Author: Nihit Ningthoujam

In this article, the author demonstrates the use of Design-to-Value (DTV) and Cost Integrated Conjoint Analysis as an approach for delivering winning product pricing strategies with high value to cost ratios. This article draws upon recently completed pricing consulting projects to illustrate the effectiveness of this approach. Nihit Ningthoujam is a customer strategy consultant (freelancer) with high focus on pricing and product optimization supported by market research and analytics methods such as Conjoint Analysis. He can be reached at View animated simulations of the illustrations in the original article here.

The Pricing Advisor, November 2020

Design-to-Value (DTV) is a data driven cross-functional approach to deliver winning products with high value to cost ratio with the objective to maximize contribution margin (total earnings available to pay for fixed expenses and to generate a profit). DTV requires in-depth consumer, competitive and operational intelligence. Application of DTV across similar products of an entire product line is much more complex, especially, because of the cannibalization among the similar products. The cannibalization effect is not easy to predict during the product design phase, before the launch of product.

In this article, I will use past project work to illustrate why Cost Integrated Conjoint Analysis is a very powerful and convenient tool for Design-to-Value (DTV). This DTV approach increased contribution margin by more than 30% and significantly reduced the Service Development Time (time taken to research, design and develop a service). Conjoint Analysis is a technique that replicates real life purchase decisions to capture consumer and competitive insights with the objective of optimizing product features and price.

If you wish to reshape your service offer, for example, here are some key questions:

  1. Is the current price optimal?
  2. Should we modify the service features?
  3. Can we deliver multiple service packages?

Here are the necessary project phases:

  • Phase 1: Identification of key service attributes and innovative features that would drive purchase decisions and prices as well as key competitive packages to be included in the study
  • Phase 2: Creation of Conjoint Analysis survey using Sawtooth Lighthouse and data collection in collaboration with a panelist
  • Phase 3: Modelling of survey data, integration of cost data and development of a simulator
  • Phase 4: Extraction of competitive and consumer insights, bench-marking of current service package and creation of three service packages to maximize contribution margin

Jumping forward to Phase 4, the following illustration demonstrates how to measure the importance of product attributes and features to identify the key elements driving customer value perception (Illustration 1):

Illustration 1

Design-to-Value (DTV) using Cost Integrated Conjoint Analysis

In Phase 3, benchmark the current service package against the competitors across Share of Customers, Share of Payslips and Share of Revenue (Illustration 2):

Illustration 2

Design-to-Value (DTV) using Cost Integrated Conjoint Analysis

Integrate cost data into Conjoint Analysis results in order to optimize each of the service attributes while evaluating the impact of each service feature on contribution margin. For example, the impact of Advanced Analytics tools on contribution margin is demonstrated in Illustration 3.

Illustration 3

Design-to-Value (DTV) using Cost Integrated Conjoint Analysis

By transforming a single service package into a line of multiple packages, you can optimize prices and features while taking cannibalization and competition into account. The impact of new packages on the bottom line are directly highlighted in Illustration 4.

Illustration 4

Design-to-Value (DTV) using Cost Integrated Conjoint Analysis

Three packages defined as Economical, Value for Money and Premium, maximized their combined contribution margin (Illustration 5). Conjoint Analysis enables hyper optimization of product or service attributes, enabling us to hit three sweet spots with the right combination of features and prices, and to answer questions such as:

  • What if Assistance Tickets Per Year of Package 1: Basic is reduced from 50 tickets to 40 tickets? What would revenue decline and cost saving levels be?
  • What if Support is upgraded to 24/7 and Uptime SLA is degraded to 99.90% in Package 2: Standard? How would it affect margin, revenue and cost?
  • Should we sell Package 3: Premium at 42 € or at 54 € instead of 48 €?

Illustration 5

Design-to-Value (DTV) using Cost Integrated Conjoint Analysis

The cost to value ratio of the new packages and the current package are illustrated below (Illustration 6). In this illustration, we can observe that:

  1. Value of Package 1 is higher at a lower cost than the current package
  2. Value of Package 2 is 1.5x higher than the current package for a marginal increase of cost by 1.2x
  3. Value of Package 3 is 2x higher than the current package for an increase of cost by 1.5x

Illustration 6

Design-to-Value (DTV) using Cost Integrated Conjoint Analysis

This led to estimated profitability of these packages (Illustration 7 and 8) and more well-informed decisions with much higher confidence.

Illustration 7

Design-to-Value (DTV) using Cost Integrated Conjoint Analysis

Illustration 8

Design-to-Value (DTV) using Cost Integrated Conjoint Analysis

In conclusion, a Design-to-Value (DTV) approach leads to higher value-to-cost ratio than the current package. These packages have the potential to increase the client’s contribution margin by more than 30%. The key of the Design-to-Value approach used in this study is Conjoint Analysis: cost Integrated Conjoint Analysis enabled to effectively hit three sweet-spots with the right mix of service features and prices. This approach permitted us to effectively skim multiple customer segments with different needs and willingness to pay.

In this example, there are seven service attributes with two to four different service features each, excluding price and brand. In other words, there were 1728 possible packages (i.e. all possible combinations of service features). Out of all these possibilities, three optimal packages were chosen and their price points were defined maximizing their combined contribution margin while taking cannibalization and competition into account. Imagine the time and costs that your team would incur to the test market acceptability and economic feasibility of 1728 possible packages without using Cost Integrated Conjoint Analysis.

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