Mimiran

If you’re reading this blog, you probably know the dramatic profit impact of small price improvements.  (For a company running at 10% net margin, a 1% price improvement increases profit by 10%.)  Yet even when companies want to improve pricing performance, they often feel like they are at the mercy of the market.

If you’re truly in a commodity market, you are in fact at the mercy of the market.  Either focus on your cost, or differentiate to decommoditize.

Few businesses are in true commodity markets.  And for a lot of these businesses, especially at the SMB level, the first barrier to improved pricing is the one the business owner can control most directly: themselves.  Many business owners look at their costs, tack on a “fair” profit, and call it a day.  Or they look at the competition and price a bit below them.  Even when costs go up, they often have trouble raising prices.

Your pricing is ultimately limited by the perceived differential value of your offering.  I’ll go into more detail on the value side in a later post, but for now let’s think about the amount of value you can capture.  You work really hard to create value for your customers.  You work proactively to make them successful.  Don’t sit back passively when it comes to capturing your share of that value.  Pricing is the monetization of value, and you should be just as proactive about that as value creation.

So don’t be the limiting factor on your pricing.  If you think you should be achieving higher prices but you haven’t asked because it makes you uncomfortable, you need to fix that.  If you are used to giving big discounts when you get nervous in sales cycles even though the value is there for the customer, you need to fix that.

Here are some exercises to help you think about this:

• The Series of Increasingly Outlandish Prices.  From Steve Blank, author of 4 Steps to the Epiphany.  When the customer asks for the price, keep getting more and more outlandish until the customers pushes back.  For example:  “It’s $1M dollars.  Per month.  Plus $2M for setup.  Plus 20% maintenance.”  The point is to help discover the price for a new offering, but it’s also useful to force you to think beyond “I think it should be about $99.”
• Double Your Price.  Someone bursts into your office and holds a gun to your head.  They tell you that you have to double your prices in a month.  (Maybe it’s your accountant.)  What would you have to change about the way you sell, your products, your services, your customers, to achieve this?  While you may not be able to actually double your prices, you can make dramatic improvements.  We used this method to double consulting prices, although it took us 2 years, not a month, to actually do it.
• Visit a Porsche Dealer.  Test drive the fastest convertible on the lot.  When it comes to negotiating the price, keep insisting that you are deciding between this car and a Hyundai Sonata that seats 5.  Keep asking the sales manager to come back with a better price.  After they get done laughing and throwing you out, compare the reaction of the car salesreps to your reaction when customers try to chisel you down on price.
• Say It to the Mirror.  It may sound silly, but if you have trouble asking for the price you think you should get, practice saying it to the mirror.  Not just the price, but why this is a great deal for the customer.  Make sure there is no hint of apology in your voice or your body language.

If you haven’t already, check out Gerhard Gschwandtner interviewing Ron Hubsher from the Sales Optimization Group on the sales negotiation process.  Ron looks at the sales process with the same philosophy I do– namely, selling value instead of price, and using that profit increase to build a much more valuable company.  However, he approaches the problem from a sales training perspective, a nice complement to the analytical approach we use.

Check it out:

Twas the night before Christmas, when all the through the house
Not a hard drive was stirring, not even a mouse.

The pipeline reports were tallied with care
In hopes that the revenue soon would be there.

The sales teams were in hotel rooms, snug in their beds
While visions of commissions danced in their heads

The CFO had his latte, and I had my cap[puccino],
because after working all night we really wanted a nap.

When in the conference room arose such a clatter,
That meant we wouldn’t make numbers– that was the matter.

To my dashboards I flew like a flash,
I knew I had found a great source of cash.

Sales Compass showed our profit on each deal in flow
Which ones were met target, and which were below.

And then what to my wondering eyes should appear
But deal approval alerts and analytics so clear.

That I didn’t need Excel, I could be nimble and quick
And I knew which deals needed which kind of trick.

More rapid than eagles the deals they came
And I whistled and shouted and called them by name.

“Now Upsell! Now Cross-sell! Now Big Deal and such!
On this one and that one there’s no need to discount so much!

