The 3 Benefits Of Prescriptive Analytics
In the last few articles we investigated the different types of analytics and focused on prescriptive analytics. At this stage you might still be wondering “what will the benefits of prescriptive analytics be in my business?”
As stated in a previous post, prescriptive analytics is more advanced than predictive analytics both in terms of complexity and business value it delivers. The main purpose of it being able to prescribe certain actions that are expected to maximise key business metrics. Essentially Prescriptive Analytics is a tool to optimize and streamline your business and its associated processes.
The benefits of prescriptive analytics have already become apparent in a number fields including but not limited to healthcare, supply chains, insurance, financial risk management (specifically credit risk management) as well as sales and marketing operations.
In the field of sales and marketing operations prescriptive analytics is assisting in highlighting a customer’s preferences in order to maximise the likelihood of a sale. How, do you ask? Prescriptive analytics learns from past transaction history, customer interactions and preferences selected to prescribe optimal interactions, such as:
- What time of day the prospect would like to be contacted;
- Which sales or marketing channel should be used; and
- What the most appropriate product for the prospect is.
For example say we have a customer called Anna. Anna is a mom of two and regularly purchases a 2 litre milk. Prescriptive Analytics would be able to identify this pattern and recommend that Anna should be contacted every 3 days at noon via a SMS to remind her to buy milk if she hasn’t done so. This process will make Anna’s life easier and assist the grocery store by retaining a customer and building goodwill with her.
The benefits of prescriptive analytics have also become apparent in the field of supply chain optimization with big companies such as UPS utilizing it to refine operations. In the case of supply chain management, imagine that a dairy farm realizes that it is short supplying a certain retail chain because of their inventory management. The said dairy farm can regulate their supply by using prescriptive analytics. An appropriate analytics model will be able to identify the inefficiencies in their operations based on historical data and accordingly prescribe the best action.
In this case an optimal solution could be to supply less cheese to retailer A and more to retailer B. What-if Scenario Analysis would also assist in evaluating the various methods available to regulate their supply and demand. By more accurately supplying the demand for various products, less waste occurs, the needs of the customers are more closely met and therefore it increases the dairy farm’s bottom line.
Lastly one area which has experienced the benefits of prescriptive analytics is the financial sector and more specifically the area of credit risk. Utilizing computational models and machine learning, credit risk departments have been able to successfully optimize their debt collections strategies. Debt collection is often a problem for retailers and banks alike which offer credit facilities. For example these credit institutions and banks can utilize prescriptive analytics to investigate on which day each of their customers are most likely to pay their installments and which debtors needed to be reminded to pay the installment. If they have these insights the prescriptive analytics could also help identify which date the creditor needs to contact the debtor and which method of communication was most effective in collecting their debt.
In the scenarios mentioned above, many of the prescribed actions can be automated. For example contacting people at specific times of day using recommended channels can be automated through integration into CRM, email services, dialling systems, text message gateways and other systems. This allows for the analytical intelligence to be applied to large volumes of decisions that would normally be too difficult and time consuming for people to make decisions upon manually.
In conclusion we have seen that there is a myriad of benefits to prescriptive analytics. The most pertinent of them being:
- Reduced risk as data driven decisions are made which enhances accuracy of decisions
- Increased revenue as a result of optimization of processes which minimize cost and maximises profit
- Increased efficiencies since issues within a process can be identified faster and actions are recommended to improve the process
Interested in exploring the benefits that your company can derive from Prescriptive Analytics?