Price level

• A segmentation based on an average price level allows classifying customers into 4 segments based on their average basket :
• Discount
• Medium -
• Medium +
• Premium

• Scoring customers according to their average basket gives the ability to adapt upsell & cross-sell strategies in function of purchasing behaviors.

• Octolis’ price level segmentation goes further with a price level score per product category.

• A customer will not have the same buying behavior depending on the type of product he buys.

• If you like chocolate, you might go for a premium product while the difference between a first-price detergent and a premium detergent might not be obvious to you.

• Thus, if your favorite grocery store recommends you first-price chocolate you most likely won't buy it.

• Smart upsell & cross-sell strategies recommend products that match your customers’ habits, at the price they use to pay for that type of product.

• Email personalization > Push recommended products in your automated emails (post-purchase, reactivation, ..) or even as a conditional section in your newsletter.

• Web personalization > Push recommended products in different steps of the web user journey, in a dynamic block of some pages, or a popup.

• Any channel > Almost all customer journey touchpoints could be used to upsell / cross-sell your existing customers (outbound phone calls, postal mail promotion, ..)

• Impact on: Upsell, Cross-sell, Average Order Value, Number of orders per customer.

• Level of impact: ⭐️⭐️️⭐️️

• A dataset containing your Customers data, with columns for:
• user_id: unique identifier of the customer.

• A dataset containing your Orders (items) data, with columns for:
• user_id: unique identifier of the customer.
• order_date: date at which the purchase was made.
• product_id: unique identifier of the product purchased.
• order_amount: total amount that was spent by the customer for this order.

• A dataset containing your Products data, with columns for:
• product_id: unique identifier of each product.
• category_id: unique identifier of each product.

• Tiers to reach the different price levels/segments.

• As this score is closely related to RFM and Product Recommendation, please refer to the relevant pages to learn more about these scores.

• The output of this recipe is a new Dataset containing a row for each of your contacts who made at least one purchase.

• In the Dataset table, you will have one new attribute per product category :
• price_level_cat1
• price_level_cat2
• …

• With the associated price_level_score (decile of customer sensitivity to discount offers (from 1 to 10).

• You can then make a join between your new Dataset and your existing Contacts Dataset to map your favorite store attributes.

• Create a new Dataset using SQL expert mode.

• Copy / past the following SQL template.

• After pasting the template, modify all variables with the names of your input columns.

• Add your Price level Segmentation Dataset as a source of your Contacts Dataset, and map the needed fields to import them into your Contacts Dataset.

sql
// Price level Segmentation SQL Template