Key Roadblocks to Data Analytics Success in eCommerce

India is one of the fastest-growing eCommerce markets in Asia/Pacific, according to research firm Gartner. This market is growing at approximately 60-70% every year, with the global players like Amazon intensely competing with the local eCommerce players like Flipkart, Snapdeal, etc. Almost all the players are showering discounts and offering big deals to win over customers.

But beyond the intensely fought battles, at the backend, a team of analysts at every major eCommerce firm is crunching huge amount of information to find out how they can better fine-tune their products or services to gain customers. Analysis of data, sometimes, reveals hidden insights, which can give a huge competitive advantage.

Unfortunately, analyzing this data has its own set of challenges. Skilled resources is one of the biggest challenges for the industry as of now. Skilled professionals are needed to evaluate business requirements and impact, and apply the big data and analytics concept and technology to them.

Prashant Malik, Co-founder and CTO, LimeRoad.com puts this in context, "The challenge in implementing data analytics solution is to get data out in the best possible format. What is the best metric that should be looked at? And all this varies with user segment that you are dealing with, platform being studied, the eCommerce category being looked at, and so on."

Other key roadblocks that prevent the success of data analytics initiatives include:

IT Readiness: The failure to involve IT from the very beginning of the analytics journey can lead to significant issues down the road if technology gaps and limitations aren’t understood up front.

Tools: A lot of analytical tools are available in the space and it becomes a challenge to select the right solution. Further, these tools are unable to provide complete answers to business problems. The companies are spending a lot of time on data integration, reporting, and analytics before they can see any improvement in their results.

Change Management: Organizations fail to understand how predictive models change the current business and technology operations-policies, procedures, standards, management metrics, compliance guidelines and the like.

Lack of End-user Involvement: Lack of end-user involvement in the planning, design and ultimate roll out of the predictive models can be detrimental to the efforts.

Another key challenge is RoI of deploying data analytics tools. Sanjay Sethi of ShopClues explains, "RoI becomes a challenge in implementing a data analytics solution. E-commerce businesses tend to focus on the scalability of their big data environments rather than quickly identifying performance bottlenecks. Adding more servers may not always help as much as performance management techniques."

In the extremely competitive space, data analytics is now not a choice but a business imperative for the eCommerce players. To gain a competitive edge and retain their market position, eCommerce firms need to quickly rise above these challenges and successfully ride the big data analytics wave.