If we drop the price here our profit will fall!
And we’ll give away all our hard work after all!”

As dry leaves that before the wild hurricane fly,
When they meet with an obstacle, mount to the sky,

So up to the Target Price the deals they flew,
With price optimization and simple comparisons, too.

And then, in a twinkling, I heard on the phone
The happy laughter of successful sales reps back home.

As I drew in my hand, and was turning around,
Across the wi-fi St. Benioff came with a bound.

He was dressed in a Hawaiian shirt, true to form
Because the North Pole is cold but The Cloud is quite warm.

A bundle of toys he had on the AppExchange,
Some expensive, some cheap, some just a bit of change.

Under his breath he was singing about “The Cloud”
And I’m not sure he knew he was singing out loud.

The stump of a cigar he held tight in his teeth
And the smoke it encircled his head like a wreath.

He was long haired and bearded, a bit tall for an elf,
And I laughed when I saw him, in spite of myself;

With the click of a mouse and a twist of his head,
He gave me great insight to move some deals out of the red.

He spoke many words, and went straight to work
And filled my page layouts with Apex and Visual Force,

And swiping a finger across his iPhone,
He approved several deals, including one of my own;

He sprang to his sleigh, to his team gave a whistle,
While data sync’d in the cloud, gently as the down of a thistle.

But I heard him exclaim, ere he drove out of sight,
Happy Christmas to all, and to all a good night.

If you’re in the software industry, don’t miss SaaS University in Dallas, January 26-28. With over 30 sessions, this event provides the best opportunity for folks in the SaaS community to learn, share, and network.

Get early bird pricing through December 21, and save an additional $100 with the code MIMIRAN100.

One of the sessions is: Maximizing SaaS Revenue through Sales and Pricing Discipline, presented by Reuben Swartz of Mimiran.

Maximizing SaaS Revenue through Sales and Pricing Discipline

SaaS companies often act as if they have no incremental costs, leading to lax discounting practices. As a result, margins fall and companies struggle to reach or maintain profitability. Through careful analysis and disciplined execution, companies can close deals faster and leave less money on the table. For small software companies, the results can be the difference between life and death (a company with 10% margins that can leave 2% less money on the table raises its profit by 20%).

In this session you will learn to analyze and evaluate the effectiveness of your revenue and pricing strategies, how to discount more effectively (and less), and examine how other companies have implemented these techniques.

Hope to see you in Dallas.

For most people, throughout most of human existence, scarcity was paramount.** Now we live in an age of not just abundance, but superabundance. The agricultural revolution created abundance– not by today’s standards– in food. The industrial revolution created abundance in manufactured goods. The information revolution not only created an abundance of communication and information, it also dramatically increased our ability to move production to cheaper locations and manage the complex supply chains that resulted.

Buyers–both individuals and businesses- benefited from a huge increase in supply, selection, and a huge decrease in price. We also ended up with a superabundance of credit, which helped fuel appetites for the endless array of cheap goods.

For sellers, however, the situation was often disastrous. Many local businesses succumbed to larger competitors with lower unit costs and lower prices. Even large, successful businesses found themselves on a treadmill, running faster and faster but never getting a sustainable competitive advantage. You might have just moved manufacturing to China, only to find a competitor had achieved lower costs in Vietnam. This is before we even get to the internet, where prices are literally going to zero in many cases.

So what can we do about this?

We need to rediscover scarcity. In many cases, we’ll have to create it. This is not as simple as producing “limited editions.” This won’t work for everybody. (If anyone has any information on how Nomenus Quarterly is doing, please let me know. The trendy magazine made the New York Times after cutting production from 50 to 10 and raising prices from $2500 to to $6500 per issue.) Rental car companies have had success raising prices after trimming their fleets. It may be easy to make a car cheaply, but having one available at the airport when you need it is a different story.

And that is the key to rediscovering scarcity. You have to understand what the customer needs that’s hard to deliver. At one point, just making something was enough. Now, whatever you can make, chances are someone else can make and offer more cheaply. In pricing, after all, you’re only as smart as your dumbest competitor, and chances are some new MBA is looking to make a name for himself by getting 25% of your market, even though it’s a dumb move for everyone. (We’ll talk about the latest round of Google v Microsoft in another post.)

