Jan 1, 1970 - Jan 1, 1970,

Want To Emulate A Startup? Here’s How.

eyeforpharma Chairman, Paul Simms, explains why pharma companies are failing to innovate as they chase short-term revenues.

In my life, I talk to pharma executives every day. Every single day. And perhaps 60% of recent conversations have hinted at something similar to what Christina, an SVP I spoke to last week, told me:

“We want to work more like a startup. We’re looking at changing our culture, at growth hacking, at our digital consciousness and pushing agile development. It’s going to be a big change but we’re doing it.”

Like a startup? Yes, it’s official. Pharma has caught the entrepreneurship bug. We’ve finally realised that R&D ain’t what it’s cracked up to be, and we’re in full-on panic mode. Innovation labs and digital maturity workshops are suddenly everywhere, like scattered dandelion seeds in the springtime.

One GSK executive told me his entire department is now undergoing mandatory training in iterative development techniques. UCB is introducing design thinking across the board; in the defiant words of my friend and EVP, Bharat Tewarie: “We have no choice. We have to do this.” Several other companies are taking their first steps.

To be honest, I’m pretty happy about this mind-shift – we finally feel the flames licking at our feet and we’re motivated to do something about it. Our historic, product-safe, conservative mindset is, I’m afraid, a burning platform, and we have an opportunity to develop the type of integrated health solutions regulators are paving the way for.

But will companies succeed? Well, on current evidence, I’m sorry but no, they won’t.

Why? I mean, the desire is there. But the reason the majority will fail comes down to one thing: because our executives are scared.

They’re scared to do what startups have to do initially through necessity – take their foot off the profit accelerator and do what’s crucial for any iterative development working:  create a learning-centric organization. An organization which prioritises knowledge over revenues in the short-term. They can’t bear to look away from short-term goals for a few seconds.

The difference is elementary
Imagine a pharma company preparing for a drug launch in two years’ time. We’re talking a major launch, a big opportunity. Something special.

But, the market doesn’t exist yet, so there is work to do. As a minimum, the company requires a global medical education campaign, a comprehensive set of patient support programs, a system for analysing real-time outcomes data internally, and co-ordination with health services to create a more holistic patient solution.

The pharma company realises the scale of the opportunity, so takes it seriously – it installs the best leadership talent it can find, asks some of the most capable existing managers to take on additional project duties alongside their current work, and sends out a missive from the CEO requesting company-wide support.

Budgets are allocated – a significant boost is provided to medical affairs, market access and launch marketing – and a new incentive plan created, one that provides additional bonus cash payments to reward a successful launch. After all, failure is not an option.

At this stage, you might think this sounds entirely normal and logical. But how would a smart startup consider the same problem?

The Startup Way
Firstly, there’s no budget, so hiring top talent is out of the question. The founder’s savings- will only cover a skeleton team at most.

Instead, the first priority is to understand if customers want the initiative and will pay for it. After all, this is an inherently uncertain venture and most of the projections are assumptions, so they need to be tested before any thought of scaling.

What’s more, if the startup is to raise enough money to build anything significant, it must prove to investors that their stake will pay off – proof that must be delivered well before they’ve got anything resembling a final product or real revenue.

So, the startup launch follows a very different plan – one based on testing hypotheses and finding proof/evidence via a different set of metrics (customer conversion rates and loyalty, not revenue or ROI!). They quickly set about the creation of a minimum viable product (MVP) launched just two months from start date – even though it’s disturbingly ugly and contains only one core feature, it provides vital data against those core hypotheses. Modern data-collection methods quickly test customer traction, and with pivot-or-persevere decisions every two weeks, rapid iterations enable the generation of a second product a mere month later.

By continuously repeating this build-measure-learn loop, within a year their product is returning excellent metrics, resonating with a majority of target customers, guaranteed to succeed when scaled up.

Did I just say ‘guaranteed to succeed’? You bet. And you don’t get there without prioritising learning, rather than revenues. For a startup, only now that solid evidence exists can investment be secured.


Life in the Amazon

Don’t like either of these approaches? There is a third way, a variation practiced daily by one of the most successful of all modern companies – Amazon.


I’ve spent a lot of time with Amazon/AWS employees recently, specifically to determine how their model might be translatable to pharma.

Amazon follows a ‘work backwards’ approach that starts with a press release at the outset. If approved, a small ‘two-pizza team’ (6-12 people) is rapidly assembled, containing a range of skills. The team then uses a ‘customer-obsessed’ approach, leveraging easily accessible internal dataflows, to determine whether a project will work.

This small-team approach is used no matter how ‘big’ the ideas are – including major projects such as the Kindle or Alexa. Internally, there is almost no hierarchy – the vast majority of employees move from one small team to another, never more than one at a time (no multitasking) – and Amazon only hires ‘builders’, those willing to roll up their sleeves and get stuck into the creation process.

