A leading healthcare technology company needed to identify existing product attributes within a product line that were not optimizing incremental customer reach.
The focus was on both product features and offers.
A survey would need to be designed, scripted, and fielded among healthcare professionals.
Scope & Methodology
A MaxDiff and TURF analysis was required to identify the best/ideal set of product attributes and build an optimal model of product combinations/attributes that would reach the maximum number of unduplicated customers.
After completing the analysis of the best product attributes, an interactive simulation of customer preferences was designed to observe all possible attribute combinations.
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Value Delivered
As a result of these analyses, the client was able to build a more effective product line consisting of the most preferred product line while also maximizing customer reach.
With this information, they determined that 90% of participants were very likely to purchase from this combination of product offerings.
Researchers love conjoint analysis. It’s a handy statistical technique that uses survey data to understand which product features consumers value more and less, and which features they might be willing to pay more or less for.
It allows you understand how tweaks to combinations of features could increase desirability and, consequently, purchase price and purchase rate. Essentially, it asks, “Would you buy this product configuration if you saw it on the store shelf right now?”
Technically, there are numerous ways to present conjoint questions but all of them invite participants to compare two or more things. For example:
Would you rather buy this in red or yellow?
Would you rather pay $5 for a small one or $4 for a large one?
Would you rather buy this one or the competitive brand?
Would you rather buy this one or keep the one you already own?
The comparisons can get extremely complicated as you strive to create scenarios that mirror the complicated options of real life, in-store choices. This is because no two products are have the exact same features. There are always multiple tiny or major things different amongst them including brand, price, color, shape, size, functionality, etc.
As you see in the example conjoint question below, participants are being asked to select from among 5 different entertainment bundles, each with a different price and selection of options. Even though this question is nicely laid out, perhaps even nicer than what you might see in a store, it’s not a simple choice!
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Quick Conjoint Dictionary
First, let’s cover some quick terminology commonly used with the conjoint method so that the tips we will offer make sense.
Attribute: A characteristic of a product or service, e.g., size, shape, color, flavor, magnitude, volume, price.
Level: A specific measure of the attribute, e.g., red, orange, yellow, green, blue, and violet are levels of the attribute color.
Concept: An assembly of attributes and levels that reflect one product, e.g., a large bag of strawberry flavored, red, round candy for $4.99.
Set: A collection of concepts presented to a research participant to compare and choose from.
Simulator: An interactive, quantitative tool that uses the conjoint survey data to help you review consumer preferences and predict increases or decreases in market share based on potential product features and prices.
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Conjoint Analysis Tips and Tricks
3 to 5: Across all attributes, levels, concepts, and sets, 3 to 5 is a good rule of thumb. With so many possible combinations of attributes, levels, and sets, the ask we’re making of participants could get overwhelmingly complicated and create a lot of cognitive fatigue. That’s why we suggest aiming for no more than 3 to 5 attributes, 3 to 5 levels per attribute, 3 to 5 concepts per set, and 3 to 5 sets. By ensuring that participants enjoy the process and can take the time to review each concept carefully, we can generate much better data quality.
Meaningful Levels: Choose attribute levels carefully. Do you really want to test 3 shades of blue or 3 flavors or apple? No. While you could choose price levels of $30, $32, and $34, they aren’t meaningfully different and wouldn’t create a lot of indecision on the store shelf. They wouldn’t create variation within your data. Try to include edge cases – options that are as far apart as you can make them while still being within the realm of possibility.
Be frugal with combinations: You already know there are combinations of attributes and levels you would never offer in-store so don’t waste people’s time and cognitive load testing them. Think carefully about which combinations of attributes and levels you would never offer together and exclude them from the test. For example, don’t waste your budget testing the least expensive price and the most expensive feature. Similarly, don’t test the value of adding an extra battery for a version of the product that doesn’t run on batteries.
Minimum number of shows: When testing a level, use it in at least 3 concepts for an individual person. Think of it in terms of a ruler – for quantitative metrics (e.g., price, length, volume, weight), you need to see whether the difference between Level 1 and Level 2 is perceived the same as the difference between Level 2 and Level 3.
Include competitors: The real market includes competitors, often many. People don’t shop for single brands in isolation and neither should they answer your conjoint questions in isolation. Include at least one key competitor in your test, and preferably at least two. Further, if your brand is relatively unknown, you may wish to incorporate a competitor that is also relatively unknown.