In an age when a device as mind-boggling complex as a supercomputer’s worth of processing power is a commodity, the silicon itself has little value. But the ability to turn it into a data center takes some skill. The ability to do it tomorrow, in a certain location, with training, monitoring, and reliability guarantees is actually really valuable.

Whatever it is you’re selling, think about how your customers use it, and how there are situations when the overall experience creates scarcity bottlenecks. This could be the fact that while Southwest flies 10 cheaper flights a day, if you actually want a seat at the last minute, you’ll be forking over $1000 to Delta. Or if your customers require extremely high reliability or precision. Or if your customers order commodities from you, but require logistical and service support to deliver them to the right people, at the right time, and perhaps even set them up. Note that this does not mean you can charge all potential customers high prices all the time. It means that certain segments place a value on your offering, at least some of the time. Understand this, deliver what they need, and price appropriately.

A lot of this comes back to the one thing that is getting less and less abundant as everything else becomes superabundant: time. If you can save your customers time, you can make money. If you can save them more time than alternative solutions, you can make a lot of money. If you’re stuck on how to create scarcity, start with the customer’s time, which is already scarce.

** This goes all the way to our genes. Before Quik-E-Marts, our craving for sugar and fat helped keep us alive. Now it gives us heart disease. For more on this somewhat-related topic, check out the New Yorker article XXXL: Why are we so fat?

In addition to the genetic arguments, the article notes that the price of food, especially calorie-rich, nutrient poor sodas and other processed food, has fallen sharply. (This is partly due to increased efficiencies in farming and industrial food processing, and also partly due to subsidies that encourage production of food that we probably shouldn’t be eating. The cheapest, simplest, least-likely-to-happen step we could take to improve our healthcare situation would be to end subsidies for corn.)

Join us for a webinar on June 16, 2009, 12:00 Noon EDT

Register

For many companies, discounts represent the largest spending category– but they’re not even in the budget. This strange omission leads to inefficient and ineffective use of discounting money. Fortunately, this leaves many companies with a lot of low-hanging fruit that can yield valuable returns, especially in a tough economy. By using discounts more effectively, companies can:

• Increase profits
• Improve cash flow
• Shorten sales cycles

Register to learn more.

Pricing is a process, not an event. This mantra is important for companies who think pricing is something they do annually, when they update price books, or in special circumstances when they add a fuel surcharge. But pricing is happening all the time, whether or not you are participating in it, because pricing is the monetization of value, and your value changes with the market.

Decades ago, many companies could get away with treating pricing as an event, but the acceleration and globalization of business has destroyed that paradigm. Within the past 12 months, market volatility has further emphasized the importance of pricing agility as an ongoing business process. Inflation, especially in energy and commodities last year, left many companies flat-footed. Then, just as they were getting around to their price increases, the economy tanked and pricing power diminished or vanished. Now many of these companies are just getting around to unbundling offerings, being more creative with financing, or just being more generous with discounts.

Unfortunately, inflation is likely to be back soon, and these companies will again find themselves at odds with the market, with lower and revenue and much lower profit than more agile companies who can ride the market waves.

We’ll talk about how to ride the market successfully in an upcoming post.

If a tree falls in the woods and no one hears it, does it make a sound? If your offering has value that customers don’t perceive, can you charge for it?

Price is limited by perceived differential value. So if you are busy creating value through market research, design, R&D, better customer service, or some other aspect of the business and customers don’t perceive it, you’re just losing money (and wasting lots of time and energy). With everyone trying to cut expenses (both in purchasing and cost of goods sold), now is a good time examine where you think you are creating value and where your customers are actually perceiving it.

The first test is whether your customers actually know about it. Everyone says they have “great service.” If you think you truly have better service, you need to be able to tell your customers why. For example, if you always get a person in 3 rings or less when you call a toll free support line, you can highlight that. Some customers won’t care. They’d rather go with a cheaper provider who doesn’t even have a phone number on their website. (This is where unbundled comes in.)

