Don’t you think if you would collect and analyze your customers’ data (who your customers are, what do they do, what they want, what is their percentages as per age, geography, income, marital status etc) you would be able to serve them better? Further, by knowing these things you can increase your profits by as much as 50%-80%. Customer segmentation is study of all these aspects. It also includes methods by which required data is collected, designing customer journeys and omni-channel marketing strategies.
The marketing budget is always limited and often sale leads are too many. Here data science can help businesses to predict which lead has maximum potential, so that budget and time and energy of marketing team are optimally used.
A loyalty increase of 7% can boost lifetime profits per customer by as much as 85%. But ensuring loyalty management is becoming increasingly challenging. 68% customers think businesses must do much more to increase their loyalty. Leverage customer data to increase customer loyalty with our help.
Cross-selling identifies products that satisfy additional, complementary needs that are unfulfilled by the original item. E.g., selling insurance with car by car-dealer, or selling comb with hair-dryer by a retailer. Up-selling is the practice of encouraging customers to purchase a comparable higher-end product than the one in question. An example of positive results obtained by a client by these tactics is shown above.
Social Media Analytics enables social-media marketing which has following benefits.
The Likelihood To Buy (LTB) model predicts near-future repeat purchasing behavior based on past transactions (e.g., calculating lifetime value, recency, frequency, and approximate purchase value), email (e.g. send, open, click frequency, volume, and recency), and browsing behavior (e.g. browse or abandoned cart counts, session duration, session recency). Its four benefits are as below:
A recommendation engine is a system that suggests products, services, information to users based on analysis of data. The recommendation can be derived from a variety of factors such as history of user and behavior of similar users. A recommendation engine can work either on Content-based filtering or Collaborative filtering or it can be knowledge-based system.
It always costs more to acquire a new customer, than it does to retain a current one, which is why there is a lot of motivation to predict customer churn before it happens. Usually churn analysis is done in three steps:
(1) Identify at-risk customers
(2) Identify pain points
(3) Identify methods to prevent churn by improving products/services/processes to remove pain-points.
