Recently, I have been discussing product positioning with a company looking to introduce a new laboratory instrument. After a few of these conversations, an idea began to gel in my head about how to look at the lab instrumentation marketplace. I’ve always liked quadrant maps/charts, as I find them to be an effective visualization tool for communication, so, after a few attempts, I ended up with this:
While it is a gross oversimplification, I think it helps to communicate several basic concepts that are critical in defining a product position when thinking about lab instruments. Let’s look at the two axes in play here:
“Must have” versus “nice to have” (x-axis)
In laboratories, there are certain instruments that are “must have”: without them no meaningful scientific progress can be made! While the specific “must haves” for any given laboratory are application/domain dependent, some common examples would be things like scales, centrifuges, shaker tables, autoclaves, microscopes, etc.
These instruments are typically required in order to measure Critical Quality Attributes (CQA’s). In the pharmaceutical development world, these are described in ICH Guideline Pharmaceutical Development Q8(R2) as: “A CQA is a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality.”
There can also be instruments that are better described as “nice to have”: while they may help augment or confirm a CQA already measured with a “must have” instrument, their presence or absence are not mission critical, despite the fact that the data they produce might be of great use for deeper insight to the scientist.
These two categories are immediately separated by management when it comes to budgeting. If the scientists simply can’t properly do their job (“must have”) without the particular piece of equipment, they go to the top of the budget list, and will be purchased when money is available. The “nice to haves” are still considered, but the onus is now on the scientists to do a much more detailed justification of scientific value, meaning a lot more work on their part (and probably the seller’s), and typically a much longer sales cycle.
New method versus existing method (y-axis)
This other dimension concerns whether the technology is an existing method or a new method (or an enhanced/improved version of the existing method). While this may seem very straightforward, it is a critical dimension to take into account when determining product positioning.
In most markets, there is a “gold standard” method used for characterizing certain materials or properties. This “standard” often exists purely based on history: it is what the first scientists used and published the initial scientific literature using. As more scientists follow up on this promising new research, they are almost always going to use identical methods, and there is a snowball effect. There is scientific relevance for doing this, as the early studies can be more easily compared if the metrology is consistent.
This does not necessarily mean that this emerging “standard” is fundamentally the “best” one for the task, but inertia is powerful! At some point, the chosen technology becomes incumbent, and many a salesperson hears the statement “that’s how it’s always been done, so that’s how we’re doing it”. The snowball gets even larger when regulatory agencies begin specifying that this particular method is used; when that happens, there is basically no turning back.
Here’s a quick example: nanoparticle characterization, specifically particle size, is a CQA of many products. The only fast and reliable technique available in the early days was Dynamic Light Scattering (DLS). Since that time, many other technologies have come along that offer much higher resolution, and produce far richer and more accurate data than DLS can. However, to this day, the first instrument bought for nanoparticle analysis by any new lab is usually a DLS machine.
Putting it all together
So, let’s go back to the quadrant diagram above and look at each individual quadrant, starting with the lower left and going counterclockwise (increasing in potential market penetration):
Quadrant 1: Existing method and “must have”: to bring a new product in here is generally very difficult. In order to be successful here, your instrument has to either displace or replace an incumbent. Essentially that is only possible when the incumbent instrument reaches end-of-life and needs to be replaced, or when demand has risen enough to warrant a second instrument. Even in those cases, the incumbent has the advantage due to the existing relationship and data available, and the natural action is to replace that system with a new model from the same vendor. While there may occasionally be opportunities here, such as when an incumbent discontinues a product line or goes out of business, my general advice would be to steer clear of this quadrant!
Quadrant 2: Existing method and “nice to have”: there is definitely more opportunity here than in quadrant Q1, but a vendor must be able to really distinguish themselves from the incumbent. That means that an instrument in this quadrant must use the same method as the incumbent, but be able to differentiate based upon “better, faster, or cheaper” criteria. If you are introducing an instrument here, then it best be significantly less expensive than the incumbent, save dramatically on operating costs, or perhaps produce “better data” than the incumbent (like higher resolution). It must have quantifiable differences versus the incumbent.
Quadrant 3: New method and “nice to have”: once again, there is greater opportunity here than the prior quadrant (Q2). Because it is a new method, this instrument has a distinct capability of augmenting the capabilities of the incumbent by producing data that is complementary or additive to the incumbent. In addition, because it is a new method, the data produced is considered “orthogonal”. Regulatory agencies and industry best practices are increasingly encouraging the use of orthogonal techniques:
To reduce (i) the risk of measurement bias and (ii) the uncertainty in decision-making
during product development, the combination of orthogonal and complementary analytical techniques are generally recommended by regulators.
Source: Orthogonal and complementary measurements of properties of drug products containing nanomaterials
See also: Q2(R2) Validation of Analytical Procedures Guidance for Industry, US FDA
Quadrant 4: New method and “must have”: this is truly the area of most opportunity, and where you may find the next revolutionary products. Instruments in this quadrant can potentially be disruptive innovation: “a small enterprise targeting overlooked customers with a novel but modest offering and gradually moving upmarket to challenge the industry leaders”. However, note that in this definition of disruptive, the initial market emphasis is on the low-end (pricewise) of the market. In the lab instrument market, the disruption is more likely to be high-end disruption, meaning that the disruptor starts with an expensive high-end product, and moves down into more mainstream pricing over time (see also: HEAD vs. LEAD: Disruptions Originating at the High- vs. Low-End of the Market):
The upside of this quadrant is that there is little to no competition (initially) with large potential growth. The downside here is that to be truly disruptive, you have to start at the far left of the product lifecycle and cross the chasm.
Back to positioning…
So, before you think about product positioning, it is important to understand where in this quadrant plot your offering fits. This directly affects how your product will be best positioned, and therefore sold, in the target market. As noted previously, I would steer clear of Quadrant 1! For the others, the essence of the position is as follows:
Quadrant 2: The product must have quantifiable differences versus the incumbent, i.e. “better, faster or cheaper” (or better yet, 2 or more of those characteristics!). Beware, because this type of market will most often degrade quickly into a “race to the bottom” based on price only! For this reason, I am not a big fan of products in quadrant 2!
Quadrant 3: The emphasis here should be on the value of the data to supplement the data of the incumbent: how does the data produced augment what the user already gets from the incumbent method, and how does that data deepen the user’s understanding of the end product. In regulated industries, emphasize the importance of orthogonality.
Quadrant 4: Products in this quadrant require the full trip over the product lifecycle, starting with “early adopters”, but hold the most potential upside in the long term. One needs to identify Key Opinion Leaders (KOL’s) in the target market early on, and concentrate on getting them to buy in to the benefits of the new technology. These KOL’s then become evangelists who help you to convince the market of the clear value of the new technology over existing ones.
Quadrants 3 and 4 are the most exciting and interesting ones, and the ones with the most potential growth. Understanding where your potential product fits in this matrix will greatly help with understanding your value proposition, and how to position the product for selling.
I welcome your comments…..
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