Next, make sure you highlight the areas you’re different. If you spend a ton of time and money talking about the ways you’re just like the competition, you aren’t doing anything to create differential value. Apple, which does a great job at commanding premium price points through strong perceived differential value, has been stepping up the attack here. As computer prices continue to fall– the fastest growing segment of the market is sub-$400 “netbooks” — Apple is selling machines costing 2-7 times that amount. They introduced a new manufacturing process that fashions the core frame of a notebook out of a single piece of aluminum. Not only does this make machines relatively lighter and stronger, but it’s hard for other companies to copy. So what? To encourage people to care, they showed a video when they debuted the new product line, and posted it on their website. The video shows actual blocks of aluminum going through “13 separate operations” to become a new Macbook.

They also claim an 8 hour battery life on their highest end notebook. Rather than just claiming longer battery life, though, they used another video to highlight why their innovation– from design to manufacturing– produces better results. They are saying that other computer makers who use off-the-shelf components might save a few bucks, but they can’t innovate like this. (The videos also sidestep the fact that these factories are not actually Apple factories at all.)

When Nike started putting “Air” in its shoes, few people noticed. When they managed to cut away part of the outsole so you could actually see the bubble inside the sole, sales skyrocketed. They “visibilized the technology.”

Now, you have to make sure that customers actually care about your great innovation. Thinking from a slightly different perspective, you can also target prospects who are likely to place value on the results of your effort. We have seen cases where a company’s “Gold” customers always got free express shipping, regardless of whether they actually cared about it. This was a huge expense, especially as shipping costs rose in 2008. Instead of being a differentiator, it was just something that customers took for granted, because they had never built the value story around it.

Lastly, if a customer does not value something that you think is tremendously valuable, take it away. Then you protect your premium with the customers who do, while protecting your margin on those who don’t. Often, customers who claim they wanted you to match the competitor’s lower price will relent when you offer to match their lower price and lower service level.

Starting from the inside out like I describe above is a way to treat the symptoms. You really want to start by figuring out what the customer values and working back from there to deliver it to them. However, this is not always possible, especially in the short term.

With a lot of companies at risk for negative growth for 2009, make sure your effort is in the right place. Areas where customers perceive strong differential value will generate a great return. Areas that customers do not value do not create a good return and divert valuable resources from those that do.

Few decisions in business or in life are without risk. The outcomes we expect from our decisions depend in some part on a set of assumptions about the world around us. For big decisions with a large impact, we often attempt to gauge the risk involved. This can give us not a single expected outcome, but a range of outcomes with different probabilities.

For example, if we raise prices 5%, we can attempt to measure the risk of decreased demand. In some cases, we can derive a mathematical view of price elasticity, but in many cases this is either impossible or more of an exercise in gaming the numbers than useful statistical analysis. Most pricing decisions are made in the latter circumstances. We still need to make decisions– after all, not making a pricing move carries risk, too. We’d like to have some idea of the risks involved, but we may have very little real data.

There’s a great article in the New York Times Magazine on Risk Management in the financial sector, arguing that overly simplified ways of looking at “Value at Risk” or VAR may have contributed to the current meltdown. In short, back in the early 90s, analysts at JP Morgan developed a methodology for measuring how much risk was involved in certain trades up to a certain expected probability. This VAR metric became the standard for judging risk across Wall Street. The beauty and the curse of VAR was that it wrapped all the risk factors up into a single number. Management could even aggregate VAR from different parts of the company into a global view of value at risk.

For a group of people who are well versed in the notion that proxies do not directly represent underlying systems, Wall Street bought into VAR. When everyone was making money, no one seemed to mind that the numbers could be fudged, that there is no way to keep track of all possible risks, and that if you run a system long enough under conditions of 99% certainty, sooner or later you run into the other 1%.

(Perhaps more intriguing than the debate over the merits of VAR is the larger systems issue. Whether or not VAR oversimplifies risks, in a surging economy, firms that take large, risky positions often outperform those who adhere to more prudent strategies. Money, acclaim, and talent flow in that direction. The safety net implied by the rescue of Long Term Capital Management, which destroyed itself in part by relying too heavily on computerized risk models, further encourages risks.)

