How to Use Retail Data Analytics to Boost Sales

Disclaimer: This is a guest blog submitted by Pohan Lin

The retail industry is very much customer-driven. Which means data is a pretty big deal.

Through consumer data, businesses can learn a lot about their products, the current market, and the audience they are targeting. It’s like opening a secret door into people’s shopping habits.

All of those insights come courtesy of data!

Let’s have a look at how to use retail data analytics to boost sales, including what data analytics is and how it can be beneficial for your business.

What Is Retail Data Analytics?

Retail data refers to any information related to your business operations, including sales, inventory, and pricing. The analytical process, then, involves collecting, analyzing, and reporting on this data.

The aim is to gather insights into the various facets of your business, from customer behavior and loyalty to supply chains and inventory levels, with the intention of improving every element of your operations.

It’s not enough to believe your business is running smoothly or to have a general understanding of your weaknesses. What truly matters is having precise insights into the effectiveness of different sections of your supply chain or customer journey.

Retail data is the key to unveiling these secrets and enhancing various aspects, including pricing and customer loyalty.

What Data Is Collected By the Retail Industry?

The retail industry collects a wide range of data from various sources and makes the most of it with data governance. Some of the key types of data collected by the retail industry include:

• Transaction data – Includes information about individual sales transactions, such as the date and time of purchase, items bought, quantity, and the total amount spent.
• Customer data – Collected data on customers’ demographics such as age, gender, location, profession, contact information, and purchase history.
• Product data – Information about the products sold, including product names, descriptions, prices, SKUs (Stock Keeping Units), and inventory level.
• Online behavior data – Data concerning website and app usage, including metrics like page views, click-through rates, time spent on pages, and instances of abandoned shopping carts.
• Customer feedback and reviews – Data from customer feedback surveys, product reviews, and social media comments.

Protection from threats like computer viruses is also a crucial concern in data management. Ensuring robust cybersecurity measures is imperative to safeguard this valuable retail data from potential breaches.

What Are the Types of Retail Data Analytics?

There are several types of retail data analytics to think about, such as:

• Descriptive – Descriptive analytics uses historical data, which is managed through a data warehouse. This kind of retail data analytics concentrates on gathering previous behaviors and finding a pattern. It’s a pretty handy way to look at data, as it can be really helpful in future decision-making.
• Diagnostic – Using diagnostics analytics lets you dive deeper into the data to pinpoint the causes of particular outcomes. For instance, if you held a social campaign that didn’t get as many clicks as you’d have liked, diagnostic analytics can help you understand why this may be.
• Predictive – This data analysis helps you anticipate possible future patterns, buyers’ behaviors, and even stock levels at specific times of the year.
• Prescriptive – Prescriptive analytics goes beyond predictive analytics by advising on specific actions or decisions based on historical and predictive data insights.
• Customer-focused – This is perhaps the most well-known and utilized type of retail data analytics. With a focus on understanding and segmenting customers, it helps retailers create personalized marketing campaigns that can encourage customer loyalty.
• Transaction-focused – Every time you make a purchase online through an ecommerce retailer, it can be tracked and linked back to you and your buying habits, and these nuggets of information can be useful to retailers.

Examples of Retail Data Analytics Applications

There’s no point gathering and analyzing big data just because everyone else is. You need to understand how to put it into practice – that is, how to read the insights to help you make better decisions.

The good news is, when it comes to retail data, there are myriad use cases. Here are just a few ways you can use data analytics as a retailer:

• Personalized marketing: Today’s online customer experience is all about personalization. By analyzing customer data, you can target specific audiences with specific offers that encourage loyalty and increase conversions. You can also pinpoint your most valuable customers and tailor your marketing to further increase their spend or reward their loyalty.
• Prize optimization: The cost of products plays a huge factor in customer purchase decisions, so you want to take the guesswork out of your pricing model. Data science helps you to analyze customer behavior, psychology, and sales to optimize prices for your particular products and company.
• Inventory and supply chain management: Your retail operation is nothing without stock, so maintaining the right levels is crucial. Analytics allow you to identify customer purchasing patterns and predict future trends to optimize inventory management. You can also monitor metrics like shipping times and supplier availability to make your supply chain more efficient.
• Recommendation system: Similar to personalization, many online retailers today implement a recommendation system to target customers by promoting or cross-selling items based on previous purchase behavior. To be effective, the systems use an algorithm to automatically analyze use data and recommend products based on preferences and actions.
• Fraud detection: If your business takes on high volumes of orders, it’s unlikely human staff will be able to spot fraudulent transactions. It’s why banks and ecommerce businesses are increasingly relying on analytics to spot suspicious activity quickly and accurately, saving a significant amount of money in the process.
• Sales forecasting: By evaluating historical sales figures and identifying trends, you can more accurately predict future sales.
• Demand forecasting: The analytical process should be seen as a long-term undertaking. By gathering data over a longer period, you can more clearly spot trends that will allow you to gauge future demand, optimizing your stock levels as a result.

