Having compiled quite an extensive amount of published life science market size data, we’ve noticed a lot of very optimistic growth rates. That led me to wonder if that optimism is warranted, given overall growth in life science R&D, or if there’s something about published market reports that cause them to overstate growth rates.
As I’m sure many of us realize, life science R&D spending isn’t exactly skyrocketing. According to the Battelle and R&D Magazine “2014 Global R&D Funding Forecast” life science R&D spending has only rose from $184.2 billion in 2011 to $201.3 billion in 2014. This equates to a 3.00% compound annual growth rate. All else being equal, we should expect to see published life science market growth rates hovering around that, with the exceptional outlier for high-growth markets. It stands to reason that growth in the markets for life science tools and services should be in line with the growth in overall R&D spending.
To determine if the published studies hold to this, we took our list of published market size data and cleaned it using the following criteria:
- Only global market size data were considered. All regional market size data were removed.
- All studies not listing a growth rate were removed.
- All studies publishing data from 2008 or earlier were removed. We only wanted to look at fairly recent data, roughly in line with the time frame of the R&D spending data from Battelle.
- Only the newest study of a particular market from any given publisher was included. If XYZ Reports had a study of the cell culture market from both 2011 and 2013, only the 2013 report data was included.
- If there was data for a directly related market and sub-markets from the same publisher, only the overarching market was taken. For instance, if data for the cell culture market and for the cell culture media market existed from XYZ reports, we would ignore the cell culture media market data in favor of the broader cell culture market data.
This data cleaning still left us with quite a large amount of data – 104 studies. These studies projected an average growth rate of 11.2%, far higher than the 3.00% increase in life science R&D spending. Weighted by the value of the market size estimate, the weighted average was still 10.4%, again far greater than life science R&D growth. In fact, the lowest growth rate from those 104 studies was 4.0% (a 2012 BCC Research study of the electrophoresis market and a 2013 Decibio study of the Life Science Research Tools Market). Even the lowest published growth rates are higher than the growth in life science R&D spending. This makes absolutely no sense.
There are a few potential ways in which our analysis may be flawed, either by bias or by failing to consider all realities. For instance:
- The data we compile only includes studies that make public the market sizes and growth rates. This excludes a number of market research companies operating in the life sciences space. While our analysis included 104 studies, these studies all came from only 7 companies. Still, there is no evidence that these growth rate estimates are far higher than the estimates from any other companies.
- Some of these market sizes may include growth from outside the life science R&D sector, such as diagnostics or life science (pharma / biotech) manufacturing. While this may be true in part, it does not explain the size of the disparity. With the exception of certain high-growth sub-markets, such as biosimilar manufacturing, IVD and manufacturing growth rates are both generally predicted to be in the mid single digits.
- Companies which publish research may be focusing on “hot” markets and more likely to release studies on those high-growth markets. This is potentially a source of bias, however there are many very well-established markets included in this analysis (cell culture, research antibodies, chromatography, electrophoresis, microscopy, etc.) and even those markets have growth rates higher than the overall life science R&D market. The same can be said for market studies that analyze the life science tools and services markets at a very broad level, such as “Life Science Research Tools,” “Life Science & Chemical Instrumentation,” “Laboratory Equipment,” and “Preclinical Outsourcing.”
- Decreases (or deceleration) in life science R&D funding may be disproportionately applied across R&D costs. It may be the case that spending on products and services tends to be more resistant to budgetary contractions than personnel, infrastructure, and other sources of cost. We do not believe this to be true, however, as much equipment and service spending is far easier to cut than staff or space.
In conclusion, we believe that it is very likely that life science growth rates are overstated in published reports, perhaps by as much as a factor of two. While we can’t be certain why such overestimates exist as we do not know how the studies were performed, we do know that they are not remotely in line with overall life science R&D growth rates and the discrepancy is very unlikely to be explained by other mitigating factors. The next time you use a commercially sold study to gauge growth rates, you may want to take them with a grain of salt and assume that they are an overestimate.
