Purchase Analytics

Price elasticity of purchase probability

% change in purchase probability in response to a 1% change in price

We have a product, which costs $2.40-. We have obtained the price elasticity of purchase probability to be -0.79. The customer at this price point is: inelastic

We have a product, which costs $3.50. We have obtained the price elasticity of purchase probability to be -2.1. The customer at this point is: elastic

We have a product, which costs $2.00. Using a logistic regression model, we have obtained the coefficient for price to be -2.3. In addition, we have estimated the purchase probability at this price point to be 0.3. Using the price elasticity formula from the lecture, what is the price elasticity? -3.22

A company has a product with a price of 2.3 dollars on the market. They have estimated that the price elasticity of purchase probability at this price point is -0.9. If you are an adviser for the marketing team of the company what would you recommend: The company should increase the price of the product, without fear of losing too many customers.

The purchase probability of a client at a price point is 0.7, while their price elasticity: -1.56. If we raise the price of the product by 1%, the new probability of purchase of the client will be: 68.44%

The purchase probability of a client at $2.56 is 0.7, while their price elasticity: -1.22. If we lower the price of the product by $0.02, the new probability of purchase of the client will be: 70.95%

Multinomial Logistic Regression - For a multiclass scenario

Deep Learning

Training the deep learning model

• Keras was built into the core TensorFlow, so most TensorFlow 2.0 methods are not referenced as 'TensorFlow', but rather Keras methods
• tf.keras.Sequential(layers) function that is laying down the model (used to 'stack layers')
• tf.keras.layers.Dense(output layer size, activation function) takes the inputs provided from the last layer, calculates their dot product with the weights and adds the bias. Also applies an activation function.
• Activation functions
  • relu
  • sigmoid
  • tanh
  • softmax
• model.compile(optimizer, loss) configures the model for training
  • Optimizers
    • Adaptive Moment Estimation (ADAM)
  • loss
    • sparse_categorical_crossentropy
      • applies one-hot encoding to the targets
• tf.keras.callbacks.EarlyStopping(patience) ensures that the model stops traning after the validation loss has started increasing. When patience=0, it will stop after the first validation increase. patience = 2
• fit()
• evaluate() returns the loss value and metrics values for the model in 'test mode'
• predict()
• argmax - Arguments of the maxima