What Are the Benefits of Data in the Retail Industry?

It’s been shown that data plays a crucial role in the retail industry and offers a wide range of benefits, including improving customer experience and product development.

Seeing as 45.9% of business owners agree that customer experience is their top priority in the next five years, it’s good to know that retail data analytics can play a huge part in that.

Image sourced from superoffice.com

Here’s a look at some of the other key benefits of data analytics in the retail industry.

• Improved Customer Understanding

  Many businesses aim to improve the customer buying experience continuously and this goes hand-in-hand with better customer understanding. That’s where data analytics comes in because it allows retailers to gain deep insights into behavior.

• Optimized Inventory Management

  Another mighty benefit of data analytics is improved inventory control. You can keep on top of inventory in the warehouse and prevent sellouts or even overstocking by using previous sales data. By doing this, you increase client satisfaction while decreasing carrying costs.
  What’s more, using this data, you can also generate more targeted marketing campaigns and relevant product recommendations.

• Effective Marketing and Advertising

  Another benefit of retail data analytics is that it allows you to get really specific. You can pinpoint certain demographics that you want to target and use customized offers and promotions to grab their attention, like emailing them a 20% off coupon for items already in their cart.

  Basically, you can create more effective marketing campaigns simply by using what the data is showing you. You can also utilize email marketing and Gmail sequence to follow up prospects.

• Improved Store Operations

  Data analytics can optimize store layouts. You can use it to plot which products sell best in what position and manage staffing levels. This will also help you uncover patterns in popular product areas and peak shopping hours.

Source: Unsplash

• Customer Retention and Churn Reduction

  With the help of data analytics, retailers can identify customers who are likely to churn (stop shopping with the brand) and implement retention strategies to keep them engaged. This reduces customer churn and increases customer lifetime value.

How to Increase Sales Using Retail Data

Now that we’ve learned about what data analytics can do for retailers, let’s dive into how to actually increase retail sales using data.

• Manage Prices

  Businesses are able to track customer purchases and combine this data with wholesale and operational rising costs – all with the help of retail data analytics.

  Transaction analysis can be used here, focusing on analyzing transactions to identify which products your customers are buying.

  For example, are customers frequently purchasing two products together?

  If so, your marketing team can then use this insight to work together and create special adverts to try and sell the two products together, or they could even create a bundle with a discount.

  You can also use this information to analyze how different price changes can impact sales.

  For instance, how effective is a 2-for-1 offer?

  Is a discount as a percentage more successful?

  And if you’re interested in uncovering other pricing-related information, such as how inflation and the holiday season influence prices, then retail data is your friend.

• Utilize Your Competitive Advantage

  Retailers who effectively harness data analytics gain a competitive edge in the market. So, you can always stay one step ahead of your competitors regarding new trends and popular products.

  Using retail analytics, you can determine the most effective pricing strategies for different products and customer segments. By taking into account important factors such as demand elasticity, competitor pricing, and market trends, as a retailer, you can set optimal prices to maximize revenue and profitability, boosting competitiveness as a result.

• Use It to Make Quick Decisions

  One of the reasons why data is so useful is because it can give you an accurate overview of where things stand right now. Real-time data analytics can truly benefit retailers by helping them to make timely and informed decisions. When you’re able to monitor data as it’s generated, you can respond quickly to market changes, inventory fluctuations, and customer needs.

  This means that you’re in a much better position to adapt when needed, sometimes saving costs in the long run through quick, data-backed decisions.

• Track Your Supply Chain

  Analytics can be used to track your supply chain functionality, focusing on something all businesses need to master: supply chain operations. This helps reduce costs and improve efficiency.

  Predictive analytics lets retailers forecast future demand for products accurately, which can be crucial for ordering stock and maintaining warehouse levels at the start of the supply chain. Using demand forecasting can help when it comes to planning production for certain items, reducing the risk of lost sales opportunities.

  For small and medium-sized firms that are operating on a tight budget, this might be quite significant. Analyzing supplier, inventory, logistical, and distribution channel data is part of the process. With all of this information about your supply chain, you’re able to see where you can cut costs and where you should be investing more, which can help with overall sales.

• Predict Retail Demand on Social Media

  Using retail data analytics software you can analyze social media trends and what’s currently being talked about online, allowing you to forecast what products might be in demand.

