Mon. Sep 21st, 2020
Analytics E-Commerce
Analytics E-Commerce
Analytics E-Commerce

Analytics E-Commerce- At present, most business owners use just 0.5% of all the big data at their possession. The bigger chunk of data remains siloed in proprietary software and external tools. However, as machine learning technologies are getting significantly better at retrieving and transforming scattered data into actionable insights, e-commerce companies are finally starting to unclog their data pipe. Below are just a few benefits that emerge as a result.

Higher Revenues from Cross-Sell and Up-Sell Campaigns

The typical customer buying journey is no longer linear – they switch between website, search Google for promo codes and, according to a blog post by Konstruct Digital, drift to trusted online sources for reviews, before returning to your website and making a purchase through another device.

Capturing and analyzing all those interactions is a challenging task for human analysts. But it hardly present any difficulties for an intelligent algorithm. By gauging and churning all those online behaviors, new-gen analytics tools can compile comprehensive user personas – data-rich profiles of different audience segments. The depth of such profiles goes beyond the general demographics data. They capture all the interactions a user previously had with a brand – products viewed, clicks, past purchases etc. – and deliver personalized product recommendations based on everything the system knows about a particular customer.

Predictive intelligence recommendations can significantly improve your business bottom line. Amazon’s product recommendation engine drives 35% of cumulative company revenue. What’s even better, the results arrive fast: companies who have already chosen to adopt a predictive intelligence solution have reported a 40.38% influence in revenue after just 36 months post-adoption.

Data-Driven Product Research and Product Development

Deciding upon new products to sell or develop is never an easy task for e-commerce brands. The idea may look good on “paper”, but eventually flop due to poor market research and product positioning. According to Hubspot, 66% of products fail within the first two years and 80% of new products stay on the shelves for less than two years.

“A lot of new e-commerce entrepreneurs tend to capitalize on-the-moment product trends, rather than develop a 360-degree industry outlook and plan ahead,” said Nahar Geva, CEO of Zik Analytics, who claims his company helped over 20,000 eBay sellers in the last one year. “But every hunch should be backed by solid data, showing you exactly what people are buying, what price they are ready to pay on average and so on. Most believe that you need to pay at least five figures to some consulting company for such research. But that’s no longer the case. Data analytics platforms can supply you with all those insights for a fraction of the cost. You just need to learn how to interpret that data”.

Data analytics platforms can supply you with all those insights for a fraction of the cost. You just need to learn how to interpret that data. “Consumers are being prominently vocal online with their demands and preferences,” said Vlad Dobrynin, CEO of, a third generation social community applying artificial intelligence (AI) to revolutionise how workers and businesses connect. “Brands that manage to capture that data and apply it to product development and how they hire their staff succeed better in the long run.”

Enhanced Pricing Strategy  

Big data analytics unlocks access to more granular insights, allowing you to surge or drop the prices depending on individual customer’s tolerance just like Uber does. Data-backed price management initiatives bring significant results in the short terms perspective: 2%-7% increase in business margins and a 200-350% average growth in ROI over a 12-month period according to Deloitte data.

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Article Credit: Forbes

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