Forecasting Consumer Demand with the Help of Predictive Analytics – A Quantzig Success Story

Feb 13, 2019

About the Client

A leading manufacturer of semiconductors and other tech components located in Canada. The client possessed decades of experience in collaborating with its buyers to design and develop powerful consumer technology.

The Business Challenge

To gain a leading edge the client’s business required them to develop consumer electronic devices by incorporating the latest technology trends. Owing to the huge data sets available today, gathering reliable data on consumer demand and replacement cycle rates was particularly challenging. This predicament was further amplified by the fact that the company is at the beginning of the supply chain and their peer companies further along the chain were incentivized to inflate demand predictions to ensure they have more than enough product on hand.

Also, due to the high production lead time, the client was unable to quickly accommodate changes in demand. From a business point of view, the inability to accurately forecast demand was a major roadblock. The client sought to leverage Quantzig’s predictive analytics expertise to build and integrate the latest predictive analytics model for better forecasting consumer demand.

Wonder what would be the right analytics approach to use to gain extreme insights into consumer data? Our experts can help you, request a free proposal today!

Top Challenges Faced by the Client

Problem Statement 1

The inaccurate demand forecasting methodologies adopted by the client led to multiple cycles of underproduction and overproduction, due to which they were unable to meet the demands of their clients.

Problem Statement 2

The client lacked the ability to integrate datasets from several internal and external sources such as-as- web data, pricing data, stock exchange data, search trends, and other public data repositories.

Problem Statement 3

The third challenge that jeopardized the company’s position as a market leader and created a 1 million dollar business problem was that their fundamental modeling concepts were obsolete and required them to adopt appropriate models to tackle the challenges arising in a rapidly shifting marketplace.

Solutions Offered and Value Delivered

To help the client tackle the three core challenges and identify their strengths and weaknesses of their forecast models, the predictive analytics experts at Quantzig adopted a comprehensive approach that revolved around conducting a thorough audit of their analytic processes.

Phase 1

The initial phase of this engagement involved a detailed analysis of the company’s data curation procedures, data sets, and data science techniques.

Phase 2

The second phase of this predictive analytics engagement focused on addressing the opportunities for improvement. To do so, we developed a consumer demand forecasting model that incorporated the best data and analytic techniques to suits the clients’ requirements.

Phase 3

The final phase of this predictive analytics engagement revolved around leveraging advanced predictive analytics methodologies to further amplify the predictive power of the new model. To maximize the efficiency the predictive analytics experts at Quantzig incorporated data from proprietary data sources.

Equipped with such expansive data, we leveraged advanced predictive analytics techniques and machine learning sciences to build an optimal consumer demand model. Throughout this process, we worked closely with the client to ensure that the predictive analytics model made accurate forecasts that best serve their operational planning and business goals.

The devised model outperformed the accuracy of the client’s previous predictive analytics models by over 80%. The use of advanced predictive analytics techniques not only offered a more fluid model but also empowered to accurately detect demand upturns and downturns. Additionally, the use of Quantzig’s proprietary data accounted for 40% of the overall increase in accuracy. The predictive analytics model also increased the lead time for accurate predictions, allowing the client to make accurate predictions months in advance — far earlier than with the previous model.

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What are the benefits of predictive analytics?

In the business world, there are several ways in which businesses can benefit from predictive analytics, which is why leading organizations are leveraging advanced predictive analytics techniques to ensure long-term success. Businesses are well aware that harnessing the power of data can help them achieve business goals, many companies still have not gone beyond collecting and storing their data. We agree it isn’t easy to deal with unstructured datasets but the benefits are rewarding if you do. Here are the top five benefits of predictive analytics:

  • Quell uncertainties
  • Gain a leading-edge
  • Influence cross-functional collaboration
  • Optimize marketing productivity
  • Achieve business goal

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