Include an opt-out: Sometimes when you’re shopping, you discover they don’t have what you’re looking for and you leave the store empty handed. Generating realistic data means we must do the same in our simulated shopping trip – let people select “None of these” and leave without choosing anything. Otherwise, people may be “tricked” into selecting options they would never choose in real life.
Easy to read: Remember that conjoint is trying to simulate decisions that would normally happen in-store. Part of the in-store experience is in-store messaging. You’ll rarely see long sentences and paragraphs in the store so avoid them in your conjoint questions too. Use words and phrases that are as close as possible to what someone might see at the store.
Use imagery: We already know that a conjoint task can be cognitively demanding. That’s why imagery helps. Not only does it help people to visualize the product on the shelf amongst it’s competitive brands, it also helps to create a more visually appealing task (mmmmm cookies!). If you can’t provide an image of your product, find other ways to incorporate visuals in the questionnaire.
Plan for a hold-back sample: When product development work is extremely sensitive or is associated with life and death decisions, e.g., medical or pharmaceutical research, don’t let your budget determine the validity and rigor of your work. Spend the money to get the sample size you truly need to test each attribute and level with the appropriate rigor. And, build time into the fieldwork and data analysis schedule to permit preliminary analyses and test the model. You might need to tweak attributes, levels, or sets prior to running the full set of fieldwork.
Don’t let the statistics think for you: You wouldn’t create an entire marketing strategy based on gender differences just because a statistically significant t-test said 14% of women liked something and only 13% of men liked it. It’s not a meaningful difference. The same thing goes for a conjoint study. Review the model yourself, carefully, regardless of how “statistically significant” it is. Think about the various options suggested by the data. The simulator might reveal that there is a set of attributes and levels that would take over the market but that doesn’t mean you must produce that combination. The human brain is mightier than the spreadsheet!
If you’re curious to learn about the different types of conjoint that are available, this video from Sawtooth Software, presented by Aaron Hill, shares details about a few types of conjoint. E2E Research is pleased to offer all of these types to our clients.
What’s Next?
Are you ready to find out what configuration of your products and services consumers would be most keen to purchase? We’d be happy to help you work though the most suitable combinations of attributes and levels and build a conjoint study that meets your unique needs.
I’ve argued for years that there’s nothing wrong with DIY research. It’s a pretty easy argument given I’ve been a DIY researcher for many years myself. Of course, I’ve also had extensive training and experience in research design and analysis so it would make sense that Do-It-Yourself research has often been my favourite path.
In reality, the problem isn’t DIY research. The problem is unskilled people not realizing that conducting valid and reliable research requires extensive training and experience. For example, as much as I’d love to DIY a brand new house for you, I have a feeling you wouldn’t be happy with it even if I read every single Dummies manual.
For the sake of this argument then, let’s consider that we’re only talking about DIY research where the person is a qualified researcher with an appropriate designation, e.g., a PRC from the Insights Association or a CAIP in Canada. (BTW, if you’re not already certified, doing so is a GREAT way to tell your clients and colleagues that you are a highly competent researcher who upholds the highest ethical standards.)
Advantages of DIY Research
Agility: Everyone has been in one of these two positions before: You just discovered you need to get a questionnaire into field RIGHT NOW, or you’re watching a questionnaire already in field and you notice that multiple research participants have just provided the same open-end answer. What do you do if it’s Friday at 6pm? You get it done! You don’t have to wait until your supplier gets in on Monday morning so that they can start scripting and be ready for field by Monday evening. When it comes to being agile, no one can get a survey in field or updated faster than a DIY researcher with direct access to their own scripting licence. DIY FTW!
Internal knowledge: Regardless of which side of the fence you usually do your research on, supplier or buyer, you’ve learned the hard way that no one can interpret brand data and tabulations better than someone who has full sight-line into the history, projected future, and context of the brand, its sister brands, and the company – the end-client insights team. The confidential research and proprietary knowledge those researchers leverage while designing and interpreting research cannot be matched by anyone else no matter how much experience they have.
Price!: It’s impossible to beat the price of DIY research. When budgets are tight and the work is essential, this makes the decision simple. But make this choice wisely. Read on to make sure you’re okay forgoing the potential advantages of managed research which could force you to unexpectedly dig into your wallet after the fact.
