How Predictive Analytics Reduces Operational Costs

By Anasse Bari, Mohamed Chaouchi, Tommy Jung

Predictive analytics is an effective tool for more than customer management. It can help you reduce cost in many ways and at different levels of the organization — planning resources, increasing customer retention, managing inventories, and that’s just for openers.

Predictive analytics is especially useful for reducing operational costs in these areas:

  • Balancing the workload across departments

  • Waging only targeted marketing campaigns

  • Effectively allocating available skills and raw materials

  • Accurately estimating demand for your products

  • Positioning your products correctly in the pricing war waged by the competition

  • Purchasing raw materials and hedging against market fluctuations

  • Producing better forecasts of your inventory needs and efficiently managing resources

There is a dollar value associated with every task performed in a company. If you can save here and there, you’ll end up substantially reducing your operational costs — which can have dramatic effect on the bottom line.

Predictive models can reduce operational costs by helping you decide when to make new orders, when to increase your marketing campaigns, as well as how to correctly price your products, manage inventories, and obtain a clear view (and a solid grasp) of your supply-and-demand chains.

By making more accurate decisions that correctly anticipate your business needs, you gain an advantage over businesses that manage their operations as a guessing game.