e-commerce data intelligence

Data reveals that e-commerce businesses that rely on analytics intelligence have shown a 19% increase in sales. In this blog, we look at why analytics is critical for online retail businesses and how it can impact different aspects of the business, such as inventory, pricing, customer service and so on.  

A global consumer forecasting company which specialises in forecasting fashion trends and delivering seasonal insights on trends and purchase patterns, was facing challenges related to data acquisition and analytics. They were unable to – 

  • Process large volumes of clothing and accessories data from retailers and categorise them 
  • Analyse the nuances associated with the varied products offered by each retailer – such as colours, material etc. 
  • Maintain data collection routines as retail websites underwent periodic changes through seasonal variations, occasions and trends 
  • Eliminate noisy data acquired from retailer websites, which may not necessarily be relevant to the end consumer 

They were looking for an analytics solution that would ease data collection, filter data by category and unique descriptions, provide visuals based on collections, styles etc, and eliminate unwanted information. 

But, what is the outcome of implementing this analytics solution? They were able to process 18 million data points and images in a day, record a 60% increase in product precision and data accuracy, and experience quicker reaction times to changes in retailer websites to name a few. 

Role of Analytics in E-commerce Businesses  

Simply put, analytics uses AI and machine learning tools to enable businesses to gather data from all sources and use it to understand customer needs better, and drive better business decisions. With analytics, businesses can; 

  • Collect data related to customer demographics (such as age, gender, location etc.), and data related to their past behaviour, interactions (on the website), and purchase patterns  
  • Use this data to generate actionable insights to acquire more customers, boost conversions, and run more relevant, targeted marketing campaigns 
  • Create a touch of personalisation which will enhance the quality of engagement with customers 

Let’s look at these in more detail. 

Using Customer Data to Deliver Personalised Experiences 

Have you observed your shopping experience on Amazon? When you log in to search for an item, Amazon customises your experience by showing you clothes, household items, furniture or food based on your past browsing and purchase history. In a way, the landing page when you log into Amazon is designed to suit your personal preferences based on your past history.  

What happens with this personalisation, is it eases your search and buying experience, and quickens your journey from search to purchase. Amazon’s personalisation strategy is a brilliant example of how brands can use customer data to deliver a better experience, without seeming very invasive. 

Data shows us that websites that personalised user experiences saw a 19% increase in sales. And, 85% of online shoppers found personalisation on their homepage advantageous, and 92% saw cart recommendations to be useful when making a purchase. 

While personalisation comes with many benefits, it’s good to understand how to design personalised marketing campaigns without violating the privacy preferences of your customers. For example, if you’re planning to incorporate personalisation into your website and campaigns; 

  • Segment your first-time visitors from repeat customers.  
  • Identify which pages they visited, what items they added to cart, which page they last viewed before exiting the site etc. This will give an insight into their preferences and browsing pattern. 
  • Engage in email or social media campaigns that allow your customers to opt-into the personalisation. That way, they are more likely to open your emails, or engage with your content online. 

The Importance of A/B Testing 

As the name suggests, A/B testing is the process of testing and analysing how users are interacting with different versions of a new website feature, content or campaign. The goal of A/B testing is to convert users, be it to make them visit the website, subscribe to a newsletter, sign up for a service, purchase a product or to engage in a survey. Some important points to keep in mind when A/B testing are; 

  • Identify a measurable goal for your A/B testing campaign
  • Segment your audience into categories and perform the testing 
  • Take note of your current performance metrics – be it CTR (click through rate), email opens, conversions or so 
  • Measure your test results with past metrics to understand how effective your engagement has been 
  • Tweak the content or campaign based on user feedback 

Forecasting Demand Through Data Analytics 

Demand forecasting is estimating the future demand for a product or service. Usually, businesses collate data from past browsing and purchase trends to analyse and predict the likely demand in future. Demand forecasting has a number of advantages; 

  • It helps you plan inventory better 
  • It gives a better understanding of why a customer did or did not purchase from your site. For example, you may have run a campaign for 100 potential customers. 60 may have visited your site, 30 may have spent time exploring your products or services, and 10 may have added to cart. However, only 5 may have purchased from your site. Analysing these trends can help you identify, for example, why only 5 out of 10 purchased your product. Was the price too high? Was the size not available? Did the product not suit their specifications? etc. 
  • Insights from demand forecasting leads to better conversions 
  • It helps design better campaigns to retarget your customers and lead them towards repeated engagement with your brand  

Using Data for Price Optimisation 

Pricing doesn’t necessarily mean providing a product or a service at an economical price. Sometimes, low costs can drive higher sales (especially during sale), and sometimes high pricing can still drive sales if it is positioned as a product that is worth the price (say a luxury product).  

A Merit expert says, “Determining the ideal pricing strategy is an ongoing process which relies heavily on analysing a number of factors such as competitor pricing, the customer demographic and their willingness to pay, supply and demand in the market, and the positioning you want to establish as a brand. In fact, price optimisation also involves price testing, to determine at what price your potential customers are willing to buy your product or service.” 

In conclusion, ecommerce analytics is a must for online businesses today because it can deliver critical insights into the overall performance of your ecommerce business, and also go into the nuances of managing your inventory better, pricing your products right, driving more effective sales and promotions, and delivering an enhanced customer experience. 

Merit’s Expertise in e-Commerce 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 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.  

To know more, visit: https://www.meritdata-tech.com/service/data/retail-data/ 

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