As we discussed last week, there are only two fundamental reasons why someone won’t buy from you. Either you are talking to the wrong person, or the prospective customer doesn’t trust you. Unfortunately for commercial professionals, the reasons why someone could lack sufficient trust in you to purchase are myriad. Doubly unfortunately, those reasons often go undiscovered. Many organizations performing little analysis of why any given sale is won or lost, others do so superficially in ways that don’t provide meaningful information. Even more confusingly, many companies think they are performing win / loss analysis when really they aren’t! They are instead utilizing other tools and methods, often in an ad hoc and undocumented manner, which provide biased or misleading information!
Performing win / loss analysis correctly is not a trivial endeavor and requires a good deal of planning, but there are many benefits to doing so. These include:
- Clearer understanding of the customer buying journey
- Better understanding of the competition’s offerings (including pricing, positioning, etc.)
- Early identification of market trends
- Better understanding customer preferences
- Understanding how you and your competition are perceived
- A built-in “warning system” which informs you if your messaging is missing the mark
- Feedback on performance of the sales team and effectiveness of sales processes
- Market feedback to help guide product development
Planning for Win / Loss Analysis
Remember that win / loss analysis is a form of market research. It requires proper planning – and adherence to the plans – to ensure that the execution yields the answers you’re looking for.
The first question that needs to be answered is: who will implement the program? This should not be your sales organization! Ideally, the people running the program and performing the interviews will be far removed from the sales process. An external agency who is familiar with your market and experienced in performing win / loss analysis would be ideal, however other internal departments or functions can be used (usually a market research or CI person / team, if you have one, otherwise the applicable product manager or another relevant marketing person would be a good choice to head the effort).
Next, decide what specific objectives you hope to achieve from the win / loss analysis. There are basics that are central to the reasons a sale is won or lost and will therefore almost always be included, such as understanding the customers’ decision criteria and knowing how you measured up against the competition across a number of key factors, but you will also have the opportunity to obtain a plethora of other information. For the sake of customer participation and limiting the cost and / or effort, you will be limited to how much additional information you’ll be able to collect. You will therefore need to determine what non-core information is the most important. Are you interested in learning more about your competitors’ offerings? Do you want to know more specifics about the customers’ buying journeys? Are you interested in the finer details of how your brand is perceived compared to the competition? For long, consultative sales a customer may be more willing to engage with you in a lengthy interview. For short, low-value purchases where sales interaction was limited or non-existent, you probably won’t find customers willing to sit through a long interview. Know what can be realistically expected from your audience and plan accordingly.
The next question you need to answer is: what opportunities will be analyzed? Given the time and / or cost required to perform win / loss analysis, it is often only applied to major product lines or service areas and / or large accounts. (We do not recommend only analyzing large accounts unless your focus is improving win rate solely to large accounts; if you want to improve the win rate for all customer classes, you need to analyze them all.) You can define which opportunities will be analyzed more narrowly to cut down on the number of interviews and amount of analysis necessary, or you can be more broad to collect information about more opportunities and then perform post-hoc analyses of specific products, markets, etc. You also need to determine the frequency at which opportunities that meet the defined criteria will be analyzed. If the nature of your business is such that you have a low number of high-value opportunities, you may want to analyze them all. If you have a high number of low-value opportunities you may want to analyze only some of them. If you will be analyzing only some, you should select them either at random or at regular intervals (for example, at the conclusion of every fourth opportunity, chronologically) to prevent bias. Furthermore, ensure your criteria don’t exclude wins! It’s just as important to understand why you win as why you lose, and understanding your wins can be even more informative.
From the defined objectives, plan your questionnaire. There are a massive number of potential questions, and if you’ve clearly laid out your objectives the questions you need to ask should become somewhat obvious, but here are a few common ones to get you started:
- What caused you to initially consider a purchase of this type?
- Which other companies / products / solutions were being considered? Which one was ultimately chosen?
- What actions on the part of our team made notable positive or negative impressions?
- What selection criteria was used to make the ultimate decision?
- What interaction influenced you most during your decision-making process?
- How did our pricing compare to the competition?
- Why did / didn’t we win your business?
