Methods for Better Allocation of Marketing Resources

U.S.-based companies spent nearly US$ 300B on marketing and advertising in 2007 (Advertising Age), but few have figured out how to optimize marketing spending for profitability and revenue growth. Deciding how to allocate marketing resources is particularly difficult because decisions need to be made at many different levels—across countries, products, marketing mix elements, and different vehicles within elements of the mix (e.g., TV vs. the Internet for advertising). The emergence of new media such as online search and display advertising, video games, virtual worlds, social networking, online user-generated content, and word of mouth marketing is creating both new opportunities and challenges for companies.

Many marketers continue to rely on common methods for resource allocation. Examples are the use the “percentage-of-sales” rule for allocating their advertising budget, and in the sales force arena, companies typically constrain the ratio of their sales-force cost as a percentage of total sales. Another common approach is to arrive at the marketing budget based on a “bottom-up” method. A marketer may arrive at the advertising budget based on the desired level of brand awareness and the cost of various media vehicles to achieve this awareness. Similarly, in the pharmaceutical industry a firm may decide how many physicians it wants to reach and how frequently they should be contacted. This combination of reach and frequency determines the required size of the sales force.

While such allocation methods are reasonable, they also have limitations. For example, competitive parity (e.g., A/S ratios) is useful only if competitors are equal in strength, have similar objectives and are acting optimally. Further, the methods mentioned above are incomplete since they do not account for how markets respond to marketing actions.

A recent paper by HBS professors Sunil Gupta and Thomas J. Steenburgh highlights a two-stage process for marketing resource allocation. In stage one, a model of demand is estimated. This model empirically assesses the impact of marketing actions on consumer demand of a company's product. In stage two, estimates from the demand model are used as input in an optimization model that attempts to maximize profits. This stage takes into account costs as well as the firm’s objectives and constraints (e.g., minimum market share requirement).

Over the last several decades, marketing researchers and practitioners have adopted various methods and approaches that explicitly or implicitly follow these two stages. The authors categorized these approaches into a 3x3 matrix, which suggests three different approaches for stage-one demand estimation (econometric methods, experiments and decision calculus), and three different methods for stage-two economic impact analysis (descriptive, what-if and formal optimization approach). They then discuss pros and cons of these approaches and illustrate them through applications and case studies.

(Click here for the link to the full working paper text http://www.hbs.edu/research/pdf/08-069.pdf.)

This paper helps answer and provide a fresher perspective on long-standing questions like:

• How to allocate advertising dollars between new and existing products?
• How to allocate resources between advertising and trade or consumer promotions?
• What is the effectiveness of word-of-mouth communication (WOM)? How effective is WOM in generating sales? Who are better disseminators of WOM?
• How a salesperson should allocate effort across customers?
• How to allocate promotional dollars between new and existing customers?
• Which customers to acquire? Which existing customers to target?
• How to allocate marketing resources across all elements of the marketing mix?

What has been the impact of the proposed process for marketing resource allocation? The following are important and powerful conclusions that are not based on a single study or a single product category, but instead are generalizable results based on several studies, products and industries:

• The average advertising elasticity is almost twice as much for new products.
• TV advertising is more likely to work when there are accompanying changes in ad creative and media strategy.
• Frequent promotion of brands made it unnecessary for consumers to switch brands and made them more likely to stockpile when their favorite brand was on promotion.
• Increasing promotion depth or frequency decrease total brand profits.
• Increasing advertising had mixed effects on brand profitability.
• Very often, increasing the advertising or promotional spending bring about a less-than-proportional effect on profits, suggesting that the market is operating efficiently and that marketers in this product category are making decisions that are close to optimal.
• The impact of WOM created by non-customers had a much greater impact on sales than the WOM created by the firm’s loyal customers, suggesting that customers unconnected with the firm are likely to be less biased and more believable and therefore should be weighed more heavily in terms of their impact on WOM.
• An optimized sales calling system is better suited to repetitive sales situations vis-à-vis one-time sales occurrences. Salespersons should follow a calling pattern that maximizes some objective, yet at the same time any system should incorporate the salesperson’s knowledge and experience. This makes the salesperson think about tradeoffs that had not been previously considered, and fosters a clearer and more consistent thinking about the customer and a better communication between salespeople and their managers.
• An optimized customer acquisition and targeting system provides a better ordering of the prospects based on their estimated probability of acquisition, helping determine which prospects to target and their priority. Also, it suggests the optimal policy of focusing all on-going marketing and sales efforts on a few customers rather than on the entirety of the customer database.

Calculating the Marketing ROI for each marketing instrument is one way to take the emotion out of the budgeting process and help the firm allocate its marketing dollars more effectively. Marketing ROI calculations are interpreted as the estimated increase in revenue for a $1 increase in spending on a marketing instrument. Therefore, an ROI with a value less than one suggests that the incremental marketing spending would not pay for itself through increased sales.

These numbers should not be interpreted as one marketing instrument being more effective than another, per se, as the ROI values are also influenced by the amount of money being spent on the instrument. While ROI numbers do not tell us what the optimal spending levels are for each marketing instrument, firms are expected to shift their spending away from instruments that produce low ROI and toward investments that produce high ROI, keeping other organizational goals or strategic interests in mind.

Marketing has been, and continues to be, a combination of art and science. With the increasing availability of data and sophistication in methods, it is now possible to more judiciously allocate marketing resources.