While this type of risk assessment has its drawbacks, it’s a lot more useful than no risk assessment at all, if used properly as a tool rather than a crutch. And it’s certainly more information than most companies have when making pricing decisions.

In the absence of strong risk models or data to fully support predictive analytics, it can be helpful to develop scenarios run with different assumptions at different levels of probability. For example, the company contemplating the 5% price increase might expect to see accelerated growth, steady growth, flat sales, a small decline in sales, or a large decline in sales depending on the circumstances. We can guesstimate best, likely, and worst-case scenarios. (This often shows that better pricing discipline with low margin customers has a positive impact even in the worst-case scenario, providing organizational support for narrowing price bands.)

Naturally, this type of modeling is best used as a tool, not a crutch. In one meeting with a manufacturer early in 2008, a senior executive stated that they expected strong commodity prices to continue for several years. They wanted to move quickly to capture that opportunity and not waste time building flexibility into tools and processes to adjust for declining commodity prices. Whether or not subordinates felt the same way, they did not express disagreement. We raised the issue but this was perceived a way for us to charge more without providing any more value and the idea was rejected. Although we tried to do the right thing, we failed to effectively convince the customer and now they are suffering under declining commodity prices.

Another important point to keep in mind is that if you are trying to determine risk for a pricing move, it’s critical to have low-level data. For example, if the average price yield is 80% of list price and you want to move the yield to 82%, you will run into all kinds of trouble if treat customers as a single entity and simply move the average price up and then assign a defection likelihood. You have to look at each customer individually, and potentially move some customers more than others, and examine customers’ defection chances individually. This may not be possible to do at the level of detail we would like to be completely certain, but it can be done using attributes of the customer to give a much closer approximation of likely behavior.

We worked with one company that was making adjustments to its product portfolio that involved, among other things, introducing new products. While the goal of the new product was to encourage people to trade-up from a less expensive offering, there was also the threat of people trading down from higher-priced offerings. At first the “value at risk” seemed enormous– too great to justify the modest expansion in the market from the new offering. However, deeper analysis revealed that while the theoretical maximum number of potential downgraders was huge, practically speaking only a small fraction would even really consider it. Running through various scenarios showed that through careful pricing of the new product, we could almost guarantee a positive overall impact. (Different prices optimized the best, likely, and worst-case scenarios.) This was more than enough information for the CEO to greenlight the new product, which proceeded to have positive bottom-line impact. Part of the reason this exercise was successful was the executive team was not simply looking at a number for comfort, but actively debated the numbers and the methodology behind them until everyone had a good “feel” for what the numbers meant and some of the risks involved.

If you are contemplating price changes in 2009, whether in list prices, contracts, or discounts, doing a risk assessment can help you find the “sweet spots” and avoid negative outcomes.

Wired recently ran a story on Experimental Economists, who model complex scenarios and attempt to optimize outcomes for large companies and government agencies. My reaction to the story was “wow– that’s what Mimiran does all the time, we just didn’t have such a cool name for it.”

Now let’s walk through how many companies make pricing decisions and how enhanced modeling can help drive better results and provide metrics to gauge pricing success.

Making Decisions without Data
A lot of companies still make pricing decisions with their proverbial gut, rather than using data-driven approaches. This may work well for comedian Steven Colbert, but it can be costly when making pricing decisions. People make pricing decisions without data because:

• They lack adequate data.
• They lack the means of handling data effectively and turning it into useful information.
• Despite the financial ramifications of pricing decisions, many organizations are rushing to make a decision and do not have the weeks or even months required to make a fully considered decision.

Limitations of Traditional Pricing Decision Models
Rather than going strictly with the gut, most companies attempt to predict the outcome of pricing moves using simple models, often run in spreadsheets. This approach has the benefit of applying some data to a problem and usually results in better decisions than the gut. However, spreadsheet modeling suffers from a serious limitation. Spreadsheet models run on averages, often imported from financial reporting systems. The “average” response to a pricing move is different than the response of the “average customer”.