Advantages of Managed Research
Leverage breadth of experience: Working with a supplier that supports many other types of companies has huge advantages. They’ve seen failures and success in multiple types of projects, companies, and industries. They’ve seen how competitive brands and categories carefully craft questionnaires and discussion guides, interpret unusual data, and solve unexpected, complex business problems. They’re a warehouse of rare knowledge and experience that every client benefits from, even when no one notices. And, they won’t incorporate the unconscious, internal biases that you might have picked up along the way from your standard internal processes.
Engage experts: Most researchers are moderately familiar with a lot of different research techniques. And, most researchers are masters of a few techniques. But being an expert in Conjoint, MaxDiff, TURF analysis, JAR analysis, or segmentation doesn’t mean you’re also an expert at running focus groups, interviews, mystery shops, or IHUTs. When you’re able to identify your own unique set of skills, you can reserve them for the projects you’d be great at and leverage the expertise of other researchers who’d be far more effective at the other projects.
Focus on high value tasks: When you can avoid spending the bulk of your time doing basic tasks like scripting questionnaires and running volumes of tabulations and simple data analyses, you get to spend more of your time on the value-add components of your business – interpreting results, acting on results, and building your business. You get to spend your time creating positive change!
Finish more projects: There are only so many hours in the days. When you’ve got a dedicated team of researchers ready at your beck and call, you can design and complete far more than one concept test, pricing study, or customer experience study every 6 months. Rejoice in the fact that more of your key projects can get done with the attention they deserve, in a timely fashion, and before it’s actually too late and damage has been done.
Get creative: Using research suppliers results in unlimited creativity. Imagine a multi-method, multi-country, multi-language study with brand new techniques applied in brand new ways. Oh my. I’m getting excited thinking about what that amazing study could look like! Ok, maybe you really don’t need to do that. But, with a larger team, you can certainly cast aside any limitations based on access to tools and build the EXACT project you need. Not just the one fits into your template.
Advantages of a Hybrid DIY Research Model
But really, why must we choose DIY research OR managed research? Why can’t we be DIY researchers sometimes, choose managed research other times, and benefit from the positives of both models?
A skilled researcher who has inherent knowledge of the brand partnering with an experienced research supplier who has in-depth and broad experience with research techniques presents the ultimate research experience. Over time, it can even lead to building a dedicated external team that’s always on call, whether it’s during seasonal highs or end-of-fiscal rush periods, or to get through that huge pile of long overdue work.
In the end, whether you choose DIY research, managed research, or a hybrid model, an informed choice is the best choice!
A top food company wanted to identify the optimal set of soup flavors for incremental reach. They needed to prevent confusion from creating too many flavors while also preventing brand disloyalty from creating too few flavors.
Scope & Methodology
A survey with a wide range of product attributes was designed. The optimum number of items per set, sets per participant, and number of versions was decided.
Based on a MaxDiff analysis, the share of preferences for each potential set of flavors was identified.
In addition, the TURF analysis identified the maximum reach for each set of flavors.
Value Delivered
The client was able to understand how many customers would prefer each set of flavors, as well as how large the market could be for each set of flavors. They were also able to identify the incremental reach associated with each set of soup flavors.
Market research is the foundation of any successful business. Within the healthcare industry, it helps us to better understand perceived strengths and weaknesses of medical devices and pharmaceuticals, gain a better understanding of key stakeholder wants and needs, gain a better understanding of the industry and competitive market space, gain a better understanding of advertising campaigns and promotions, and create fair and profitable pricing strategies. Let’s address each of these areas individually.
(Of course, feel free to skip to the end for a list of healthcare/pharma conferences and podcasts!)
Better Understand the Product Strengths and Weaknesses
At the heart of a successful business is a carefully researched and designed product or service that meets the key needs of its target audience. By conducting well designed surveys and product/sensory tests via IHUTs or Central Location Tests, you can understand:
What needs does your product meet and what unmet needs need additional development?
What features of the product are unique within the broader, competitive category and can serve as your unique selling points?
How is the product correctly and incorrectly used suggesting needs for training or redesign?
How is your product used in unanticipated ways such that new needs or audiences could be addressed?
Does the memorability of your product require improvements in terms of its features, branding, colors, or logos?
Should certain product lines be expanded or reduced based on growing or decreasing market needs?
Better Understand the People: Patients, Caregivers, Physicians, Healthcare Workers, Payers
While a quality product or service is being build, it’s important to understand the perceptions of all key stakeholders. From users to buyers and those who will be recommending the product, it’s imperative that each group understand the strengths and weaknesses of the product in order to ensure maximum success. Using questionnaires, business intelligence, and secondary research, there are a number of key questions you will need to understand about your key stakeholders:
Who is your target audience in terms of their demographic, psychographic, family, social, economic, and health characteristics?
