📽️ A video demonstration for this use case will be made available soon.
Use Case Situation Description
Monitoring market feedback through realization analytics KPIs in the process industry enhances pricing strategies, boosting sales alignment with market trends and customer preferences. This data-driven approach offers optimized pricing, higher profitability, improved customer satisfaction, and competitive advantage and all can be done in Pricefx.
Value Projections
View trending for top/bottom performing accounts (3a - global)
Price change realization via margin mix waterfall / revenue margin trending (3b - customer)
Product level trending (3c - customer)
Prescriptive Design Requirements
As a Pricing Manager/ Product Manager, I want to understand the impact of costs, market changes, competition, and other factors on price changes.
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I want to make decisions based on review and long-term tracking.
The overall design requirements are summarized in these articles:
User Stories
The following are user stories associated with this use case.
Data Requirements
In Customer Insights Accelerator, we use Data Load to aggregate data from the Transactional Datamart by customer, product and pricing month and we store it in Aggregated Data source. It helps improve the dashboard performance because:
· The system queries data in a smaller data set.
· Some data need to be pre-processed before showing on dashboards as trend values, product, and customer classification etc.
Note: There are some charts and portlets whose data are queried from Transactional Datamart directly, not from Aggregated Data source, such as:
· Customer Global View Dashboard: Customer Summary portlet
· Customer Detail View: Customer Summary Portlet, Waterfall chart
Aggregated Data Mappings
The required fields from the Transactional Data mart are Customer ID/Name, Product ID/Name, Pricing Date, Invoice Price, Gross Margin, Quantity and TX Unique ID. Each field should be mapped to the corresponding attribute using the customer-insights-accelerator Advanced Configuration.
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Company Parameter PFXTemplate_CustomerInsights_Configuration
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Category |
Category of the configuration |
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Key Name |
Key name of the configuration |
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Value |
Defines the value corresponding to the keyset |
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Is Default |
If set to Yes, the dashboard will use the value of this item as a default value |
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Order |
Order of items in the same Category |
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Key Label |
Label will be shown on the chart/input |
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Note |
Which logic type is used |
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Company Parameter PFXTemplate_CustomerInsights_Customer_Segment
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Source Type |
Data Source types from which we get data for the customer segments. They include: DMDS (Data Source), C (Customer Master), CX (Customer Extension). |
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Source Name |
Data source name |
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Source Field |
Source Field in the Data Source. |
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Field Label |
Label of the field in the Data Source |
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Is Active |
If set to ‘YES’ the dashboard will add this field in the segmentation |
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Is Customer Id |
If this field is customer ID in a Data Source, set the value to ‘YES’. It helps the system get the correct “Customer Id” field in the source. Not required for C or CX, only for DMDS. |
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Company Parameter CurrencySymbols
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Name |
3 characters representation of the currency. I.e., USD |
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Symbol |
The symbol to get displayed in the dashboards. I.e., $. Defaults to the currency name. |
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CI_QuoteType_Mapping
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Dashboard Name |
Initial support only to CustomerInsights_CustomerProductsPortfolio |
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Portlet Name |
Might be one of Trends, Pricing Opportunity or Selling Opportunity |
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Quote Type |
Defaults to __DEFAULT__ and can be set to any other custom quote type. |
Aggregated Data Calculations
Additional details about the calculations can be found on Page 2 of this document or in the Glossary web page.
LEARN MORE: Customer Insights: Technical Overview | Data Flow | Glossary
Out-of-Scope
Out-of-scope business functions and features can be configured, but are not included in the Chemical Industry Catalog.
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Pricefx should not be seen as an ETL tool, data must be sent in accordance with the prescriptive data model, any required transformations will be considered custom configuration effort.
Solution Design
This solution is implemented using the Accelerate Customer Insights Package (CIP).
LEARN MORE: Find out all there is about Customer Insights Package, here.
Customer Insights Accelerator Dashboards:
Customer Insights – Global View
User inputs:
Time filter Last period (L3M, L6M, L12M, …):
→ [Start date] = First Month Beginning Date
→ [End date] = Previous Month End Date
E.g.: Today is 2021-08-06 => L3M: From 2021-05-01 to 2021-07-31
Time filter Up to Date (YTD, QTD, MTD, …)
→ [Start date] = Period Beginning Date
→ [End date] = Current date
E.g.: Today is 2021-08-06 => QTD: From 2021-07-01 to 2021-08-06
Customer/Product Rank Bucket
Counted items shown in the top or bottom of the list are based on the Rank Bucket input.
