In the highly competitive landscape of the retail industry, data is the only thing that can help stakeholders make crucial business decisions. The exponential growth of technology has granted retailers access to a wealth of information, known as Big Data, enabling them to make data-driven decisions. Big Data has the power to reshape the retail industry, unlocking its potential for growth and informed decision-making. And it is, therefore, imperative that retailers invest in a robust analytics stack to truly leverage the many benefits of retail analytics.
In this blog, we’re going to address the challenges retailers face while integrating their business data, look at the different solutions of those challenges, and the benefits of investing in the solutions.
But first, let us take a look at the status of the global retail industry.
How is the Global Retail Industry Faring?
Ever since the pandemic hit humankind and changed the face of the planet, industries have witnessed a seismic shift in the way they functioned. It all came down to one simple thing–Adapt or Perish! Companies across verticals and around the globe started shutting down their operations due to an inability to redesign their marketing campaigns and stay afloat. Just like every other industry, the retail industry had to pace up its digital transformation and diversify its marketing mix.
According to this report, there has been an increasing adoption of industry 4.0 across the retail sector.
“In retail 4.0, several operations and processes in the retail industry, like inventory management, customer service, customer accounts, supply chain management, and merchandising management activities, became digitized and automated. It is further expected to bolster the growth of North America’s big data analytics in the retail market during the forecast period.”
Therefore, it can be inferred that for the retail industry to continue thriving in the post-pandemic world, there is a pressing need to automate its data integration process to take the right call about marketing, sales, pricing optimization, etc. And this is where a superior quality data pipeline, like Hevo Data, saves the day.
What is Retail Data Analytics?
Retail analytics or retail data analytics is the process of collecting, analyzing and drawing insights from volumes of data generated by the retail industry. This data is collected from brick-and-mortar stores, e-commerce platforms, and several catalog outlets.
For instance, in retail, data from marketing campaigns helps identify top-selling products, effective promotions, and customer engagement drivers for increased sales. By prioritizing successful campaigns, retailers can enhance profitability and optimize business performance.
What are the Benefits of Retail Data Analytics?
The importance of retail data analytics does not only involve transforming the brick and mortar stores but also the online space, with advanced technologies and practices. From managing inventory and supply chain to allocating budget and resources optimally, retail analytics offers a myriad of benefits, a few of which are discussed below.
- Optimizing and Scaling Your Business: The primary function of retail data analytics is to integrate data across multiple data points to keep tabs on every aspect of the business. Data analytics, when appropriately deployed, can help retailers manage their stock, build prediction models, track market trends, among many other things.
- Enhancing Your Marketing Performance and Improving ROI: Retail analytics helps in improving your marketing performance, and subsequently, the ROI. Data visualization tools can offer you deep insights into how your marketing strategies are faring and which element of the campaign is driving growth.
- Personalizing Your Customers’ Shopping Experiences: Another importance of retail data analytics is that it helps retailers understand their customers’ sentiments, brand loyalty and target them with offers, discounts and promotions accordingly.
For instance, fashion e-commerce stores can analyze data to understand what kind of clothes a particular customer browses and adds to their cart. This insight can help retailers display pop-up ads, sponsored posts and even suggestions to personalize the customer experience and hence, increase conversions.
- Improving Your Product Pricing: Another crucial benefit of data analytics in the retail industry is that you can improve the pricing of your product and services by analyzing a number of factors. The number of people who abandon online shopping carts, sales volume of a product when the price drops, competitors’ pricing for the same product and/or service, are a few factors that can help retailers improve their pricing and generate sales.
Some Common Use Cases of Retail Data Analytics
Retail analytics encompasses various use cases that provide valuable insights and drive improvements across different aspects of retail operations. Here are some common use cases of retail analytics:
- Customer Segmentation: Customer segmentation allows retailers to target specific customer segments with personalized marketing campaigns, product recommendations, and tailored experiences. This helps improve customer satisfaction, increase engagement, and drive sales.
- Store Performance Analysis: Analytics enables retailers to evaluate and optimize store performance. By analyzing data on sales, foot traffic, conversion rates, and other metrics, retailers can make informed decisions regarding store layout, staffing, inventory management, and product placement to enhance the overall customer experience and drive sales.
- Customer Lifetime Value (CLV) Analysis: By examining customer data, purchase history, and customer behavior, retailers can estimate the long-term value of a customer. CLV analysis helps retailers prioritize customer acquisition, retention, and loyalty strategies, enabling them to allocate resources effectively and improve overall profitability.
- Fraud Detection: By analyzing transactional data, customer behavior patterns, and historical fraud cases, retailers can detect anomalies, suspicious activities, and potential fraudulent behavior. Advanced analytics algorithms and machine learning techniques help identify fraudulent transactions and reduce financial losses.
Types of Retail Data Analytics
Now that we have discussed the use cases and benefits of retail data analytics, let us glance through its different kinds.
Descriptive analytics involves summarizing historical data to gain insights into what has happened in the past in the retail business. It provides information about the sales figures of the last month and the top-selling product for a specific period.
Diagnostic analytics helps retailers understand the relationships and correlations between different variables. For example, it can reveal the factors behind a sudden decline in sales or identify the reasons for customer churn.
Predictive analytics focuses on forecasting future outcomes and trends based on historical data and statistical modeling techniques. By using predictive analytics, retailers can make proactive decisions, optimize inventory levels, plan promotions, and allocate resources more effectively.