- Who was involved in the purchase decision?
- Were you comfortable with the product features / company’s capabilities? Which were most / least important?
- How do you perceive our company? How do you perceive our competitors?
- Would you be likely to recommend our solution to others?
A common issue with win / loss analysis questionnaires is the tendency to focus almost exclusively on the latter stages of the buying journey. Remember that the early stages of the buying journey are often more influential. Ensure you ask questions that will inform you how well you are setting the stage for a win, as many lost opportunities aren’t simply failures to close.
If you end up wanting to ask more questions than they reasonably can, remember that not every interview needs to ask the same questions. If you feel that a question has been sufficiently answered, change it out and ask another which would provide more new knowledge. You can also have multiple sets of questions and rotate through them to collect input, albeit less of it, on a larger number of some ancillary questions of lesser importance. (We strongly recommend always asking a set of “core” questions which directly address the most influential reasons for winning or losing.) If you ultimately want to ask more questions than would be feasible in an interview, you can create an accompanying questionnaire to collect additional data. This can be particularly useful if you wish to collect sizeable amounts of quantitative data which can be easily collected via an online survey or similar tool. Just remember that everything you ask a customer to do effectively has a conversion rate. Asking your customer to do two things will invariably lead to an increased number of incomplete data sets from respondents who either did not take / complete the interview but completed the questionnaire or vice versa.
Preparing for the Interview
Determine who will conduct the interview. Similarly to choosing the person or team to run the program, it’s best if the interviewer is not on the sales team. The interviewer should never be someone who was involved in the sales process for that particular customer. That consideration aside, the interviewer should be someone who is familiar with the product or service being sold, familiar with the market, understands the sales process without being too intimate with the sales team, and will make the respondent feel comfortable with the interview process.
Interviews should be scheduled with the customer or prospect very soon after the opportunity has ended. A good rule of thumb is that if more than a month has passed since the opportunity was closed or lost, don’t conduct an interview. Details of their decision journey and interactions with various companies need to be fresh on their minds in order to obtain accurate information, and collecting inaccurate information is often worse than collecting no information at all. When scheduling the interview, let them know exactly what to expect and what topics you are going to discuss. If there were multiple people involved in the prospect’s decision, they should be interviewed separately as they may have differing opinions and these differences can be stark at times. If you interview them collectively, you run the risk of those differing opinions not being expressed or falling victim to groupthink.
Before the interview, the interviewer should sit down with the sales team / person who was handling the opportunity and document some facts and perspective regarding the opportunity. How did the opportunity arise? Was there any previous relationship with the prospect? What tactics and sales tools were they using and why? Were there any noteworthy challenges during the process? What was the result and was it anticipated?
Performing the Interview
Interviews are generally performed by phone, although analogous communication tools such as teleconference can be used. In-person interviews can be performed as well so long as the customer is local and the interview can be performed without becoming cost-prohibitive. Being able to see the interviewee an be helpful, as gestures and body language can convey feelings which can in turn be used to help guide the conversation. (The interviewer’s impressions obtained from body language should not be documented as it could introduce a large degree of subjectivity. Additionally, when performing win / loss analysis across cultural borders, body language could be misread due to cultural differences.)
Any expectations of confidentiality should be discussed up front. As some purchasing processes involve sensitive information, ensure the interviewee(s) feel comfortable using any information necessary to fully explain themselves while knowing that any confidential information will not be recorded or shared.
The interview should have a “script” to ensure the interviewer asks all the questions, although some of which will likely vary slightly interview-to-interview (in phrasing or approach, not in intent) based on the nature of the opportunity and how the interview progresses. However, the questions on the script should be taken to be a minimum of the questions that need to be answered. A good interviewer will probe the interviewee to uncover the underlying reasons behind their answers. Simply surveying the interviewee by asking a set list of questions in sequence is a waste of a live interview and a good way to end up with incomplete information that is difficult to understand and / or leaves a lot of opportunity for guesswork. The ability to be meaningfully spontaneous is dependent on the interviewer’s knowledge of the market, the product bring sold, and the details of the opportunity and sales process for that specific prospect.
