Real-Time Data

Real-time data is information that is available instantly, with minimal delays or time lags. In the e-commerce and retail space, real-time data is collected through various sources, including social media, mobile apps, websites, and point-of-sale systems.  

A Merit expert says, “Real-time data, as we all know by now, give brands an ongoing insight into how their customers are interacting with their business. It enables them to tweak every single function, and make it more efficient, personalised and impactful.”  

Today, more than ever, as brands go global and cater to a wide variety of customers from different cultural backgrounds and preferences, real-time data can go a long way in ensuring that the business remains relevant and competitive. 

Personalisation of Customer Experience 

One of the primary benefits of real-time data is that it allows businesses to personalise their customer experiences.  

By tracking customer behaviour in real-time, retailers can offer tailored recommendations and promotions based on their customers’ preferences and buying history. This not only improves the customer experience but also increases the chances of conversion, as customers are more likely to buy products that they are interested in. 

Optimise Inventory Management 

Real-time data can also be used to optimise inventory management. By tracking inventory levels in real-time, retailers can identify which products are in high demand and quickly restock them. This prevents stockouts and ensures that customers can always find the products they need.  

Additionally, real-time data can help retailers identify slow-moving products and adjust their inventory accordingly, reducing the risk of overstocking and minimising the need for costly markdowns. 

Monitor Live Sales Performance 

Real-time data also allows retailers to monitor their sales performance in real-time, enabling them to identify trends and respond quickly to changes in the market. This can be particularly valuable during peak shopping periods, such as Black Friday and Cyber Monday, when retailers need to be able to respond quickly to changes in demand.  

Real-time data can also help retailers identify potential fraud and security threats, allowing them to take immediate action to prevent losses. 

Optimise Pricing Strategies at Opportune Moments 

Real-time data can be used to optimise pricing strategies. By monitoring competitor prices and customer behaviour in real-time, retailers can adjust their pricing strategies to stay competitive and maximise profits.  

For example, if a competitor lowers their prices, a retailer can quickly respond by lowering their prices as well, preventing customers from switching to a competitor. Similarly, if a retailer notices that a particular product is selling well, they can increase the price to maximise profits. 

Be More Responsive in Improvements to Customer Service 

By tracking customer feedback and complaints in real-time, retailers can respond quickly to issues and improve the overall customer experience. For example, if a customer complains about a product, a retailer can quickly respond by offering a refund or replacement, preventing negative reviews and improving customer loyalty. 

Optimise Marketing Campaigns 

By tracking customer behaviour and preferences in real-time, retailers can target their marketing efforts more effectively, increasing the chances of conversion.  

For example, if a retailer notices that a customer is interested in a particular product, they can send them targeted promotions and recommendations to encourage them to buy. 

Increase the Chances of Preventing Fraud 

Finally, it can help businesses prevent fraud by identifying suspicious transactions and patterns in real-time, enabling them to take action before the fraudulent activity causes significant damage.  

Did you know? Fraud is a serious problem for ecommerce and retail businesses, with losses from fraudulent transactions estimated to reach $130 billion by the end of 2023, according to Juniper Research. 

How Walmart Used Real-Time Data to Drive Business Decisions 

A significant example of a brand that uses real-time data to drive business decisions is Walmart. The use of real-time data has equiped them to optimise inventory management, enhance customer experience, and improve operational efficiency.  

Here are some of the ways in which Walmart use real-time data: 

  • Manage inventory levels by tracking sales, stock levels, and customer demand. This helps to reduce stockouts and avoid overstocking, which can lead to waste and increased costs. 
  • In-Store Analytics to analyse customer behaviour in-store, such as foot traffic and product interactions. This helps to optimise store layouts and product placement, which can increase sales and customer satisfaction. 
  • Price Optimisation based on competition, demand, and other factors. This helps to improve profitability and increase customer loyalty. 
  • Operational Efficiency: Walmart uses real-time data to optimise its supply chain and logistics operations, such as tracking shipments, managing inventory levels, and predicting delivery times. This helps to reduce costs and improve efficiency. 
  • Customer Analytics: By analysing customer behaviour and preferences, such as shopping habits, purchase history, and social media interactions, Walmart have been able to personalise marketing campaigns, improve customer engagement, and increase customer loyalty. 

In addition to the above examples, Walmart also uses real-time data for energy management and sustainability. 

Merit’s Expertise in Ecommerce Data and Intelligence    

Our state-of-the-art ecommerce data harvesting engine collects raw data and provides actionable insights;  

  • Three to four times faster than standard scrapers    
  • At lower cost    
  • With Increased accuracy (up to 30% compared to standard scrapers)  

Our powerful, new scraper engine can gather massive data sets from multiple sites and geographies in real-time so you can stay informed on customer behaviours and market trends. 

Merit’s eCommerce and retail data engine provides a high degree of confidence in insights generated from analytics – thanks to confidence in the data quality and access to enriched data.  

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