The retail industry has been significantly transformed by technology and Data, Particularly by the rise of e-commerce. Amazon, a giant in the e-commerce sector, which is responsible for approximately 50% of the U.S. e-commerce market. However, despite the strong growth of online shopping, it’s estimated that over 80% of global retail sales still happen in brick-and-mortar stores. This means the retail industry is a unique blend of both online and offline experiences. Statista says In 2022, the global retail market generated sales of over 27 trillion U.S. dollars, with a forecast to reach over 30 trillion U.S. dollars by 2024.
This coexistence of online and physical retail environments has given birth to a new retail format known as “omnichannel retailing.” Omnichannel retailing provides a seamless shopping experience for customers, whether they’re shopping online from a mobile device, a laptop, or in a brick-and-mortar store. This trend is shaping the future of the retail industry, creating exciting opportunities for retailers to engage customers and provide personalized shopping experiences.
Ever noticed that you show interest in one kind of advertisement on Facebook and all the channels from YouTube, Social media and news channels will begin showing you these advertisements? After getting influenced so many times across various screens which are mobile, TV, or Laptops, in the next few days you arrive at the shop to check out the products personally. The journey of today’s customer is truly multifaceted and this results in countless potential paths that any particular customer might embark on.
Today in-store shopping experience is also marked by the use of technology. Think Google says, Nearly 80% of shoppers will go to the store to buy when they have an item they need or want immediately. A global survey found that 56% of in-store shoppers used their smartphones to shop or research items while they were in a store in the past week. Today anyone visits the store online before visiting it offline.
The fact that retail is both a rapidly changing field due to technology, yet remains grounded in traditional brick-and-mortar stores, is a testament to the adaptability and resilience of the industry. It also brings our attention to the fact that technology is helpful in collecting data and there is a huge amount of data being generated and can be crunched for intelligence.
The application of new technologies and the data-driven decision-making process within the retail sector epitomize what is known as Retail Analytics.
What is Retail Analytics?
Retail analytics is all about collecting, processing, and understanding the data that flows through retail stores, websites, apps, or various other customer touchpoints to achieve customer 360. It helps understand customer behavior, optimize inventory, manage the supply chain, forecast demand, and boost company performance, profitability, and overall customer experience.
At its very core, retail analytics strives to achieve three primary goals: understanding customer behavior, identifying patterns, and driving operational optimization to enhance overall business performance. It is also keeping track of customer interactions at all times and at all levels. It is incredibly complicated but the use of new technology and efficient data management is the key.
Companies worldwide are relying on Retail analytics to take a variety of important decisions like opening a store in a particular city or particular street which is next to the biggest competition. It also could be decisions on what products on what shelves makes sales go higher, The products next to check-out counters which helps in impulse buying, The number of checkout counters, discounts, and loyalty points for customers.
The list is endless, almost every decision in a retail store can be linked to Data and thus to retail analytics. This is not only store analytics we are talking about adding shopping apps, loyalty engines, websites, messaging apps, and other outreach channels like email.
Achieving Customer 360 is what customer wants but it is incredibly difficult to implement. Customer wants if you had one interaction with the company, the company should use the points from that interaction and suggest you next steps for smooth onboarding. Now imagine there are 100s of touch points, so many human interactions involved the company is most likely to lose track of what is promised and what is not. In spite of this difficulty, it is imperative to track data and give the customer what they need. There is no respite, no escaping from Retail Analytics or Efficient Retail Data management.
Now Imagine being a VP at a retail leader, like a Vice President at a renowned store like Shopify store or Walmart and you will be amazed at the data processing that happens. Walmart collects and analyses almost 2.5 petabytes of data every hour for almost every data point of customers. Shopify stores have processed over 1 billion orders and generated over $200 billion in sales. All these are not possible without an active Retail analytics data management system in place.
It’s like having a treasure trove of valuable information at your fingertips, enabling you to make informed decisions that drive profitability. It’s like having a crystal ball that reveals what your customers really want and how to give it to them.
In the vibrant and dynamic landscape of the retail industry, understanding performance metrics is not just an advantage; it’s a necessity. Measuring Key Performance Indicators (KPIs) are also important. Check the top 10 KPIs to track for Retail companies and 50 KPIs for an e-commerce company.
Evolution of Retail Analytics
Retail analytics has undergone a significant evolution, progressing from manual inventories and basic sales records to sophisticated systems driven by technological advancements. With the introduction of electronic cash registers and point-of-sale (POS) systems in the 1980s, retailers gained more accurate sales data and streamlined transaction processes.
In the 1990s, the concept of data warehousing emerged, enabling the consolidation of data from multiple sources into a centralized repository. This facilitated a more comprehensive analysis of retail data, leading to the development of business intelligence (BI) tools.
The proliferation of digital technologies and the exponential growth of data in the 21st century gave rise to the era of big data in retail analytics. Artificial Intelligence (AI) and Machine Learning (ML) are now employed to churn data at massive levels but there was one more change coming and no one predicted it before it actually happened. It is the advent of omnichannel retailing, where customers interact with brands through multiple channels and it has changed Retail significantly.