Post-Interview Analysis and Assimilation of Knowledge
Soon after each interview, send the customer a message to thank them. As with any customer interaction, a win / loss analysis is a branded experience and you want to ensure the customer experience is a good one in order to earn future business and cultivate brand advocates.
There is no single, correct way to analyze the information from a win / loss analysis because the information, and the kind of information collected, will vary based on the questions you are trying to answer and potentially other factors as well (as discussed earlier). However, data analysis provides ample opportunity to derail your win / loss analysis. It’s likely that most of your data is qualitative. If your organization has a tendency to be political, various groups may try to influence how the data is analyzed or presented in order to make themselves look better or further their own ends. It’s the job of the person managing the program to ensure this does not happen. Any quantitative data should be handled using proper statistics, and qualitative data should be analyzed in a way that is logical, defensible, and allows you to extract the necessary insight. Applying semi-quantitative methods to the analysis of qualitative data may help, but you shouldn’t limit yourself to them. Whatever methods you use to analyze the data, you need to ensure that they are consistent!
Once the data is distilled into knowledge, you need to ensure that it is utilized! When there is enough analyzed information to answer at least some of the questions that you defined in your objectives, a report should be drawn up and a meeting called with people from all departments who would stand to benefit from the resulting knowledge. (Depending on your company policies and culture, the reports and analysis may also be made available to anyone in the company who cares to learn from it, or restricted on a need-to-know basis.) At this meeting, the data and analysis are discussed, lessons learned are shared, and ideas can be generated for ways to improve – these ideas are the foundation for change. The results should inform your sales processes, market segmentation, product development, messaging, marketing communications, sales collateral, and other areas.
If you’ve obtained answers to some secondary objectives, you can remove the associated questions from the interview script. These may be replaced with questions to fulfill other knowledge objectives. Remember, however, that the primary purpose of win / loss analysis is to understand why you win or lose business! The core questions facilitating the answer to that question should, under most circumstances, not be removed or replaced. If you find yourself desiring the answer to other questions more than the answer to why you are winning / losing business, then you should use a different tool or approach which is more suited for the information you seek to gain. You may, however, rotate through other product lines or service categories in order to obtain information specific to other areas.
A recent Gartner study (“Tech Go-to-Market: Three Ways Marketers Can Use Data From Win/Loss Analysis to Increase Win Rates and Revenue“) found that less than one third of organizations conduct win / loss analysis properly. The same study found that win / loss analysis can increase win rates by as much as 50%! That should be no surprise. Understanding is the foundation upon which improvement must be built. Sure, win / loss analyses require a good deal of rigor and effort, but that 50% should be well worth it.
In most market research, you start with a hypothesis or a set of assumptions and you take it from there. Those assumptions often aren’t conscious – for instance, when asking a user to rank a set of product attributes you’re assuming you know which attributes are most important – but they’re almost always present. For most research that’s fine, however for problems that are large and unknown these assumptions hinder our ability to identify a solution. This is true not just for traditional market research efforts, but for analytics-driven research as well. Diving into data – big or small – to try to answer a question doesn’t guarantee a correct result.
For those big, vexing problems, we need another approach.
Customer Research Without Assumptions
Serendipitous discoveries require that we shake those assumptions. We need to be able to observe and learn without our questions or research getting in the way of themselves. In order to do this, we need to adopt a customer-centric perspective. We once again need to stop thinking about the customers and start thinking like the customer.
Obviously, in order to perform research you still need to know what it is that you’re seeking to understand. We therefore still need to be able to ask questions and set goals, but we need to ensure those goals are assumption-free. To do so, start with the problem you want to solve or question you want to ask, and then convert that into a broad but addressable customer-centric issue. In other words, you need to be able to frame it as a human experience.
Addressing problems framed in this manner is not something that can be done with surveys or some kind of defined Q&A process – both require questions which embody assumptions. Instead, such research must be primarily observational. You want to be able to gather information in an open-ended manner. Questions should only be asked in response to observation. The primary issue in the design of such studies must be: what about our customers can we observe in order to gain the necessary understanding?
