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Reinforcement Learning (RL) For Acquisition
What business questions that RL models can help answer?
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How much of your sales can be attributed to marketing efforts?
-
What is the contribution of TV, radio, and digital channels towards your business KPIs?
-
What is the most efficient way to maximize the cumulative long-term gains from marketing efforts?
-
How can the customer journey be managed to increase ROI?
What are the business goals that RL models can help achieve?
-
Increase the effects beyond tradition acquisition marketing
-
Maximize the cumulative long-term gains from marketing efforts
-
Optimize cumulative gains through an interactive customer engagement process
Reinforcement Learning (RL) could be the next solution to gain more valuable customers and incremental values for your business over time. RL Algorithms can solve some of the problems marketers have been trying to solve in the past especially tracking the customer journey.
To "reinforce a habit" is a popular practice done in Marketing Analytics, and it is one of the ideas behind reinforced learning, which is to leverage machines to influence people.
The basis of advertising is "salesmanship" in mass media. Take sales as an interactive process - a two-way flow of influence between salesperson and customer. During this process, there is usually a multiple-step interaction for the salesperson who does not always convince the consumer to make a purchase. Thus, the goal is to communicate with a consumer in an effective way that the consumer will be converted.
The process mentioned above is just as important as the final outcome. In most cases, the process is advertising. Advertising can be used to gain revenue or to achieve other goals that depend on the marketing campaign objectives.
If you think the process as that you were playing a game. You need to try multiple strategies to achieve the highest result over time. Normally, you set a strategy at the beginning. Since the game is dynamic, and you have to be responsive to the settings. That is how a Marketing Analyst's strategy changes according to consumer reacts. Marketing analysts keep employing different strategies until one of them works. This is the basic idea of reinforcement marketing. If your marketing efforts are paying off, then the analyst can use it for higher cumulative gain, out of the more dynamic or interactive stretch.
When thinking about RL, it is important to highlight the interaction effect between you and the consumer, the status, and whatever action you can take. With this framework, a marketing analyst can take a more holistic view and take into account all the different factors that can affect customers browsing to purchasing decisions. The key is to look for that synergy knowing what action to take, which is a key advantage of an RL model.
What structure and types of data are required for an RL model?
We will need three datasets, customer dataset, transaction and response dataset, and communication dataset, including customer profile, product profile, media spending, historical transactions, conversion data, exposure data, etc.
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What business questions that RL models can help answer?
-
How much of your sales can be attributed to marketing efforts?
-
What is the contribution of TV, radio, and digital channels towards your business KPIs?
-
What is the most efficient way to maximize the cumulative long-term gains from marketing efforts?
-
How can the customer journey be managed to increase ROI?
What are the business goals that RL models can help achieve?
-
Increase the effects beyond tradition acquisition marketing
-
Maximize the cumulative long-term gains from marketing efforts
-
Optimize cumulative gains through an interactive customer engagement process
Reinforcement Learning (RL) could be the next solution to gain more valuable customers and incremental values for your business over time. RL Algorithms can solve some of the problems marketers have been trying to solve in the past especially tracking the customer journey.
To "reinforce a habit" is a popular practice done in Marketing Analytics, and it is one of the ideas behind reinforced learning, which is to leverage machines to influence people.
The basis of advertising is "salesmanship" in mass media. Take sales as an interactive process - a two-way flow of influence between salesperson and customer. During this process, there is usually a multiple-step interaction for the salesperson who does not always convince the consumer to make a purchase. Thus, the goal is to communicate with a consumer in an effective way that the consumer will be converted.
The process mentioned above is just as important as the final outcome. In most cases, the process is advertising. Advertising can be used to gain revenue or to achieve other goals that depend on the marketing campaign objectives.
If you think the process as that you were playing a game. You need to try multiple strategies to achieve the highest result over time. Normally, you set a strategy at the beginning. Since the game is dynamic, and you have to be responsive to the settings. That is how a Marketing Analyst's strategy changes according to consumer reacts. Marketing analysts keep employing different strategies until one of them works. This is the basic idea of reinforcement marketing. If your marketing efforts are paying off, then the analyst can use it for higher cumulative gain, out of the more dynamic or interactive stretch.
When thinking about RL, it is important to highlight the interaction effect between you and the consumer, the status, and whatever action you can take. With this framework, a marketing analyst can take a more holistic view and take into account all the different factors that can affect customers browsing to purchasing decisions. The key is to look for that synergy knowing what action to take, which is a key advantage of an RL model.
What structure and types of data are required for an RL model?
We will need three datasets, customer dataset, transaction and response dataset, and communication dataset, including customer profile, product profile, media spending, historical transactions, conversion data, exposure data, etc.