Glossary (Customer Insights)

Customer Health Score
Product Health Score

Calculated based on the fields Revenue Trend Last 12 months and Margin Trend Last 12 months.

The Revenue Health Score and Margin Health Score are set according to the Revenue and Margin monthly or quarterly change (trend) in the last 12 months ( the maximum value is 100, the minimum value is 0) and to this classification:

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Example:

Revenue Trend Last 12M = -19.23% => Revenue score = 25
Margin Trend Last 12M = -18.84% => Margin score = 25

Health Score = Revenue Score * Revenue Weight + Margin Score * Margin Weight

The weight value can be set (in the configuration in Price Parameters) between 0 and 1 for each (the default value is 0.5 for each); the summary of these two has to be equal to 1 (e.g. Revenue Weight = 0,5, Margin Weight = 0,5 => 0,5 + 0,5 = 1).

Customer Classification by Revenue

This category should be calculated based on last 12 months.

Calculation:

• Get sorted (Descending) sum of revenue per customer.

• Calculate contribution value of each customer = ∑Revenue of customer / ∑Revenue of all customers

• Calculate cumulative revenue contribution per customer.

• Assigns Customers into different classes based on cumulative revenue contribution for the last 12 months:

A ≤ 20%
B ≤ 50%
C ≤ 95%
D rest
The thresholds are configurable.

Example:

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Product Classification by Volume

This category should be calculated based on last 12 months.

Calculation:

• Get sorted (Descending) sum of volume per product

• Calculate contribution value per product= ∑ Volume per product / ∑Volume per all products

• Calculate cumulative volume contribution per product.
  Assigns Products into different classes based on cumulative volume contribution for the last 12 months:

Very High Volume ≤ 10%
High Volume ≤ 20%
Normal Volume ≤ 75%
Low Volume rest

The thresholds are configurable.
Example:

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Product Type

Classification whether the product belongs to Commodity or Specialty, is based on Margin % = Margin Value / Invoice Price value (Margin field depends on your mapping field.)

Commodity ≤ 40%
Specialty > 40%

The thresholds are configurable in the PP table.

Make sure the value = ∑ Value of a product per all customers in the last 12 months (not per one customer).

=> We will have only one record to for Commodity, Specialty products based on the data for the last 12M.

When select YTD, MTD etc., the chart will sort out products that have transactions in that period; if a product does not exist in that period of a customer, it will not count.

E.g.: We have 30 products Commodity, 70 products Specialty in the last 12M.

For customer X in YTD, only 50 products have transactions, 20 of them is Commodity, the rest is Specialty, so the chart will show this accordingly.

Cross Sell Opportunity

Formula = Average (Invoice) Price per Customer Segment and per period and for Products not bought by Customer

Example: Segment buys Product A, Product C and Product E, Customer A bought only Product A and Product E = > cross-sell would be an average Invoice Price of Product C per Customer Segment.

Customer Segment is defined in the configuration (Price Parameters) – a list of fields the segment consists of.

Revenue Increase

If Quantity per Product and Customer < average Quantity per Product and per Customer Segment, then:

Formula =  (average Quantity per Product and per Customer Segment - Quantity per Product and Customer) * average Price per Product and Customer

If Quantity per Product and Customer > average Quantity per Product and per Customer Segment, then:

Formula: Revenue Increase = 0

Example: Customer Segment buys Product A in Average 500 quantity and Customer bought 350 quantity, then Revenue Increase = (500 – 350) * average Price per Customer. If Customer bought more than 500 quantity, then Revenue Increase = 0.

Customer Segment is defined in the configuration (Price Parameters) – a list of fields the segment consists of.

Selling Opportunity

= Cross Sell Opportunity + Revenue Increase

% Products buying

Formula = Number of Products buying in this period of a customer / Total of Products traded in this period * 100

Example: With YTD, total 200 products are traded. Among of them, customer A buys 50 products.