When to Take an Assumption-less Approach
The assumption-less approach to market research should only be undertaken for big, unknown problems. If you are even moderately familiar with the customers and the market, can envision a defined set of possible outcomes with a good degree of certainty, and can frame a set of hypotheses, then the problem is capable of being defined in a manner which does not necessitate an observational, assumption-less study.
The assumption-less approach is best when:
- You are highly unfamiliar with the customers, market, or problem.
- The problem at hand is novel.
- Other methods of research or analytics cannot be used or have failed.
- You cannot define a set of likely outcomes and have no hypothesis to test.
Aided by technology, many life science marketers who perform market research are increasingly relying on a combination of surveys and analytics to perform market research. These methods, however, cannot answer all questions. Hypothesis-driven market research imparts assumptions which can confound the understanding of unknown problems. To best tackle those big problems, take an assumption-less approach and perform an observational study which seeks to better understand unadulterated customer experiences.
Lab Manager Magazine routinely publishes surveys, often multiple per month, which briefly assess scientists’ utilization and preferences with regards to a particular laboratory technology. These surveys basically amount to free market research. They don’t go terribly in-depth, but if you’re looking for some basic information (such as feature preferences) about a particular type of instrument, they are a wonderful resource. Here at BioBM we archive them for in-house use and have a bookshelf lined with the physical magazines, but we thought this would be a good resource to share with everyone. Since Lab Manager doesn’t have an easily sortable list, we created one for you here. Every Lab Manager product survey from January 2011 to today is included, and we’ll be coming back periodically to update the table.
This resource has moved. You can find it here: https://biobm.com/resources/list-of-lab-manager-surveys/
Life science companies rarely speak with their customers as often or as deeply as they should. You can make the common excuse about scientists being distant and antisocial (which I would like to go on record as saying is complete nonsense) but many companies actually start out being good at speaking with customers but then lose that trait as they grow. Why? Simple – taking the time to speak with customers isn’t something that’s easily scalable. It’s easy to view large amount of customer interaction as unnecessary and cut it in the name of efficiency. Or a company might just become large enough that it makes a lot of financial sense to automate the heck out of everything. While marketing automation and customer relationship management automation are very powerful tools that we strongly advocate, they should not displace real conversations with your scientist-customers, for a number of reasons.
1) Customers love good support.
Nothing says “we don’t care about you” like a robotic confirmation email sent from a DO-NOT-REPLY email address. While you can still do better without actually speaking with the customers, your customers will appreciate getting an email from a real person (or at least what looks to be an email from a real person) with the ability to reply to that person and ultimately get a response. It shows that you care enough to give them some of your time, if they want it. And while some customers may abuse the privilege, most will not and it gives you the opportunity to create a lot of goodwill. It’s great for your brand and great for customer-retention.
Of course, you don’t need to wait until after the sale to have a conversation or to demonstrate great support (but we’ll address that in a minute).
2) You WILL learn things.
Want feedback on your product? Want MORE and BETTER feedback? Want to learn what the customer is thinking when they’re contemplating a purchase or perusing your website? You could fire off an email asking them to take a survey to try to win an iPod, and that might be useful if you’re dying for quantitative data to perform some large-scale analysis, but in most situations you’ll be better served and you’ll almost always get a better response from just striking up a conversation. Have an actual person type an email to a few people who bought your product three months ago and ask how things are going. I’m sure most of you would be genuinely interested in how the customer feels about your product, so let that interest shine through. Show them that you have an interest in them and you care about what they think and how things are working out.
Of course, you don’t need to wait until after the sale to have a conversation and learn about your audience (almost there…)
3) It can be great for conversion.
You know those live chat boxes that you occasionally see popping up asking if you want to chat with a representative? Or the popup-like “lightbox” that appears after you’ve been on a website for 10 seconds where you’re asked if you’ll take a 4-minute survey? Those both seem pretty silly and useless and they often are, however their failure is more due to design than their intention. Customers will speak with you during their buying journey, and you can effectively prompt them to do so on your website (or just about anywhere else). Whether you’re making use of live chat or simply encouraging users to call or email, try to start a conversation as early as possible without being forceful or gimmicky about it. Not only will you help your conversion by answering questions and helping to simplify the customer’s buying journey, but you’ll also learn a lot about how they make their buying decisions and demonstrate good support all at the same time.
