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Mapping the Future of Marketing Success Using MarketSim Agent-base Marketing Analytics ABM Marketing Analytics #PRMSTailWag @ProRelevant Objective To show how agent-based modeling can work in consumer markets To show how the MarketSim agent based modeling works (at least for the purchase funnel) MarketSim consumer behavior model Consumer decision behavior Purchase funnel Probability of seeing message Relevance Shopping Inertia/loya lty Becoming aware Purchase frequency Purchase Intent Availability The probability of choosing one item over another is based on the net utility derived from each of these three dimensions versus the price to be paid for the item Price Probability of choosing an item $ ¥ € £ The modeling process calculates a relative weight for each dimension MarketSim Animation Purchase funnel The following slides are an animation to describe how Media drives purchase intent and purchase The impact of Pricing, Distribution and Brand Relevance are also important drivers of sales, and will be made available in a separate animation The animation will progressively describe the process, moving from a simple example to a more complex example Probability of seeing message Becoming aware Purchase Intent MarketSim Animation Consumers Agents =>> Agents represent consumers They are virtual consumers They respond to market stimuli based on rules and parameters you determine A full ABM describes consumer behavior in a chosen category. When properly calibrated it provides an accurate model and simulation tool of real consumer behavior in the category Let’s begin Example: Simplification 1: Media drives Awareness. Awareness drives purchase. Let’s assume that the only important consumer behavior is the ability for media to drive consumer awareness a) The higher the awareness, the more items that would be purchased b) Media directly drives awareness: Awareness directly drives purchase c) Further let’s assume that everyone is not aware Initial Awareness Simulation Media Investment GRPs Week 1 100 Consumers Unaware Consumers Aware Simulated Purchases 1,125 Initial Awareness Simulation Week 2 100 100 1,125 1,125 GRPs Media Investment Week 1 Consumers Unaware Consumers Aware Simulated Purchases Initial Awareness Simulation Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 100 100 50 0 50 0 100 1,125 1,125 563 0 563 0 1,125 650 875 575 165 300 175 315 GRPs Media Investment Week 1 Consumers Unaware Consumers Aware Simulated Purchases Actual Purchases Difference - 475 250 12 165 263 175 810 MAPE: 93% Initial Awareness Simulation Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 100 100 50 0 50 0 100 1,125 1,125 563 0 563 0 1,125 650 875 575 165 300 175 315 GRPs Media Investment Replay: Consumers Unaware Consumers Aware Simulated Purchases Actual Purchases Difference - 475 250 12 165 263 175 810 MAPE: 93% This model indicates that there is a correlation between the hypothesized media and awareness, and awareness and purchase, but most likely there is something else going on. A traditional MMM might estimate in this way, if all the nuances of consumer behavior aren’t properly included in the model Simplification 1: This simplification is made up of 2 rules with 2 (or more) parameters 1) Number of Aware Consumers = A * (Media GRPs) 2) Product Sales = B * (Number of Aware Consumers) Calibration If these two rules are correct and if we know the advertising from the past and if we know the past ‘real’ sales then we can determine the value of the parameters A and B that provide the best ‘fit’ for our model Best fit (Error) is defined as the average error per period between actual and simulated sales For the above example we have A = 3 and B = 3.75 With these two values for these two parameters the fit (error) is 93%. Could we get the fit to be better? How could we improve the fit? Obviously there is more to marketing than a simple relationship between advertising, awareness and sales. What about purchase intent? What about brand relevance? What about loyalty? Let’s try A = 3 +0% and B = 2.25 -33% With these new values the error is (56%) Awareness Animation Improved Nr. 2 Media Investment GRPs Week 1 100 Consumers Unaware Aware Consumers Simulated Purchases 675 Awareness Animation Improved Nr. 2 Week 2 100 100 675 675 GRPs Media Investment Week 1 Consumers Unaware Aware Consumers