by Bob Walker | Apr 6, 2023 | Conjoint analysis, Data science, Marketing and strategy
Potential buyers of a product or service consider many factors when deciding what to buy, and potential sellers must decide on the factors to offer and at what price. Buyers typically place their own economic self-interests first: spend the least amount of money, time, and effort to get the product or service they want. Companies want to satisfy customer needs but also have a competing objective: to find the factors that maximize appeal, revenue, and profit.
The general class of research designs that deals with intersecting (conjoined) variables in a structured manner is called conjoint analysis (covered in a previous post). Conjoint analysis involves reaction to, and statistical relationships between, factors in choice decisions. A “factor” is part of the choice criteria used to make a buying decision. A factor also has characteristics, features, or benefits that vary called “levels”. Simple examples of factors are price, quantity, and size. Some real-world examples might include:
- An industrial paint sprayer company might offer a unit with multiple hose lengths, paint capacity, nozzle diameter, or spray wand length.
- A new credit card with different levels of an annual fee, number of airline points per dollar spent, levels of travel protection coverage, or access to airport lounges.
- A laptop manufacturer may offer different screen sizes, keyboard layouts, processor speed, graphics cards, memory, hard disk size, or bundled software.
In each of these examples there might be just one or two levels per factor, or perhaps many more. In its entirety, one set of combinations (factors x levels) could be rendered as a series of concept description (e.g., known as a “full profile” design) typically used in new product development and optimization. In other cases, such as competitive preference testing, options are shown as sets of choices to respondents (e.g., choice modeling or discrete choice designs). We then obtain demand estimation through intent to buy or intended volumetric consumption.
As complexity increases (more factors with more levels), the number of possible combinations of products grows exponentially. For example, four factors with three levels each produces 34 = 81 unique combinations. Theoretically, we could show all 81 combinations to a single respondent – but would we want to? Poor quality data and exhausted subjects are two good reasons not to.
Conjoint analysis solves for the problem of too many combinations by showing each subject a randomized subset of all possible combinations, also known as a partial factorial design (i.e., a subset of the complete factorial design). Features or characteristics of a product or service are sequentially exposed using concept descriptions and simple explanations. In most studies we recommend a basic explanation of the exercise to familiarize respondents with the category and tasks. Concept features and levels then vary by subject in the overall matrix of combinations. In simplistic terms, this akin to Swiss cheese: we know what the overall shape is (complete design), but there are holes inside (partial exposure).
In conjoint analysis we strive for balanced “orthogonal” designs in which an equal number of factor x level combinations are shown to a respondent, usually as a set of even numbers (i.e., 8, 12, or 16). As expected, more complex conjoint designs require much larger sample sizes for statistical reliability. From this we interpolate demand curves for each factor (e.g., price, size, quantity) and also assess overall demand for the optimal combination of factors that maximize consumer appeal. In addition to demand curves for each factor, and identification of the optimal combination of factors, additional outputs include simulation and revenue estimation for the optimal set (there may be multiple) of product configurations.
Deficiencies of Conjoint Designs
Conjoint designs assume that:
- Screening criteria accurately reflect the true target market.
- Respondents are consistent and rational in decision making.
- Alternatives are meaningfully different (subtle differences may produce no test effect).
- Realistic combinations of factors and levels are always shown (non-confounding design).
- Buyers and buying decisions seek maximum benefits at the lowest price. This may not be true depending on a category, such with overwhelming altruistic or social cause benefits.
- Price has linear and important, but not overwhelming, influence on choice. Respondents may quickly become sensitized to pricing which may exaggerate true differences.
As you might tell from the above, the design and the screening criteria, as well as the factors and levels chosen for exposure, are a huge factor in making sure that the conjoint results ultimately make sense. A simulator provides scenario testing using different combination of features and using the utility scores that were generated from the respondent sample. Conjoint utility scores are converted to a scaled value (in the above case, a 65% interest level). When applied against an interested target or segment, an estimate of penetration can be developed. Sales estimation would need to be confirmed with additional forecasting and analysis (an additional stage of work).