It’s very easy to get out of the habit of having meaningful conversations with customers. By ensuring that you take the time to speak with the customers you’ll be doing a valuable service to your company and helping your scientist-customers at the same time. There’s simply no substitute for real conversations.
Your life science company could have a stellar new product or a unique new service. It could be wonderfully differentiated and offer your customers a unique value. If you fail to effectively communicate that differentiation and value, however, than your marketing is still going to flop.
As we’ve discussed before, life science marketers often resort to facile claims to describe their products, and in most cases that not only leads to messages that are devoid of real meaning but also leads to messages that are not unique or differentiated. Even when meaningful claims are made, competing products / services tend to describe themselves in the same ways, using similar attributes. Your product may be differentiated, but if your messages are largely the same then how can scientists tell that your product is better than the competition? They can’t, which is why it is so important to not only differentiate your product, but convey a unique positioning in your marketing message as well.
One of the best and easiest ways to make sure that your positioning and value claims are unique is to perform an attribute analysis. An attribute analysis is a market research technique that determines how competing products / services are outwardly positioned* by looking at their marketing communications and seeing how they are defined.
To perform an attribute analysis, first list all the competing products or services and collect references which you will use for the attribute analysis. Webpages and pdf brochures are generally the best options in terms of content and accessibility, however product manuals and other more technical documentation may be used, as may marketing materials that are generally less accessible such as webinars or email blasts. Have at least two references for each product whenever possible, although more is better. Secondly, collect all the attributes that are used for each product. Note that attributes should be counted – you want to know how many times each attribute is used rather than simply if it is used. Attributes can include descriptive terms, features and specifications. The list of attributes can easily become larger than is valuable, so basketing similar terms is recommended (for a basic example, “fast” “rapid” and “quickly” could all be basketed under one attribute, and you could assign ranges for specifications such as “read lengths between 200 and 300 base pairs”) as is ignoring unimportant specifications or features (example: for many products, few people may care about weight). Once attributes are counted, you can group them into categories as well. You then have laid out in front of you a numeric map (or a visual map, if you plot the attributes) of the positioning of competing products and services. The data can be analyzed in various ways.
Having performed the attribute analysis, you will be able to see what claims are commonly used and which are uncommonly used. You can combined this with market knowledge of scientist needs to find positioning opportunities; positions that align with customer needs but which are not used by competitors.
*I use the term “outwardly positioned” because many companies do not have their positioning formalized or do not effectively translate their positioning into effective marketing messages. This erroneously leads to different outward and inward positions, where the company believes the product has a certain positioning but the positioning communicated through its marketing is different. You could also call these externally-facing and internally-facing positions.
Life science market research can be a tricky, and often expensive, endeavor. You need to find a suitable population that meets your study requirements, recruit individuals to actually participate in the study, and building the survey. You have to worry about introducing bias, sufficiently powering your study, ensuring your population is representative, and many other factors. However, there is one tool that can, in many situations, make your research easier, faster, and cheaper: AdWords.
Yes, that AdWords. Pay-per-click Google Adwords.
AdWords has many desirable qualities that one would want in a market research platform. It has a huge audience, and our in-house research has shown that it is overwhelmingly the search engine of choice within the life sciences. Google is used by a billion unique individuals every month. The audience is easy to segregate (albeit in limited ways: by keyword or by geography). It’s easy to reduce both population bias and question bias. With market research via AdWords, you often don’t have to even ask a question – the question is implicit rather than explicit since the unwilling participants are looking for information, products, services, or content. While you’ll be limited by language, in most places English is the language of science and users select themselves by using relevant keywords.
That said, AdWords is obviously not designed for market research and its capabilities as a life science market research platform are understandably limited. You only get to “ask” each participant a single question. The types of information you can gather are relatively limited. You can’t segregate the audience by job title or other useful demographic information.
