Putting a Price on Value

In the course of my consulting work, I often work with clients (who are themselves consultants and service providers) to review their pricing models. The goal is to help my clients clarify what they are charging for, to ensure that both they and their own customers are comfortable with the price. What often emerges is that on its own, “time-based” pricing is becoming harder to justify, unless there is a clear understanding of the resulting value created and transferred.

adding-valueAmong some of the major consulting and professional service firms, there is a growing awareness that pricing based on billable hours alone is no longer sustainable. This in turn is forcing firms to review how they put a price on their work. They recognise the need to shift from billing clients for “time and materials”, to generating license fees and royalties for the use of proprietary IP, and to offering “XaaS” models that comprise a blend of “always on” retainer and actual service delivery, neither of which is wholly based on time or effort spent.

At the same time, many input costs are actually decreasing:

  • Reduced staff overheads via offshoring and outsourcing
  • Cheaper technology (although we consume more of it)
  • More open source tools and freeware available
  • Ubiquity of BYOD
  • Greater use of remote working, telecommuting and hot desks

What this means for the clients I work with is that they need to have a better grasp of the amount of effort applied and the level of expertise they deliver to their customers. If there are significant parts of the project costs that have to be measured by actual time spent, then it is important to make sure that the customer understands the effort required.

How else can consultants and professional service firms demonstrate value, other than by billable hours alone?

To begin with, clarify exactly what the customer thinks they are paying for. There can be nothing worse than consultants spending most of their time and effort on tasks or activities where the customer does not see a material benefit, or which the customer does not value.

Clearly, if there are measurable and quantifiable outcomes for the customer, then that is a good basis for demonstrating value. For example, direct cost savings to the customer, or reduced opportunity costs in terms of time to market or other factors. However, it may be harder to demonstrate the direct benefit of some qualitative outcomes, at least in the short term.

Some pricing models include a consultant “success fee” coupled to a share of revenue, profit or costs savings (which can be high-risk for consultants if they have no control over the implementation and execution). Other consultants are working with their clients to co-create products and services, which can generate standalone revenue streams from the shared IP. Others are adopting more collaborative approaches to consulting which build long-term value through the quality and nature of the relationships which are more like partnerships than transactions. This can remove the customer’s anxiety that the “meter is always running”, although such arrangements still require expectations to be managed through agreed boundaries and clear rules of engagement.

One model I use with clients is to figure out the nature (as well as the amount) of the value they are being asked to deliver, based on why the customer is buying, as much as what they are paying for. Some of the factors to consider include:

  • Risk mitigation – is the customer in effect buying an insurance policy, transferring their own risk, or reducing their exposure to risk?
  • Must have – is the customer having to meet a regulatory or compliance obligation?
  • Best practice – does the customer aspire to be among the best in their industry?
  • Competitive advantage – is the customer getting something unique or hard to replicate?
  • Peer pressure – does the customer need to meet a recognised standard or level of competence?
  • Situational – does the customer need to build or acquire appropriate skills and capabilities?
  • Urgency – is the customer willing to pay more for a speedier service? (This is one area where time-based pricing can still be relevant!)

It’s also important to understand how customers are funding their purchase. For example:

  • which cost centre is paying for the service?
  • what is the purchasing criteria?
  • what cost/benefit analysis has been done?
  • is there a specific budget allocation, or is it coming out of existing operating costs?

Of course, consultants are frequently hired to bring an alternative (and sometimes critical) perspective to their clients’ problems. In which case, getting an external opinion has value in itself, and the customer should accept there is a cost associated with having access to someone else’s brain – even if it is only for a few hours.

Finally, for an alternative perspective, I would refer to recent comments made by Ash Maurya (author of “Running Lean”, and creator of Lean Canvas) when he was in Melbourne. Talking about how to scale startups, he made the observation that, “selling time [as a consultant] is not scalable … There’s only 24 hours in a day.”

Next week: Bridging the Digital Divide

101 #Startup Pitches – What have we learned?

During the past 3 years of writing this blog, I have probably heard more than 100 startup founders pitch, present or share their insights. Most of these pitch nights have been hosted by Startup Victoria, with a few on the side run by the Melbourne FinTech Meetup and elsewhere.

Image sourced from Startup Victoria Meetup

Image sourced from Startup Victoria Meetup

Based on all these presentations, I have collated a simple directory of each startup or pitch event I have covered or mentioned in this blog, as well as a few key accelerators and crowdfunding platforms.