From the same research study, a max-diff analysis of individual features was performed (i.e., based on preference ranking). Scores are preference “votes” based on a series of choice exercises, ranked from high to low. You can see from this slide that higher paint transfer efficiency (through the nozzle), ease of operation, higher paint transfer efficiency (from container), reduced mist, and less likelihood of bursts or droplets were the top items. Mechanical function items were lower on the preference hierarchy
This snapshot attempted to show the range of conjoint designs and approaches that could be used depending on a client’s needs. Each individual design is based on the considerations of the business decision that needs to be made and the number and complexity of choices to be evaluated by respondents.
For more information, please contact:
Bob Walker, CEO
Surveys & Forecasts, LLC
https://safllc.com
+1.203 255.0505
by Bob Walker | Apr 1, 2023 | Analytics, Conjoint analysis, Marketing and strategy, New product development
In today’s crowded marketplace, it’s more important than ever to understand what drives consumer preferences. That’s where conjoint analysis comes in. Conjoint analysis is a research method used to understand how consumers value different attributes of a product or service.
At its core, conjoint analysis involves presenting consumers with a series of hypothetical product or service profiles, each containing different combinations of attributes. Respondents or prospects must make choices which require that they “trade off” between options. For example, if you were conducting conjoint analysis for a new car, you might present participants with combinations of attributes, such as price, fuel efficiency, horsepower, and style. By analyzing the choices consumers make between these profiles, including price, researchers can determine the relative importance of each attribute in driving consumer preference.
There are two main types of conjoint analysis: traditional full-profile conjoint analysis and adaptive conjoint analysis. Traditional full-profile conjoint analysis presents participants with a fixed set of product profiles, while adaptive conjoint analysis uses a computer algorithm to adjust the profiles presented to each participant based on their previous choices.
Conjoint analysis has many applications in marketing research. It can be used to optimize product design, pricing, and marketing messaging. For example, if a company is considering launching a new product with different feature sets at different price points, conjoint analysis can help determine the optimal combination of features and price to maximize sales.
Conjoint analysis can also be used to understand how different customer segments value different attributes. This can include emotional drivers, such as attitudes and beliefs when building a brand’s core positioning. By analyzing the choices of different demographic groups, researchers can gain insight into how different segments of the market value different features.
While conjoint analysis can be a powerful tool for understanding consumer preferences, it’s important to keep in mind a few limitations. For example, conjoint analysis assumes that consumers make rational choices based on the features presented to them. In reality, consumers may be influenced by factors outside of the product attributes themselves, such as brand loyalty, emotional appeal, or social issues that affect their choice criteria. Price is also highly biasing, hence options must be presented in a realistic context.. Additionally, conjoint analysis is only as good as the attributes and levels included in the analysis, so it’s important to carefully consider which attributes to include.
Overall, conjoint analysis can be a valuable tool for marketers looking to understand consumer preferences and optimize their product offerings. By presenting hypothetical product profiles and analyzing consumer choices, conjoint analysis can provide insights into how different attributes drive consumer preference, helping companies make data-driven decisions about product design, pricing, and marketing.
Buyers of your product or service will consider many factors when deciding what or how to buy, and as a company you must decide on what to offer, and at what price, to maximize profit. If your product development roadmap has stalled, conjoint analysis is definitely something to consider. For more information about conjoint analysis or other research services, please visit the Surveys & Forecasts, LLC website or get in touch at info@safllc.com.
by Bob Walker | Mar 15, 2023 | Marketing and strategy, Marketing research, Media Planning, Multi-touch Attribution
Companies that focus on buyers who have bought your brand – even at a minimal level – are significantly more likely to shift more of their total share of consumption to you. First you must find them, and then target them effectively with incremental ad spending.
