Interview with Manit Parikh, Chief Strategy Officer, DTDC Retail

With Cypher 2016 just 2 weeks away, Analytics India Magazine brings its readers a series of Interviews with Cypher 2016 Speakers. This being the first in the series. AIM recently interviewed one of its esteemed speaker at Cypher 2016, Manit Parikh, Chief Strategy Officer, DTDC Retail. The interview revolved around his talk at Cypher and the role he plays in the analytics industry. Manit Parikh, a senior management executive, has more than 11 years of International experience in the corporate and consulting sector. He experience ranges from being in executive management to playing entrepreneurial roles with hands-on large scale business strategy, global Market, Credit & Operational Risk, to carrying out complex, enterprise-level risk, Big Data, Quant & Banking analytics with data and model validation for clients in BFSI, FIG, Retail, Logistics & eCommerce. Here is the excerpt from the interview. AIM: Would you like to share with us about your talk at Cypher? Manit: Infusing Analytics into the decision-making process: To embed an “Analytics first” philosophy into the organization, leaders need to identify the business problems, define the appropriate key performance indicators and use analytics in their decision-making processes along with organizing and governing analytics capabilities across the Organization. AIM: Tell us about your journey in the analytics industry. Manit: Over 11 + years of my career I have used analytics as a back bone for my success either to understand the client, their business and how to help them grow their business by using predictive analysis for cross selling and up selling. If I have to sum up, analysing data in a predictive manner has allowed me to gain professional accolades along with huge growth monetarily and a pool of great minds in my network. AIM: Why do you think analytics is needed in an organization? Manit: There is clearly a need for organizations to use deeper, more comprehensive Analytics to improve performance by addressing different issues, including:

• Getting closer to the consumer: Intense competition for consumer loyalty means that companies need the ability to draw deeper consumer insights from “big data” and make quicker, fact-based decisions.
• Optimizing the supply chain: Increasing pressure to reduce costs while simultaneously increasing service levels is also driving a need for improved decision making throughout the supply chain.
• Strengthening relationships with the retailer: Retailers have direct access to the shopper, have a wealth of information at their disposal, continue to mature their Analytics capabilities and are now expecting this level of sophistication from their suppliers.
• Better managing talent: How to hire, manage and deploy the right talent across the business to meet global marketplace needs.

AIM: Would you like to highlight the benefits of using analytics at DTDC Retail? Manit: Predictive analytics has benefited DTDC retail in the below key areas:

• Has improved Customer Engagement & Increased Revenue
• Launched promotions that were better targeted for the customers
• Enabled optimize Pricing to maximize profits
• Increased efficiency in Inventory Management
• Minimized fraud by proactively detecting it
• Optimized cost of Customer Service
• Analyzed data resulted in making better decisions In Real-Time

AIM: Would you like to share about a specific use case of analytics that has brought significant value to DTDC Retail? Manit: 1.Launched promotions that were better targeted for the customers: Promotions are a must-have for our business to succeed but it’s not easy to get them right. Predictive analytics allowed us to correlate data from multiple sources to determine a personalized promotion that worked with our customers resulting in 18% increase in traffic to the website with 30% converted in sales. 2. Enabled optimize Pricing to maximize profits Traditionally we used to use A/B or Bandit Testing to set prices for different products and came up with the optimal price that could result in maximum profits. Problem we faced was, each price was set manually & was prone to error. By using predictive analytics we took a different approach by building a model to support real-time pricing that used input from various sources like Historical Product Pricing, Customer Activity, Preferences & Order History, Competitor pricing, Desired Margins on The Product, Available Inventory. This enabled us to crack the complications associated with setting prices with different variables in the supply and demand equation. AIM: How do you think ‘Analytics’ as an industry is evolving today? Manit: Analytics today has gone Prime Time. Needless to say that business analytics has come a long way. Enterprises of all sizes now can apply analytics and data modelling tools on top of Big Data technology to extract meaningful trends and insights that can quickly deliver valuable business intelligence, agile visualizations and advanced predictive analytics—on any device or platform at any time. Number-crunching and reporting that used to take hours gets accomplished now in 30 seconds, at a fraction of the cost. In today’s business world the impact of social media is so grave that even the smallest businesses rely on Twitter, Facebook and advertising on LinkedIn or Google to find new customers and enhance their brand. But they don’t know what the impact is on their business, or what they can do to improve it? A strong business analytics solution can help them extract valuable data from social media, as well as point-of-sale and customer records. Analytics gives companies a much more complete look at their businesses, allowing them to make quicker, more-informed decisions as well as reduce costs and increase efficiency and productivity. Plus, there are more user-friendly dashboards and mobile applications that provide concise, detailed, sorted information any time, any place. AIM: What are the most significant challenges you see in the Analytics space? Manit: Analytics depends on sufficient volumes of high quality data. The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then deciding what subsets of data to make available and easily finding high quality data scientists.