Still, you can get insights on a surprising amount of questions. For example, the following information can often be reasonably obtained using AdWords:
• The relative popularity of a basket of products or brands
• Which prospective name for a new product would be better
• What method are researchers using more often
• What nations are most frequently using a particular method or type of product
• An attractive price for a particular laboratory product
• A fair deal more…
Note that some of the above information would require the use of Google Analytics (or similar) in conjunction with AdWords.
While not a fully capable replacement for traditional market research studies, a lot can be done with Google AdWords for as little as $0.10 per participant ($0.10 is the minimum cost-per-click in AdWords). Next time you’re looking for market data, especially if the data you’re looking for isn’t terribly complex, you may be able to save your life science company a lot of time and money by turning to the reliable old pay-per-click advertising platform.
Small life science companies are surrounded by uncertainty. How can we improve our service to customers? What new product would be of greatest interest to scientists? How can we be more certain that our strategic direction is in sync with future realities? What can we do to add value to our products? How can we attract new segments of the market? All of these are almost constant questions among all companies, but small companies are the most likely to leave them unanswered or do an insufficient amount of research to confidently answer them. Especially in rapidly changing markets such as the market for laboratory products and services, having solid information on which to base your company’s actions is highly important.
The Importance of Good Information
“I love talking about nothing. It is the only thing I know anything about.” – Oscar Wilde
All businesses need to understand the potential risks and rewards of any specific course of action. Beyond being a principal tenet of the practice of risk reduction, it is essentially a core business need. Businesses act on this basis. If the expected reward from a specific course of action will result in a return that justifies the amount of risk, then this action is taken. But how do you even know the risks or rewards of a hypothetical future action? … The answer? Market research. Market research provides the information that allows the quantification of uncertainty and risk.
For example, say a company that develops and sells cell lines for research purposes is considering which of a choice of new cell lines to commercialize. Without appropriate information, the choice would effectively be a poorly educated guess. Even if the company has the experience to “feel out” where the demand lies, they will be acting on a short-sighted intuition with little information to justify it. One cell line may be in more demand today, but the market for it may be shrinking while another is growing, and therefore another may have greater demand in the future and provide a better return over the lifetime of the product.
With a well-designed study, almost any question about the market can be answered, and the information discovered can be extremely valuable in reducing risk and uncertainty and maximizing returns.
Types of Market Research
“Be curious always, for knowledge will not acquire you; you must acquire it.” – Sudie Back
Market research can be segmented into two distinct types: secondary research, and primary research. Secondary research utilizes information that already exists. This may consist of mining databases, utilizing demographical data, analyzing existing research reports, etc. Primary market research involves reaching out directly to individuals within the target market. Primary market research can be in person, online, or via any other mode of communication, and may involve interviewing, surveying, questionnaires, etc. Either type may be quantitative or qualitative, although secondary market research is almost always quantitative.
Making Market Research Work for You
“Knowledge is of no value unless you put it into practice.” – Anton Chekhov
The first issue of importance when conducting life science market research, and one that you will have a large part in answering, is understanding what you want answered and who should be providing the answers. What information is it that you are looking to collect? Will this information answer the question you have in mind? Will answering that question help you reduce uncertainty in ways that are relevant to your business needs? Who should be answering this question to make the answer relevant? Would there be a subset of life scientists who would best answer the question, or maybe lab managers, or perhaps even distributors? These questions need to be answered to ensure the relevance of the market research study.
The next issue is the study design. How should the information be collected. Would secondary or primary research be most appropriate (or a combination of both)? How important is the question? Do you need a very thorough, and therefore more expensive, study or would a less thorough or less structured study be sufficient? How should the data be collected and analyzed?
The last and most important issue is using the data! No matter how much market research you do, it’s not going to help you unless you apply the information to help guide your decision-making.
The life sciences are rapidly evolving and in a near constant state of change, and uncertainty and risk are abound because of it. Utilizing properly designed and executed market research can give your life science company a more certain future, improved returns, and the ability to act with confidence.