What have we learned over that time?

First, apart from the constant stream of new startups pitching each month, it’s been impressive to witness the Melbourne startup community collaborate and support one another.

Second, some of the international founders who have spoken are among the rock stars of startups – and we are fortunate that they have been willing to spend time in Melbourne.

Third, a number of the local startups who have pitched during this time have become well-established and well-known businesses in their own right.

This all means that besides creating great products and services, and being willing to share their experiences, the founders have helped aspiring founders and entrepreneurs to appreciate the importance of:

  • product-market fit;
  • working with agile processes and lean startup models;
  • tackling prototyping and launching MVPs;
  • learning what to measure via key metrics;
  • figuring out funding; and
  • knowing when to pivot or fold.

Looking at the cross section of pitch nights, panel discussions and guest speakers, there are some significant trends and notable startups to have emerged:

Industry focus: Not surprisingly, the pitches are heavily biased towards FinTech, MedTech, Education, Digital Media, Enterprise Services and Consumer Services. There are a some key startups focused on devices (e.g., SwatchMate and LIFX); a smattering in recruitment, fashion, gaming, health and well-being, property services, social media and even logistics. But there are surprisingly few in environmental technology or services.

Business models: Two-sided market places abound, as do customer aggregators, sharing platforms (“the Uber for X”, or “the AirbnB of Y”), freemium apps and subscription services (as opposed to purely transactional businesses). There are also some great social enterprise startups, but surprisingly no co-operative models (apart from THINC).

Emerging stars:  Looking through the directory of startups, some of the star names to have come through during this time, based on their public profile, funding success, awards (and ubiquity at startup events….) include:

CoinJar, LIFX, Tablo, SwatchMate, etaskr, DragonBill, Culture Amp, Eyenaemia, Timelio, Moula, nuraloop,  Konnective, OutTrippin and SweetHawk.

Acknowledgments: Some of the startups and pitches in the list are just ideas, some don’t even have a website, and some didn’t get any further than a landing page. However, I have not been able to include all the startups that turned up at Startup Alley, nor the many more startup founders I have met through these events (but whom I didn’t get to see pitch or present), nor the startup ideas that were hatched during the hackathons I have participated in. And there are a few startups that I could not include because I heard them pitch at closed investor events. Finally, I am and have been very fortunate to work with a number of the startups listed, in various capacities: Brave New Coin, Ebla, Re-Imagi, Slow School of Business and Timelio. To these startups and their founders, I am extremely grateful for the opportunities they have given me.

Next week: Putting a Price on Value

 

When robots say “Humans do not compute…”

There’s a memorable scene in John Carpenter‘s 1970’s sci-fi classic, “Dark Star” where an astronaut tries to use Cartesian Logic to defuse a nuclear bomb. The bomb is equipped with artificial intelligence and is programmed to detonate via a timer once its circuits have been activated. Due to a circuit malfunction, the bomb believes it has been triggered, even though it is still attached to the spaceship, and cannot be mechanically released. Refuting the arguments against its existence, the bomb responds in kind, and simply states: “I think, therefore I am.”

Dark Star’s Bomb 20: “I think, therefore I am…”

Dark Star’s Bomb 20: “I think, therefore I am…”

The notion of artificial intelligence both thrills us, and fills us with dread: on the one hand, AI can help us (by doing a lot of routine thinking and mundane processing); on the other, it can make us the subjects of its own ill-will (think of HAL 9000 in “2001: A Space Odyssey”, or “Robocop”, or “Terminator” or any similar dystopian sci-fi story).

The current trend for smarter data processing, fuelled by AI tools such as machine learning, semantic search, sentiment analysis and social graph models, is making a world of driverless cars, robo-advice, the Internet of Things and behaviour prediction a reality. But there are concerns that we will abnegate our decision-making (and ultimately, our individual moral responsibility) to computers; that more and more jobs will be lost to robots; and we will end up being dehumanized if we no longer have to think for ourselves. Worse still, if our human behaviours cease making sense to those very same computers that we have programmed to learn how to think for us, then our demise is pre-determined.

The irony is, that if AI becomes as smart as we might imagine, then we will impart to the robots a very human fallibility: namely, the tendency to over-analyse the data (rather than examine the actual evidence before us). As Brian Aldiss wrote in his 1960 short story, “The Sterile Millennia”, when robots get together:

“…they suffer from a trouble which sometimes afflicts human gatherings: a tendency to show off their logic at the expense of the object of the meeting.”

