Real time analytics: A competitive Edge of Retailers

Some retailers outperform their competitors even when both are selling the same products. Similarly, some businesses have better conversions. All this is done because of the effective use of the collected data using real-time analytics. Yes, most retailers today are using data analytics. Therefore, to have a competitive edge in the retail industry, real-time retail analytics is paramount.

In Real-time analytics the data and related resources are used for analysis as soon as it enters the system. This real data analytics has benefited around 56% of the retailers, according to a survey by Solace. And in this blog, we’re going to see exactly how real-time analytics in retail is possible. So, let’s get started without further delay.

Seizing Opportunities: Real-time Analytics for Retailers

Capitalize Real-time Market Trends

The traditional data analytics environment is not agile enough to make quick shifts for upcoming market trends. To tap into emerging trends like identifying hot products or adjusting product offerings, real-time analytics is necessary.

To enhance Operational Efficiency

There are various moving parts in retail operations, from inventory management to in-store operations. Using traditional analytics methods, it can take a while to derive which operation needs optimization.

Enhancing Customer Experience

Traditional data analytics systems take quite some time to derive insights. Retail analytic eliminates delays that can cause a hindrance in delivering a better customer experience.

Real-time Actionable Insights

Processing information in data analytics systems takes time. A real-time analytics environment can process data and generate insights using retail analytics.

Large Scale Data Integration

Retailers have diverse data sources including websites, point-of-sale systems, inventory management systems, and much more with large-scale incoming data. Real-time analytics in the retail environment is necessary to Integrate and analyze large-scale data sources in real-time.

Effective Infrastructure Scalability

Scalability challenges can arise for retailers due to sudden fluctuations in data volume leading to system downtime. The real-time analytics environment ensures that systems can adapt to larger datasets whenever required.

Roadblocks in Real-time Analytics Retail Integration

Data Latency

Achieving true real-time analytics in retail requires low-latency data. Any delay in data processing can impact the usefulness of the insights. So, retailers must invest in high-performance infrastructure to minimize latency.

Talent & Expertise

A retail analytic environment requires talent with specialized skills and expertise. Retailers may find it hard to recruit and retain data scientists with specialized skills.

Transforming the Retail Sector: Impact of Real-Time Analytics

To make timely decisions, businesses must first understand their customer preferences. With this customer intelligence, they can customize products and services to meet the specific needs of their target audience.

The infographic below lists different types of customer intelligence that are used in real-time analytics to target customers.

four types of customer intelligence

Contextual Recommendations: Personalization with Real-time analytics

Context-based recommendations in retail analytics analyze current events and suggest non-intuitive recommendations with the help of a machine learning model. Real-time analytics focuses on delivering personalizing suggestions for better business opportunities.

Let’s see some of the benefits of real-time analytics in retail that help retailers stay competitive.

Increased Conversion & Retention Rates

  • Machine learning in retail analytics meets customers’ immediate needs and can significantly influence their purchasing choices by monitoring their interests, social activities, income levels, and other minute details.

factors influencing consumer's buying behavior

Better Upselling Opportunities

  • With real-time data analysis, retailers get detailed insights of consumer’s buying behavior. Retailers can upsell their customers new products or a new product version. By analyzing real-time data factors like the day, month, and income level of consumers.

Fine-tuned Suggestions

  • A real-time data analytics system will analyze real-time clues like search query, typing pattern, how many times a search query was modified, and other similar hints to fine-tune the list of product suggestions in real-time.

However, it is quite tricky to execute all these processes synchronously with each other without Collison. But once the environment is set up, everything runs smooth as butter. Let’s see how data flow and utilization of real-time analytics in retail is better.

Quick Decision Making: The Core of Real-time Analytics

A real-time retail analytic data warehouse eliminates batch data processing inefficiency by storing and processing live incoming data from different business endpoints within seconds or minutes of time intervals.

This real-time data warehouse helps businesses in

1. Achieving Omni-Channel Retail

According to a study by risnews nearly 71% of retailers observed a lack of real-time visibility, the biggest obstacle in achieving omnichannel retail. Real-time retail analytic

For instance, in Binary Semantics we have a BI solution that can clean and link all your retail data, and produce amazing visuals to show you where your business is today and also predict what will happen tomorrow. All these real-time insights generated with Binary Semantics can be viewed in your phone.

Similarly, Binary Semantics also integrates advanced analytics, real-time data integration, and omnichannel marketing functionalities to construct a cohesive perspective of customers. This involves the seamless integration of data from diverse sources.

This tackles issues in real-time visibility and helps in achieving an omnichannel retail system to deliver near real-time updates of retail stores and site interactions.