For example, a financial services company wanted to make some pricing changes to bring them closer to competitors’ price points and price positioning. Spreadsheet simulations indicated that the move was feasible and desirable. The average customer would not experience too great a change. Unfortunately, this model had no way of showing, or even knowing, that the “average” customer came from aggregating a wide spectrum of individuals. The average of all of these customers seemed like it would react reasonably well to the proposed pricing change. Looking at the customers as individuals, and then aggregating their responses gave a very different view. High end customers would experience an awful lot of consumer surplus, while low end customers would have to take too big a price increase.

Experimental Economics
Experimental economics allows companies to model the possible outcomes of a pricing decision at the level of an individual customer, then aggregate those results to product an expected outcome.

Nuanced data models allow prediction of customer behavior based on customer benefit. In the financial services example, we were able to show that certain pricing discounts had a far lesser impact on customer behavior than first predicted. We were also able to show that certain pricing incentives and bundles encouraged customers to consume services in a way that was unprofitable for the firm. By rearranging some of these bundles, we helped the customer achieve positive return on investment in the project before we had even generated final recommendations.

Another example involves a high tech manufacturer looking to take advantage of list pricing opportunities and tighten discounting variance in the field. Preliminary analysis showed an opportunity of over $20M, which provided justification to bring in external expertise. More detailed modeling that we performed, however, revealed several obstacles to achieving the $20M savings. First, list price opportunities were impossible to capture reliably because of the maze of contracts and negotiated discounts. The first look at the list pricing opportunities suggested that list price changes correlated strongly with actual prices. When we looked deeper, however, we found an almost random scatter of relationships between list price changes and final price outcomes. The average outcome may have born some resemblance to the change in inputs, but it was as likely to be caused by general market forces and inflation as pricing moves, despite a huge effort put into the annual price review process. Using this information, we could focus the pricing team more productively on managing the final price. Here we saw that many underperforming customers targeted for margin improvement were under contract and would not see price changes for some time. Some others bought primarily low margin products. The problem for these customers was not the pricing, but the product mix. We were also able to find customers getting discounts far beyond expected ranges for premium products. Focusing on these customers, there was an immediate, actionable $3M opportunity. How does turning a $20M opportunity into a $3M opportunity constitute success? First, the larger opportunity was not actionable. Without more granular detail, there was no way to capture it. Secondly, it was not actually there. There’s a pricing joke “what is the difference between getting a promotion for capturing a $3M pricing opportunity and getting fired for capturing a $3M opportunity? Promising $20M and promising $2M.”

Merging behavioral data and pricing data can provide even more insight. For example, looking at pricing and usage data for a software company helped us design a pricing model for a new edition. We were able to assess at the level of an individual customer who might downgrade from a more expensive edition, and whose usage patterns would justify higher price points than the company had originally intuited. This led to appropriate fencing between editions and pricing that addressed both “casual” and “hardcore” users separately. The company could provide great value at a good price to the hardcore users and good value at a great price for the casual users. Aggregate information would have led to a single price point which would have left money on the table for the hardcore users and still not provided compelling value for the casual users.

Many of these models depend on inputs whose values are not exactly knowable. However, detailed models help you infer ranges for inputs, such as the percentage of customers who leave over a price increase versus the percentage who opt for cheaper substitutable product. In addition, the models can run with a range of inputs, based on customer feedback, surveys, market data, and expert opinion. Any one of these sources could be wrong, but by combining multiple sources, we create a distribution of inputs and a distribution of outputs, which leads to worst-, best- and likely-case scenarios.

Putting Theory into Practice
While the benefits of detailed experimental economics are compelling, few customers undertake such activities. There are several reasons for this, foremost among them a lack of awareness that such capabilities exist (hopefully writing this piece will help). Many companies also lack time, data, expertise, and money. As the Wired article notes, developing these capabilities can require 6- and 7-figure annual investments. However, we have been able to deliver these projects in many cases under $100K. In addition, our software not only provides the models, it also tracks performance against the predictions of the model, enabling you to tweak pricing if needed, and providing built-in proof of value. Plus, creating these models is a lot of fun and is certainly an eye-opening experience as our customers get a much deeper view into the performance of their businesses.