This has all the resources and code I would be using/creating while learning the price elasticity and optimisation.
Price is an important driver of company's profit. As per a quantitative survey of 2500 companies following drive the profit
Price 11.1% Variable Cost 7.8% Volume 2.3% Fixed Cost 2.3%
two infographics to be placed in here
Complexity of Pricing
two infographics to be placed in here
Across the business, there may be potential for conflict when it comes to pricing a product.
Traditional approches
- Cost based
- Competition based
- Customer based
A link to article which explains the same
price-optimisation/Traditional Price Approches.pdf
three infographics which explain each of the traditional method
Scientific Approach
Links to Article which explain the same
AI based Approach
Link to the infographic which explains AI based approach
Stage I: Reach
Reach is the initial stage of the buyer’s journey. The key is to attract more visitors and provide an engaging experience that will lead to a purchase.
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Smart Content Curation: This stage is about showing visitors content relevant to them based on what others like the prospect have bought in the past. In short, this can be a form of recommendation engine that includes products, offers and content.
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Programmatic Media Buying: This relates to the use of propensity models to more effectively target ads to the most relevant customers. AI can help by determining the best (and worst) sites to be used for ads.
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AI Generated Content: AI content writing programs can select elements from a dataset and structure a ‘human sounding’ article that is personalized to a specific prospect. For banks and credit unions, AI writers can assist with quarterly earnings reports and market data.
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Voice Search: To improve reach, voice technology driven by AI is about utilizing the technology developed by the major players (Google, Amazon, Apple) to help increase organic search traffic using digital personal assistants.
Stage II: Act
The second stage of the consumer journey is intended to draw the consumer in and to make them aware of your products and services.
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Propensity Modeling: Propensity modeling uses large amounts of historical data to make predictions about the real world. Machine learning at this stage helps to direct consumers to the right messages and locations on you website as well as to generate outbound personalized content.
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Ad Targeting: Propensity models can process vast amounts of historical data to determine ads that perform best on specific people and at specific stages in the buying process. This allows for more effective ad placement and content than traditional methods.
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Predictive Analytics: Using propensity models can help determine the likelihood of a given customer to convert, predicting what price a consumer is likely to convert, or which customers are most likely to make repeat purchases. The key here is accurate data.
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Lead Scoring: Scoring leads is the process of using predictive analytics so that a sales team can establish how ‘hot’ a given lead is, and if they are worth devoting time to.
Stage III: Convert
This is the important stage of moving a consumer from being an interested prospect to being a customer or member.
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Dynamic Pricing: Dynamic pricing uses machine learning to develop special offers only for those prospects likely to need them in order to convert. This means you can increase sales without reducing your profit margins by much, therefore maximizing profits.
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Re-Targeting: Propensity models can help determine what content is most likely to bring customers back to your site based on historical data. This optimizes re-targeting ads to make them as effective as possible.
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Web & App Personalization: A very powerful tool, you can use propensity models to personalize a web page or app based on where the consumer is in their journey to purchase.
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Chatbots: Chatbots use AI to mimic human intelligence, interpreting consumer inquiries and completing orders. If you are interested in building a chatbot for your brand within the Messenger platform, Facebook has created instructions for how to do so.
Stage IV: Engage
Once a purchase is made, it is important to continuously build engagement and loyalty with the intention of expanding the relationship and potentially generating referral business.
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Predictive Customer Service: Predictive analytics driven by machine learning can be used to determine which customers are most likely to either become dormant or leave altogether. With this insight, you can reach out to these customers with offers, prompts or assistance to prevent them from churning.
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Marketing Automation: Machine learning can and predictive analytics can be used to determine the best times to make contact with a customer, what words should be used in the communication and much more. These insights can improve the effectiveness of your marketing automation efforts.
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1:1 Dynamic Emails: Predictive analytics using a propensity model can use previous behavior to promote the most relevant products and services in email communication as part of the onboarding process. The results from these communications are then fed into the models to improve results in the future.