A recent study by the Mobile Marketing Association focused on a common consumer category and showed that those with a 20%-80% propensity to buy a brand are much more responsive to incremental advertising. Those living in the “tails” of the buyer distribution are either (a) basically non-responsive because they buy so little from you, or (b) are already so loyal that additional ad spending has no incremental effect. Within this middle band of buyers, the power of your incremental targeted advertising is very strong. The bonus: ad campaigns built around the moveable middle also improved reach.
So who are these buyers in the so-called “moveable middle” of your brand’s overall profile of users? Unlike promotionally-driven “brand switchers”, the moveable middle segment represents buyers at many different levels of past purchase behavior who relate to your brand story. Their responsiveness to advertising grows as their share of category needs approaches the center of this distribution. Decreasing marginal returns occur at the tails. The movable middle holds many more attitudinally receptive, persuadable buyers as defined by their mid-range probability of buying your brand.
The result of this recent research shows that the moveable middle of a company’s product user base can be 5x-10x more responsive to advertising than buyers at the tails. This makes incremental advertising and marketing spending against this middle segment of buyers an incredibly fertile area to allocate incremental ad dollars because the return on ad spend (ROAS) is so high. There are several reasons why this is true:
- Those who are already buying your company’s products or services are not buying all of their category needs solely from you. This volume can be significant, but it is likely hidden from you. This implies that they have much larger volumetric needs than they are directly telling you as one manufacturer.
- There is a high probability that products in the category have not been effectively differentiated, and benefits or features that are important to buyers have not been captured by you. Additional ad spending highlights those differences and motivates additional purchases. Optimizing your messaging is clearly indicated here, too.
- Buyers who are already buying 20%-80% of their category needs from you are more receptive to your message. This forces them to exclude other alternatives once your message is received.
- The movable middle already has the advantage of familiarity with your brand or product. They know the things you can provide. Advertising doesn’t need to work as hard among people who have familiarity. It doesn’t have to generate awareness: rather it simply refreshes you in consumers’ minds.
Marketing and advertising plans focused on the moveable middle almost always yield better response curves to incremental media spend than dollars spent on reach and frequency. Recent academic research has attempted to dispel this notion. They believe that marketers should target broadly (i.e., to all buyers). It is true that buyers continuously enter and exit a category, hence on its face this makes some intuitive sense. However, this thought process (a) ignores that buyer entry and exit can be quite slow, and as a result (b) the ROAS of a reach-only based plan will be low. It takes significant amounts of time and ad spend to generate even small increases in awareness-to-trial conversion. A company may not have a 3-, 5-, or 10-year window to see if their strategy was effective at bringing in new buyers. By then, the company could be out of business!
Certainly, some ad spending must focus on long-term brand building, but with increased direct and digital relationships with customers (1st party data), it makes no sense to ignore your own ability to target those we know are receptive to your product story. Focusing on the movable middle is an “outcomes” or “performance-based” marketing concept that complements your longer-term brand building initiatives. Strategic brand marketing is needed to support the core product story and promote user differentiation, but properly targeted media delivery generates much higher reach-to-conversion, and therefore longer-term retention of the customer to the business. And it has the nice side effect of increasing reach, too.
Caveat: over-targeting or over-promoting can have negative longer-term consequences that can diminish responsiveness to additional media spending: there must be a balance. Exclusively focusing on the movable middle at the expense all other initiatives to build brand equity fails to recognize that new buyers do enter the category from sources other existing buyers. But the evidence that focusing on the moveable middle of your own brand’s buyer distribution is a smart and effective way to start..
At Surveys & Forecasts, LLC we have conducted numerous targeted media and research programs that focus on the movable middle, and proved the ROAS power of incremental spend. For more information, contact us info@safllc.com. We look forward to hearing from you!
by Bob Walker | Mar 15, 2023 | Customer experience, Marketing and strategy, Marketing research
In the acquisition process, revenue metrics are critical to assess business value. However, the need to assess customer health is often overlooked. In customer-facing businesses, this is especially dangerous.
