Marketing and Conjoint Analysis

If you undertook an MBA program then likely in your Marketing 101 class you read that ... marketing is about "all the processes involved in getting a product or service from the seller or manufacturer to the ultimate consumer."

This would have included creating a product or service concept that is sticky enough with high switching costs, identifying who is likely to purchase it (target segment, target market), promoting it (advertising or promotion) and moving it through the proper selling channels (route to market and distribution channels) and servicing the customer going forward by listening to them (voice of the customer to capture their requirements). 

You would have then heard about the 4P's of marketing but at Santa Clara University (SCU) we view it as 2+1+1 that make up the 4P's.

Some people say marketing it is about "what you say" and "how you say it" when you want to explain the value of how awesome your product or service is and why people should consider buying it. With this some view marketing as simply advertising or a sales pitch, or a glossy brochure or a web press release, and some today see it as a social media tweet, blog post, or a keynote announcement.

Overall I feel is that marketing sits at the crossroads between a business and a customer and is that bridge connecting them– the goal of marketing is to service the wants of the buyer with a product or service that meets the need and then some! Based on the interests of the business (some prefer profit, people, planet: aka the triple bottom line).

At a more fundamental level, marketing can be viewed as the process of understanding your customers and their needs, while building and maintaining relationships with them, and exchanging products or services in return for monetary payment with the hope of continued ongoing business.

Ultimately you would want to design and integrate network effects with planning into the product, you would want to develop an ecosystem that amplifies the stickiness to lock in the customer and make it really hard (high switching costs) for them to step away and leave. You see examples of ecosystems pictured on the left. But, to address the customers needs, some people think you need to perform a Vulcan mind-meld of sorts to really understand them and know what they are thinking and how they are deciding.

I mean wouldn't it be simple just to ask customers casually "hey whaddya want?" listen to them and build it? Could you harness the data from their verbal responses and actually derive insights from first-hand feedback that was actionable and uncover insights?

The problem is a great deal of market research data today is descriptive, e.g., asking customers questions, tabulating their responses based on what they said — then looking to see if there are similarities or trends in responses. The fallacy is that if a respondent was asked the same question over two weeks, they might provide different responses over that period vs. consistent responses as their response is subjective on how they felt that day. This method of casual questioning is not predictive, and the responses captured are subjective and overall, this is not a scientific enough of an approach. Is there a more scientific way to do it? Now that's a great question.

Every consumer looking for products and services usually considers many attributes and is making conscious choices and decisions based on some trade-offs on what matters to them. Do the respondents value high quality, low price, higher reliability, longer warranty, or faster delivery? Or, do they seek better customer service and design aesthetics, care about sustainability and whether you use recycled products, or is the brand more important to them?

Consumers are forced to make trade-offs, weigh various feature criteria before making a decision. Some trust friends, others read product reviews, others listen to social media influencers, some ask sales assistants for advice.

Ultimately, understanding customer choices and preferences can lead to uncovering valuable insights into what product features matter more, what matters less or things they don't care about. It is also helpful to find out what they are willing to pay more for and why. Understanding the relationships between features and the constraints can help designers design products that hit the right target segment, use the 2P+2P (4 P's of marketing), target the demographic correctly, and have the right social media marketing strategy to promote it, have the right route to market and their channel partners lined up to support in order to create a supply chain surplus for all players. Maximizing the value while optimizing the product or service is every marketer's dream, challenge, and opportunity. 

Conjoint analysis is a market research approach developed in the 1970s that is still relevant today. It is founded with a mathematical model and was derived from the conjoint measurement framework which is far more precise. Back in the 1970s, academics such as Green and Rao 1971; Johnson 1974; Srinivasan and Shocker 1973 introduced conjoint analysis for measuring buyer tradeoffs among multi-attributed products and services. Both Paul E. Green and V. “Seenu” Srinivasan in 1978 applied a well-known method termed Conjoint Measurement proposed by mathematical psychologists (Luce and Tukey 1964) and adapted a linear programming (LINMAP) procedure for rank-ordered data as well as a self-explicated approach to solving complex problems through conjoint analysis.

