JavaScript-Enabled Services, SaaS, and Open Source: Friction Free Models that Drive the Reallocation of Capital

What do JavaScript-enabled services, SaaS, and Open Source all have in common?

They are product delivery models that dramatically reduce the cost, time, and resource requirements to test products and their purported value.

The risk to trial is mitigated and individual users can experiment and validate value in isolation of the broader enterprise.

More than ever, companies that focus on reducing the risk and resources required to trial their product or service are outperforming "heavy" footprint product companies.

The capital markets are highly efficient and dollars quickly flow to the highest yielding assets. IT markets, however, are characterized by high degrees of friction that artificially limit capital reallocation and flow.

Typical IT frictions include: required asset requisitions, proprietary interfaces, multi-department decision making, multi-level budget approvals, lack of connectivity, lack of resource and expertise, and behavioral inertia.

It truly pays to ask what are the exogenous barriers that artificially limit value testing and access.

Today's fastest growing companies seek to optimize two core things:

1) friction free adoption and
2) hard "value per unit" analysis; such as price per click, price per CPU, price per seat.

Capital flows to the highest yielding assets.

Once economic actors are able to validate the "value per unit," the dollars will flow:

• at a rate proportional to the relative increase in yield (value/cost) from product A to product B and
  
• at a rate inversely proportionate to the number of barriers that limit the free flow of capital to product B (the higher the # of frictions, the slower the reallocation to the economically advantaged unit of value).

In summary:

• market are efficient and capital flows to the highest yield assets
• products that provide a higher yield (value/cost) will attract capital
• ex. cost per action, cost per seat, cost per CPU
• product delivery models that reduce frictions will see faster capital allocation
• efficient product test, validation, and delivery mechanisms stimulate capital flows
• equity value creation is a function of the amount of total capital at risk and the rate of reallocation from one class of assets to the next
• ex. total capital at risk = total ad spend market
• ex. "value unit" = cost per sale
• ex. "yield" comparison = $100 sale/$2 cost per click= 50x vs $100 sale/$15 per telesales call = 6.66x, or 7.5x differential in yield
  
• ex. "friction" = JavaScript implementation of AdSense vs setting up telesales trial

In designing a start-up's strategy, outline:

• the targeted pool of capital
• the economic unit of value in question
• the differential in yield from model A to model B per given unit of value
• barriers to capital reallocation from model A to model B
• barriers that protect model B from replication
  

IRR Multiplication Table

The attached IRR Multiplication Table is a very useful reference tool.

The data table calculates IRR by years (x-axis) and multiple (y-axis).

Dan O'Keefe, who I worked with at Pequot Ventures is the brains behind the spreadsheet. I suggest printing it out and keeping it by your desk.

When you are on the phone you can impress your friends/boss by quickly reeling off the IRR on a 5x over 5 years (38%), 10x over 6 years (46.8%), 3x over 3 years (44.2%) etc.

Start-up Sales Management

My prior post provides a model for how to forecast revenue over the life of an operating plan. Another classic start-up conundrum is how to forecast revenue for this quarter.

Sales forecasting is a notoriously difficult problem and start-ups generally learn the hard way that sales meetings, prospect interest, and apparent momentum do not translate into purchase orders in any where near the time and speed one would hope.

Professional sales management forecasting techniques can help eliminate emotion and excitement ("We had such a good meeting, I know they are going to buy!") out of the process.

Missing a sales forecast really hurts no matter what size company you are. However, given that most start-ups are not profitable, missing a top line revenue number can have disastrous impacts on cash burn, employee morale ("we are working so hard and getting nowhere"), and shareholder confidence.

While there are many different models out there, I will share one with you that works well for the companies that I work with in conjunction with an investment in a CRM system, like Salesforce.

As a first time CEO or manager, a managing a sales pipeline by sales stage can improve forecast accuracy.

A key, however, is that the whole sales team buy into the process and be religious wrt its application. Top leaders must constantly evaluate where an opportunity is relative to the key sales milestones and if sales reps are realistically categorizing various opportunities.

Sample Pipeline by Sales Stage

• Prospect New (10% probability - telemarketing lead or tradeshow follow-up)
• Prospect Engaged (20% probability - webex, phone contact, early requirements discovery)
• Technical Evaluation (30% probability - demo/presentation completed, NDA executed)
• Budget Qualification (40% probability - major discovery requirements phase)
• Proposal Submitted (50% probability - confirm budget, test commitment)
• Technically Selected (60% probability - building ROI analysis with customer)
• Contract Negotiations (70% probability - reviewing proposals, technically selected)
• Getting Final Signatures (80% probability - selected, budget confirmed)
• In Purchasing (90% probability - waiting for fax to ring!)
• Closed (100% - purchase order in house!)

When forecasting revenue, try to match each sales engagement against the milestones/stages listed above. The forecast is then equal to the sum of the dollar weighted opportunities by stage.

Another key question is what is the required sales pipeline coverage ratio - ie divide the pipeline by the target and you get the coverage ratio...a typical rule of thumb is that you want $3-4 of pipeline for every $1 of targeted revenue.

The coverage ratio, sales cycle, conversion ratio of prospect to closed...all will help identify the required investment in lead generation/marketing necessary to hit the number.

