Tangerine Customer Value Segmenter

Value Segmentation Report

Content Part 1 – Introduction & Summary Results

Appendices - Explanation of the Approach

• Introduction

• Data Used

• Summary of the Approach

• What is meant by ‘Value’

• Key Findings

• The Segmentation Dimensions

• Segmentation Output Summary

• Scoring the Dimensions

Part 2 – Detailed Results and Findings

• The Segments

• Customer Base Value Distribution

• Allocating Customers to Segments

• Order Volumes & Values by Value Segment • Customer Gains & Losses by Value Segment

• Value Changes by Value Segment • Calendarisation of Value by Value Segment • Order Volumes & Values by Value Segment • Product Purchases Split by Value Segment • Value Segment Purchases split by Product Group • Customer Type Split by Value Segment • Value Segment Split by Customer Type

Part 1 – Introduction and Summary Results

Introduction

Summary of the Approach

• This is the output from a customer value segmentation study carried out in August 2019

• Data provided from our CDS system with financials at the GM (Gross Margin) Level

• Conducted by Primary Customer Data Ltd. from the UK

• Value figures are annualised to enable comparison between customer with different tenures

• Based on a widely adopted analytical and framework approach

• Three dimensions of value considered:

• The objective is to: Understand where the value really sits in our customer base and what the core differences are between higher and lower value customers

• Current annualised value • Value growth / decline trajectory • Value consistency over time

• Dimensions weighted to give all customers a Weighted Value Score (WVS) • WVS used to allocate customers to one of 10 working-level value segments: • • • • •

Critical Few Very High Value High Value Medium High Value Medium Value

• • • • •

Medium Low Value Low Value Very Low Value Upper Tail Lower Tail

• These can be aggregated for operational purposes

10 Key Findings • The top 491 customers (1% of the base) deliver over 40 million of annualised value • The lowest value 40% of customers (almost 20,000 of them) deliver 8.5 million • Increased customer value is driven by purchase volume far more than by average purchase value • Our net customer attrition of 3% is made up from 18% real attrition and 15% real gains • Our highest value and lowest value segments are typically growing in value but mid-ground segments are decreasing in value • Our medium value segments have less seasonality in their purchases than high and low value segments

• ‘Home Cinema’ is the most frequently bought product by our highest value segments • ‘Components’ are mainly bought by lower value segments • Over 50% of our Critical Few customers are in Business Services or Public Services

• Over 50% of our Lower Tail customers are in Retail or General Commercial

Summary of Segmentation Output

Part 2 – Detailed Results & Findings

Customer Base Value Distribution

Customer Base Value Distribution (contd.)

Order Volumes and Values by Value Segment

Order Volumes and Values by Value Segment (contd.)

Customer Gains and Losses by Value Segment

Customer Gains and Losses by Value Segment (contd.)

Value Changes by Value Segment

Value Changes by Value Segment (contd.)

Calendarisation of Value by Value Segment

Calendarisation of Value by Value Segment (contd.)

Order Volumes and Values by Value Segment

Order Volumes and Values by Value Segment (contd.)

Product Purchases Split by Value Segment

Product Purchases Split by Value Segment (contd.)

Value Segment Purchases split by Product Group

Value Segment Purchases split by Product Group (contd.)

Customer Type Split by Value Segment

Customer Type Split by Value Segment (contd.)

Value Segment Split by Customer Type

Value Segment Split by Customer Type (contd.)

Appendices– Explanation of the Approach

The Data Used

What is meant by ‘Value’

• All data provided by us and sourced from the customer transaction data store (CDS)

• The ‘Value’ used throughout this document is the Annualised Gross Margin

• No Customer Identifiable Information is provided – only system reference numbers

Gross Margin • IS the “GM” figure provided from CDS • = Price paid by the customer less discount less the cost of the product/s to us

• Transaction data provided covering January 2009 to June 2019 • Data provided was at the individual order level covering: • • • • • • •

Customer ref (anonymised) Customer category (Sector Code) Order number Order date Product category code Quantity Value (at Gross Margin Level)

• 1.5 Million Order records were provided in total

Annualised • Value is ”Annualised” to give a meaningful comparison between customer with different tenures • Also prevents distortion of results by products with long re-purchase cycles

The Segmentation Dimensions

Scoring the Dimensions

• Segmentation based on three dimensions:

• Each customer will have base result for each dimension. For example:

1. Annualised Value 2. Value Trajectory (Rate of increase or decrease)

• Each Customer is allocated a standardised score between 1 and 10 for each of the segmentation dimensions. For example:

3. Value Consistency (Lack of ‘Spikiness’ over time)

• Weightings of dimensions 2 and 3 can be set to 0, meaning the segmentation is only based on Annualised Value • The weighting used in the production of this output are: Annualised Value

70%

Value Trajectory

20%

Value Consistency

10%

• The scores allocated are based on a simple decile split for each dimension.

•

The highest 10% of values are allocated a 10

•

The next 10% of values are allocated a 9

•

And so on until

•

The lowest 10% of values are allocated a 1

The Segments

Allocating Customers to the Segments

• 10 Analytical Value Segments

• Customers allocated an overall Weighted Value Score (WVS) based on their dimension scores and weightings

• Simple, working titles are: •

Critical Few

•

Very High Value

•

High Value

•

Medium High Value

•

Medium Value

•

Medium Low Value

•

Low Value

•

Very Low Value

•

Upper Tail

•

Lower Tail

• We can review these names for communicating internally • For operational purposes we may group some of the segments

• Customers ranked by their Weighted Value Score and split into percentile groupings • The percentile splits select for your segmentation are: