Finding Product Market Fit in the Industrial Marketplace
Published on : Sunday 07-06-2020
The process of finding industrial product market fit in the post-Covid-19 era will require a deep empathetic understanding of the situation. Derick Jose elaborates.
When, the world around us just flipped upside down in the last 4 weeks – did you ever imagine that you have to think twice before touching your own face? We have now entered an extremely VUCA – Volatile + Unpredictable + Complex + Ambiguous – world triggered by two black swan events – oil price and the virus. As every company navigates the new world, they will reinvent themselves to survive and stay relevant. The heart of this new process will need to answer that one transformative unanswered question:
How do we find resonance + relevance in a Post Covid world?
We at Flutura distilled 5 key signals for zeroing in on Industrial Product Market Fit. They are:
- Signal-1: ‘Average to Average Sell’ vs ‘Complex Consultative Sell’ signal
- Signal-2: Industrial ‘Painkillers’ vs Industrial ‘Vitamins’ signal
- Signal-3: ‘Small fish in a big pond’ vs ‘Big fish in a small pond’ signal
- Signal-4: ‘Users is excited, but unwilling to shift behaviour’ vs ‘User signals behavioural change/willingness to pay’, and
- Signal-5: ‘In-house from scratch’ vs ‘Partner with a product company’ signal.
Why is it important?
Industrial startups (Robotics, Sensor AI, Autonomous vehicles and Satellite intelligence companies) and even large ‘Big Iron’ companies (Rockwell, ABB, Honeywell, Siemens, Hitachi, etc), will be forced to do 3 things – rewire Business Models, accelerate Experimentation and find New Product Market Fit... Getting Product Market Fit is the difference between survival and extinction for industrial AI startups. Those who get product market fit breakthroughs in well-defined time intervals, before the financial runway gets exhausted will get escape velocity and be ready to blitz-scale.
Digging deeper into industrial product market fit
Last week Krish and I were watching a TV program in Houston, which was talking about recent events in terms of 2 eras BC (Before Corona) and AC (After Corona). Everything we took for granted and knew to be true will now have to get revisited and re-imagined in an ‘AC era’. Industrial AI cohort will experience the steepest learning curves we have ever encountered in our lifetimes.
So, how can startups respond?
How can industrial AI companies navigate a ‘guided Google map’ like pathway for finding industrial product market fit in a post Covid market place (in weeks, not months)? (Specially in energy and engineering industries)? Let’s unpack each of the above using real life industrial examples Flutura experienced in US, Germany, France and APAC.
1) ‘Average Salesman to Average Customer’ sell vs ‘Consultative sell to Mature Customers’?
The first signal can be discerned by asking a simple question: Can an average sales executive sell to an average customer in 3-6 months? (or does it need a lot of 'high cerebral consulting' to execute the sell?) (Thank you FalconX for this valuable insight!!!) Framed another way, ‘Can an average sales executive make an average customer perceive movement of the needle on an operational metric (say yield outcomes or energy outcomes or quality outcome) he/she cares about?’ For example: Post Covid, contact less remote troubleshooting from command centres of instead of ‘visit based’ troubleshooting of expensive equipment is a ‘Painkiller’ offering. A lot of OEMs who had analog equipment want to go digital now... This can be sold by an average sales executive and will not require a ‘Sales Rockstar’ based consultative sell. Whereas an industrial AI offering powered by sophisticated AI technique with fuzzy linkage on a process which does not have critical mass of tagged outcome data to learn from will be a consultative sell.
PMF metric to watch: #of ‘non early adopters’ in the mouth of the sales funnel who find the pitch from average sales executive and requesting for a demo.
2) ‘Industrial Vitamins’ vs ‘Industrial Painkillers’?
1- Industrial Vitamins solve an immediate pain with minimum guaranteed outcomes (MGO) in a well-defined timeline.
2- Industrial Painkillers do deliver results, but in invisible ways which are intangible ... Just like vitamins).
3- The strength of the cause and effect relationship between product and problem is stronger for painkillers than vitamins.
Also thinking processes are changing. The last 3 weeks have seen a dramatic rewiring of the economic buyers mental model. There is a tectonic shift from hope to fear...When the house is on fire, there is no time to ‘think’. It will be that way for at-least the next 12 months.
In light of the above, Flutura feels that there will be accelerated selling for Industrial Painkillers and 12 month hold for Industrial Vitamins, so:
1- How can we see the world through the eyes of an industrial economic buyer who is awake at night thinking ‘Who can solve my top 5 immediate pains which have pre- approved budgets in frugal way?’
2- Is our industrial offering going after a specific budgeted problem or is my offering going after a ‘un-budgeted’ problem?
3- Are industrial users are actively looking (not waiting to be educated on an intangible RoI) for an immediate solver?
We are confident that if we are solving an immediate top 5 budgeted pain, the Industrial Buyer will grab the solver from your hands. If you are selling a vitamin (‘This is not budgeted for’ or ‘product aiming to solve problem No 8’ or ‘Solution looking for a problem’ or ‘Aiming at an intangible/unattributable outcome’), we need to spend time educating. Honestly we do not think they have the time, energy or $ to get educated when a sword hangs over the business outcomes they care about. For example: An innovative Virtual Reality based solution for HSE (health, safety and environment) simulation could be an Industrial Vitamin, but a ‘Cloudless Edge based Vibration Anomaly Detector’ for a 13000 kW motor powering pharma chemical production is an Industrial Painkiller.
