How to Use Retail Data Analytics to Increase Sales

by Deputy Team, 9 minutes read
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Did you know that around 125 retail companies filed for bankruptcy in 2020 alone? Besides, the pandemic only made it more difficult for physical retailers to survive. 

Today, consumers expect a seamless transition between online and in-store shopping and a personalized retail experience. Retailers are constantly juggling multiple things, ranging from changing consumer preferences to declining profit margins and fierce competition from e-commerce stores.

But despite the challenges, some retailers have managed to stay on top of their game. How? The secret behind their success is advanced retail data analytics. 

This guide will help you understand the basics of retail data analytics, its different types, and how you can benefit from it. We have also added some effective strategies on how to increase your retail sales, so keep reading. 

What is retail data analytics?

Retail data refers to the information collected to quantify a business. It is usually a huge volume of data that is used to reveal patterns and trends associated with consumer behavior. Retail data analytics is defined as the process of using retail data or big data to optimize product pricing, improve customer loyalty, and track supply chain movements. 

In the retail sector, data analytics involves gathering and studying retail data such as sales volume, inventory, and pricing to identify trends, forecast outcomes, and make smart business decisions. 

It helps business owners get valuable insights into the performance of their stores, vendors, customers, and products and then use that to increase revenue.

What are the types of retail data analytics?

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Broadly, there are four types of retail data analytics: descriptive, diagnostic, predictive, and prescriptive.

#1. Descriptive analytics

Descriptive analytics involves collecting raw data from various sources such as ERP, POS terminals, inventory systems, and so on. The data is then used for generating useful insights into historical and current performance. 

For example, the most common retail descriptive metrics would be determining year-over-year sales growth, the number of new or repeat customers, pricing changes, etc. 

The purpose of descriptive analytics is to use data to describe the activities taking place in your business. However, this type of analysis does not address why your business has performed in a certain way. This is where diagnostic analysis steps in.

#2. Diagnostic analytics

Diagnostic analytics is a form of retail data analytics that helps retailers use data to understand the ‘why’ of certain business situations. It uses statistical analysis, algorithms, artificial intelligence  (AI), and machine learning to identify anomalies, highlight potential issues, and find correlations between data points. This allows businesses to understand areas of improvement and find ways to resolve their challenges.

#3. Predictive analytics

After you have identified the ‘what’ and ‘why’ of your business, predictive analytics helps you explore the next steps. Using the findings from descriptive and diagnostic analytics, predictive analytics helps you forecast future trends for your company. This is done by automatically identifying clusters and exceptions and using complex algorithms and statistical methods to predict future business performance. 

To give an example, predictive analytics helps you find out the expected sales for the upcoming period, the number of POS counters you need to adjust to the store rush, and more. 

#4. Prescriptive analytics

Prescriptive analytics is the most advanced type of retail data analytics. With the help of algorithmic AI, prescriptive analytics can help retailers understand the next steps they need to undertake in order to achieve the best possible outcome for their business. Typically, prescriptive analytics runs simulations on a given number of different initial conditions, such as different pricing models, product assortment, and allocation, and chooses the conditions that generate the highest profit. 

Benefits of using data analytics in retail

Here are the advantages of using retail analysis for your business.

#1. Helps understand consumer behavior

One of the biggest problems faced by retailers is understanding buyers’ persona. With retail analytics tools, they can get a 360-degree view into their customer’s identity, behavior, and journey. According to a study of 350 retailers and brand manufacturers, the retail analysis helped them increase their client retention rate by 55% and improve their customer targeting by 51%. 

Retail data analytics helps entrepreneurs better understand their target audience and their preferences, which, in turn, helps them create more effective and personalized marketing campaigns that fetch increased conversions and sales. 

#2. Analyzes past and present trends

Descriptive retail analytics helps businesses identify and gain valuable insights into their historical and current performance. For instance, you can find out which season or month you have the highest footfall and ensure that your store is not understaffed to provide a seamless shopping experience to your customers. 

In short, you can find the correlation between two or more sales factors, any anomalies or potential issues, and explore future improvement areas. 

#3. Improves inventory management

With automated insights, retailers can improve their inventory management. By forecasting demand, businesses can accurately predict the volume of supplies they require for the upcoming sales cycle and ensure that they are proportionately stocked. Further, the retail analysis leaves room for automation as it can trigger rule-based actions in inventory management software.

#4. Helps optimize costs

Retail data analytics can help organizations save costs in multiple ways. By accurately forecasting future performance indicators, businesses can capitalize on the right sales opportunities, improve customer experience, and manage inventory more efficiently. Moreover, retail analytics also helps companies to streamline operations, launch effective sales and marketing strategies, and understand customer behavior more accurately.

What are KPIs in retail?

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As with other industries, key performance indicators (KPIs) are also prevalent in the retail sector. Some KPIs used in retail include sales per square foot, sales per employee, conversion rate, gross profit margin, net profit margin, year-over-year growth, average transaction value, and so on.

KPIs indicate the performance level of your retail store in various areas. It helps determine if a company can meet its goals or not and make profitable decisions. Let us understand a few KPIs in detail.