Does it work? Well, if you have any doubt over the productivity of this approach, consider that AWS (the smaller, B2B, non-retail side of Amazon) released more than 1400 products and services in 2017.

Turn up the contrast
Consider five major differences between the traditional model and these two alternatives:

1. The traditional approach isn’t suited to uncertain environments
Face it, the standard hierarchical model of most large companies is built for one thing – generating maximum returns from existing products. That’s why specialist departments exist – they hone their capabilities and produce economies of scale, but also become inflexible silos where people work in a set way. Those with ‘experience’ call the shots, rather than letting the market decide. Employees are encouraged to report success with ‘vanity metrics’ rather than the truth. However, uncertainty calls for different measures and an entirely different style of rapid experimentation from adaptable, cross-functional teams.

2. The traditional approach risks everything
It might be an era-defining project for your company but why commit so many resources before you know if it will work or if customers want it? Instead, start with the end in mind and build something that resembles a completed product quickly. If you can’t build a product, why not create a description of the product – a website, brochure or instruction manual – and launch that to see if you get take-up?

3. The traditional approach makes people unfocused and unhappy
Notice how the startup and Amazon models both eschew multitasking. People work on a single project so they can dedicate their energy without interference. Employees are also empowered; senior managers should be give others a taste of the freedom they enjoy.

4. Investment is allocated, not metered
In traditional environments, projects are often chosen through an archaic, often-unwritten entitlement system. At Amazon and many startups, all projects have small budgets and only those that show signs of working are scaled up.

5. You say you embrace failure, but you don’t
Tell me truthfully; is your reward plan based on financial success, thereby punishing failure? How is failure managed to enable company-wide understanding of what to avoid next time? Amazon failed so spectacularly with the Fire Phone that most people have never heard of it, yet it was one of their biggest investments. No problem – an honest assessment was made, lessons were learned, technologies were adapted for other projects like Alexa, and staff simply moved onto other project teams. Nobody was fired or their pay downgraded.

The harsh lesson is, if you want innovation, you need to be prepared to break from a revenue-first incentive model. Most of you never will.

Where pharma is going wrong
Take a look at this diagram; in my earlier example, we saw how traditional pharma companies (even those that think they’ve introduced entrepreneurship) tend to begin at the top of the pyramid and work downwards.


They start by hiring a big cheese with twenty years of experience, the person with the winning smile that makes the key decisions. Together with their management style, this defines the company culture, and middle managers then define the processes needed, which, in turn, drives the accountability and productivity of the organization.

The smart startup, however, moves in the opposite direction – because it has to. Accountability is baked-in from the outset by defining and measuring customer assumptions. Decisions are made from here, setting the ‘build-measure-learn’ process in motion, which, with frequent findings and rapid progress, gives rise to a can-do culture. This attracts the right people.

(By the way, in case you’ve ever wondered why clever young graduates tend to abstain from working in pharma (and ‘redefine healthcare and make a difference’) and opt instead for a career at Facebook, this is a big reason why.)

The direction of travel, up or down this pyramid, determines whether you’re an organisation suited to optimising an existing model, or an organisation suited to finding a new model. Whether you’re driven by experience and strength, or by learning from new customers.

So, if you believe Darwin’s adage – that it’s not the strongest who survive but the most adaptable – then you may need to fix your company sharpish.

Forever dumb: Why learning requires courage
Last month, I ran a two-day off-site executive leadership meeting for a pharma company. The eleven leaders were taken step-by-step through the above reasoning, with various exercises along the way. Heads nodded. Hands clapped. Then, Amazon executives were brought into the room to demonstrate how a large company can follow this model and grow revenues. Everyone agreed this was the kind of company they would love to lead.

Then it came to the crunch point: are you ready and willing to become a true learning company?

Everybody froze.

This is not surprising. Deprioritizing metrics like market share and gross revenue is an extreme shift. Rewarding people based on the knowledge they generate – through success or failure – takes balls.

It means staring into the abyss and realizing there is nowhere else to go. It’s about smiling knowingly as you quickly change direction.

Ironically, there are many people inside pharma companies who do know how to learn iteratively. “We actually know how to learn from failure quite well – we expect it and do it every day – in R&D,” an SVP from Astellas told me recently. “But as soon as a molecule becomes approved, we attach a budget and a target to it – and anything that fails badly in commercial is a career-ender.”

Make no mistake; figuring this out how to bring a scientific approach to learning into our commercial organisations could be the only way to avoid our collective terminal decline. Those of us in sales, marketing, access and medical can no longer rely on R&D for innovation – it is our responsibility to find new non-drug opportunities, and fast.

We must be prepared to start with the customer, not the drug, work backwards and recognize that pharma’s dumb era needs to end.

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Jan 1, 1970 - Jan 1, 1970,