Note: Omnichannel retail is a strategy where consumers are targeted through multiple physical and digital touchpoints like stores, websites, social media, etc.

omni channel retail ensures

2. Optimized Inventory Management

According to a study by Prime AI, retailers lose 20-50% of overall sales due to insufficient inventory. Real-time retail analysis can help them analyze point-of-sale data to predict demand for their top-selling and least-selling items, reducing the risk of stockouts and overstocking.

Research by Charge Detail found that 37% of customers purchased fashion items from a new brand because an item was out-of-stock from their regular supplier.

But with Binary Semantics we use machine learning and predictive analytics techniques to swiftly visualize data and deliver easily interpretable results in real quick time the stocks can be maintained regularly.

benefits of real-time pos system

3. Pricing Optimization

Real-time analytics in retail can help retailers in adjusting their product pricing dynamically. By analyzing competitor pricing, demand-supply fluctuations, and consumer interest retailers can change product pricing in real time to ensure maximum revenue.

Binary Semantics has a comprehensive data analytics platform. It can enable your retail industry to make smarter decisions regularly with its advanced AI, and high-performance cloud platforms. Thereby resulting in a truly data-driven business.

Enhanced Brand Perception with Real-time analytics

Real-time sentiment analysis through machine learning is highly effective in spotting subjective opinions in live social content and customer feedback. A positive brand image encourages powerful word-of-mouth marketing, surpassing other forms of promotion.

For instance, using platforms like Altair and QLIK can help in structured data-driven decision-making in retail analysis. This can help retailers in enhancing their brand perception in multiple ways. Such as.

Live Campaign Analysis

By performing real-time data analysis on social media promotional events, brands can target their marketing strategy on what customers like more. Moreover, identifying what customers do not like also becomes useful as it helps in quick decision-making to rectify any negative business impacts.

For example, luxury fashion house Balenciaga faced controversy with their holiday promotional campaign. They removed the ad shortly after its launch and issued a public apology for causing offense to consumer’s sentiments.

Due to real-time analytics, the brand reduced the damage, otherwise, the brand image would’ve deteriorated to a greater extent.

Influencer Analysis

Businesses are increasingly using influencer marketing to connect with customers and build a strong brand image. However, the reputation of influencers can impact businesses positively or negatively. With real-time data analytics, retailers can monitor audience sentiments related to their influencers to safeguard their brand image from potential harm.

For example, when Cristiano Ronaldo removed a pair of Coke bottles, he unintentionally hammered the company with a loss of $5 Billion per share on the stock market.

Prevent Cyberbullying

Real-time analysis of audience sentiments, negative feedback, and competitor campaigns is invaluable for proactively addressing cyberbullying. As digital presence grows, it’s important to guard against subtle attempts by haters and competitors to downgrade your brand image.

Remember the controversial ad of Pepsi where a kid uses two Coke cans to buy one Pepsi. Today competitors are promoting similar ad content via social media, and you may never know how customer sentiments may change for your products.

Future Trends Driving Better Real-time Analytics

Edge Computing

Using edge computing retailers can reduce challenges associated with latency and data delay. In edge computing the processing of data takes place closer to the source. So, retailers can process data in their warehouses in real-time.

Edge computing will empower instant decision-making, especially for retailers with physical retail stores.

Conversational Analytics

With the rise of voice commerce and smart chatbots, retailers can use conversational analytics to understand the sentiments of voice commands made by consumers while shopping.

Conversational analytics will help businesses analyze the conversational sentiments of audiences while making purchases, browsing, or interacting with platforms via voice commands.

Augmented Analytics

Augmented analytics in retail focuses on automating the retail analysis process, such that real-time analytics becomes more accessible to non-technical users as well.

From data preparation to insights generation, augmented analytics aims to simplify everything for non-tech users.

Key Takeaways

Real-time data analytics allows increased conversion and retention rates, efficient customer order fulfillment, and improved inventory management.

Real-time analytics in retail help influence consumers’ buying behavior for better conversions.

Targeting customers from different endpoints for omnichannel is efficient with real-time data analysis.

Real-time analysis helps in analyzing live audience sentiments for better promotional event outcomes.

Maintaining a positive brand and preventing cyberbullying is feasible with real-time analytics.

Real-time analytics in retail can effectively address potential bottlenecks and give businesses an edge over competitors by considering various aspects. However, implementing a real-time analytics system is a complex task that demands a deep understanding of both business and analytics.

At Binary Semantics, we have been offering data analytics services for over three decades, serving numerous businesses in meeting their analytical requirements. Real-time analytics in retail has driven substantial growth for many businesses. Connect with our team of experts to discover how your business can benefit from data analytics optimization.

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