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Intro

What is Product Analytics? Your Ultimate Guide | FullStory

Fundamental marketing theory​

  • STP - Segmentation, Targeting, Positioning
  • Marketing mix
  • Price elasticity
  • Neural networks

Customer Analytics​

The first part of the course focuses on how to perform customer segmentation, using a hands-on approach. It involves the application of hierarchical and flat clustering techniques for dividing customers into groups. It also features applying the PCA to reduce the dimensionality of the problem, as well as combining PCA and K-means for an even more professional customer segmentation

Purchase Analytics​

The second part of the course explores both the descriptive and predictive analysis of the purchase behavior of customers, including models for purchase incidence, branch choice, and purchase quantity. Not only that, but it also covers the application of state-of-the-art deep learning techniques to make predictions using real-world data

STP Framework​

STP is a fundamental marketing framework. It can be applied to all areas of business and marketing activities

  • Segmentation
  • Targeting
  • Positioning

Segmentation​

  • The process of dividing a population of customers into groups that share similar characteristics

  • Observations within the same group would have comparable purchasing behavior

  • Observations within the same group would respond similarly to different marketing activities

  • Divide customers into segments based on age, income, gender

  • Consumer data

    • Demographic
    • Geographic
    • Psychographic
    • Behavioral
      • Purchase frequency
      • Time of purchase
      • Purchase quantity
      • Product ratings

Targeting​

  • The process of evaluating potential profits from each segment and deciding which segments to focus on

  • Selecting ways to promote your products. You can target one segment on TV and another online

  • Examining customers' perception. (Involves psychology and usually budget constraints)

  • Consideration factors

    • Segment size
    • Expected growth
    • Competitors' offerings

Positioning​

  • What product characteristics do the customers from a certain segment need?
  • Shows how a product should be presented to the customers and through what channel
  • In fact, this process is so important, that it has a framework of its own called: Marketing Mix

Marketing Mix​

Develop the best product or service and offer it at the right price through the right channels

  • Purchase probability
  • Brand choice probability
  • Purchase quantity

4 Ps of Marketing​

  • Product - Product features; Branding; Packaging
  • Price - Product cost; Long term price changes
  • Promotion - Price reduction, display and feature
  • Place - Distribution: intensive, selective, exclusive

Physical and Online Retailers​

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Price elasticity​

Price elasticity is: the percentage change in an economic outcome of interest with respect to one percent change in a respective price

It is expected that: units sold from a brand would increase if the unit price of the brand decreases and would increase if the unit price of a competitor brand increases

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  • Price elasticity of purchase probability
  • Price elasticity of brand choice probability
  • Price elasticity of purchase quantity

Pearson Correlation​

  • Linear dependency between variables

df_segmentation.corr()

  • Ranges from -1 to 1

Standardization​

For standardizing features. We'll use the StandardScaler module

# For standardizing features. We'll use the StandardScaler module.
from sklearn.preprocessing import StandardScaler

# Standardizing data, so that all features have equal weight. This is important for modelling.
# Otherwise, in our case Income would be considered much more important than Education for Instance.
# We do not know if this is the case, so we would not like to introduce it to our model.
# This is what is also refered to as bias.
scaler = StandardScaler()
segmentation_std = scaler.fit_transform(df_segmentation)