PMF metric to watch: Average time to make a sale (3-6 months) vs Average conversion rate across funnel stages.
3) ‘Small Fish in a Big Pond’ or ‘Big Fish in a Small Pond’?
Many successful products started off by finding a beachhead sub segment at a SIC code level (Standard Industry Classification) where they can be a ‘small fish in a big pond’ and slowly sequencing their move to an adjacent SIC code. For example: We at Flutura targeted 516905 SIC code (Adhesives and Sealants) with our Cerebra Digital Assistants and Cerebra Engineers Work Bench to understand hidden cause and effect relationships which liberate operational $. Instead of carpet bombing all ‘SIC codes’ we cherry picked a beachhead SIC code sub-segment ripe for entry. For example the need to do augmented and accelerated process root cause analysis using engineer’s work bench is relevant to 516905 SIC code (Adhesives and Sealants) whereas doing augmented, automated and accelerated cause analysis of electrical motors for offshore processes which comes under SIC code 3731) may not have the industry winds in its favour . So ‘Think SIC code level beach head’ for product entry where you can be a big fish in a small pond before finding defined pathways to sequence your moves to adjacent SIC codes. (A relevant set of sub segments are available at https://www.naics.com/sic-industry- description/?code=5169)
PMF Metric to watch = TAM/SAM of the beachhead SIC code.
4) ‘User is excited’ vs ‘User signals behavioural shift & willingness to pay’
Status quo is an option is the biggest competition for startups with Industrial customers... Status quo manifests itself in many ways in our industry using EXCEL, Matlab, handwritten python code, traditional BI, using reports generated by Osisofts Pi solutions still work while trying to extract actionable intelligence from sensor streams. The behavioural change tipping point threshold or incentive to shift behaviour is often underestimated.
So the key signal here can be discerned by asking 2 core questions:
1- Did the economic buyer see differentiating features in the industrial AI product which are significant enough to switch behaviour and pay for?
2- What is the minimum guaranteed impact on a quantifiable operational outcome because of this changed behaviour and new delta features?
This is an important blind-spot for a lot as enthusiastic startups who are a little too attached to the solution! Startups fail to see the human aspects (motivations + fears + incentives) of user behaviour required for finding product market fit. Many at times we have a strong educated hunch about a ‘white-space’ in the Industrial problem space (say for example a quality whitespace, reliability white space, production white space, etc). In order to test we develop prototypes and MVP and during validation sessions sense the feedback signal wrongly. Startups mistake an excited user (‘Wow I really think this is a cool feature’) for a 'willing to pay[ user (‘Tell me how much would something like this cost’).
Often we noticed polite customers trying to be nice. Mistaking their polite encouragement for willingness to buy is dangerous in our view. As a company trying to dig deeper it’s important to understand the cause and effect relationship between proxies for behavioural change and the trigger to write a check.
PMF metric to watch: Number of written letters of intent signalling potential to enter into a commercial engagement.
5) ‘In-house from scratch’ vs ‘Partner with Product companies’
Depending upon the nature of the business and certain operational solvers will be the source of competitive advantage for organisations. For example autonomous drilling operations both onshore and offshore in hazardous conditions could be a source of advantage for upstream OEM equipment manufacturers. The upstream OEM makers initial instincts are to ‘do it in-house’ and it could take a very long time before they decide to partner based on their internal initiatives experience.
PMF metric to watch: Number of cases where after demo there is no movement for 2 months.
Closing thoughts
Finding Product Market Fit in the industrial marketplace in an AC (After Covid) era differs from finding product market fit in a PC (Pre-Covid era which was willing to bet on innovation and the future of the operations). In today’s world finding industrial product market fit process requires a deep empathetic understanding of 'After Covid' mental model rewiring experienced by the economic buyer. It requires a very structured PMF process which can executed in days not months. It requires high velocity feedback loops which stress tests the top 10 riskiest business model assumptions. It’s like playing the Rubik cube... So many dimensions have to align in a short span of time and we believe it will if there is learning velocity. We at Flutura also believe that no adversity should get wasted. There are hidden seeds for startup renaissance hidden somewhere. As Joshua Graham, the Mormon missionary said famously: ‘I survived because the fire within me burned brighter than the fire around me’. Good luck from all of us at Flutura as you embrace your new realities... Intensify your inner fire and develop new wings to fly in a radically altered PC (Post Covid) world. Stay safe, Stay strong!
Derick Jose is Co-founder, Flutura Decision Sciences & Analytics. Flutura is changing the way industrial operations are conducted in Energy & Engineering industries by using AI to impact measurable business outcomes! Flutura is a pioneering Decision Sciences company leveraging new streaming sensor data from IoT & AI based algorithms embedded within its platform CEREBRA. Flutura’s platform is being used by upstream companies and downstream refineries/process chemical industries in Houston, Dusserdolf, Chicago, Shanghai, Paris and Tokyo.