Retailers now have to track customer journeys across online and offline touchpoints, analyze customer behavior, and provide personalized experiences, making retail analytics more challenging than ever before. Customer-centric analytics allows businesses to understand preferences, anticipate needs, and deliver tailored offerings and this is giving rise to Customer 360 which we will discuss later.
Components of Retail Analytics
Retail analytics is like solving a puzzle using information from a store. We collect data about sales, customers, and other things. Then we look at the data to find patterns and understand how things are going. This helps us make smart decisions about prices, what to sell, and how to make customers happy. This also helps us to achieve what we call Customer 360. There are four main steps to retail analytics before you even begin with Data Analytics.
- Data: We gather information from cash registers, customer programs, websites, and social media or almost all touchpoints for the customer and start managing the data.
- Analysis: We study the data to find patterns and learn important things about our customers and how the store is doing or the website or the email campaign you just sent.
- Insights: The things we learn from analyzing the data help us make good choices about pricing, products, and other important parts of the store or the website or app. This is when you know how customers is behaving, what products are selling, and why.
- Action: Finally, we take action based on what we learned. This might mean changing prices, improving products, or making the store or online experience better for customers.
There are different types of retail analytics, this is usually the same statistical techniques that we apply to any data situation.
- Descriptive analytics: What’s happening
- Diagnostic analytics: Why it is happening
- Predictive analytics: What will happen
- Prescriptive analytics: What to do
What changes is the situation we apply this statistical or data analytics technique? Some key areas where retail analytics can be applied include:
- Sales Analysis: Identify top-selling products, understand sales trends, and forecast demand.
- Customer Segmentation: Grouping customers based on common characteristics to tailor marketing campaigns, personalize customer experiences, and improve customer retention.
- Inventory Management: Optimize inventory levels, reduce stock-outs, and minimize excess inventory.
- Pricing Optimization: Analyzing pricing data and market dynamics to determine optimal pricing strategies that maximize profitability and competitiveness.
- Store Performance Analysis: The performance of individual stores or retail locations to identify strengths, weaknesses, and opportunities for improvement.
- Promotional Effectiveness: Measuring the impact of marketing and promotional activities to assess their effectiveness and return on investment.
- Market Basket Analysis: The customer purchase patterns to identify product affinities and cross-selling opportunities.
- Supply Chain Optimization: Improve supply chain efficiency, reduce costs, and enhance overall operational performance.
- Marketing analytics: Measuring Online Customer behavior, social media responses, Advertising, ROAS, and ROI on performance marketing or Advertising spending. Data analytics can be applied to various kinds of marketing use cases.
Achieving customer 360 is the aim of Retail Analytics
In the modern world of Omnichannel Retail and e-commerce, The larger aim for retail analytics, with all tools and techniques, and data management is to achieve a Holistic view of customer activities which is called Customer 360.
Customer 360, also known as a 360-degree view of the customer, refers to a comprehensive and holistic understanding of a customer’s interactions, preferences, interactions, and behaviors across multiple touchpoints and channels.
It involves aggregating and integrating data from various sources to create a unified profile of each customer. A Customer 360 view takes into account both online and offline interactions, such as purchases, website visits, customer service interactions, social media engagements, messaging, responses to emails, App activity, and more.
The Purpose of Retail Analytics is to achieve Customer 360 and a higher Customer Lifetime Value (CLV). Customer lifetime value (CLV) is a metric that measures the total net profit a company can expect to generate from a customer throughout their entire relationship with the company. A Good example is Apple: Apple products, such as iPhones, iPads, and MacBooks, tend to have a high CLV because they are often replaced every few years. The average annual CLV for an Apple customer is estimated to be $2,000. Adobe Creative Cloud: Adobe Creative Cloud is a software subscription service that offers a suite of creative tools for designers, photographers, and other creative professionals. The average annual CLV for a Creative Cloud subscriber is estimated to be $1,200.
In today’s omnichannel world, companies must prioritize retail analytics to achieve customer 360. By leveraging data and advanced analytics, businesses can gain insights into customer interactions, preferences, and behaviors across various channels. This enables personalized experiences, optimized operations, and long-term customer loyalty. Retail analytics is the key to success in delivering seamless and tailored customer experiences in the modern retail landscape.
At the same time, Effective data management is crucial for successful retail analytics. It involves collecting relevant data from various sources and integrating it into a unified view for Customer 360. One cannot achieve excellence in Retail analytics unless supported by a world-class Data management platform. SCIKIQ Offers Data management solutions for Retail Analytics and achieving customer 360. Data Analytics companies like DAAS LABS have their own Centres of excellence for Retail. By mastering data management, businesses can unlock the power of retail analytics to gain actionable insights, improve customer experiences, optimize operations, Supply chains, and drive growth and profitability in the dynamic retail landscape.