If the whole list has 12 customers and Customer Rank Bucket =10, then the top part of the list shows 10 customers and the bottom part shows 2 customers.
Customers/Products columns show top/worst aggregation of data,
driven by inputs on the left-hand side of the dashboard.
Columns with Trends show increase/constant/decrease metrics based on Data Load calculation results. There is no direct relation between these two types of metrics and columns.
Customer classification
This category should be calculated based on the 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
Thresholds are configurable.
Example:
Product classification
This category should be calculated based on the 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
Thresholds are configurable.
Customer Health Score, Product Health Score
Calculated based on fields Revenue Trend Last 12 months and Margin Trend Last 12 months.
Revenue Health Score and Margin Health Score are set in accordance with
Revenue and Margin monthly or quarterly change (trend) in the last 12 months
(maximum value is 100, the minimum value is 0) and to this classification:
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
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)
Portlet 1
Displays some typical figures regarding a chosen customer or a group of customers.
All values are aggregated on the Customer(s) and Category levels.
If a Category value is not selected, it will be hidden in the portlet.
Displays some typical figures regarding a chosen customer or a group of customers.
Customer displays a list of [Customer ID (Customer Name)] following a filter group.
When no customer is selected, it is left empty.
Health Score Last 12M is re-calculated by the Trend L12M of the group.
Pricing Opportunity = ∑ Revenue below target
Selling Opportunity = ∑ Up Sell + ∑ Cross Sell
L12M trends are calculated on a monthly basis.
Note: Data queried from Datamart
Portlet 2
This portlet shows a list of “inactive” Customers, so that user can identify Customers which there are no transactions in the chosen time period.
Config Inactive-Customer-classification
Applied filters: Customer(s), Time filter
Not applied filters: Customer Rank, Customer Rank Bucket, KPI, Customer Class, Customer Health Score
Months Inactive = Current Month - Last Active Month
Portlet 3
The pie chart shows relative sizes of customer classification based on Revenue in the last 12 months.
Ability to see data as a table. Ability to drill down.
When hovering over an individual customer pie share, detailed information is displayed.
Portlet 4
The pie chart shows relative sizes of customer classification based on Revenue in the last 12 months.
Ability to see data as a table. Ability to drill down.
When hovering over an individual customer share, detailed information is displayed.
Portlet 5
Table shows the total value which can be gained for products that customer bought and did not buy in the given period.
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Cross Sell (Evaluate non-purchased products)
a) The value that customers should spend by purchasing products which they did not buy in the past, but other customers did buy.
b) If value = 0, customer bought full products set presented in the given period.
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Up Sell (Evaluate purchased products)
a) Value that a customer should spend by purchasing a product which they already bought in the past.
b) If value = 0, customer purchased products that all are above average point in the customer set.
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Opportunity: Total additional value that can be obtained for customer, evaluated for both purchased and non-purchased products.
Portlet 6
The table shows a list of customers that can be targeted to sell more products at bigger volume.
Top of the list represents the highest opportunity - these customers are under the average point in the customers set.
Bottom of the list represents the lowest opportunity. (hidden)
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Revenue below target (Evaluate purchased products)
a) If value > 0, customers have reached revenue under the average point among the customers set,
it indicates the value that customer should obtain to reach average base.
b) If value = 0, customers have reached revenue above the average point among the customers set, they are good customers.
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% Product Buying = how many products (in % ) customer purchased in the given period
a) If value = 100%, customer purchased full products set presented in the given period.
It correlates with Cross Sell Opportunity = 0.
b) If value < 100%, customer did not purchase full products set presented in the given period.
It means this customer must have Cross Sell Opportunity > 0.
Portlet 7
Calculation granularity: monthly
Customer Insights – Customer Detail View
User inputs:
For details on user input see 1. Customer Insights – Global View.
Portlet 1
Displays some typical figures regarding the chosen customer.
