Pricing can be a thorny task. Pricing challenges and intense competition in ecommerce markets have shot up drastically in the emerging age of internet because of price transparency. There is always a cheaper alternative or a costlier alternative of almost everything you see on an e-commerce website. Any person with a high threshold of time would explore all the options before investing the money into something. Although, pricing is not always the criteria for buying, the quality of product, availability of product range, delivery service and customer experience of the application are also important criteria but sure is one of the important ones.
Traditional approaches such as segmenting and clustering are not enough to create a competitive advantage over other pool of players. Today all we look for is a smooth customer experience under our budget. Businesses must seek to maximize the potential of analytics to determine each customer’s propensity to buy. Machine learning factors in all possible variables that help the marketer devise the right strategy to generate the desired engagement with customers – from Keeping a simple snap shot to what kind of price range product are in their saved items, how much time they have spent luring on one particular item, what kind of advertising brought them to website, was it price-sensitive or product efficient? Which customers are most likely to purchase more of the same product or another product? What is his approximate income? In consumer marketing they may compare age, gender and zip code to other likely buyers. In business marketing, relevant demographics may include job title, industry, and geography.
There are two main uses for likelihood to buy predictions: which customers to focus on and how much money, including discounts, to spend on each customer.
- Choosing the right audience: This is pivotal to optimize marketing ROI, because onboarding a customer can be expensive. If you can target your audience and make the communications more relevant, the purchase rate would significantly shoot, say 10 percent, reducing the cost to reach each buyer drastically. So, targeting the right people at right time with apt prediction is the moat. Using this strategy, it is possible to significantly reduce discounts as a part of their customer acquisition strategy.
- Right Discounting: As per one of the researches on an organization US a retailer was able to reduce by 9% the number of customers who just lured by giving them high discounts. Using machine learning, we should analyse the likelihood to buy and discount sensitivity of all of its customers. If a customer is likely to buy with an offer of 10 percent off, you don’t need to give them a 70 percent discount. Using machine learning, the company identified the right discount level for different groups of customers. Buying propensity score can be assigned to the customer based on his demographic attributes, Income range, behavioural patterns. And the customer can be categorized accordingly.
Summing up, in a red ocean of ecommerce, to build a competitive advantage use of Machine learning and metrics such as buying propensity score and psychographic data is the need of the hour to expand market share and maintain profitability.
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Ayesha Kapoor is currently working with IDcentral (A Subex Company) as a growth Marketer. She is a post graduate in management from Symbiosis Institute of Digital and Telecom management with marketing as her majors. She is creative head who loves to read and explore different avenues in the field of Marketing, Branding and Advertising.