Without a clear “line of sight” into customer satisfaction and retention, an acquiring company (e.g., family office, VC, or angel investor) may overlook evidence that indicates an acquisition may not result in incremental revenue or synergy with existing businesses.
For businesses that are sold using a revenue multiplier, a significant miscalculation can result in a lower exit price. Both family offices and business owners do not realize the hidden factors that can negatively impact a sale. As the deal size grows, those risks increase.
Conversely, hidden positive factors can surprise to the upside, and support a significantly better acquisition price or sales story. As part of an acquisition and revenue assessment, looking at the entirety of your customer or user base is essential to understand the real value of the business now and into the future.
Who benefits from a revenue and customer health assessment?
Investors and family offices of every size can benefit, but there are three distinct beneficiaries of a revenue optimization and customer health assessment when a business is bought or sold. They are:
- The business owner who wants to maximize his or her exit value.
- The potential buyer (family office, angel investor, or VC) who wants to minimize the price paid.
- Another party who may be in dispute with the owner, and who wants to minimize the price that they pay (or conversely, maximize their ownership share of the business).
Conflicting interests can be addressed through a well-executed revenue and customer health assessment using research. The approach used by Surveys & Forecasts, LLC evaluates revenue potential and customer health in a systematic and independent manner:
- We employ a variety of tools to estimate prospects for growth, forecast volume, share, and satisfaction.
- We profile relevant products, brands, and services to assess your competitive position.
- We combine findings to provide an unbiased estimate of revenue potential. We can combine this with financial professionals who have the tools to assess performance in the absolute and relative to a peer group.
- We work with clients on a per project or ongoing retainer basis to provide guidance and market intelligence for the business.
Additional detailed information can be found here, To discuss whether a revenue optimization and customer health assessment is appropriate for an acquisition your office is considering, please get in touch at info@safllc.com or at +1.203.255.0505.
by Bob Walker | Oct 24, 2022 | Analytics, Communications, Marketing and strategy, Marketing research
A recent conversation on A/B testing with a client revealed an interesting perspective about messaging and positioning. The client, extolling their company’s rigorous A/B testing approach, failed to recognize a simple but scary fact: it is easy to compare multiple versions of a sub-optimal message. In the end, you end up with a “less-worse” version of an already weak message. This is not equivalent to building brand value over time — and building a moat around your brand’s essence.
What were they thinking?
The client had overlooked the obvious by ignoring underlying reasons to buy. Instead of testing which alternative was more persuasive based on price, the more important question they should have been asking was: what is the underlying motivation behind purchase? What segments, personas, or buyer types fall into our wheelhouse? Why should our brand be considered in this crowded category? This client, and so many others, seem to miss a simple tenant of marketing: why give away your marketing advantage so early in the game?
This client’s products have significant performance advantages over others in the category, yet they were A/B testing multiple executions built around being a lower-cost, value alternative. If they had taken just a little time to understand buyer behavior, they would have realized that price can be a relatively small factor in the buying decision when the brand looms large.
In this case, A/B testing was fueling a race to the bottom. By choosing the “less worse” option, the client had already decided that they would primarily compete on price, pushing them deeper into a commodity mindset for the customer.
When misused, A/B testing behaves like a cost-reduction test. There are many instructive lessons here: a well-known case is Maxwell House coffee. Over the course of a many years, the company increased its use of lower quality beans in the blend to cut its COGS. It conducted taste tests to make sure that consumers did not detect a difference when compared to the previous blend. But market share began to fall. Why? Because they never tested it against the original formula. What if there were thousands of Maxwell Houses across the globe instead of Starbucks? In the same way, test between meaningful options, rather than confine your evaluation to a narrow set of sub-optimal choices.
Be smart. A/B testing works best when the strategy is well-defined and plays to your advantage. First figure out what that is. Focus on highly persuasive messages that support your brand, rather than identify the best way to discount your business into oblivion. Don’t give away the store when you don’t have to.