All values are aggregated on the Customer(s) and Category levels.
If Category value is not selected, it is hidden in the portlet.
Click “View Customer Products Portfolio” - opens new tab “Customer Products Portfolio” dashboard.
L12M trends are calculated on a monthly basis.
Note: Data queried from Datamart
Portlet 2
Shows Revenue and Margin values over several months in the given period.
Portlet 3
Waterfall chart is used for understanding all the components of the final price and margin.
Initial and the final values are represented by whole columns,
while the intermediate values are denoted by floating columns.
LEARN MORE: For more details see Waterfall Dashboard.
Portlet 4
This chart helps users identify the reason for the gap in revenue for 2 time periods.
Allows to compare two years or quarters +
optionally filter for on product(s) and/or customer(s).
LEARN MORE: For more details see Revenue Breakdown Dashboard - Fields Definition.
Portlet 5
Shows relative sizes of product classification based on the margin %.
Specialty Products: Higher than the defined margin %
Commodity Products: Lower than the defined margin %
Based on an average margin achieved by the product during the last 12 months
(The thresholds are configurable).
Portlet 6
Portlet 7
Bar & line chart
shows the number of transactions for the last 12 months + estimation for the next 3 months.
Current month is viewed as a future month (as it has not ended yet).
Portlet 8
Bar & line chart
shows Revenue and Margin for the last 12 months
+ estimation for the next 3 months.
Current month is viewed as a future month (as it has not ended yet).
Customer Insights – Customer Products Portfolio
User inputs:
Portlet 1
Widget displays some typical figures regarding the chosen customer, showing the segment that the customer belongs to.
L12M trends are calculated on a monthly basis.
Note: Data queried from Aggregated Data source: Trend YTD value here is the “Customer YTD [revenue/margin/quantity] trend”.
In Customer Detail View dashboard, data is queried from Transactional Datamart: Trend YTD value to be recalculated dynamically if product filter is selected.
Meanwhile, in Customer Products Portfolio dashboard: Trend YTD value does not change regardless of any product filter.
Portlet 2
Bar chart shows Revenue and Margin split into ten buckets to visualize the number of products needed to cover each bucket (cumulative contribution).
Portlet 3
Pie chart shows relative sizes of product classification based on the margin %
Specialty Products: Higher than the defined margin %
Commodity Products: Lower than the defined margin %
Based on an average margin achieved by the product during the last 12 months
(Thresholds are configurable).
Portlet 4
Bar chart shows products (bars) by Revenue below target descending
+ cumulative Revenue below target (line).
Grouped by product ID.
Portlet 5
Hidden
Portlet 6
Calculation granularity: quarterly
Portlet 7
Table shows Average Unit Price per Product ID and Customer Classification by Revenue.
Customer Revenue Class A/B/C/D: Average invoice price per Product and Customer classification.
Overall: Average invoice price per Product and all customers
Customer: Average invoice price per Product and customer.
Average invoice price calculated based on data over the last 12 months.
Time filter has no effect in this table.
Portlet 8
Sum Revenue below target by input value in product attribute filter.
Chart label reflects input value in product attribute filter.
Portlet 9
Table shows total value to be gained from products customer bought and not bought in given time period.
Grouped by the input value in product attribute filter.
Portlet 10
Table shows Revenue below target value and % Revenue below target.
Grouped by input value in product attribute filter.
Table shows Revenue and margin for top/worst Products
Note: Green/red color in the Product column does not correspond to the Health score. See how the coloring works.
Create a Quote from CI Dashboard
You can create a quote from the Customer Insights - Customer Products Portfolio dashboard, specifically from these portlets: Trends, Pricing Opportunity, and Selling Opportunity.
Steps:
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As inputs, select:
a. Customer and Time Filter which transactions exist.
b. Product Attribute = “Product Id”
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Select a row in the result matrix.
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Click the “Create Quote” button.
A new quote is created and opened, and it includes the parameters passed from the dashboard.
Quote Type is defined in the PP table “CI_QuoteType_Mapping”.
If Quote Type is not defined in PP, the quote is created with the “default” Quote Type.
Example: Quote created from Trends, Quote Type = default (CPQ)
Example: Quote created from Selling Opportunity, Quote Type = CPQ
