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CMO Insights and Analysis from Deloitte

Advanced Modeling Can Improve Marketing ROI

Generally used in financial forecasting, modeling may be a useful tool for other departments, including marketing and customer relations.

Organizations are increasingly turning to predictive analytics and modeling to help drive their businesses and execute on strategic objectives. As they do, many executives are facing new challenges and questions: What are the risks associated with models? How should these models and analytics be managed? And, how can an organization use models to gain a competitive advantage in the marketplace?

Often associated with financial services, modeling is being used more frequently across many industries, from manufacturing and retail to utilities and life sciences. Moreover, organizations are applying analytics to help with marketing and customer relations as well as finance functions. A marketer who wants to determine which type of coupon would be most effective with consumers, for instance, could use modeling rather than a traditional survey or demographic sampling to predict that success.

“Such a model, given key inputs, is capable of forecasting which combination of coupon-shopper and geography could result in the highest probable purchase rate. When done properly, such models can inform the broader marketing and communication processes,” says Michael Juergens, a principal with Deloitte Risk and Financial Advisory at Deloitte & Touche LLP.

The Nuts and Bolts of Analytics and Modeling

Business leaders have a broad spectrum of tools available to them to drive competitive advantage, including artificial intelligence, regression analysis, natural language generation (NLG), natural language processing (NLP), simulation modeling, and others. Analytics, meanwhile, often involves mathematical models of varying complexity (Figure 1). The basic level—descriptive—comprises methods focused primarily on gathering data into a structured format and performing simple statistical procedures that describe the data set, such as means, standard deviations, key risk indicators, and key performance indicators.

At the opposite end, the highest level in the model continuum is prescriptive, comprising modeling methods in which the inputs themselves may have variability. These models may represent the most complex level of analytical capability. They also can generate the greatest risks.


“Organizations should balance modeling complexity with the return on investment, the inherent risk of employing advanced mathematical methods, and the cost of sourcing the required data. The most complex approaches may not always automatically yield the optimal result,” says Mike Bailey, a principal with Deloitte Risk and Financial Advisory at Deloitte & Touche LLP.

To meet certain objectives such as competitive advantage and operational efficiency, organizations can build entity-specific analytics and models. Generally speaking, there are three steps to designing custom analytics or models:

Step 1—Identify existing processes that could be replaced by a model. Assess critical decision-making or forecasting processes to identify the ones that could be replaced or enhanced with analytics and modeling. Evaluate the identified process for quality (how reliable, accurate, or beneficial the process has been) and efficiency (how labor- or time-intensive it is).

Step 2—Determine the modeling options. Consult with team members across the organization with relevant skill sets, such as programming and data science expertise, to identify potential model options. Determine the ease with which relevant data can be extracted, models built or implemented, and processes adjusted. Input data may include tracked transactional data involving customers and vendors, industry groups, and other external data. Weigh the costs and benefits of investment in the model.

Step 3—Build the analytics and models. This process not only includes the design and coding implementation of the model but also the detailed testing and monitoring needed to make sure it is performing as intended. It is important to document the model theory, input data, uses, limitations, and plan for monitoring performance over the long run.

Models can help organizations gain a competitive advantage or improve operational efficiency, but they can also increase the risk of financial loss, harm to customers or clients, improper managerial decisions, or damaged reputation.

As such, leading organizations are implementing model risk management (MRM), a structured approach that defines roles, responsibilities, policies, procedures, and controls that can mitigate the potential adverse impact of model use. A typical MRM framework is designed to mitigate risk at each stage of the model’s life cycle and facilitate broader risk management activities.

“MRM is now often recognized as good operational hygiene and sensible risk management across industries, many of which are designing MRM activities that are tailored to their environment and risk appetite,” says Clifford Goss, Ph.D., a partner at Deloitte Risk and Financial Advisory at Deloitte & Touche LLP who specializes in MRM. “By implementing and maintaining a strong MRM framework, organizations can focus on leveraging the significant benefits that models can offer for driving competitive advantage.”

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