For decision support, a growing number of Marketing Directors are turning to Marketing Mix Models (MMM). By turning to predictive marketing methods, Marketing Managers can determine the best way to allocate their budgets and maximize their marketing and sales strategies.

The typical Marketing Mix model is based on multivariate statistical methods, neural networks and time series forecasting models that use sales, marketing and ROI data as inputs to determine the optimal allocation of resources and project the impact that various strategies will have on volume, revenue and profitability.

With the adoption of data warehouses by most companies, marketing mix models are gaining popularity particularly in the Consumer Packaged Goods, Financial Services, Automotive, pharmaceutical and Hospitality industries. In fact, P&G, Kraft, Coca-Cola, Pepsi and other Fortune 500 companies are already considering Marketing Mix Models as an integral part of their marketing planning.

As CEO’s demand increased accountability and effectiveness, tools like Marketing Mix Models are becoming increasingly important.  Marketing Mix Models allow marketing organizations to know how best to allocate spending, or to justify spending at all.

What Is The Marketing Mix?

The term "marketing mix" refers to the control ingredients (subject to internal and external constraints) in a marketing plan that a marketing manager can control. These Marketing Mix ingredients included product planning, pricing, branding, distribution channels, personal selling, advertising, promotions, packaging, display, servicing, physical handling, and fact finding and analysis.

Marketing Mix - 4P (Product, Price, Physical Distribution/Place, Promotion) Decisions
Product Decisions - The term "product" refers to tangible, physical products as well as services. Some examples of the product decisions include: Brand name, Functionality, Styling, Quality, Safety, Packaging, Repairs and Support, Warranty, Accessories and services. Product line extentions fall into the category of product decisions. Conjoint Analysis is a statistical technique used in Marketing Research to determine how people value different features of an individual product or service.  Before a product line is expanded, a careful analysis should be conducted to determine if a unique need exists that isn’t already satisfied with another product within your line. This type of analysis involves the use of TURF Analysis or Shapley Value Analysis.

Pricing Decisions - Based on a company's pricing objectives, a company can have various pricing strategies and methods. Some ommon pricing decisions include: Pricing strategy (skim, penetration, etc.), suggested retail price, volume discounts, and wholesale pricing, payment methods, payment terms and discounts, seasonal pricing, product bundling, price flexibility and even price discrimination. Conjoint Analysis is a common technique used in identfying optimal product pricing and features and has become a critical component in strategic planning and new products development.

Physical Distribution - A company must be able to deliver its good or service to the customer. Some examples of distribution decisions include: Distribution channels, Market coverage (inclusive, selective, or exclusive distribution), Specific channel members, Inventory management, Warehousing availability, Distribution centers, Order processing, JIT, Speed of delivery, Transportation costs and Reverse logistics

Promotion Decisions - represents the various aspects of marketing communication, that is, the communication of information about the product with the goal of generating a positive customer response. Marketing communication decisions include: Promotional strategy (push, pull, etc.), Advertising, Search Engine Optimization Personal selling & sales force, E-commerce and paid search campaigns, Sales promotions, Public relations & publicity, Marketing communications budget. One of the most advanced and accurate methods for determining an optimal promotion strategy is through the use of highly accurate multivariate statistical design experiments for advertising tests and Marketing Mix Models.