A customer life-cycle as depicted in the figure above has three phases ‘Enhance the profitability of the existing customers’, ‘Retaining existing customers’ and ‘Acquire new customers’. ‘Enhancing the profitability of the existing customers’ has the highest potential for enhancing the revenue/customer. Since the existing customers are more likely to trust your products and services, it is easier to up-sell and cross-sell with minimal resistance.
Due to the digitization of various consumer services, targeted marketing has proven to be more promising and cost-effective than mass marketing as in earlier eras. For example, a thirty years old person would be more likely to go for home loans or car loans than a student. A newly married couple may consider holiday packages. It will be more effective to identify the potential buyers of your products and send the promotional messages and offers only to them. This is called segmentation.
The fierce competition in the market also induces the risk of a loyal customer getting lured away by the exciting deals offered by the other retailers in the market. Customer critics and feedback on the social networking sites also play a key role in determining the reliability and value of the brand in the market. Therefore, to increase the revenue and sales, it is imperative to retain and delight the existing customers by identifying their needs and turning them into value buyers.
A cross-sell happens when the existing customer is convinced to purchase a new item, which otherwise, he must not have purchased. Data mining brings out the correlation between the products from the buying history of the customers and brings out the useful associations which may not be so obvious.
An existing customer has trust in your brand and is likely to buy more products from you. Acquiring new customers is expensive and time-consuming. Increasing the value of the buyers by approaching them with the right offers enhances customer satisfaction and trust in the brand. In this way, many organizations leverage the opportunities to cross-sell and up-sell by studying the customer’s purchasing power and inferencing their future needs.
Association mining can facilitate useful insights which help in taking profitable decisions like:
- Customers buying more meat products on the weekends may indicate they are buying the products in bulk for the rest of the week. So, no offers on the meat products on the weekends.
- Most customers who buy pizza also buy cola. So stacking pizza and cola in adjacent racks or providing an offer on cola on buying pizza provides a happy shopping experience.
- Optional services bought by the telecom users (roaming packs, message packs, do-not-disturb etc) show the best way to bundle these services.
- Items purchased on credit (eg. car) provide insight into the next product that customer is likely to buy (eg. car insurance, music system).
- Unusual combinations of insurance policies may indicate fraud.
A ‘propensity to buy’ model predicts the next purchase from the demographic, psycho-graphic and past buying history of the customer indicating the opportunity for cross-selling.
An up-sell is the marketing strategy to make an existing customer upgrade his purchase by buying more expensive products/ services. A customer running a basic version of a software can be convinced to buy the premium version. A customer looking for a smartphone with fewer features can be encouraged to buy a phone of more price by justifying the value of the product based on its higher features. Both cross-sell and up-sell are widely practiced in the market and plays a vital role in the growth in sales and revenue of the organization.
Enterprises are considering various strategies for effective Social Media marketing. Several digital analytics platforms provide a wealth of insights on the business health of an enterprise. I have scrapped the tip of the iceberg trying to explain the customer dynamics in e-commerce. I will write a series of blogs on different aspects of customer behavior in the days to come! Stay tuned.
– Nikita Naidu, Data Scientist, Cappius India