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Analytics Magazine

Executive Edge: Six ways of value-creation through analytics in E-commerce

July/August 2014

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By Rohit Tandon and Shruti Upadhyay

Increasing popularity and access to the Internet has changed the way marketers are interacting with customers. These customers are smart, well informed and empowered, as Internet connectivity is available to them at their fingertips and on the go. It has therefore become imperative for organizations to be on the customers’ online radar with respect to new products or services and to be able to influence their choices.

Not surprisingly, according to one study, 34 percent of marketers are generating leads through Twitter. India’s online retail market grew at a staggering 88 percent in 2013 to $16 billion and continues to grow. These examples are a testimony to the growth of e-commerce. The Internet deluge has opened an assortment of opportunities. Customers are able to buy high-end fashion and designer shoes, book hotels, buy movie tickets and you-name-it.

Therefore, an opportunity exists for business research to capture, compile, churn and store colossal bytes of information about customers, suppliers and operations. This is what we call the age of “big data.” We believe that this age is a natural progression in online business and is here to stay. We are already seeing a surge in adoption of digital channels such as social media, e-mail marketing and display ads in e-commerce. Imagine the amount of data this has created for marketers to lay their hands on for analysis. Despite that, in the race to utilize the online space, marketers may be focusing more on advertising and less on analysis of the data that could potentially increase sales.

In our opinion, understanding the customer behavior becomes more complex in business-to-consumer companies and more so in a 24/7 e-commerce business that sells technology products in an increasingly commoditized industry. A strong analytics foundation may make e-commerce a thriving and successful channel of sales. Businesses, therefore, are increasingly creating customizable campaigns for their installed base customers and improving sales effectiveness through e-commerce.

For example, pricing and merchandising decisions need to be taken in real time, and the need to have real-time insights is ever-increasing. To make these decisions faster and better, marketers would need to quickly analyze their digital marketing strategies by mining data exhaustively and cost effectively through advanced analytics.

Key Drivers of Increased Revenues

An organization’s ability to achieve its goal of increased revenues and margins would depend heavily on its ability to improve three key drivers: 1) volume of customer traffic to the online store (number of visits); 2) customer conversion (percentage of conversion); and 3) basket size (revenue per average order size). Analytics has a very important role to play in this value chain. So while organizations may have the best talent with an analytical mindset and eagerness to apply it, we need to equip data scientists in organizations with the right tools and insights.

Conversations with analytics professionals reiterate our belief in some of the following must-haves that will elevate an organization’s e-commerce agenda to the next level:

1. Development of best-in-class tools and techniques are a must to build scalable solutions and tackle the optimization of key drivers.

Over the years various products such as SAS have provided excellent development environments, but every data scientist had to start from scratch and depend on their “personal” techniques to tackle new problems. However, in recent years, data scientists and organizations are now moving toward using templates and building packaged models and solutions to reuse and replicate technologies with ease.

One of the first such pilot solutions within HP was developed for HPDirect.com’s demand generation function, where global analytics developed V.1 of a series of demand generation models. These models also paved the way for the development of customer targeting models. In most organizations, such initiatives if implemented have the potential to lay the foundation for similar opportunities with other business functions such as planning, store operations and category management. When an organization reaches such a stage of maturity, that’s when true “return on data” (ROD) is possible.

2. The three Ws …whom, what, when. Traditionally, marketers have used a uni-dimensional approach to target customers. However, results show that these can be sub-optimal and might have an adverse effect on customer loyalty and brand image. Answering questions such as whom to target, what to offer and when to offer bring a paradigm shift in garnering customer interest and loyalty. These help rank customers on their propensity to re-purchase, and lead to preferential treatment of the right customers with the right product portfolio or allow marketers to understand when to offer discounts.

Effective tools and modeling will also note clues on probability of customers picking one product over another or repeat customer behaviors. This brings us back to the importance of using effective, proven analytics tools and techniques.

3. Automate and innovate. Creating and applying big data algorithms will help organizations in taking appropriate actions. Many of them are programmed automatically, save time and allow better decisions faster. Creating a robust tool-based ecosystem that allows creation of funnels that track visitors, bounce rates, conversations, etc., is vital to a successful Web analytics initiative.

4. Site search analytics. Tracking site search is a very useful resource that allows you to know what your visitors are looking for in your website. Is the search engine directing the customer to your website or redirecting them to the next best option in absence of the product? Keeping tabs on this will help companies increase customer loyalty and sales.

Another application of site search analytics allows you to understand what is being searched on your website. By understanding this, marketers can influence the site layout and design so that visitors are able to easily locate answers to common queries or the most searched products.

5. Marketing spend optimization. HP’s online store uses a mix of marketing vehicles to reach different customer segments with different communication and buying preferences. Optimizing spend on various marketing vehicles is critical to optimizing demand generation efforts as well. However, determining which marketing mix is most beneficial to the business is not an easy process, requiring not only a scientific approach to analyzing spend and revenue, but also a test-learn-optimize culture. For example, ongoing analysis of the response to different types of marketing vehicles helps in identifying the best fit for a particular type of message. Based on such analysis, one can decide if a banner would work best vis-à-vis a customized landing page, or would an e-mail campaign be the best option.

6. Connect marketing with warehousing. In large supply chain environments, an accurate forecast of orders that get shipped out of the warehouse on a daily basis can be tracked using predictive analytics methodologies to enable accurate warehouse space/staffing allocation in order to meet the aggressive shipping timeline.

In conclusion, marketers can apply data mining and advanced analytical skills to derive key insights to better understand drivers of Web traffic and reasonably accurate traffic forecast for use in business planning. We sense that if companies use data accurately, they can easily exhibit a three to five times growth of the online business and will make analytics easily replicable across different functions of the organization.


Rohit Tandon is vice president of corporate strategy and worldwide head of Global Analytics at Hewlett-Packard. As part of HP’s corporate strategy team, he helps drive the analytics ecosystem to support HP’s vision and priorities through delivery of cutting-edge analytical capabilities across sales, marketing, supply chain, finance and HR domains. He was recently named one of the top-10 most influential analytics leaders in India for 2014 by Analytics India Magazine. Shruti Upadhyay is a manager with HP Global Analytics.

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