine learning and cloud.” 

A Case study on retail KPIs from a global home needs company 

Let’s look at a case study of how a global retail giant adopted retail KPI analytics into its daily operations. 

The problem  

A global home needs company with a revenue of over USD 4 billion was relying on legacy systems to monitor and report weekly export, sales and revenue data. There were multiple sources of truth, the data security was lax, and its reporting process lacked visualisations.  

The solution  

To bring more process and efficiency in the entire sales process, the company partnered with Tableau and Azure to build a comprehensive data intelligence solution.  

Using SQL Databases and Data Warehouses, the retail giant deployed data at scale and with adaptability, simultaneously allowing IT teams to govern the same data and ensure it remains secure. 

The Result 

Its system security strengthened and improved, it was able to complete its weekend reporting in minutes (a process that took all day and a ton of manual reporting), its daily sales reports and product performance reports helped teams make more informed decisions, and overall, the retail giant was able to focus more on growing its business, rather than spending most of its resources on interpreting and reporting data. 

There are a number of metrics retail businesses can track based on their business objectives. Some fundamental ones that need to be on the list are; 

  1. Sales Tracking 

This is the most basic metric that retailers need to track. How much sales are you earning every day, every week, every month? During what time of the day, week or year are sales at their peak? Where are most of the buyers coming from? Which products are recording higher sales?  

These metrics will help retailers tweak their sales campaigns, manage on-the-floor resources, plan inventory better and ultimately boost profts. 

  1. Gross Margins 

This metric helps retailers track the cost of goods they own, and the revenue generated from the sale of these goods.  

Tracking gross margins will help retailers make key decisions around which products to keep and dispose of, come up with better pricing strategies, and assess the profitability of the goods they are stocking and selling. 

  1. Inventory Turnover:  

This helps retailers identify which products are moving fast or slow, and how much revenue they are generating from each of these products. This will help them plan which products to stock, and which to remove, design strategies to dispose of low-demand products, and so on. 

  1. Customer Acquisition Cost (CAC) 

Like the name suggests, it is the cost of acquiring a new customer through marketing and advertising strategies. CAC tells retail marketers how to improve their campaigns to reduce CAC costs and improve customer experience. 

  1. Conversion Rate 

This metric measures how many customers convert into buyers when they visit a store or online site.  

Tracking this metric will help retailers understand how to place and price their products, how to market them, and identify gaps in customer service – which could potentially lead to higher conversions. 

  1. Average Order Value 

This metric measures the average order value of every transaction by each customer. Tracking the AOV will help marketers understand how, where and when to cross-sell and upsell products to increase cart value. 

Merit’s Expertise in Retail Data and Intelligence    

Our state-of-the-art retail data harvesting engine collects raw data and provides actionable insights;  

  • Three to four times faster than standard scrapers    
  • At lower cost    
  • With Increased accuracy (up to 30% compared to standard scrapers)  

Our powerful, new scraper engine can gather massive data sets from multiple sites and geographies in real-time so you can stay informed on customer behaviours and market trends. 

Merit’s eCommerce and retail data engine provides a high degree of confidence in insights generated from analytics – thanks to confidence in the data quality and access to enriched data.  

To know more, visit:

Related Case Studies

  • 01 /

    AI Driven Fashion Product Image Processing at Scale

    Learn how a global consumer and design trends forecasting authority collects fashion data daily and transforms it to provide meaningful insight into breaking and long-term trends.

  • 02 /

    Automated eCommerce Data Harvesting at Scale and Speed

    This company reduced their cost by 15% & automated 80% of their quality assurance & data integrity checks.