Prescriptive analytics provides recommendations and actionable insights on what actions to take to optimize business outcomes. It can help retailers determine optimal pricing strategies, inventory allocation plans, store layouts, and marketing campaigns.
Each type of analytics serves a specific purpose and offers unique insights, but when combined, they provide a powerful toolkit for retailers to drive growth and success in the ever-evolving retail industry.
What are the Common Challenges of Retail Data Analytics?
As crucial as it is for retailers to leverage Big Data to drive their business processes and streamline their operations, retail data analytics comes with a few challenges, especially while dealing with large data sets. In this section, we’ll discuss those challenges and understand how it affects the retail industry.
- Data Quality and Integration: Collecting data from different sources can often lead to redundancies, inconsistencies and errors. In addition to that, retailers also often find it challenging to integrate this data, transforming and standardizing it for analysis. Ensuring that the data collected from disparate sources is accurate is imperative to draw valuable and actionable insights.
- Data Privacy and Security: One of the biggest challenges that retailers face while collecting and processing data is the need to comply with different data privacy laws and regulations. As these regulations keep getting complex, complying with every nitty-gritty keeps becoming difficult. For instance, laws such as the General Data Protection Regulation (GDPR) have strict conditions about data residency and where it is being processed. Retailers must ensure that they have a robust data protection and access framework like RBAC (Role-Based Access Control) in place to keep their customers’ sensitive information like banking details, social security number, date of birth, etc., safe.
- Technology Infrastructure: Investing in an excellent technology infrastructure is of utmost importance for scalability of the business and computational skills for large data sets. However, this could be incredibly challenging as identifying the right technology amongst a hundred others on the market is cumbersome, and not to mention, expensive.
For instance, the e-commerce industry, which is a big part of the retail industry, needs to integrate data to understand customer behavior and their purchase history, among other elements. However, there are a few challenges that the industry faces.
- Manually building and maintaining an in-house data pipeline that could ingest data from various sources consumes a lot of time, effort, cost and engineering bandwidth.
- Integrating e-commerce data with different marketing automation is often challenging, leading to inaccurate analysis.
Analytics Solutions in the Retail Industry
Analytics solutions empower retailers to gain a deeper understanding of their customers, optimize operations, improve decision-making, and stay competitive in a rapidly evolving marketplace. In this section, we’re going to deep dive into some of these solutions and understand how they could help retailers drive business growth and success.
- Data Warehousing: A very common analytics solution used in the retail industry is data warehousing. Data warehousing provides retailers with a consolidated view of their business data. It allows them to combine data from multiple sources, such as point-of-sale systems, online sales platforms, customer databases, etc, into a single source of truth.
- Business Intelligence tools: Business Intelligence or BI tools, like Tableau and Power BI, allows retailers to manage and organize their data neatly. Using these applications to visualize data gives retailers a comprehensive understanding of what is driving their sales, key areas they need to work on, etc.
- ETL tools: ETL tools, like data pipelines streamline the data flow and ensure that retailers have access to clean, reliable, and actionable data insights. Retailers can either opt to build their own data pipelines or choose an automated and fully-managed third-party pipeline, like Hevo Data, saving their costs and engineering bandwidth.
Here’s How Hevo Helped Deliverr Unlock the True Potential of their Data
Deliverr, a California-based e-commerce technology startup leverages proprietary data coming in from different sources, Machine Learning algorithms, and a network of warehouse partners to make strategic decisions about their inventory, and hence, make blazing fast e-commerce deliveries to their customers.
However, manually creating data pipelines seemed laborious and time consuming. These pipelines could not handle incoming schema changes efficiently, and there was also a lack of visibility into the pipeline’s status.
After extensive research, the data and analytics team at Deliverr zeroed in on Hevo.
What Made Deliverr Choose Hevo Over the Others?
The folks at Deliverr chose Hevo after trying out other tools like Fivetran, Stitch and Matillion for a month owing to its ease of usability and features like auto-mapping. They realized that using Hevo, they could set up their pipeline in just a few minutes, without writing a single line of code.
With Hevo, they were able to improve their query latency by 25-40%, even when their data volume grew exponentially. Plus, they were also able to enhance their data warehouse reliability from 97% to 99.8%
Hevo helps us centralize our data to Snowflake and effectively use it for reporting. Deliverr’s product and engineering teams manage their own set of microservices, each team creates Hevo data pipelines on its own, without needing help from data engineers or DevOps.Emmet Murphy, Staff Software Engineer, Deliverr
From the discussion that followed, we realized that data is nothing less than the lifeblood of the dynamic and ever-changing landscape of retail, and the benefits of analytics in retail are many. Retail data analytics empowers businesses to extract meaning from the vast amounts of data they generate. By leveraging advanced techniques, retailers can gain a competitive edge, enhance customer experiences, and optimize operations.
We also discussed the kind of challenges that retailers face while integrating data to make valuable decisions. However, the silver lining is that these challenges are not without effective and practical solutions, like automated data pipelines, BI tools, and advanced analytics solutions.
If you’re keen on scaling your business, opting for Hevo might be the best decision you make today. Hevo’s fault-tolerant data pipeline offers you a secure option to unify data from 150+ data sources and store it in a data warehouse of your choice. Plus, its unbeatable pricing makes it one of the best in the marketplace and a practical solution for your data integration needs. If you are interested, you can try Hevo by signing up for the 14-day free trial, and see if it fits your needs.