% Products buying of customer A in YTD = 50/200 * 100 = 25 %

Revenue, Margin and Volume Trend Last 12 Months

First, the respective metric (revenue, margin or volume) is calculated for every single time unit.
Customer level: time unit = month
Product level: time unit = month

The least-square approach is used to compute the trend. See the Fitting a trend: Least-squares section in the Linear trend estimation Wikipedia article.

Formula:

• x is the month number (like month 1 would be the first month in scope)

• x̄ is the average month number

• y is the metric to consider, like margin

• ȳ is the average metric to consider, like average margin

To convert the trend value â back to a percentage, the following formula is used: â / average(metric)

Periods with no transactions are considered to have zero revenue, margin, and volume, and are included in the trend computation.

Find the example in the the following attachment:

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This calculation is used in the following portlets:

Portlets affected by this trend calculation change:

1. Global View:
   Customer Summary (Trends, Health Score)
   Customer Health Summary (Health Score)
   Trends (Trends)

2. Customer Detail View:
   Customer Summary (Trends, Health Score)

3. Customer Products Portfolio
   Customer Summary (Trends, Health Score)
   Trends (Trends)
   Product Health Summary (Health Score)
   Average Invoice Price per Customer Revenue Class Last 12M (Product Classification)

Revenue, Margin and Volume Trend YTD

Formula: The least-square approach is used (see the Last 12 Months computation above).

This calculation is used in the following portlets:

Portlets affected by this trend calculation change:

1. Global View:
   Customer Summary (Trends, Health Score)
   Customer Health Summary (Health Score)
   Trends (Trends)

2. Customer Detail View:
   Customer Summary (Trends, Health Score)

3. Customer Products Portfolio
   Customer Summary (Trends, Health Score)
   Trends (Trends)
   Product Health Summary (Health Score)
   Average Invoice Price per Customer Revenue Class Last 12M (Product Classification)

Number of Transactions

Total Transactions in a given period / Total days in a given period

Revenue Below Target

Average Invoice Price =  Average Unit Invoice Price for the particular Product ID and particular Customer Segment

Below Average Price = the particular Unit Invoice Price - Average Unit Invoice Price

Revenue Below Target = if Below Average Price < 0 then Below Average Price * Quantity * (-1) else 0

% of Revenue below Target

Formula = Revenue Below Target / Revenue

(calculated for Products with Revenue Below Target above zero)

Pricing Opportunity

= Revenue Below Target

Customer Segment

To group customers by common characteristics, the Customer Segment is defined in configuration (Company Parameters) – a list of fields the segment consists of, e.g. customer size, region, country etc.

According to values aggregated on the Customer Segment level, certain KPIs for particular customers are calculated, e.g. cross sell.

Average Invoice Price per Customer Revenue Class Last 12M

• (1) = ∑ Invoice Price of Product / ∑ Qty of Product (per customer Last 12M)
  -> In Simulation, this field is called “Product Avg Price By Customer“

• (2) = ∑ (1) of Product Y of all customers in class A / Number of customers in class A that have product Y
  Do the same with class B, C , D.

• (6) Overall per product = Average { (2),(3),(4),(5) }

Core Customers

Core: Customers having cumulative revenue contribution in the last 12M <= 80% (or by a PP) .
Long tailed: The remaining Customers/Products having cumulative revenue contribution in the last 12M > 80% (or by a PP).

Core Products

Core: Products having cumulative revenue contribution in the last 12M <= 80% (or by a PP).
Long tailed: The remaining Customers/Products having cumulative revenue contribution in the last 12M > 80% (or by a PP).

• For Products this is calculated per Customer Segment (as per the Segment definition).

Projected Nr. of Transactions, Margin and Revenue

(for the next 3 months)

Projected measure for the next 3 months (calculated as adjusted linear trend based on the first 3 months of the period).

Calculation:

• Projected measure for the particular month = linear trend for the particular month * Seasonality Adjustment

  • Seasonality Adjustment = (measure for the (particular month - 12 months) ) / Average of the value for the last 12 months)

  • If the projected value is negative, then we return 0 instead.

measure = Number of Transactions, Margin or Revenue

Example of Revenue projection calculation:

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