Retail key performance indicators for evaluating sales data

#1. Sales per square foot

Sales per square foot are defined as the volume of sales generated per square footage of sales space in your store or outlet. The formula for sales per square foot is:

Net Sales/Amount of Sales Space

It is one of the best methods to evaluate the sales productivity of a company. It can help understand whether a business is making the best use of the shop space. In order to improve your sales per square foot ratio, you can start by optimizing your store layout, keeping competitive prices, having a unique product assortment, encouraging customers to spend more time in your store, and training your staff to sell more.

#2. Sales per employee

Another KPI that helps you calculate the number of sales generated per unit is sales per employee. It is computed as: 

Net Sales/Number of Employees

Sales per employee is a good indicator of performance, as it helps you differentiate between high-performing and underperforming team members. Using the KPI, you can create employee schedules, incentive plans, hiring, rostering, and compensation models for your business. You can integrate your POS system with the CRM platform to track the revenue generated by individual employees. 

The CRM will collect sales data from your POS software to track employee sales performance and create a leaderboard to determine excellent and poor-performing team members. 

Using the data, you can set smart goals for each employee, invest in sales training programs, and conduct friendly competitions to motivate sales personnel to perform better.

#3. Conversion rate

Conversion rate refers to the proportion of in-store visits to the number of shoppers who made a purchase. The formula for calculating the conversion rate is:

Number of Sales/Total Number of Shoppers

Conversion rate enables you to understand how good your team is at selling or convincing visitors to buy a product. It is one of the most important KPIs for retailers, as a high amount of traffic to your store won’t amount to much if it does not lead to greater sales. 

#4. Net profit margin

Net profit margin is the percentage of revenue a retailer earns for every unit of sales. It is computed as:

(Net Profit/Total Revenue)*100

Net profit margin takes into account all expenses, including wages, marketing, transportation, overhead, and so on. It’s a strong indicator of a business's success – a high net profit margin means the company is profitable. On the other hand, a low net profit margin indicates that a retailer needs to reduce its expenses or minimize expenditures on company operations.

#5. Year-over-year growth

Year-over-year growth helps you compare the current year’s performance with that of the previous years. Here’s how you can compute year-over-year performance for your company:

(Current Period Revenue-Prior Period Revenue)/Prior Period Revenue*100

A positive and increasing year-over-year growth indicates that a business is in good health and is on track to achieve its goals. By contrast, negative or declining year-over-year growth shows that a business is falling behind and needs to take steps to improve its performance.

#6. Average transaction value

Average transaction value tells you how much shoppers spend in your store at a time. It is calculated as:

Total Revenue/Number of Transactions

This KPI helps you gauge whether consumers are spending more on expensive products or buying larger quantities. Accordingly, you can implement sales strategies to ensure you get the most out of your shoppers while they explore your store.

How to increase retail sales

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If you are looking to boost your retail sales, here are some ways to do it.

#1. Understand your buyers’ persona

The first and foremost step to maximizing sales revenue is understanding consumer behavior. Using retail data analytics, you can keep track of your customer's buying patterns, purchase history, individual preferences, and preferred channels for communication. This information helps you determine how to reach out to your consumers for better engagement and customize offers tailored to their needs. What’s more, retail analysis can help you gauge facts for better customer retention and personalization that will help you optimize costs and boost sales.

#2. Predict retail demand

The combination of big data and algorithms in retail data analytics helps analyze social media and online shopping trends. This, in turn, can enable businesses to forecast what products will be in demand accurately. Accordingly, stores can stock their inventory and manage it efficiently. 

For instance, brands like Walgreens and Pantene partnered with local weather channels in various regions to account for weather patterns to customize product recommendations. When the companies anticipated a rise in humidity levels, they began advertising anti-frizz products and offering in-store promotions to attract customers. As a result, Walgreens saw a 10%  increase in the purchase of Pantene products and a 4% rise in the sale of other haircare products in the following months. 

#3. Optimize supply chain management and logistics

Retail analytics helps you optimize back-end supply chain management by using simple threshold models to determine changes in demand. Further, modern retail analysis software allows retailers to accurately track historical purchases to forecast the demand for certain goods and manage their inventory more efficiently.

In addition, retail data analytics can help you anticipate in-store staff requirements. Using accurate demand forecasts, retailers can better understand when to expect high footfall and schedule in-store staffing and work hours. 

#4. Speed up the buying process

Despite high-quality products, customers might be discouraged from visiting certain stores. One of the factors behind it is the long waiting queues behind checkout counters. And with online shopping on the rise, stores must find ways to deliver a seamless customer experience.

One of the ways to do so is by implementing mobile POS software to reduce delays in checkout. Each sales rep should have their own tablet or mobile device, which they can use to process transactions quickly. A digital POS system can reduce wait times and help sales team members better serve clients. 

#5. Manage prices to boost sales

By leveraging retail analytics, businesses can track purchases and combine the data with real-time wholesale and operational costs. This can enable sales teams to evaluate how price fluctuations affect the bottom line and determine optimal pricing levels. Moreover, retail data analytics can help determine other factors that influence product prices, such as weather, festive season, technological innovations, inflation rate, and so on.

Wrapping up

Retail data analytics help businesses achieve optimal pricing, improve inventory and supply chain management, and enhance customer satisfaction. Using the combination of big data, machine learning, and complex algorithms, retail analysis is able to identify trends and patterns which are typically overlooked by sales forecasting models. 

Additionally, retail analytics also uses KPIs to evaluate business performance and provide an accurate prediction of demand, enabling retailers to plan ahead and make informed business decisions.