Why predictive analytics is important for successful ecommerce website – Top Ecommerce Startups

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Most of the ecommerce starups or new website builders focus on ecommerce website builders, ecommerce website templates, ecommerce website designs or free ecommerce website tools to build their ecommerce website.

After successfully building one such website, they go to market with their SEO, SEM and marketing tools to make it the BEST ECOMMERCE WEBSITE to compete with the other BEST ECOMMERCE WEBSITES out there.


How predictive analytics helps ecommerce website?

First let’s understand what is predictive analytics – Predictive analytics is a technic that uses machine learning to analyse data and make predictions. Ecommerce website ignores this because of the complexity and costs involved in implementing.
With Big Data, both of these things are changing, as more affordable solutions are now available that can be used by companies of all sizes.

Smart Search:
Smart Search
A visitor’s interaction with a ecommerce website starts with a search, providing smarter results or smarted recommendations based on past searches or click through would help in better results as per the visitors choice
Smart Recommendations:

recommendations
An effective rule engine should be built to recommend products apart from the one the visitor has searched, recommendations and shown based on the browsing patters and historical data of the visitor if the price is something one is looking at or the features is something a visitor is look at or if it’s some things else, every product had got different attributes like Price, quality, brand, feature, using these data can increase the conversion rate
Pricing Management:

pricing management
Predictive analytics analyses pricing trends based on sales information to determine right price – this is done by analysing historical data for sales, product and region and customer segment.
Local Currencies:

Local Currency
This is one of the basics, when a visitor enters a website, products price should be in local currency, one should be given an option to change the country if required. This would give your visitor better shopping exp. rather working on currency converters manually.
Season based selling:
Since ecommerce is global store front, based on region and session the products hast to be featured. People searching us United States on a black Friday would be for discounted product, so the search or recommendation should be giving higher priority to show results based on discounts and not with MRP.
Lateral recommendation:
This is something done in physical store front, where products are placed in such a way that people might buy one product along with other, in a case if a visitor is buying gift for his Kid, it’s likely he would buy some educational products for his kid.
Summary:
Better understanding of historical visitor data will help in serving them better and also increase the conversion rate. This helps in understanding trends, segmentations and regions which makes decision making easier for the business and customer.


Also See 20 Startups (Logos) Acquired by Yahoo’s CEO

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