Media mix modeling (MMM) helps marketers optimize their ad spend in a privacy-friendly, data-driven way. But how do you know if your model is producing realistic results? One powerful way is by comparing your MMM’s estimated elasticities to established advertising elasticity of demand benchmarks. Note that advertising elasticity of demand is sometimes abbreviated as AED and we’ll use both terms in this post.
AED tells us how much demand changes in response to advertising spend, providing a sanity check for your model’s outputs. We’ll break down AED benchmarks, explore how factors like competition, product lifecycle, and industry type impact AED, and explain how to use AED to validate your MMM results.
What Is Advertising Elasticity of Demand?
AED measures the responsiveness of sales to advertising spend. The formula is:
AED = % change in demand / % change in advertising spend
For example, if a 10% increase in ad spend leads to a 2% increase in sales, the AED is 0.2.
MMM helps estimate how much each advertising channel contributes to sales. However, if your model suggests that increasing ad spend by 1% leads to a 5% sales lift, while industry AED benchmarks suggest 0.2 – 0.3, there’s a problem. By cross-referencing your MMM outputs with AED benchmarks, you can identify unrealistic attribution, potential model biases, or data issues.
Advertising Elasticity Benchmarks by Advertising Type
Based on meta-analyses (Sethuraman et al., 2011; Bayer et al., 2020), here are typical AED values across media types:
| Advertising Type | Short-Term AED | Long-Term AED | Key Takeaways |
|---|---|---|---|
| Paid Search (Google Ads, PPC) | 0.24 – 0.27 | Higher carryover possible | High intent, strong conversion impact |
| Online Display Ads | 0.24 | Moderate carryover effects | Supports branding & retargeting |
| Television (TV Ads) | 0.12 – 0.29 | 0.48 for durable goods | TV drives long-term brand equity |
| Print Advertising | Lower short-term AED | Higher long-term AED | More effective over time than immediately |
| Offline Advertising (Billboards, Radio) | 0.08 – 0.12 | Limited long-term impact | Hard to measure direct effects |
These figures show paid search appears to have the highest short-term AED (~0.27) since it captures high-intent users. Our own industry experience shows search can be high elasticity though generally less than 1.0 — much of it is driven by product lifecycle, awareness, and competitiveness considerations. TV and print ads show stronger long-term AED (~0.48 for durable goods), supporting brand-building efforts.
Note also that in industries with long established non-durable goods with inelastic demand, e.g. for non-durable goods such as beer, wine or cigarettes, AED may be low to nil (Henningsen et al, 2011). Our focus will generally be on industries with innovative products and service, e.g. the TMT sector, and as a result we tend to see higher advertising elasticities.
Factors That Influence Advertising Elasticity
AED is not a fixed number—it varies based on competition, product type, and product lifecycle stage.
(A) Services as a Durable Good: Higher Long-Term AED
AED tends to be higher for durable goods because they:
- Require higher involvement—consumers take more time to decide.
- Have long purchase cycles—advertising builds awareness over time.
- Depend on branding and trust—consistent ad exposure influences choice.
Many telecom, media, and technology services (e.g., mobile plans, broadband subscriptions) function like durable goods:
- Customers commit long-term (contracts, subscriptions).
- AED is higher over the long run (~0.48, similar to durable goods).
- Advertising is sometimes more about brand equity and long-term retention than immediate conversions, although we’ve seen effective tactical, promotional advertising work well with short lags (days to weeks instead of months).
In this case, you should consider the long-term effects of ads, rather than over-prioritizing short-term performance metrics.
(B) Competition May Increase AED
- In competitive industries, AED may be higher because:
- Advertising plays a crucial role in differentiation.
- Consumers have more choices, making ad exposure more impactful.
- In monopolistic markets, AED may be lower since customers have few alternatives.
For instance when looking at Telecom markets with multiple competitors (e.g., Verizon, AT&T, T-Mobile, Comcast), AED tends to be higher because brands must fight for switchers. In regional broadband monopolies, AED may be lower, since customers have fewer provider choices.
(C) Early-Stage vs. Late-Stage in the Product Lifecycle
Advertising elasticity is typically higher in the early stage of a product’s lifecycle and declines as the market matures.
- Early-stage AED tends to be higher
- New brands/products require advertising to build awareness.
- Consumers are forming preferences, making ads more influential.
- Mature products tend to have lower AED:
- Customers already know the brand.
- Advertising acts more as a reminder than a driver of new demand.
In the Telecom example we discussed earlier, 5G broadband and fiber-optic internet services (early stage) should show higher AED than traditional cable plans. Another consequence is that an MMM that underestimates early-stage advertising elasticity might also undervalue new product advertising.
How to Use AED to Validate Your MMM
1. Compare Your Model’s Elasticities to AED Benchmarks
- Extract each channel’s estimated AED from your MMM.
- Compare to industry benchmarks.
- If your MMM suggests an AED of 0.8 for TV ads, which is meaningfully above the expected 0.12 – 0.29, it might overestimate TV’s impact.
2. Check for Competition & Industry Effects
- If your business operates in a competitive market, generally expect a higher AED than the industry average.
- If you sell durable or subscription-based services, generally expect longer-term AED effects.
3. Identify Over-Attribution to One Channel
- If your MMM shows one media channel driving 80% of sales, check if its AED is wildly higher than benchmarks.
- If TV is underweighted but paid search is over-attributed, your model may fail to account for branding effects.
4. Adjust for Product Lifecycle Effects
- If you’re marketing a new service, e.g., 5G home internet, your estimated AED should be higher than for mature telecom plans.
- An MMM showing static AED values might misallocate spend across growth vs. mature offerings.
Case Study: Fixing an Over-Optimized MMM
A telecom provider ran an MMM for its Consumer business and got these AED estimates:
| Channel | MMM AED Estimate | Industry AED Benchmark | Mismatch? |
|---|---|---|---|
| Paid Search | 0.85 | 0.24 – 0.27 | Yes (Too High!) |
| TV Advertising | 0.08 | 0.12 – 0.29 | Yes (Too Low!) |
| Display Ads | 0.22 | 0.24 | No |
In this case, recommended fixes include incorporating lag effects for TV to capture brand-building impact, and recalibrating search attribution to factor in brand lift from TV.
Final Thoughts: AED as an MMM Sanity Check
Always compare your MMM’s advertising elasticities against benchmarks to check for realistic results. When necessary, adjust for competition, product lifecycle, and industry effects (especially for telecom). Consider recalibrating models that over-attribute short-term digital channels and undervalue brand-building efforts.
Want a more accurate MMM? Read our full guide to Media Mix Modeling.
References
Bayer E. et al., The impact of online display advertising and paid search advertising relative to offline advertising on firm performance and firm value, International Journal of Research in Marketing, vol 37 (4), 2020
Sethuraman R. et al., How Well Does Advertising Work? Generalizations from Meta-Analysis of Brand Advertising Elasticities, Journal of Marketing Research, June, 2011
Henningsen S. et al., Determinants of Advertising Effectiveness: The Development of an International Advertising Elasticity Database and a Meta-Analysis, German Academic Association for Business Research, vol 4 (2), 2011
How Can We Help?
Feel free to check us out and start your free trial at https://app.g2m.ai or contact us below!
