How To Boost Your Customer Revenue Using Predictive Analytics
Businesses use various technologies to boost customer revenue and predictive analytics is one of them. With predictive analytics, a business can analyse past and current data to make predictions and assumptions. These impact the decision-making processes of businesses, from sales and marketing strategies to growth and expansion.
A data science consulting company may use techniques like machine learning, data mining, and predictive modelling when developing a predictive model for a client. These clients come from different industries like healthcare, retail, finance, and law enforcement.
There are several ways an organization can use predictive analytics to improve customer experience and boost customer revenue and a data science consulting company can provide the organization with the necessary data to make these predictions.
Customer Needs
If you compare the sales and marketing strategies that were trending five years ago with the trends for this year, you will notice several differences and changes in targets and objectives. The main reason for this is a change in customer needs and buying patterns. These changes have a significant impact on an organisation’s sales or marketing strategies and a business that wishes to boost customer revenue will, without doubt, focus on current customer needs.
However, what is trending at present alone will not suffice. Businesses must also look at the future, especially in terms of customer needs and purchasing patterns. A business can thus use predictive analytics to predict customer needs, whether it is about when the customer will need to purchase an item again or when they will be considering something new.
With predictive analytics services, a business can study data and make predictions on consumer behavior. They can then develop and implement sales and marketing strategies that take into consideration these predictions. This will, in turn, boost customer experience and revenue.
Customer Preference
When a customer is shopping for an item on an ecommerce platform, they will be shown store suggestions and recommendations as well as similar products. Even when checking out, a customer will be given a final chance to add related items to their order.
These recommendations rely on customer feedback and preference displayed while they are browsing for the item and aim to improve the customer’s shopping experience. Similar recommendations take place on streaming platforms and food ordering and delivery platforms.
These real-time recommendations can help a business boost customer revenue and a big data analytics company will be of great use in this regard.
Lead Scoring
Sales teams have been using lead scoring for far longer than they have been using predictive analytics but there is no denying that predictive analytics services have significantly improved the efficiency and accuracy of lead scoring and similar practices.
Lead scoring can be used to automatically analyse data like demographics and make comparisons with existing sets of data. A sales team can use this information to determine which leads are most likely to convert to customers and predictive analytics services can improve the accuracy of this process and make it less time-consuming.
When a sales team can easily access information about potential clients, they can improve sales and marketing strategies and convert customers easily. This, in turn, allows a business to increase their customer base in a shorter period of time and boost customer revenue.
Flight Risk Factors
It is a given that a business will invest in the right technology to increase customer conversion. However, expansion and growth rely on customer retention as well. A data science consulting company can provide a business with the required data sets to identify flight risk factors and determine which customers are most at risk of leaving.
When a business is aware of the customers they are at risk of losing, they can develop sales and marketing strategies that specifically target this group and reduce churn rate.
Pricing Model
Businesses change their pricing models over time, taking factors like age and gender into consideration. With predictive analytics and similar technologies, a business can change their pricing models based on the datasets collected on customers.
When developing a pricing strategy, an insurance company, for instance, can take into account data on how often and how well people drive vehicles. This data can be collected from in-car sensors. This applies to all industries and a business that wishes to boost their customer revenue by changing their pricing model can work with a big data analytics company to better understand their customers and their purchasing patterns.