Simulated Purchases Awareness Animation Improved Nr. 2 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 100 100 50 0 50 0 100 675 675 338 0 338 0 675 650 875 575 165 300 175 315 GRPs Media Investment Week 1 Consumers Unaware Aware Consumers Simulated Purchases Actual Purchases Difference - 25 200 237 165 38 175 360 MAPE: 56% Awareness Animation Improved Nr. 2 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 100 100 50 0 50 0 100 675 675 338 0 338 0 675 650 875 575 165 300 175 315 GRPs Media Investment Replay: Consumers Unaware Aware Consumers Simulated Purchases Actual Purchases Difference - 25 200 237 165 38 175 360 MAPE: 56% 3rd Animation For the above example we have A = 3 and B = 2.25 With these two values for these two parameters the fit (error) is 56%. Could we get the fit to be better? Let’s try A = 2 -33% and B = 3 +33% With these new values the error is (54%) Initial Awareness Simulation Nr. 3 Media Investment GRPs Week 1 100 Consumers Unaware Aware Consumers Simulated Purchases 600 Initial Awareness Simulation Nr. 3 Week 2 100 100 600 600 GRPs Media Investment Week 1 Consumers Unaware Aware Consumers Simulated Purchases Initial Awareness Simulation Nr. 3 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 100 100 50 0 50 0 100 600 600 300 0 300 0 600 650 875 575 165 300 175 315 GRPs Media Investment Week 1 Consumers Unaware Aware Consumers Simulated Purchases Actual Purchases Difference - 50 275 275 165 0 175 285 MAPE: 54% Initial Awareness Simulation Nr. 3 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 100 100 50 0 50 0 100 600 600 300 0 300 0 600 650 875 575 165 300 175 315 GRPs Media Investment Replay: Consumers Unaware Aware Consumers Simulated Purchases Actual Purchases Difference - 50 275 275 165 0 175 285 MAPE: 54% In summary: a simple two rule model The error has been reduced to 54% MAPE But more could be done This model uses only 2 simple equations Media and awareness Awareness and purchase. But it doesn’t tell the whole story. There’s much more to it. Why is this important For new brands we can now determine the trade-off between driving awareness and other valuable consumer behavior dimensions. Based on our objectives and our brand’s status in the market we can now determine key tactics and strategies to drive value for our brand. More awareness would help small or newly launched brands More purchase intent would help larger more well-known brands Adding more realistic awareness After purchase, a consumer is usually always aware and this awareness lasts for a long time. It is a step-function: Media leads to awareness Consumers are either aware or unaware (1 or 0). There are no partial awareness states as in the previous example If the consumer isn’t already aware, there is some probability, they will become aware when seeing an advertisement Probability of becoming aware if they see the media 50% Awareness (either 1 or 0) = The probability of becoming aware is dependent on the media quality and seeing the media Awareness leads to purchase = 3 per aware agent Purchase leads to near permanent awareness Animation with Awareness Nr. 4 Media Investment GRPs Week 1 100 Audience Consumers seeing media Consumers Aware Unaware Simulated Purchases 600 Media Investment GRPs Animation with Awareness Nr. 4 Week 1 Week 2 100 100 600 1,200 Consumers Audience seeing media Consumers Aware Unaware Simulated Purchases Media Investment GRPs Animation with Awareness Nr. 4 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 100 100 50 0 50 0 100 600 1,200 1,500 1,500 1,800 1,800 2,100 650 875 575 165 300 175 315 Consumers Audience seeing media Consumers Aware Unaware Simulated Purchases Actual Purchases Difference - 50 325 925 1,335 1,500 1,625 1,785 MAPE: 430% Media Investment GRPs Animation with Awareness Nr. 4 Replay: Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 100 100 50 0 50 0 100 600 1,200 1,500 1,500 1,800 1,800 2,100 650 875 575 165 300 175 315 Consumers Audience seeing media Consumers Aware Unaware Simulated Purchases Actual Purchases Difference - 50 325 925 1,335 1,500 1,625 1,785 MAPE: 430% But this can be improved further Awareness doesn’t usually lead directly to purchase For those consumers that are aware, there is some level of purchase intent Purchase intent is delivered by seeing an advertisement. The level of purchase intent is dependent on the quality of the advertisement