How does the patient journey evolve from the onset of symptoms through to diagnosis, treatment, management, and recovery while understanding medical, emotional, financial, and social needs and situations?
What personal experiences do patients have within the category including adverse events from your brand and competitive brands?
Which stakeholders come into contact with your treatments, medical devices, or healthcare facilities e.g., buyers, administrators, payers, technicians, clinicians, patients, families?
What does each stakeholder group need, want, feel, and prefer?
What drives each key stakeholder group to choose, use, buy, and recommend your brand vs competitive brands, e.g., clinicians, patients, payers, buyers, sellers
Which stakeholders will influence your target audience to consider using or buying treatments, medical devices, or facilities?
Better Understand the Placement, Industry, and Competitive Market Space
Every product or service exists within a broad ecosystem of competitive brands and companies. By conducting questionnaires or secondary desk research, you can understand a wide range of business problems such as:
Who are your primary and secondary competitors locally, globally, and virtually?
What product, physical, emotional, social, and economic needs is the market needs failing to address?
How has the competitive landscape changed over the last year and how might it forecast into the next 3 to 5 years within your country and potential expansion countries?
Where are the white spaces to develop new products, extend services, or open new locations?
Can secondary data help us understand how large our existing market is and how large it could be while remaining profitable?
Better Understand Promotions, Advertising, and Campaigns
With a great product or service built and the target audience well understood, a marketing campaign is normally required to reach out to the target audience and introduce them to your offering. Using questionnaires or data analytics, a number of key questions can be answered:
Which online and offline information channels do your users and buyers use to learn about new products, gather recommendations, or make purchases?
What types of messaging would be most successful at reaching your target audience and differentiating your brand from competitors?
What types of ads would be most effective with each of your audience segments when considering likability, meaningfulness, believability and the likelihood to act?
What types of healthcare marketing campaigns are more likely to be successful?
What types of brands, companies, or influencers would your users and buyers like to be incorporated in an integrated marketing campaign?
Which concepts are most memorable and would generate the most action from your target audience?
There is more to pricing than picking a number that will generate profit. A price that is too high can reduce physician recommendations and insurance coverage. A price that is too low leaves achievable profit on the table. A final price can only be determined by understanding your true profit margin, market pricing, and stakeholder needs. To build the most effective pricing strategy for your medical device, pharmaceutical product, or service, conduct the appropriate surveys, interviews, and secondary research first.
Based on secondary research, how are competitive products on the market currently priced?
Using questionnaire data, what type of pricing strategy is most appealing to healthcare administrators and payers?
What type of pricing strategy would facilitate product recommendations from clinicians and physicians?
Which user segment has the least and the greatest revenue potential?
Building a successful medical, pharmaceutical, or healthcare product or service requires a foundation of well designed and executed research coupled with well analyzed and actioned results. Whether you’re tasked with supporting the growth of an innovative new brand or helping a company understand their buyers and their business, our team has more than ten years of experience helping researchers, marketers, and brand managers generate great quality healthcare data and insights for the questions outlined above. Please feel free to email your project specifications to our research experts using Projects at E2Eresearch dot com. We’d love to help!
A market leader in healthcare imaging equipment needed to understand the relative importance of imaging product features and estimate how those features would affect sales of a new product.
Scope & Methodology
A list of product attributes was developed for a survey
Product users rated the product features and post sales operations
As part of a MaxDiff analysis, best and worst scores were calculated for each attribute and score differences across all available matrices were rationalized
Comparison scores of each attribute were used to design a new product and estimate their impacts on sales
Value Delivered
The client was able to understand the relative importance of a large number of product features. In particular, they learned that down time is ~3 times more important than price.
Include competitors: The real market includes competitors, often many. People don’t shop for single brands in isolation and neither should they answer your conjoint questions in isolation. Include at least one key competitor in your test, and preferably at least two. Further, if your brand is relatively unknown, you may wish to incorporate a competitor that is also relatively unknown.
Use imagery: We already know that a conjoint task can be cognitively demanding. That’s why imagery helps. Not only does it help people to visualize the product on the shelf amongst it’s competitive brands, it also helps to create a more visually appealing task (mmmmm cookies!). If you can’t provide an image of your product, find other ways to incorporate visuals in the questionnaire.
