5 Tips for B2B Product Bundling Research
When B2B marketers work on bundling and pricing strategies, many questions arise. Are we leaving money on the table? If we increase prices, how many customers will we lose? Should we use a good-better-best bundling strategy? Is there a slimmed-down version of our product that appeals to cost-sensitive prospects? Is there a market for a premium bundle of features and services?
B2B marketers have always struggled with these questions, so it is no surprise that product bundling studies have always been one of the most common use cases for primary market research. This spans informal customer interviews to structured, large-scale quantitative surveys. While the scope and nature of these studies vary, based on our twenty years of helping B2B marketers conduct product packaging and pricing research, many general best practices cross verticals and solution types.
We offer the following observations and recommendations as prompts for your thinking the next time you conduct product bundling research.
1. Define what you want the bundles to be
In general, primary research activities produce more actionable results when used to test hypotheses and assumptions. Fishing expeditions often provide interesting but not actionable insights. Product bundling research is no different.
We recommend you identify a rough definition of what you want the bundles to look like or what you expect will appeal most to the market. This helps determine the features that don’t need to be tested. There are likely features that you know will only be included in your top-tier, most robust package, and others that will be included in every package. While in the ideal world, bundles would be based purely on customer needs, in the real world, some bundles are more feasible for providers than others from an engineering or financial standpoint. There is little to gain in asking customers and prospects about their opinions of things that will not change.
Cutting out the features that don’t need testing identifies the elements in the grey area – the ones that are up for debate. The focus of the research – from the number of questions asked, techniques used, etc. – can focus on sorting these middle attributes.
2. Focus on the most important elements/features/functionality
It can be tempting to ask about every feature within your solution set. However, it’s worth taking the time and effort to cull this list down to a critical set of features and functionality based on what you already know about customer needs. For example, going into a project, you will likely know that certain features are nice-to-have or table stakes. For example, customized reporting is often a nice-to-have feature for SaaS solutions that target SMBs. Conversely, security is often a table stakes condition but not a feature that drives the selection of a solution when all vendors meet the table stakes criteria.
In most cases, you don’t need research to provide a more nuanced view of these features. Instead, it is better to focus on the features you have strong hypotheses about in terms of them being both important to the market and a feature customers view as a differentiating factor. Again, this can help identify aspects of your category that you can own.
3. Determine in advance what “good” level of interest is
Before starting the data collection, it is helpful to determine a priori what you define as a positive level of market interest in the packages. The results from packaging research don’t always identify a clear winning package. In addition, the results often fall into an interpretive grey zone. For example, if ~15% of customers would pay for the package or feature, is that a good result? A disappointing one? Knowing the relative value of a response at the beginning helps set the tone for the analysis and reporting.
4. Don’t get caught up in artificial precision
A general bit of advice for any B2B research study is not to get caught up in artificial precision when analyzing and interpreting the results. Most bundling and pricing research techniques were designed for consumer markets which generally have the following characteristics: Easy to define products and features (relative to B2B solutions). Buyers tend to be well informed – they understand the product features, how to use them, and the benefits they provide. There are few influencers beyond the buyer. The sale doesn’t involve a professional sales team, there aren’t six tangential stakeholders involved in the decision, etc. On the research side, consumer studies typically have robust samples – usually hundreds, sometimes thousands of data points.
In contrast, B2B decisions and research are a bit fuzzier. Relative to consumer solutions, B2B solutions are more complex, the features are harder to define, buyers are less informed, and many other factors influence the decision. Due to feasibility and budget considerations, sample sizes are much smaller on the research side. B2B quantitative studies with 50-100 data points are not uncommon. With this in mind, you cannot look at a B2B study with the same level of precision as a consumer study.
For example, in a consumer packaging study of counter-top dry-fryers that used a conjoint analysis, the difference in the share of preference between two products of 19% and 24% is likely statistically meaningful. However, in a B2B study of SaaS HR applications, a difference of that magnitude will be less meaningful due to the smaller base sizes. As a result, you need to bring a higher level of context and inference into the analysis of a B2B data set.
5. Don’t over engineer your study
The overall theme running through these recommendations is: Try not to over engineer your research design. Instead, try to stay focused on the features most likely to make a difference to your success. Avoid the ones that are less important either to the client or to how you configure the product. When selecting your research design, start by clearly articulating the options on the table and then create the most straightforward research design. There are many exciting and sophisticated packaging and pricing research techniques available, but not every study needs them. Sometimes they are overkill.
A fundamental question to ask the team is: How many possible configurations and options are really on the table? Perhaps you just need to identify the handful of features that customers would pay more for. Maybe you just need to know how many customers would buy a premium package. On the other end of the continuum, it may be the case that you need to identify the optimal combination of features that will help you compete against competitors.
Further guidance
Guiding packaging and pricing is one of the most compelling use cases for primary research. The above recommendations should help you design your next study. If you have any questions about developing the right-sized research study for your solution, just get in touch. We’d be happy to provide our perspective. You can reach us by going to our contact page.
Read additional perspectives on packaging research in these posts:
Alternatives to conjoint for identifying feature bundles