Data Analytics: The ultimate power for businesses to unlock the true potential of e-commerce

The e-commerce sector has grown exponentially with the advent of startups selling unique things or selling things uniquely on the internet. The growth of online businesses is equally attributed to the rise of internet users in the country, accounting for 780.27 million as of May 2021. Everything is available online within a few taps on your smartphone, from ordering groceries to getting consultations from doctors. Owing to this impressive pace and scale, the e-commerce market is likely to reach US$ 111.40 billion by 2025, with more innovations to be unveiled in the coming years.  

However, before setting hopes high, let's read another side of the same story. 

Several online startups flounder to run profitably, and in fact, 90% of them fail within the first five years of inception, as per market estimates. The reasons are multiple: poor online marketing strategy, low or no online search visibility, cash crunch, competitor's dominance, poor timing, lack of right talent, etc. In a nutshell, all these players lack in-depth market research and do conduct that; they need to unlock the power of data analytics. 

How can data analytics drive the growth of e-commerce businesses?

Track and enhance customer buying journey 

By leveraging data analytics tools, businesses can capture a user's past interactions with brands and go through the products they viewed, items they bought, and so on. These inputs provide a clear picture of customers in terms of their likes and dislikes while also giving access to products with high demand, what time of the year the need for specific products are likely to rise, and other such market trends that are essential for businesses to enhance their existing offerings to expand their customer base and widen their reach in the ever-evolving market. 

Personalized experience

With access to the aforementioned inputs, businesses can augment their communication strategies and thus attract more prospects or retain existing customers. For instance, they can send customized emails containing special discounts to some users, show targeted ads to different consumers, implement up-selling/cross-selling strategies to individuals, etc. The world's largest e-commerce giant Amazon is a great example. For instance, it recommends a range of products from different categories that the user has been browsing to shortlist and put products in front of them that they are likely to click, explore more, and eventually buy. 

Improved buyer sentiment analysis

Several online brands extract relevant data from customer reviews. For instance, Yunting CEM, a customer data analytics platform, collects reviews and conducts sentiment analysis for prominent players, including Haier, Unilever, and Huawei. These companies create multiple product lines and sell millions of products online. As a result, using Natural Language Processing technology, companies can analyze customer sentiments and know precisely how buyers think of a particular product. For example, marketers of Huawei can easily find out if consumers have positive, neutral, or negative sentiments about any feature, design, battery life, or overall performance of their smartphones. 

Enhanced customer service

There are multiple ways through which data analytics can improve the customer service of online businesses. For instance, companies can enable their customer service staff to increase the overall response timelines by monitoring the average response speed. Similarly, they can improve service quality by sharing questionnaires and collecting customer feedback in real-time. Also, they can identify issues associated with the delivery process and avoid possible logistics challenges by monitoring the delivery time of the goods. In short, companies can deliver delight to consumers precisely the way consumers expect from them.

Optimized pricing

Moving away from the conventional research models that heavily rely on manual processes, businesses can monitor competitors' pricing in real-time using data analytics. Since the market keeps fluctuating, companies must stay updated with the pricing factors by considering a large data set, which is only possible through data analytics. For instance, Octoparse is a famous big data extraction tool that can extract product details from various e-commerce sites and export those inputs into Excel, HTML, and other formats. Companies can also find essential product information like its name, price, color, weight, rating within a few clicks. 

Demand forecasting

Data analytics enables online retailers to leverage historical sales data and other past experiences to predict future sales and estimate future stockings ahead of time. They can also prepare effective marketing campaigns using these qualitative inputs to attract customers and establish a strong brand presence in the market. A few decades ago, when technology wasn't invented, predicting the number of products was an impossible task, especially during the festive seasons when sales go high and business owners need to maintain working capital efficiently. However, with data analytics, prediction is not only possible, but it is also more accurate than ever. 

In closing

Data analytics has given online businesses a lucrative opportunity to search, decipher and explore the hidden growth aspects of the e-commerce sector. It has made them more prepared and responsive even to uncertain market conditions, enabling companies to innovate their existing product offerings and serve customers more seamlessly than ever before. 

The article has been written by Sudeshna Datta, Co-Founder & Executive Vice President, Absolutdata