As consumers, decision-making criteria is based on the evaluation of the overall desirability of a product or service among competing products and brands. The consumer's approach is based on a function of the value of its separate (yet conjoined) parts (parts worth) which forms the notion of conjoint measurement.

Conjoint analysis use models to infer buyers part worths for attribute levels based on importance. This Conjoint analysis approach allows researchers to enter the parts worth into a buyer choice simulator to predict how buyers will choose products or services based on the value and rank they assign. This approach can combine real-life products, services, or scenarios and employ statistical regression techniques to model actual market decisions.

A conjoint framework allows e.g. enterprises to quantify the decision making criteria that consumers value and what they choose to trade-off between different products and services. The overall goal is to quantify the respective values they place on different features or component parts (parts worth) of an offering. By understanding precisely how consumers make decisions and what they value in potential new or existing products or services, allows products and services to be defined to hit the optimal sweet spot of features that balances the value to the customer against cost of creating the product or service to the company while also forecasting the potential pent up demand or choice share (not market share) in a hyper-competitive market.

In summary, Conjoint analysis is a popular method for efficiently measuring the trade-offs for analyzing survey responses of product and pricing research that helps uncover feature preferences, the sensitivity of price in buying decisions, forecast choice shares, and predict the likelihood of adopting new products. Conjoint analysis is a framework that has a proven method for simulating:

• How consumers might react to new products, revamped, or changes in a current products feature.
• Measure a consumer’s willingness to pay for specific product features relative to other features.
• Measure brand value, test advertising claims, and see if packaging influences consumer behavior.
• Price elasticity of demand and understand customers sensitivity to price changes.
• How to price products against competitors, review potential feature cannibalization and customer preferences all in order to make more profitable pricing decisions within a company.

On your first ever market research survey using ASEMAP and Qualtrics (demographics, psychographics, and product usage) software. Once you have captured the data you may import the files into IBM SPSS and run ANOVA statistical regression, along with Chi-Square cluster analysis. This may uncover that all or a few (or none) respondents of the demographic variables are likely to be statistically significant at the 0.05 level. These findings do not conclude that our product or service has not produced good results, you may still find substantial differences across benefit segments, and you may need to run a follow-up study with new questions or deeper questions. From the findings, you may wish to consider offering a product line, rather than a single point product.

Based on the size of respondents in the market survey, you may find the target market segment is also relatively small, perhaps it is new targeting enthusiast segment and early adopters, and perhaps the early majority has not yet emerged. You could, for instance, advertise your complete product lineup broadly to all potential customers of a product category if there is a fit based on your data. Customers, even if they are not able to be targeted by demographic variables, could self-select the products they prefer most from your branded product line vs. those of your competitors. Lots of data have to be analyzed, weighed, and insights drawn before you make the optimal decision.

Reflect, collaborate, share your findings in your teams and across teams, then listen to other diverse points of view. Choose your decision-making criteria and then let the best ideas win at the end of the day.

Once you have zeroed in on what features are valuable, you can run price sweeps on the data to find the optimal selling point. Know which features are not important and drop those from your plan of record and march forward with designing and manufacturing your product or service.

While doing this you would likely want to survey the competitive landscape and utilize Michael Porter's Five Forces Model, now a Sixth force termed complimentors has been added. This would provide you insights if you can bend the forces to your favor or understand where the challenges are so that you can devise strategies to win. For superior performance, you have to ensure you can win before entering a market or have unique qualities that differentiate you, or features that disrupt the market such that incumbents and new entrants cannot replicate you too quickly. Patents are key along with being first to market helps.

Porter explains his five forces model quite well, pictured above.

The rest is execution, not an easy step, and from your fast failures, learn, adapt, and retry. There will be winners and losers, some companies will disrupt industries again and again and cultivate a customer following almost like a cult. Others businessness may become "me too companies" or happy being a fast follower.

