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Discounting Diagnostics

Abstract

Identify when discounts are made in the sales process, and how they are impacting specific customer segments.

Discounting can be one of the most challenging issues holding back your revenue growth. This blueprint helps you identify when discounts are being made in the sales process, and how they are impacting specific customer segments. This can lead to pricing model and discounting processes changes that optimize customer lifetime value.

Using this blueprint

Main use cases for this blueprint include:

  • Create visuals to identify customer segments where you can charge more based on similar company size and industry.

  • Compare your pricing across different customers to see where you are underperforming based on industry benchmarks or organizational goals.Identify customer areas where the range of

  • Identify customer areas where the range of pricing offered is too wide.

Blueprint requirements:

  • One year of transactional information

  • Number of licenses purchased

  • Industry the customer operates in

  • Industry benchmarks of competitors

Required columns:

  • Number of licenses purchased to calculate the price per license and segment customers based on size.

  • Revenue to divide the number of licenses to get the price per license.

  • Customer industry to segment customers into the different industries they operate in.

  • Industry benchmark to compare your results against the industry to see if you are selling your product at too much of a discount or premium.

Tip

We recommend using this blueprint once a year to see how much your pricing varies in key customer segments, and how you are performing compared to the industry.

Using Discounting Diagnostics

  1. Import customer and sales information.

    1. Orders (Data): Gives the pipe a data set containing revenue and the number of licenses purchased. Ensures the data contains customer demographics, such as the industry.

    2. Industry Benchmarks (Data): Benchmarks the data by industry and deal size, containing max, min, median, Q1 and Q3 price per seat.

    3. Deal Size Benchmarks (Data): Benchmarks the data by deal size, containing max, min, median, Q1 and Q3 price per seat.

  2. Price per seat and segment customers.

    1. Purple tools are used to assign customer segments by the amount of licenses purchased and calculating the price per license.

    2. Filter For Only Seats on Cloud Products (Filter): Filters data to only include cloud products.

    3. Create Customer Groups (Case): Creates customer segments based on licenses purchased.

    4. Calculate Price Per Seat (Formula): Calculates price per seat by dividing deal revenue by licenses.

  3. Create pricing visuals for customer size segments.

    1. Blue colored tools are used to create pricing visuals based on customer size.

    2. License Group Percentiles (Percentile): Calculates median, Q1, Q3, min and max for each license group.

    3. Create Result Column (Formula): Creates column to separate benchmarks from actuals.

    4. Boxplot License Group (Union): Union benchmarks with actuals to create boxplots for comparison.

  4. Create pricing visuals for industry boxplots and heatmap based on license count.

    1. Orange colored tools are used to create pricing visuals for each license count by industry.

    2. Industry Heatmap Data (Aggregate): Calculates the average price per seat for each combination of license size and industry. This will be used for the industry heatmap.

    3. License Group Industry Boxplot (Percentile): Calculates the median, Q1, Q3, min and max for each license group and industry.

    4. Create Result Column (Formulat): Creates a column to separate benchmarks from actuals.

    5. Union Actual and Benchmark Values (Union): Unions benchmarks with actuals to create boxplots for comparison.

    6. Join Industry Benchmarks With Actuals (Join): Starts a subbranch to identify existing industry-deal group combinations by comparing with benchmark data.

    7. Filter by Joining Only On Industries and Deal Groups in Transaction (Join): Removes hidden industries in your data by joining on the industry-deal group combinations that display in the subbranch.

    8. Industry Boxplot License Group 1000 or Less (Filter), Industry Boxplot License Group 1001-5000 Seats (Filter), Industry Boxplot License Group 5001-10000 Seats (Filter) and Industry Boxplot License Group Mega Deals (Filter): Filters to create boxplots for comparing actual with benchmark license sizes by industry.