Marketing executives make many strategic decisions across various spending categories, products, and markets. For example, they may choose to invest in online advertising for product A, launch a price promotion for product B, and engage in a sponsorship for their full brand.
Executives therefore need a metric that assesses and compares the productivity and accountability of their many marketing engagements. Return on marketing investment (ROMI) is the logical metric of choice.
Ideally, ROMI metrics would be single numbers that executives could easily compare across marketing activities. For example, a manager might want to state with confidence, My search advertising campaign yielded a return of 60%, well above average for ad campaigns for our brand and exceeding last years return of 45%. Importantly, the return calculations would be made on net marketing contribution, found by multiplying revenue increase due to marketing by gross margin, subtracting marketing investment, and dividing the result by marketing investment.
Unfortunately, the reality of marketing does not lend itself well to simple ROMI performance metrics, and executives must understand the metrics determinants before deploying it across tactics strategically.
Download Article
Get this article as a PDF
ROMIs Determinants
Consumer response to marketing activities is not linear. Research shows it is typically concave, with diminishing returns to scale, or S-shaped, increasing and then showing the diminishing returns (). As a result, the profit response to marketing spending is typically inverted-U-shaped (). And ROMI depends critically on marketing spending.
In the hypothetical search advertising example, ROMI might be 150% for the first $10,000 spent, 40% for the next $10,000, and negative at higher spending levels. Firms therefore cannot compare ROMI across different marketing campaigns or media, unless they spend the same amount on each.
Academic and practicing marketing analysts have long recognized this challenge. Instead of reporting ROMI in papers, academics focus on top-line productivity metrics, such as sales lift due to marketing, net profit, contribution to overhead, or marginal ROMI (i.e., return on last dollar spent).
To use ROMI correctly, marketers must understand consumer response patterns and the accounting consequences of spending. Researchers have carefully examined marketing spendings impact on short-term and long-term profitability, customer lifetime value, and other strategically important metrics. But as much as practitioners favor ROMI as a simple yardstick, they must focus on ROMIs determinantstop-line performance enhancement, profit margins, and marketing coststhen derive the metric on a case-by-case basis. In so doing, they cannot expect a simple return metric that can easily be compared to others ().
ROMI for Marketing Tactics
Most marketing effectiveness studies examine individual actions, particularly advertising. Some focus on marketings direct impact on sales to derive the profit and ROMI implications.
With improved intermediate consumer attitudinal data, especially digital metrics like clicks and likes, analysts can derive ROMI in two steps: (1) Estimate marketings lift on an intermediate metric (e.g., clicks a digital ad generates) and (2) determine how the intermediate metric translates into future sales (i.e., the conversion rate) (; ). Marketers can make the necessary inferences using historical data and econometric methods, experiments, or a combination of the two ().
In the digital world, marketers have extended their ROMI models to account for the full consumer journey, which allows advertising to reach the right people at the right time (). The analysts make a distinction between first-purchasers (customer acquisition) and repeat-purchasers (customer retention, upselling, and cross-selling), as their ad responsiveness has been shown to differ (). Combining the two effects enables analysts to estimate marketings impact on customer lifetime value ().
ROMI for Marketing Strategy
In the context of marketing strategy, which typically combines multiple instruments, ROMI must focus on long-term performance impact, specifically sustained performance growth (). have shown that long-term sales growth is more sensitive to investments in product and distribution than advertising and sales promotions. Indeed, firms cannot expect advertising or sales promotion ROMI results to have a sustained impact.
Marketers can use ROMI to examine how marketing investments enhance critical assets known to improve long-term business performance. have demonstrated that marketing assets are more important in driving firm value than individual marketing actions. They find the meta-analytic firm value elasticity of brand strength is .3 and that of customer relationship strength is .7, while for advertising spending, the elasticity is only .04. use the meta-analytic results to recommend the following strategic marketing allocations: Invest 61% of budget on customer-related assets, 28% on brand-related assets, and 11% on market share.
Research has shown that customer-based assets correlate strongly to customer satisfaction, and customer satisfaction movements can relate to stock price changes (). Published reviews have also been shown to influence customer satisfaction with new products, and have found that review quality elasticity is about .7, while that of advertising is .11, according to .
Summary
Marketing executives and academic analysts have taken significant interest in ROMI. While executives would like to have one number to gauge their marketing investments performance, oversimplification can lead to significant future spending errors.
For individual tactics, such as advertising and sales promotions, firms must derive ROMI by measuring marketings lift on top-line performance and conducting a marketing cost analysis. Marginal ROMI, found by determining return on last dollar spent, might serve as a unifying metric, being positive for underspending, negative for overspending, and zero for right-spending. But for more strategic marketing decisions, firms should use long-term growth measurement and/or changes in brand or customer relationship assets driving long-term performance to derive ROMI.
Citation
Hanssens, Dominique M. (2024), Using Return on Marketing Investment Effectively, Impact at JMR, available at /wp-content/uploads/2024/07/Using-Return-on-Marketing-Investment-Effectively.pdf.
References
Ataman, Berk, Harald J. van Heerde, and Carl F. Mela (2010), , Journal of Marketing Research, 47 (5), 86682.
Danaher, Peter J. and Harald J. van Heerde (2018), , Journal of Marketing Research, 55 (5), 66785.
Deighton, John, Caroline M. Henderson, and Scott Neslin (1994), , Journal of Marketing Research, 31 (1), 2842.
Dekimpe, Marnik G. and Dominique M. Hanssens (1999), , Journal of Marketing Research, 36 (4), 131.
Dinner, Isaac M., Harald J. van Heerde, and Scott A. Neslin (2014), , Journal of Marketing Research, 51 (5), 52745.
Edeling, Alexander and Marc Fischer (2016), , Journal of Marketing Research, 53 (4), 51534.
Edeling, Alexander, and Alexander Himme (2018), , Journal of Marketing, 82 (3), 124.
Farris, Paul, Dominique M. Hanssens, James Lenskold, and David Reibstein (2015), , Applied Marketing Analytics, 1 (3), 26782.
Floyd, Kristopher, Ryan Freling, Saad Alhoqail, Hyun Young Cho, and Traci Freling (2014), , Journal of Retailing, 90 (2), 21732.
Fornell, Claes, Forrest Morgeson III, and G. Tomas Hult (2016), , Journal of Marketing, 80 (5), 92107.
Gupta, Sunil, Donald R. Lehmann, and Jennifer Ames Stuart (2004), , Journal of Marketing Research, 41 (1), 718.
Hanssens, Dominique M., Koen H. Pauwels, Shuba Srinivasan, Marc Vanhuele, and Gokhan Yildirim (2014), , Marketing Science, 33 (4), 53450.
Hanssens, D.M., Leonard J. Parsons, and Randall L. Schultz (2001), , 2nd ed. Kluwer Academic Publishers.
Krishnamurthi, Lakshman, Jack Narayan, and S.P. Raj (1986), , Journal of Marketing Research, 23 (4), 33745.
Mantrala, Murali K., Prasad A. Naik, Shrihari Sridhar, and Esther Thorson (2007), , Journal of Marketing, 71 (2), 2644.
Sethuraman, Raj, Gerard J. Tellis, and Richard A. Briesch (2011), , Journal of Marketing Research, 48 (3), 45771.
-
-
Marketing News
Strategies for Leveraging AI in the Customer Experience
-
Marketing News
How to Optimize the Freemium Sales Model