Long live logic, but longer still live common sense!

Next week: 101 #Startup Pitches – What have we learned?

 

The latest installment of Startup Victoria #pitch night

The numbers were out in force for the August edition of Startup Victoria‘s monthly pitch night. A full house (no doubt helped by a new beverage sponsor…) heard from another batch of startup hopefuls, operating in very different sectors: medtech, recruitment, food logistics and domestic services. Despite some AV issues, this event showcased some interesting businesses, all of them demonstrating some impressive early stage traction.

In order of appearance, the night’s pitches came from:

VideoMyJob

Launched in April 2016, this online tool allows recruiters and hiring managers to film, edit and share their job ads. The business already boasts more than 60 clients (some of them very high-profile), with the data suggesting an 82% higher success rate in hiring outcomes. This performance is largely attributed to the simple fact that candidates spend up to 4 minutes watching a video ad, rather than the average 12 seconds candidates spend reading a text-based ad before they submit an application.

The tool, which runs on a mobile device, includes a tele-prompt feature, in-app editing functions, a one-step process to publish to social, plus e-mail. Customer pricing is based on a $79 monthly subscription to place unlimited video ads. One reported benefit for clients is much stronger candidate shorts lists.

Given the changing dynamics in the recruitment market, where companies are finding themselves competing for talent and striving to become employers of choice, any new hiring solution has the potential to be a game-changer. Which is what the founders are probably banking on as their exit strategy, with a likely trade sale to a complementary recruitment platform.

PredictBGL

This medtech startup (previously known as ManageBGL) offers an app-based solution to help diabetes patients manage, monitor and predict their blood glucose levels. Despite regular patient testing, according to the founders, 80% of the data is actually ignored.

Able to offer more “real-time” testing, the app claims to fix wrong insulin doses within 3 hours (not the usual 14 days with traditional clinic-based testing), offers more precision dosing, and predicts patient levels up to 8 hours ahead.

It also has the option to incorporate live exercise data (from wearables), and serve patients who can’t afford expensive insulin pumps. As well as paying a monthly subscription, patients are also paying for insights based on the data. With a $10 per month fee, over 80% customer retention rates, and around 600 sign-ups per month, the app is breaking into the US market.

Asked about potential risk factors and the margin for error in patient testing, the founders explained that the user results are somewhat conservative, so they are embarking on clinical trials to refine the analytics.

Jarvis

Billed as “your very own personal butler”, Jarvis is one of a number personal concierge services, catering to the time-poor, inner-city residents who want to outsource domestic chores and errands.

From $33 per week (and an average of $55), Jarvis differentiates itself by offering a more personal touch, because the business hires and trains employees, rather than using freelancers or contractors.

Launched in January 2016, Jarvis is experiencing 20% growth per week, 90% customer retention, high referral rates and generating 10-15% margins. The founders are working on their logistical efficiency – routing, grouping – and deploying scalable technology – such as cluster algorithms. Pat of the attraction for clients is the fact that Jarvis does not see itself as a transactional service like some freelance and task-based apps and platforms.

The panel of judges asked about the risk of being disintermediated (by their own employees going direct to client). Jarvis claims that their key defense is the proprietary Butler app for employees.

Pantreeco

Last up was Pantreeco, which was established in 2014, with the goal of building “productive partnerships in food” by streamlining the logistics and supply chain communications between food suppliers and buyers.

A self-styled “co-commerce” solution, Pantreeco includes a messaging tool between producers, wholesalers, distributors, restaurants, cafes, grocers and providores.

Offering a freemium SaaS model (based on a per customer per channel basis plus commission), Pantreeco is in the process of taking its model to overseas markets via some major international expansion.

Asked by the judges about the competition, such as TradeGecko and Unleashed, the founders stress that they are not simply an e-commerce or inventory management solution. Instead, Pantreeco developing a range of integration services in response to customer demand – e.g., invoicing, accounting, communications as well as inventory management with 3rd party platforms such as Xero, ZenDesk and SalesForce. They also have plans to on-board major enterprise clients in the food and beverage industry.

Based on the audience voting, Pantreeco took out the honours on the night.

Next week: When robots say “Humans do not compute…”