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  • Writer's pictureNihit Ningthoujam

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

Updated: Jan 11

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.

I will draw upon a recent consulting project to illustrate why Cost Integrated Conjoint Analysis is a very powerful and convenient tool for Design-to-Value (DTV). This DTV approach enabled to identify levers for the client which 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. In my previous article, I have elaborated using examples of two consulting projects to illustrate the power of Conjoint Analysis in pricing and product optimization.

I was approached by Le Club du Pricing français (French Pricing Club) for a pricing project conducted with their team, for one of their members specializing in ERP solutions and more specifically in SaaS products for enterprise services. The objective of the project was to reshape the service offer in order to produce 3 new packages. In order to get there, we were asked key questions:

  1. Is the current price optimal?

  2. Should we modify the service features?

  3. Can we deliver multiple service packages?

The study was conducted in four 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

Directly jumping into the Phase 4, the importance of product attributes and features were initially measured to identify the key features that would drive customer value perception (Illustration 1):

Illustration 1

The market simulator developed in Phase 3 was used to bench-mark the current service package against the competitors across Share of Customers, Share of Payslips and Share of Revenue (Illustration 2):

Illustration 2

Cost data was integrated into Conjoint Analysis result in order to optimize each of the service attributes while evaluating the impact of each service features on contribution margin. For example, the impact of Advanced Analytics tools on contribution margin is demonstrated in Illustration 3.

Illustration 3

Furthermore, the market simulator facilitated to transform a single service package into a line of multiple packages with the objective to maximize contribution margin. The market simulator allowed to optimize prices and features of the new packages while taking into account the cannibalization effect as well as existing packages by competitors. The impact of new packages on the bottom line were directly highlighted on the simulator (Illustration 4).

Illustration 4

Three packages were defined that maximized their combined contribution margin. These packages were positioned as Economical, Value for Money and Premium (Illustration 5). I would like to emphasize that Conjoint Analysis enables hyper optimization of product or service attributes. In this project, it enabled us to hit three sweet spots with the right combination of features and prices. For example, it enables us to precisely and easily answer the questions such as:

  • What if Assistance Tickets Per Year of Package 1: Basic is reduced from 50 tickets to 40 tickets? What would be revenue decline and cost saving?

  • 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

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

The bottom line impact of these packages were estimated using the market simulator (Illustration 7 and 8). These forecasts permitted the client to take well informed decisions with much higher confidence.

Illustration 7

Illustration 8

In conclusion, Design-to-Value (DTV) approach enabled the recommendation of three service packages with higher value-to-cost ratio than the current package. These packages have the potential to increase 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 to effectively skim multiple customer segments with different needs and willingness to pay.

Furthermore, the time and cost efficiency of Conjoint Analysis is remarkable. In this study, there were seven service attributes excluding price and brand. Each attribute had two to four different service features. In other words, there were 1728 possible packages (i.e. all possible combinations of service features). Out of these 1728 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 cost that your team would incur to test market acceptability and economical feasibility of 1728 possible packages without using Cost Integrated Conjoint Analysis.


Nihit Ningthoujam

I am a pricing consultant. I have helped more than 30 businesses realise their monetisation potential by quantifying product value based on internal intelligence, historical data and customer insights. Although 60% of my clients are in SaaS sector (B2B & B2C), I have also delivered projects across CPG, pharmaceuticals and automotive sectors.

Very frequently renowned global consulting, private equity and marketing research firms also collaborate with me to bridge expertise gaps. Some of these firms are Pricing Solutions, MG Pricing, Le Club du Pricing français, Movens Advisory, 3H Partners and Ingear Research.

I pursued Grande Ecole MSc in Management from ESSEC Business School, Paris and Bachelor in Engineering Physics from Indian Institute of Technology, Delhi.



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