Using Prescriptive Analytics In Up- And Cross-Selling
Being client centric and focusing on strong customer relationships are key to every business as this allows you to maximise the customer lifetime value from your existing client base. To enhance customer relationships and drive product interest and purchases, the correct products need to be recommended at the right time.
Cross-selling includes the practice of selling additional products or services to existing customers. The aim of cross-selling is to increase the profit derived from customers and also to keep the customer satisfied such that their needs are met.
Cross-selling is desirable for most businesses as selling an additional product to an existing customer is cheaper than the cost of selling the same product to a new customer. However unlike the acquiring of new business, cross-selling involves an element of risk that existing relationships with the customer could be weakened. For that reason, it is important to ensure that the additional product or service being sold to the customer closely meet their needs.
Some examples of cross-selling include:
- Suggesting a complementary product to the one being sold,
- suggesting a similar or related product that can be sold together; or
- making recommendations of products that other customers purchased.
Upselling is a technique whereby a seller approaches the customer to purchase more profitable products, upgrades or other add-ons. It is common practice in large businesses to combine upselling and cross-selling to maximize profit.
Some examples of upselling include:
- Upgrading a contract on contract renewal,
- adding additional services to an existing contract,
- selling value added products from affiliates to your customer base,
- selling an extended service on a contract; or
- selling a luxury finishing on a product.
Prescriptive analytics attempts to recommend an action to be taken in order to realize an optimal outcome in the business. Prescriptive analytics utilizes the data, the business rules and processes and machine learning algorithms to enable users to identify the actions needed to drive predicted outcomes and to gain insight into the potential impact of each alternative action.
The data derived from the business which would be utilised to make predictions typically include: historical campaigns, transactional, customer, products and contract information. In addition to this customer interactions is a powerful source of information;
All of this data is used to train machine learning models to predict how customers behave given their specific attributes at any given time.
In summary, prescriptive analytics are used in up- and cross-sell campaigns to:
- Accurately select customers who have the highest likelihood of purchasing an additional product or service;
- Accurately select customers who have the highest likelihood of upgrading their existing services or products;
- Explore customer buying behavior and leverage product basket analysis to accurately predict products that other customers will like;
- Employ up-selling techniques to recommend products that help to retain customers for a longer time;
- Maximize profit and drive more individual customer purchases by recommending complementary products or products that more closely match the customer’s’ needs;
- Understand customers and provide them with relevant and timely information to ensure they remain satisfied and loyal.
These types of features and insights all form part of BusinessOptics Sales and Customer analytics solution. The BusinessOptics modelling platform can help make your customers happy by knowing and giving them what they want, while growing your business at the same time.
Schedule a demo today, to learn more about the BusinessOptics platform and the various solutions we provide!