Quickborn Consulting participates in Retail Technology Conclave 2012
With an ever-growing focus on the emerging markets, Quickborn Consulting continues to grab every opportunity to interact with retail industry players to discuss challenges and opportunities. The Annual Retail Technology Conclave conducted by the Retailers Association of India at the Bombay Exhibition Center saw industry experts from across India participate in a 15 hour event spread over 2 days the 17 - 18 of May 2012.
The event was kick-started by a warm welcome by Kumar Rajagopalan, CEO of the Retailers Association of India (RAI) urging CIOs and technology leaders to mutually make the event a fulfilling experience. B S Nagesh, Chairman of RAI spoke about the myriad of opportunities and how technology companies can bring best of breed retailing business practices from their experiences in other markets to India and increase the success rate of IT implementations in the Retail Industry which currently sulks at less than 10%.
Indian Retail and the IT opportunity
With the sessions commencing, Krishna Kumar Natarajan, Vice Chairman, NASSCOM and MD & CEO, MindTree Consulting talked about retail in India being a great contributor to the growth story with the market size pegged at US$ 425B(2010) but is still controlled by the 95% unorganized and fragmented players. He expressed a positive sentiment that by 2015, the organized retail will reach 9% and 20% by the year 2020 thus offering a huge potential for growth. The opportunity areas being:
• Rural Potential
• Mobile Adoption
• Disposable Income
• Generation Next
According to Natarajan, the IT spending in Retail has grown by 17.5% in FY 2011 and is expected to grow at 16% (CAGR) over FY 2011-2020. He identified the Role of IT in enhancing Business Intelligence through Analytics; decreasing infrastructure spends for mid-size retailers through cloud adoption; connecting with the informed consumers through social media and contribute to the enterprise mobility.
IT Companies are responding to these opportunities by:
• Creating Centers of Excellence
• Creating tailor-made products for the Indian market like the touch-proof/spill-proof design specifically suited for rough Indian conditions
• Moving to open source platforms to reduce the set-up and running costs of software
• Collaborating with partners to develop low cost solutions for small Indian retailers
Predictive Analytics in Retail
Professor Galit Shmueli, SRITNE Chaired Professor of Data Analytics and an Associate Professor of Statistics & IS at one of India’s top B-School Indian School of Business delivered a great introduction of predictive analytics to industry folks obsessed with BI. Galit started with a striking example of how Indian e-commerce differentiates itself from the rest of the world by doing a lot of ‘Cash-On-Delivery’ orders, where the customer pays once the product has been delivered. All e-commerce companies with serious business are concerned about whether the customer pays once the product is delivered.
She mentioned that companies including India’s first $1B e-commerce company - Flipkart are worked about reverse logistics for non-performing deliveries – and how they should be motivated to question themselves “Will this customer pay?” – and to answer this question using predictive analytics.
There was a brief talk on how predictive analytics differ from Reporting and other BI tools which are at best real time. With predictive analytics retailers could ask questions into the future with existing information (categorized data).
In the process of prediction, it is pivotal to identify what you want to discover, without which the predictive modeling becomes directionless. There was a clear discussion around measuring (determining outcomes and predictors) that would help to identify the data samples that need to be extracted and identifying the right model to evaluate the outcomes. There were 4 examples of using predictive analytics in the retail context that were mentioned:
1. Generating a personalized promotional offer to a customer
2. Which employees to train?
3. Customer Churn: Which members are likely to renew their membership?
4. Product level demand forecasting
Using the following algorithmic techniques and using ‘holdout’ data:
1. Classification and regression trees
2. Regression Models
3. K-Nearest Neighbors Modeling
ReTechCon – continued its tradition of bringing in new debates, one such was the CIO vs. CFO debate and how technology companies can help CIO justify budgets in an industry plagued with implementation failures with the onus always on the CIO being the key driver of the project. It was unanimously agreed that unless there is a project sponsored within the business team the implementations will always suffer the risk of being unused to its potential.