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Data science and analytics: What e-commerce CXOs can’t ignore

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By Hoshi Mistry – Principal and Head of Operations for ‘Digital Services’ and ‘Customer Operations’, eClerx Services Limited

The emergence of e-commerce is the most recent example of digital flux across industries. Ecommerce has been established for a few decades, but has just recently become mainstream for all enterprises, large and small, with the global epidemic forcing laggards into the new paradigm. Because of this transformation, huge volumes of useable data are generated by customers’ online travels and buying patterns. Utilising Data Science and Analytics (DSA)
techniques, this data may now be processed and learned from, allowing for more precise company plans and decisions with more realistic outcome projections.

Impact of the pandemic on specific challenges in e-commerce
Due to pandemic limitations, the retail industry suffered. Businesses who couldn’t or didn’t want to become digital had to close down or combine with larger companies to avoid financial losses. And the big retailers shifted to a hybrid approach with a heavy emphasis on internet sales. With the epidemic came new enterprises that recognised the subtleties of an internet business strategy and were quick to react. As a result of the pandemic, Worldpay FIS predicts India’s e-commerce market would increase by 84 percent to USD111B by 2024. DSA is effective across all corporate functions, but we will focus on three to demonstrate its applicability: Marketing, Sales, and Operations.

Role of DSA in drawing the Marketing Strategy

To effectively drive marketing efforts, a company’s marketing division must improve customer outreach, experience, and retention. DSA helps the marketing team and the CMO choose the best marketing strategy, consumer segmentation, and marketing budget. With DSA, the team can undertake real-time dynamic segmentation to help with consumer profiling and tailored recommendations, based on customer interactions. Targeted consumer outreach maximises budget usage and increases conversion rates.

Customer journey and lifecycle optimization helps map customer journeys, analyse and offer “next-best activities”. To construct a churn propensity model, the team has to know “who are the customers at risk of leaving?” and “what are the sources of churn?” Determining the fundamental reason of a positive or negative customer feedback helps the firm avoid a sudden income decrease and helps the marketing team measure and predict any likely negative impact on sales. The back-end integration of all customer-centric data helps develop 360-degree perspectives of customers. Endless customization possibilities exist with this solution. For example,telecom customer service representatives may already know a customer’s purchase history, subscription type, and contact preferences.

DSA boosts effective Sales planning & navigation
Sales heads are also vital for a company’s growth, as they focus on generating revenue. Product recommendation and affinity models forecast a product’s saleability. They also evaluate product bundling and advise sales teams on cross-sell/up-sell opportunities. Lead conversion optimization helps boost sales funnel conversion by giving specific and actionable driver analyses of successes and failures. Shelf performance measurement and optimization is a solution that measures and optimises product performance across many digital shelves. Customer evaluations and comments are used to assess the success of the company’s products and services, among other key markers such as out-of-stock conditions, price sensitivity and price elasticity, impact of product marketing, content effectiveness, gap between self and competitor products assortment and their sales estimation.

Competitive analytics compares a company’s offerings to similar things sold in the market. Prices, promotions, assortments, and characteristics of each product can be compared. This solution’s main goal is to increase market share and product search share. Price optimization helps sales teams make key decisions like product pricing, discounts, and markdowns while conforming to corporate and industry requirements. The complete sales performance measurement suite and driver analysis provides overall sales performance, identifies pockets of good and poor sales performance, as well as its key drivers for sales heads.

DSA supports Operations in an agile organisation
Achieving departmental alignment, resolving key operations risks and issues and directing corporate planning initiatives are examples of how Chief Operating Officers strive to enhance everything they touch. Risk modelling assesses and categorises various business hazards. Within the risk profile summary, it highlights the primary risk factors and suggests possible remedies. Demand, labour, inventory, and other business elements can be forecasted. This approach seeks to deliver recommendations based on precise gap and driver evaluations. The Command Centre is a drill-down anomaly detection and early warning solution based on a data lake architecture with aggregated levels and a consumption layer tailored to the COO office’s needs.

Conclusion
Despite their limitless potential, these data sets and processing methods are not without issues. In the lack of the relevant tools, models, and processes, decision makers are forced to make decisions based on incomplete, out-of-date, and disconnected data. Analytics solution providers help business executives incorporate DSA into their decision-making processes so they don’t have to read tea leaves to make critical business and career decisions.

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