Ecommerce is a fast-moving and dynamic system. It is much different than regular commerce systems. The customers in ecommerce system have very small barrier to exit. It’s just one click away. On the other hand, once a customer enters a regular store various marketing methods (including visualization, touch, smell etc.) could be utilized to keep him engaged in the store.

It is very important in ecommerce to keep the customer engaged and interested. The statistics on ecommerce show that there is a big demand and large payoff for personalizing the offerings each of your customers gets when they visit your site.

  • 59% of online shoppers believe that it is easier to find more interesting products on a personalized online retail store
  • 56% are more likely to return to a site that recommends products
  • 53% believe that retailers who personalize the shopping experience provide a valuable service
  • 45% are more likely to shop on a site that offers personalized recommendations


Personalization extends beyond remembering the customer’s last purchase and showing recommendation based on their last purchase. The organization needs to use all the data that is available about the customer (social media interaction, offline purchase patterns, marketing campaign response, survey response, geographic information, public records, weather data etc.) and create a compelling experience for the customer that engages them and encourages them to take action and visit again.

This is a challenge in organizations where the legacy systems could not handle the volume and variety of the data that needs to be analyzed to create personalized recommendations for the customer. We have specialized in using Big data technology to handle precisely the same problem.


As part of our ecommerce analytics offerings we provide the following verticals. ecom1

  • E-commerce backend analysis
  • Web-site optimization analysis
  • Customer experience analysis

Our advanced e-commerce offerings include:

  • Target Marketing:
    • Marketing campaign performance scorecard
    • Customer segmentation and  behavior analysis
    • Spend analysis on customer categories
  • Online ad effectiveness:
    • Dynamic segmentation
    • Ad performance scorecard
    • Ad position optimization
  • Market mix of different channels:
    • Internet advertisement channel usage and profitability analysis
    • Marketing mix optimization for online campaigns
    • Identifying ideal mix of online and offline media for marketing
  • E-commerce Optimization:
    • Customer behavior analysis
    • Conversion rate maximization
    • Search engine optimization
  • CLTV and Churn Analysis:
    • Estimate and project number of customers of a given competitor(s)
    • Predict at risk customers
    • Calculate life time value of customers
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