Lift Analysis is an insightful ecommerce metric that measures the direct impact of a specific marketing campaign on consumer response.
Lift Analysis is a unique statistical model used, predominantly, in ecommerce platforms to evaluate the effectiveness of a marketing campaign or promotional effort. A comparative measure, it juxtaposes the results of marketing activities under two conditions- controlled and exposed. The control condition involves normal operations without applying the marketing campaign while the exposed condition allows a cohort to experience the promotional content. The technique calculates the ‘lift’ which is the rate at which consumer response is improved with the marketing or promotional efforts.
The traditional formulation of MMM (Multiple Linear Regression) looks something like the following:
Sales = β0 + β1 TV + β2 Radio + β3 * Newspaper + Other Factors + Error
In this formula, Sales represent total sales, while TV, Radio, and Newspaper represent various marketing channels/flights. β1, β2, β3 are the increment in sales for each unit rise in investments in those respective channels.
For instance, if Campaign A has a conversion rate of 12% after exposure and a conversion rate of 6% in a non-exposed group, the lift would be 12%/6%, yielding a lift score of This denotes that Campaign A increases conversions twice as much as if there were no Campaign A.
In an era where ecommerce platforms utilise multiple marketing strategies that range from email marketing to social media ads, discerning the effectiveness of each campaign is paramount. Lift Analysis, by evaluating the direct impact on customer response, allows for identification of most effective campaign efforts breaking down the amassed customer metrics.
The granularity of Lift Analysis can be enhanced by micro segmenting the audience based on attributes like age, geography, purchase history, device type etc. A more focused targeting strategy improves the relevancy of the conclusions drawn by the Lift Analysis. The use of AI and ML techniques can also elevate predictive analytics capabilities aiding proactive strategies.
The factors affecting Lift Analysis include the quality and relevance of the marketing campaign, sample size of the controlled and exposed group, the segmentation of the target audience, duration of the campaign, and competitive environment.
lift analysis works in tandem with other ecommerce metrics like conversion rate, click-through rate, and average order value. A higher lift score generally implies a greater chance of improving other metrics. The conversion rate—a crucial metric for e-commerce platforms—is a primary element in calculating the lift. The lift analysis can also greatly influence click-through rates by assessing the efficiency of promotional efforts and applying improved strategies based on the findings.