Companies that invest in marketing analytics as a “discipline” can measure, manage and analyze performance to maximize marketing effectiveness and optimize return on investment (ROI).
At DataFactZ, we bring more than a decade of business analytics experience and a marketing analytics team headed by Ph.D.s who can build and optimize state-of-the-art simulation models. Our strengths in statistics and mathematics, simulation, modeling and optimization provide the most effective analytical foundation to address complex business and strategy issues. As our approach to advanced analytics is interdisciplinary, we can draw from other areas in customer analytics in order to complement marketing analytics, such as customer life value, customer retention, or upsell and cross sell.
The strategies mentioned above provide only a basic foundation that should be complemented by several other areas in advanced analytics, in order to establish a successful marketing analytics strategy, such as:
Choice modeling is the most accurate process available for predicting human decision-making behavior. Choice models predict with great precision how customers will react to a particular product or service offering.
Our analytical experts can design the appropriate choice model to accurately predict customer behavior. Our approach to choice modeling is custom-tailored to the specific objectives of a project because each product has distinct characteristics and is received differently in the marketplace. We collaborate with businesses to help us understand the thought process behind products and services and to build successful choice models. Our choice models can help you:
Marketing mix modeling (MMM) uses statistical analysis to estimate the impact of various marketing tactics on sales and forecast the impact of future of tactics.
DataFactZ builds effective MMMs utilizing advanced statistical techniques for a deep understanding of your industry to measure and predict the performance of your marketing mix and maximize ROI for marketing investments.
Big data technologies can also be leveraged to determine the effectiveness of spending by channel. This approach statistically relates marketing investments to other key performance indicators such as sales and external data sources (Economic data, Promotional data, Competitive data, Product data. Pricing data etc.). Below is an example of the type of simulation that can developed using Market Mix Models for business planning and strategy for marketing.
Market Mix Modeling Simulation
Most businesses offer a range of products, however, a large product portfolio presents challenges in terms of deciding how to allocate investments across the board.
Each product makes a different contribution to the organization’s bottom line. Some products may come at an expense to produce; others cause an increase or decrease in market share. Some contribute to significant revenues, and some have greater marketing expenses.
How can an organization manage a well-balanced product portfolio while growing and gaining market share? We can help your organization optimize the product portfolio by analyzing consumer behavior to determine how to expand with new products and phase out under-performing products. By analyzing and understanding product combinations and the strength of these relationships, we can help you determine how to upsell and cross sell and develop effective promotions through the following process:
The below diagram illustrates a type of decision tree that we build using BigML and other third party tools.
Brand equity is one of a company’s most valuable assets. Understanding how this value is created, where it is created and the relationship between brand value and business value is vital to strategic decision making. Brand equity has several dimensions, including brand awareness, brand image, and brand association.
Using the conventional methods, brand equity analysis can be performed using customer survey data collected through marketing efforts. However, traditional survey data sets do not portray the full brand experience, such as product performance and pricing, and survey findings cannot be correlated with the overall outcomes and financial performance.
At DataFactZ, we go beyond these conventional methods to establish a complete picture of brand management by building predictive statistical models that can correlate information from marketing efforts, survey data, brand awareness, and sales. Today’s customers are actively engaged with brands on social media, providing an opportunity to consistently monitor brand awareness, recognition, popularity of products and services and more, adding another dimension to measure brand equity. Our interdisciplinary approach to brand management coordinates brand equity management with customer retention to ensure these efforts support each other. Our brand equity management services can help you: