Our Analytics Foundry
Customer Life Value (CLV) and Retention Models
Our Customer Life Time Value (CLV) models help brand teams evaluate a true life time value for their customers. CLV is generally underestimated because of shorter duration time frames. This leads to estimating customer acquisition program ROIs that are lower than actual ROIs.
Our customer retention models help brand teams understand if their promotional investments are helping in increasing the retention probabilities of their customers.
Our customer retention models help brand teams understand if their promotional investments are helping in increasing the retention probabilities of their customers.
Promotional Response and Forecasting Models
We use bayesian dynamic models to estimate marketing mix models. Our models allow promotional effects to vary over time in response to the changing market conditions. Our approach decomposes a sales / market share time series into trend, cyclical, seasonal, and promotional components without any need to detrending the time series and thus compromising the data. Our models are superior to the traditional SCAN*PRO models.
Brand Equity / Baseline Models
Our dynamic brand equity models help brand teams understand how brand equity evolves over time and how different promotional spends contribute to the brand equity evolution. For example, our models can unravel the relationship between advertising, brand equity and sales / market share.
Understanding Marketing Interventions
We can help the brand teams understand the full / long term effects of marketing interventions. For example, pricing promotions are often associated with short term increases, post promotion dips and long term impacts. Measuring the short term effect is not enough to estimate the intervention ROI.
Optimizing promotional spends over time
Our optimization models can help brands decide how to allocate promotional funds over time to reach brand objectives over a given time period. For example, how many GRPs are required to reach the targeted markets share over a given time period? Should advertising allocations be increasing / decreasing / steady over time?
Differentiating Advertising Campaigns
Awareness tracking studies are often used to understand the effectiveness of advertising campaigns. We can help the brand teams differentiate the effectiveness of different advertising campaigns / copy themes. Advertising campaign effects may be different for different themes.
Our approach recognizes non-linearities that are ignored in the traditional adstock modeling process.
Click TrackerEval to see a case study.
Our approach recognizes non-linearities that are ignored in the traditional adstock modeling process.
Click TrackerEval to see a case study.
Advertising Frequency
Should we blitz, pulse or maintain steady advertising campaigns? We use bayesian modeling and genetic algorithms to help brand teams decide on an advertising frequency policy that maximizes brand awareness / sales.
When should we replenish the ad
When should we start thinking of replacing ad copy? We recommend changing the ad when it's effectiveness depreciates to 50% of its original effectiveness. Our bayesian models can predict that time of change.