Additional software pricing errors that zap revenue and profit
In my previous post I discussed the three worst pricing errors I see B2B software companies making. Here are three more nearly as harmful to revenues and profits—and equally avoidable.
- Three more B2B SaaS pricing errors:
- How to simplify pricing for sales teams and customers without hurting your company financially
Three more B2B SaaS pricing errors:
1. Layer error-propagating structures on top of pricing tiers
Be careful what you build on
In some software companies, the adverse effects of price tiering pervade operational processes and organizational thinking. People lose track of the fact that customers buying at the same pricing tier may have nothing else in common. They may vary widely by industry, how they run their businesses and, most importantly, how they use and derive value from the software.
Yet this essentially arbitrary grouping of customers may become the basis for important company strategies and decisions. People will start thinking of customers within a pricing tier as the same and allow that assumption to guide pricing, marketing and product development. “That’s the group that buys between $50K and $250K”—even though there are often significant differences between them (size of purchase for one).
Case in point: I once heard a financial person explain that, for every pricing tier, he had calculated the average of all the net prices paid by customers at that tier. He then proposed those averages as new discounted price points for his revised pricing tiers. That didn’t make much sense to me since the net prices he averaged were pretty random—the results of wide swings in discretionary discounting.
Moreover, they included outliers, such as a massive sale in that magic catch-all tier (“10M+”), which further skewed the average. Then, because some of the averages were lower in higher tiers, certain data points were thrown out under the rationalization that they were invalid for some arcane reason. That made the average prices line up better but made the whole calculation even less justifiable.
What would have happened if the company had adopted these proposed new discounted prices for its pricing tiers? Since this company was accustomed to hiding its list prices, buyers would have assumed the new prices in each pricing tier were list prices and expected to start negotiations for discounts from there. Savvy buyers doing their competitive intelligence research may have come up with numerous list prices for the same product, and at volumes that didn’t quite align with the seller’s tiers. As a result, the salesperson could have ended up in a vicious cycle of negotiation over the price point.
Randomness begets more randomness, and errors beget more errors. I’ll go so far as to say that in the worst cases, pricing tiers becomes a self-propagating error-producing, non-forecastable, unmanageable operational structure.
2. Allow clashing rate cards to produce multi-product disasters
Weird math effects skew, slow and sink deals
The problems I’ve discussed get multiplied in deals encompassing more than one product.
Selling a bundle comprising multiple products with different price structures creates significantly more friction and difficulty for buyers than even products with differently priced modules. And if you’re also applying different discounting structures, you can end up with chaos.
It’s a bad situation, and it happens more often than you’d think. Since pricing tiers, once instantiated across the organization, are so complex and costly to change, software companies often leave them alone for existing products while trying to create more financially advantageous pricing tiers for subsequent products. So Product 1 in the bundle has the old pricing tier structure, and Product 2 has the new one, and no one has figured out how to price the bundle in a way that absorbs and aligns these differences.
Pricing can get so weird, so lacking in rationale, that there is a complete divergence from the company’s price list. Buyers scratch their heads in befuddlement, and salespeople find it impossible to defend value with a straight face. To make the deal, they may negotiate an arbitrary flat price–essentially, a made-up price. This can seriously hinder the software company’s ability to get paid fairly for value. It’s a glaring example of simplification that financially hurts the seller.
3. Fail to realize you’re in pricing neverland
Tiers, outliers, averages and unexamined assumptions can lead you far from reality
Bad data from pricing errors like these can have far-reaching effects if it is believed and used for decision making. While this happens all the time in the software business, here’s a simple example from outside the industry that conveys the problem:
The seller in this case was a clothing manufacturer trying to price bundled size-assorted t-shirts. Based on averaging data from previous sales, the marketing lead insisted the company’s customers typically bought specific configurations of sizes (such as 20 small, 50 medium, 30 large) and that these quantities should be the basis for the new packaging and pricing. But people don’t buy averages, and averages can obscure what customers actually do buy. (For instance, an average of 50 doesn’t necessarily mean most customers buy 50. Instead, it might mean that one customer bought 101 and another bought one.)
In this instance, no one had validated marketing assumptions by closely examining the sales data. Advised to do just that, the company discovered that the bundle configurations it was planning were far from what its typical customers were buying—in fact, none of its customers purchased those combinations of products in those sizes!
If you’re doing this “managing in the aggregate” and relying on unvalidated assumptions, you’re in pricing neverland. I call it that because pricing tiers, averages and outliers obscure important nuances and customer behavioral trends. As a result, pricing can get so out of whack that it becomes completely separated from reality.
I also call it neverland because it can be challenging to return from there to rational pricing that enables your company to thrive and improve. For example, imagine your CFO says the company is not generating the revenue it needs. What can you do?
Producing more revenue by changing pricing tiers probably won’t be a cost-effective option since it would require too much organizational agreement and change. Of course, you could increase list prices. Still, you’d probably never see that on the bottom line because the potential additional revenue would likely be absorbed by salespeople offering deeper discretionary discounts.
That leaves the option of lifting revenue by building more value into your products. But to move fast enough, you’ll probably have to bring in third-party components, which can create pricing inconsistencies like those described in pricing error #2… you’re still in neverland.
Here’s one way back to reality: Create new product bundles better fitted to your customers’ needs and priced optimally using the process outlined in the next section.
How to simplify pricing for sales teams and customers without hurting your company financially
I’ve talked about how the worst B2B SaaS pricing errors involve overcomplication, counteracted by attempts at simplification that hurt the company by further reducing pricing effectiveness and business performance.
To avoid these blunders, go for simplicity from the beginning. Treat pricing as an element in the design of your product rather than something you tack on at the end of development before going live. Gather detailed quoting data (for example, our LevelSetter software collects every quoting action, via API, taken by your sales team or partners and a wealth of information about buyer and salespeople behavior patterns). Then validate all of your assumptions during rollout and make the appropriate improvements to the model based on real-world usage and transaction data.
Optimal pricing models emerge from this design mindset equipped with a deep understanding of how your customers will use your software to derive business value. Potential customers who are similar in this respect can be seen as classes of buyers. For monetization purposes, they are a far more accurate and powerful means of segmentation than the more typical buying personas.
This deliberate design process involves finding ways to absorb within your pricing model the various ways you will be charged for third-party components and services. The goal is to create a single, rational, uniform, value-based pricing model (including a structured discounting framework) extending across all SKUs the sales team offers. In addition, pricing should be simple enough that it can be readily explained by salespeople and easily understood by buyers.
With this approach, discounts are earned, not given. Salespeople can fluently explain how pricing works and what buyers can do to increase their discount. They can show buyers how adding more capability (module or product) or adding more volume will affect their net price. If that’s not of interest, they can remove that component of the deal, snapping the net price right back to where it was. The math works; there are no mysteries. Customers are treated fairly, with everyone having the same opportunities to improve their net price. Net prices are predictable, enabling the company to better forecast and manage the revenue stream.
To find out more about optimizing pricing for value-based selling, keep reading this blog, or contact me: firstname.lastname@example.org.