• Do your market research.
• Decide what your core values are and stick to them.
• Make a brand you are proud of, stand for something, and if customers value your brand as much, they will make it their own and they will tell others.

You need to plan, strategize, and execute well. Hire smart people, let them try, listen to them, collaborate together, iterate, experiment, tinker, and innovate, and win, and then do it again. It's never easy but armed with the right frameworks, models, and case study experience you have more insights, perspectives, to generate ideas, foster teamwork, collaborate, gather data, analyze it, uncover insights, and know-how to strategize and execute well.

Remember to stop, listen, see, think, and do! Mix the right technologies, adapt to the changing customer needs and wants. Continuous education, have rigor and deep passion for what you do. Learn to value other people ideas, share your own, and work collectively, play the long game and cross the finish line together with teamwork. You are stronger together, inspire others, challenge the process, and recognize everyone's efforts and abilities. Value other peoples ideas as much as your own. Consider differing points of view and remain mindful.

Many thanks to Dr. Kirthi Kalyanam at Santa Clara University for his Bringing Technology to Market class. His class was enlightening, Kirthi is a true marketing genius having worked in retail, online retail businesses and his marketing acumen runs deep. He truly understands marketing frameworks has great guest speakers who visit his class providing valuable insights, sharing stories from their past, challenges, failures, and their journeys. He helps students gain deep marketing insights through probing questions and has great case studies to discuss (some are old Harvard ones, some really old! but useful) all done in a cohort setting at Santa Clara University Leavey School of Business.

Many thanks to Dr. V. 'Seenu' Srinivasan of Stanford University for being a guest professor and teaching us Conjoint Analysis. He is one of the godfathers of Conjoint along with the late Dr. Paul E. Green. Learning Conjoint first hand from Seenu was a delight, he clearly understands the framework, application, constraints, and limitations along with workarounds. He is energetic and kind. His grading and feedback are always fair, if you get to meet him you will like him.

Many thanks to Dr. Tammy Madsen of Santa Clara University for teaching us Porter's Five Forces framework, along with SWOT analysis, VRIO frameworks and more. Tammy speaks fast but what she does says is spot on, so listen very carefully.

Without a competitive strategy and network effects built-in along with understanding the difference between your price and cost indicating how much utility you are providing your customers. You may have a product and it may sell but is your company driving superior performance? So, consider the various frameworks to see how it stacks up against the competition. Figure out how to get the organization out of the way of your product execution and understand switching costs and your customers better than your competitors.

Many thanks to Dr. Naren Agrawal of Santa Clara University as no marketing strategy is complete without an accompanying supply chain. Marketing and supply chain management go hand in glove e.g. like Oreos and Milk! Naren taught us various models from the newsvendor model which is useful when customer demand that is uncertain, moving averages framework, seasonality model for products with long execution cycles and short order windows, and exponential smoothing. Is your supply chain optimized? Is your supply chain your competitive edge. Well, perhaps you need to look into all the players and drive changes to optimize it. We also covered some great Harvard Business School case studies from Benihana, Barilla Spa, Root Beer Simulation and the Bullwhip Effect, HP Deskjet Printers, to Sport Obermeyer, and Patelco Credit Union. Naren also had experienced influential leaders from the industry come to class and share their wisdom, learnings, and insights over the years. It was a true joy to participate and learn about supply chain management.

Santa Clara University EMBA program (Executive Marketing Program) is second to none. It truly has passionate professors who are leaders in their respective research fields. The professors are passionate about what they teach, the cohort program has a small intimate class size so questions can be asked and answered.

Toby McChesney (Sr. Assistant Dean) runs a nice tight ship and is picky who gets the honor to attend the program based on the following criteria: years of work experience, interest in SCU, overall goals of furthering your education, and your demeanor if it will work and fit within the cohort program. If you earn a slot, get ready to learn and have an enjoyable experience on this award-winning EMBA program that is ranked #13 nationally.

Thanks for reading, please sound off and comment below or click like if you learned something or it reminded you of a recent class or made you want to attend SCU and want to enroll in an EMBA/MBA/MS program!