  Social media can be overwhelming, but social media analytics are super helpful to businesses in predicting retail demand. Not only can it let you know what appears to be trending, but it can also help you learn more about click-through rates and advise you if your money is being well spent. Using data and algorithms, you can better predict retail demands, which can be a great tool to increase sales.

  To ensure accurate data collection in this process, consider employing residential proxies, which can provide a more authentic representation of online user behavior.

  It’s important that retailers understand how to use social media to their advantage. To ease yourself into this, you can leverage various software tools, such as Salesforce Facebook integration.

Retail Analytics Tools

Collecting swathes of data across your entire business may sound daunting. But a range of tools are available today that make the process that much simpler. Here are a few designed to help retailers optimize their approach.

• Point of Sale Systems

  Chances are you already have a point of sale (POS) system, but are you leveraging all of the data it contains? POS tools like Magestore, Square for Retail, and Shopify gather information on everything from products and customers to staff and sales.

  Source: blog.littledata.io

  These nuggets of information are invaluable for gaining a deeper understanding into customer behavior and spending, top-performing sales members, and top-selling products. Sales data also helps you analyze figures in each sales channel, which is ideal if you operate a brick-and-mortar and online site.

• Inventory Management Apps

  Managing stock levels manually is a constant headache. By automating the process, you not only make it more efficient but can also gain valuable insight into stock-level analytics, from shrinkage and inventory turnover to holding costs.

  Some POS tools, such as Magestore and Lightspeed, come with built-in inventory management software, while dedicated apps include InFlow, Sortly, and Megaventory.

• Business Intelligence Software

  Business intelligence tools like SiSense, Looker, and Power BI are brilliant for visualizing and analyzing large amounts of data across the entire business. You can view real-time metrics, from sales and revenue to expenses, to assess your strategy and adapt as needed.

  Some will even allow you to customize the retail analysis dashboard, so it’s an ideal solution for larger businesses facing more complex decisions.

• Retail Analytics Platforms

  With so many retail companies competing today, it’s no surprise there are more dedicated retail analytics platforms on the market. Software like SafetyCulture, Tableau, and DataWeave are specifically designed for businesses in the retail industry to boost their productivity, effectiveness, and competitiveness.

  You can gain various insights using features such as AI analytics, data visualization, and pricing intelligence and even integrate it with other retail solutions such as your POS or CRM.

When Should You Upgrade Your Retail Analytics?

Businesses of all sizes can benefit from data analytics, and even if you’re already using it, chances are there’s room for improvement. Particularly as your retail business grows, it’s crucial to stay ahead of issues before they arise by getting an accurate picture of your operations and customers.

If your business is just getting off the ground, there’s no time like now. Implement analytics from the get-go, and you’ll have a far better chance of success in those challenging early stages. At this point, you may choose to start by analyzing a particular section of your operations and work from there.

Remember that as your business grows, so too will the volume and complexity of data you collect. There will come a time when you need to update your analytics, which might include tweaking the process you use to gather or report on data or upgrading the tools you use to do so.

Ask yourself whether you are still experiencing common issues – gaps in the supply chain, seasonal over-stock, or ineffective recommendations, for example. Also, consider whether your data now requires a more in-depth analysis or you must manually adjust forecasts. If any of these rings true, it’s a clear sign an upgrade is in order.

Using Retail Data Analytics to Boost Sales

According to Global Market Insights, the retail analytics market size exceeded $10 billion in 2022. Moreover, it’s expected to grow at over 24% CAGR between 2023 and 2032. So it’s safe to say that big data is going nowhere any time soon.

It’s clear that retail businesses that leverage data can boost sales. But it’s how you use the data that makes all the difference. Once you’ve used the appropriate tools to uncover all that information, you can extract the bits that matter to you. Then, you can create a unique, personalized customer experience based on historical sales and future predictions.

With research from McKinsey showing that grocers’ personalized promotions result in a 4-8% sales increase, the benefits of creating a bespoke experience are obvious. Using data to analyze future demand can be equally beneficial.

For example, Walgreens and Pantene partnered with The Weather Channel to create a Haircast campaign and app. The idea was to monitor weather patterns and customize relevant product recommendations. Predicting a rise in humidity levels, the brands began promoting anti-frizz products with in-store promotions to seal the deal.

The campaign resulted in a 24% increase in sales at Walgreens and a 4% sales lift across the entire hair category. Meanwhile, Pantene’s Walgreens stands saw a 37.5% increase in merchandising goals thanks to increased visibility.

So, data can truly benefit your business, and there are plenty of ways to use analytics creatively. Start gathering insights into your business and customers today, and you’ll soon be tapping into your retail business’ full potential.

Author’s Bio

Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing. Pohan Lin has also written for domains such as BigCommerce.

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