Elasticity | Definition
Elasticity measures the percentage change in an outcome (such as sales or conversions) resulting from a 1 percent change in a marketing input (such as advertising spend or price), providing a standardized metric for comparing the responsiveness of different levers and channels regardless of their absolute scale or measurement units. This concept, borrowed from economics, provides the common language necessary for strategic discussions about where marketing investments generate the greatest return.
The power of elasticity lies in its scale-independent nature that enables direct comparison across vastly different marketing contexts. Comparing the raw coefficient for television (measured in thousands of impressions) to social media (millions of impressions) to search advertising (clicks) creates an apples-to-oranges problem that obscures which channel actually drives stronger response. Elasticity solves this by standardizing everything to percentage terms; for example, a television elasticity of 0.15 means a 10% increase in TV spending generates a 1.5% increase in sales, directly comparable to a social media elasticity of 0.22 or a search elasticity of 0.08—immediately revealing that social media delivers the strongest response per percentage point of investment. These standardized metrics enable portfolio optimization discussions in a common framework.
Different elasticity magnitudes carry distinct strategic implications for budget allocation and channel prioritization. High-elasticity channels (approaching or exceeding 1.0) indicate strong responsiveness where marketing investments drive proportionally large sales increases, suggesting these channels deserve priority in growth scenarios. Low-elasticity channels (below 0.1) may still generate positive ROI but require larger absolute investment changes to move the business needle, perhaps better suited for maintenance spending rather than growth investment. Understanding these dynamics through response curves reveals not just current elasticity but also how it changes as spending increases and channels approach saturation.
The temporal dimension adds crucial context to elasticity interpretation. Short-term elasticities capture immediate campaign responses within days or weeks, reflecting tactical performance that matters for in-flight optimization. Long-term elasticities incorporate accumulating adstock effects over months or quarters, revealing total impact including delayed and carryover responses that short-term measures miss entirely. Example: A television campaign might show modest short-term elasticity of 0.12 but long-term elasticity of 0.28 once all carryover effects manifest—meaning patient investment strategies generate 2.3x better returns than impatient tactical approaches would suggest. Kochava MMM calculates both short- and long-term elasticities across all channels with continuous updates, enabling marketers to balance portfolio strategies between channels optimized for immediate response and those building cumulative value over extended horizons.
