Early adopter

📊 Business Context

What is it about?
  • The Early Adopter (EA) segmentation classifies customers according to their likelihood to buy new products.
  • The more new products the customer buys, the higher the EA segment is (4 segments in total: EA --, EA -, EA +, EA ++).
  • Segmentation restricted to customers with at least 3 purchases (others are not segmented)
  • A customer is considered an early adopter if he purchases a product within 90 days after its first sale.
Why focus your efforts on early adopters?
  • The term early adopter refers to “Diffusion of Innovations” (1962), a book in which Everett M Rogers the communication scholar and sociologist, describes 5 types of adopters for products and provides insight into each of those types.
    • The graph below highlights these 5 types.
  • Early adopters attributes:
    • They will often have a degree of “thought leadership” for other potential adopters.
    • They may be very active in social media and often create reviews and other materials around new products that they strongly like or dislike.
    • They will try to obtain more information than an innovator in this decision-making process.
What are the main use cases?
  • Push new products to EAs first :
    • Through any channel: Email & web personalization, loyalty programs, dedicated events to try new products.
  • Encourage review generation from these clients:
    • Gift or discount after review.
    • Questionnaire after purchase.
    • Free samples.
  • Facilitate access to information needed by EA: ROPO use cases.
What business impact can I expect?
  • Impact on: Retention, New sales.
  • Level of impact: ⭐️⭐️️⭐️️⭐️

↔️ Inputs & Outputs

Data input
A dataset made of Orders with at least the following attributes :
  • order_id: unique identifier of the transaction.
  • customer_id: unique identifier of the customer who made the purchase.
  • order_date: date at which the purchase was made.
  • order_details: list of product ids within a purchase.
A Customers dataset with the following attributes
  • customer_id: unique identifier of the customer who made the purchase.
  • A dataset containing your Products data, with columns for:
    • product_id: unique identifier of each product.
    • category_id: unique identifier of each product.
User Settings
Users can adjust the following settings according to their needs :
  • The number of new products to purchase to get to each segment (EA --, EA -, EA +, EA ++).
  • Segment restriction:
    • Default segmentation is restricted to customers with at least 3 purchases (others are not segmented).
    • The default period for a product to be considered new is 90 days (from its first sale).
Expected output
  • The output of this recipe is a new Dataset with 3 output columns :
    • first_purchase_date : first purchase date per product.
    • new_product_purchased_count : part of new products purchased per customer.
    • early_adopter_score : decile of customer sensitivity to new products (from 1 to 10).
  • You can then make a join between your new Early Adopters Dataset and your existing Contacts Dataset to map your early_adopter_score for instance.

🖥️ Implementation in Octolis

1. Prepare your input data
Make sure that you have built an Dataset containing all your orders.
In case you have several order Sources (e.g. eShop & PoS) you need to group all sources into an orders master Dataset.
2. Apply the SQL Template “Early adopter Segmentation”
  • Create a new Dataset using SQL expert mode.
  • Copy / paste the following SQL template.
  • After pasting the template, make sure to modify all variables with the names of your input columns.
  • Add your Early adopter Segmentation Dataset as a source of your Contacts Dataset, and map the needed fields to import them into your Contacts Dataset.
// Early adopter Segmentation SQL Template