The first three business-related data points that an organization can readily capture are the customer’s age, gender, and lifestyle choices. I chose these three and bundled them together because they inform about the customer’s demographic, thus help craft a brand image that is more acceptable to them. For instance, if the customer is a 24-year-old male and is a vegan by choice, then a brand image or marketing ad that fails to meet some of the aspects of his worldview will result in them paying no attention to the ad or brand (Liu & Yi, 2017). If, however, targeted advertisement or brand image engage the customer and result in their acquisition, their retention requires business analytics and customer relationship management tools. As a result, the customer’s preferences, likes, and dislikes need to be continuously monitored, especially on social media. These three business-related data points help to model and predict what the customer and how to craft an engagement strategy to keep them as valuable customers.
It is not just about customer acquisition and retention and brand imaging, however. Eventually, the customer will have to make the purchase decision. The distribution channel, such as how the product will be delivered to the customer, will be determined by at least three other data points: the customer’s physical address, history, and line of work (occupation). While the address tells about the average time for delivery, the history tells about the mode of delivery, such as in-store purchase or shipping after being sold on an online shopping platform. Occupation, on the other hand, tells how busy the customer is and, as a result, makes the delivery mechanism (in case of online shopping) flexible without interrupting their work. The final business-related data point that an organization would require is the customer’s financial bracket. The information is useful not only in making pricing decisions, but when targeting specific demographics with their products and services. After all, brand imaging and customer engagement are useless if the target customers cannot afford what is being sold to them.
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These data points, when combined, help a company select different pricing strategies for the products and services. For instance, information about the customer’s consumption patterns and preferences could help the company implement a dynamic pricing strategy. In this case, the price would change with the demand for a product or service. For instance, the price could increase if profitability is key or reduce if competitive advantage is the goal. Furthermore, reducing prices in times of high demand and supply is necessarily not a bad strategy due to the economies of scale. On the other hand, if the company is selling a service only, such as an email marketing tool, then a freemium pricing strategy is more appropriate. Freemiums are common in software products where the customer is given a basic version of the product, and if they like it, they pay for the upgrade to access more features, especially premium features.
On the other hand, customer preferences and habits could decide the preferable and most appropriate pricing strategy. If the customer prefers to buy products at discounts and clearance sales, then the high-low pricing strategy could be used. The objective would be to at least break-even when the product is being sold at a high price. Later on, the price will be dropped to accommodate the customer’s preferences, at which point the company will be profiting instead of losing. The key to success, however, is to minimize the production cost because there are a lot of risks involved. If a competitor is selling its product at a discount at a time when the company has its prices high, then the customer is more likely to go after the discount.
References
Liu, P., & Yi, S. P. (2017). Pricing policies of green supply chain considering targeted advertising and product green degree in the big data environment. Journal of Cleaner Production , 164 , 1614-1622.