If the coverage ratio is ~1, one can be sure the target will not be hit. Missed targets kill cash as gross and net burn become one in the same. It truly pays to forecast revenue in a disciplined and realistic manner, especially given the high cost of start-up capital.

While a rigorous process is not sufficient to hit the number, I believe it is a necessary condition to doing so in a predictable and repeatable manner.

Forecasting Revenue

This post addresses a key question for start-ups, how do you model and forecast sales?

Please note that the technique below is best for enterprise-oriented companies rather than consumer Internet companies.

Forecasting Revenue
A key mistake start-ups make in raising money relates to how they model future revenues. This post explains a bottoms-up approach to forecasting revenue. My favorite bottoms-up forecasting method is the productive sales rep model.

In this model, future bookings are NOT a function of market share, size, and penetration rates ($500m market x .005 penetration, or $2.5m) but rather of how many mature sales reps are in the company and the expected sales rep quota and productivity.

A top-down approach is simply too hard to handicap and fails to ensure that a company matches an investment in sales resources with projected bookings and revenue.

First Model Bookings
Bookings = mature reps x quota per rep x productivity
Bookings = purchase orders
Mature reps = the number of reps with sufficient market and product experience to be effective (typically six months with the company)
Quota = bookings quota per year (typically $1-2m per rep in a start-up, and $2+m per rep in a mature company)
Productivity = percent of total quota achieved, on average, by the sales force

Therefore, for a start-up, with two mature reps entering the year, one rep joining in January, a $1.5m quota, and an expectation of 75% productivity, bookings would equal:

2.5 (mature reps) x $1.5m (quota) x 75% (average productivity as % of quota), or $2.8125m.

Then Assume Bookings Mix and Revenue Recognition Policy
To get to revenue, we then need to assume 1) a revenue recognition policy and 2) a bookings mix across license, maintenance, and professional services. This mix is typically 70% license, 15% maintenance, and 15% professional services.

Wrt revenue recognition, license revenue is recognized either at the time of sale for perpetual model or ratably for SaaS vendor, while maintenance and professional services revenues are recognized pro-rata over the course of the year/project. For example, assuming the bookings mix above a $1m purchase order (booking) on April 1 would contribute the following revenue in the year:

License revenues: $1m x 70%, or $700k
Maintenance revenues: $1m x 15% x 9/12, or $112.5k
Services revenues: $1m x 15% x 9/12, or $112.5k.

While there were $1m in bookings, revenues would be $925k, with the $75k difference on the balance sheet at year-end as deferred revenue.

The key issue is make sure that sales projections are tied to tangible investments in sales resources and are based on reasonable assumptions of sales rep productivity, time to maturity, and quota. Finally, think through how bookings translate to revenue. A sophisticated approach to the problem will go along way in gaining credibility with a prospective investor.

Reid Dennis

This week IBF held the 18th Venture Capital Investing Conference.

Reid Dennis, the founder of IVP, received a lifetime achievement award. His acceptance speech proved both educational and inspirational.

A few highlights follow:

• Reid began investing in Silicon Valley start-ups in 1952, 55 years ago!
• His first job out of the GSB paid $425 per month
  
• In 1952, there were NO public electronic companies in Silicon Valley
• HP went public in 1957 and sold 10% of the company at the IPO for $4.8m dollars
• The first 25 electronics companies required total capital of $300k each and private individuals formed the basis of the early syndicates
• Reid founded IVA in 1974 and IVP in 1980
  
• Reid's firms, IVA and IVP, spawned many of today's top firms
• Redpoint spun out of IVP
• TVI spun out of IVA
• August and Benchmark spun out of TVI
• Reid played a key role in two pivotal moments in private equity history
• the 1978 reduction in the capital gains tax from 49.5% to 28%
• the 1978 change in ERISA laws that allowed pension funds to invest retirement funds in alternative assets
• in 1975, prior to the relaxation of ERISA laws, the entire VC industry raised $10m
• He spoke of the need to work with Washington to eliminate tax and regulatory disincentives that limit the free flow of capital to innovation and entrepreneurs
• With the "carry tax" under discussion in Congress, he warned the audience that the golden goose is at risk if today's industry leaders do not forcefully fight to protect the industry

Bill Gates' commencement speech yesterday at Harvard spoke of the challenge in understanding complexity. He quoted George Marshall who told Harvard graduates,

"I think one difficulty is that the problem is one of such enormous complexity that the very mass of facts presented to the public by press and radio make it exceedingly difficult for the man in the street to reach a clear appraisement of the situation. It is virtually impossible at this distance to grasp at all the real significance of the situation."

I would argue that the impact of cuts in capital gains taxes, ERISA safeguards, etc on the health and vitality of the economy are similarly complex. It is hard to appreciate the link between capital gains tax policy, innovation's access to capital, and regulation on an economy's vitality. Yesterday's speeches by our industry leaders, however, warned of the peril of missing the connections and causality between policy and innovation.

Other speakers included Ed Glassmeyer, the founder of Oak Investment Partners. While he spoke eloquently on the history of Oak and the state of the industry, one story really stood out for me. It took him 4.5 years to raise Oak I.

The chance to see Dick Kramlich, Ed Glassmeyer, Gary Morgenthaler, Lip Bu Tan, Dixon Doll, Reid Dennis, etc recount history, discuss the state of the industry, and prognosticate on the future proved really inspiring.