Each ad delivers 1 unit of purchase intent for those consumers that see the ad and are aware Purchase intent always decays (typically exponentially, e.g., 50% per period) – for our example, we assume it is linear decay. It goes from 1 to 0.5 to 0.0 But there can be no purchase intent if the consumer is unaware Awareness can be forgotten if the consumer doesn’t see an advertisement (over some period of time). There is some probability of becoming unaware for those consumers that are aware, but have never purchased in the category If the consumer becomes unaware they lose their purchase intent Let’s assume that purchase takes place if purchase intent reaches a certain level The quality of the creative and the quality of the media placement determines the level of purchase intent delivered to the consumers with each media impression Animation with Awareness & Purchase Intent Media Investment GRPs Week 1 100 Consumers Audience seeing media Consumers Aware Unaware NoPurchase Purchase Intent Simulated Purchases 600 Media Investment GRPs Animation with Awareness & Purchase Intent Week 1 Week 2 100 100 600 900 Consumers Audience seeing media Consumers Aware Unaware NoPurchase Purchase Intent Simulated Purchases Media Investment GRPs Animation with Awareness & Purchase Intent Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 100 100 50 0 50 0 100 600 650 900 875 600 575 150 165 300 300 150 175 300 315 Consumers Audience seeing media Consumers Aware Unaware NoPurchase Purchase Intent Simulated Purchases Actual Purchases Difference - 50 25 25 15 0 25 15 MAPE: 6% Replay: Media Investment GRPs Animation with Awareness & Purchase Intent Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 100 100 50 0 50 0 100 600 650 900 875 600 575 150 165 300 300 150 175 300 315 Consumers Audience seeing media Consumers Aware Unaware NoPurchase Purchase Intent Simulated Purchases Actual Purchases Difference - 50 25 25 15 0 25 15 MAPE: 6% Some results Purchase intent is available only for those consumers/agents that are aware. Purchase intent is different from awareness, where purchase intent is a value as opposed to an on-off function like awareness The new parameters associated with this simple animation are: Purchase Intent Those agents that are aware have some level of purchase intent Simulation #4 Simulation #5 4 4 Aware of Media 2 (50%) 2 (50%) Purchase Intent - 3 (Aware + 50% previous week) 600 900 430% 6% Seeing Media (100 GRPs) Simulated Purchases MAPE MarketSim Agent-Based Modeling The preceding animations represent the findings of consumer behavior based on many years of experience modeling consumer behavior in many categories and markets. There are other aspects not yet included in the animations that are necessary to build a complete model How do consumers respond to pricing How does the in-store experience influence their purchases How does their brand perceptions influence their purchases How do these parameters get determined? What is calibration? Calibration leads to best fit where the error rates between simulated lead to the lowest level of error and the errors are as random as possible What we do, who we work with Industries CPG / FMCG Financial Insurance Regulated utilities Beverages Carbonated, spirits Dairy Infant Formula Personal Care Software / Internet Telecom Airlines 44 The ProRelevant Team Guy Powell / Atlanta MBA University of Chicago, Published author (x3 books) Global Speaker & Trainer Marketing ROI Ramesh Sundararajan / Singapore Post Graduate Engineering from BITS, Pilani Masters in Management from The Indian Institute of Management Steven Groves / Charlotte Publisher author (x2 books), 7 years Computer Associates Global Speaker & Consulting Businesses on Online Marketing & ROI Pulak Ghosh / Delhi Professor Quantitative Methods and Information Systems (IIM, Bangalore) Chief Data Scientist & Member of the Board of Advisors. Strategic Decision-making Tools Drive long term strategic change in marketing decision-making methods Develop & Support Analytics Software • MarketSim® ABM Marketing Analytics • Preznc® Report Competitive Online Presence Assessment Global Experience North America Russia Malaysia Hong Kong Vietnam Kenya ABM Marketing Analytics Competitive Online Assessment www.ProRelevant.com Sri Lanka Singapore Japan Morocco Indonesia Tanzania +1 404-816-4344 - Atlanta, USA #PRMSTailWag @ProRelevant
