ricing is a key component of any SaaS company's business model. It's the only way for you to generate revenue from customers and it has a huge impact on your customer acquisition cost (CAC). But when it comes to setting prices, there are many factors at play: competitive landscape, market demand, product positioning and more. With so many things in flux, how do you know what price is right? How much money do you want to make? These are the questions that many business owners ask themselves when they are deciding what to do with their website.
One of the most important aspects of pricing is determining how much to charge. Pricing too high and you risk losing customers, while pricing too low may not generate enough revenue to cover basic costs. For this reason, it's crucial that you get your pricing right the first time.
It can be difficult to decide what the right price for your SaaS is, especially when you're trying to compete with other companies. In this blog post, we will discuss 5 easy ways that you can de-risk pricing experiments and increase revenue.
Don’t impact your existing customer base
Worried approximately angering your current clients with pricing experiments? Pricing experiments must be examined with new signups and now no longer together with your cutting-edge paying clients.Pricing could be very sensitive! You should not affect your current client base till you’re assured approximately what pricing experiments are successful (and which can be deemed failures).
Receive direct customer feedback
At first I just said that pricing experiments shouldn't hurt your current customer base, but you can incorporate feedback from existing customers into your new registration pricing experiments. “Long before you change your software price tag, you need to start with an existing customer. Send a survey and ask if you're willing to pay more for a particular feature or new offer for your company. "Masu," writes Cara Hogan. For example, Groove discovered one issue with its first pricing plan after receiving feedback from paid customers. It was overwhelming and we decided to buy something complicated from Groove, "said Alex Turnbull, CEO of Groove. Based on feedback from paid customers, Groove has experimented with different pricing structures for new sign-ups. The company also continued to research website visitors to determine the best price for SaaS. In the end, Groove reduced the price to the simplest and simplest form of "one price". All inclusive. There is no additional charge. 14 days free.
Trial price based on current customer usage
According to Sniply co-founder Mike Cheng, "A solid pricing strategy needs to take into account not only the different users of the product, but the different uses of the product." However, we do not recommend testing your pricing. Current customer base. Instead, we recommend that you reject SaaS pricing experiments by testing prices that better match existing customer usage data. This pricing experiment strategy is more complex, so let's take a look at a working example.
The project management app Planio "analyzed the distribution based on the various criteria that make up the plan: the number of users, the number of projects, and the price plan at the time of analysis. Comparing customer usage with pricing plans, "there was a clear discrepancy," writes Planio's growth marketer Thomas Carney. Based on the number of users per account: 75% of customers were classified into Planio's two lowest price points while 40% of the existing customer base has less than 6 users and fits the Silver plan.
The company found that the split was that a team with 7 active users paid the same as a team with 30 active users. "Our conclusion was that our price scale was not tailored to customer behavior." They created a new price segment based on the data points found in the usage analysis. The new price test applies only to new registrations and increases the average MRR (Monthly Revenue) per new customer by 100%. Optimizing the plan based on consumption / consumption not only increases the MRR of new businesses, but also leads to subsequent expansion of the MRR. For more information, see Future Proof: How to Model SaaS Prices for Enhanced MRR.
Frequent testing in a small cohort
Mathilde Collin wrote a great blog post explaining that frequent price testing and testing in a small cohort from the front line has become a "secret weapon" for the company's growth. "We have moved from an annual iteration to a three-week iteration. If we make changes here, we will compare the new cohort with the previous cohort. Does the cohort behave the same? If the new cohort is better, use the new pricing plan. If not, just revert to the pricing structure that worked well before, "Colin writes.
Front pays particular attention to the following during and at the end of each experiment:
Income: "If you raise the price, you should increase your income. If your price increase is at the expense of your number of acquisitions, your income will reflect that. "
Commitment: "Price changes should have a positive impact on user activity. That is, we want to attract users who are interested in our products. If price changes reduce our exposure, go to the previous iteration. Please bring it back. "
Retain: "It's important to make sure you're getting users to stay. Make sure the number of acquisitions doesn't obscure the churn rate issue."
The secret to more frequent and continuous testing is to make small, step-by-step changes more often so that the impact on the general public is rarely overlooked. Sure, the end result of 10 different small pricing experiments and one big pricing experiment can produce the same results, but it teaches you more about your paid conversions and what they are willing to pay. Will do it. Colin tests a small cohort and states: Therefore, we are gradually raising prices with a steady rhythm, rather than raising prices significantly and rarely. "
Decide whether to “ A/B test, or not to A/B test?“
A / B testing is especially popular in SaaS marketing, but it gets a lot of praise in price experiments. For early SaaS enterprises, Tunguz probably points out that your enterprise doesn't have "a key element of A / B testing-heavy user traffic." He shares a screenshot of Optimizely (below). "To detect a 20% change in target metric with 90% certainty, if it occurs in 3% of visitors, it requires 12,000 visitors per test, in other words, 2 in an AB test. We need 24,000 visitors to test one variant, "Tunguz wrote. The vast majority of early-stage SaaS companies do not have this type of volume.
Some of the additional issues raised by other reviewers (applies to early stage and advanced SaaS businesses). Sales team saw potential customers when they visited the pricing page of their website. How do you know the price? If a prospect has not visited the website yet, how does the sales team know what price to quote them? If you're in the B2B space, multiple contacts in the same company are likely to be doing research on your SaaS (and competitors). What if different people within the same company see different pricing? Some A/B testing tools will allow you to enable IP Targeting in A/B tests, which ensures contacts on the same IP see the same test/price.
Unfortunately, if they're in different office locations, are using their home computer, or viewing your pricing on a mobile device, the IP targeting won`t be of assistance. Alex Birkett, CXL Growth Marketing Manager, offers a low-risk A / B price testing method. We do A / B testing on two different pricing pages, but when a prospect clicks to sign up, everyone promotes the cash at the same (lower) price. register. This tactic isn't suitable for everyone, but it does allow you to collect purchase intent data in a risk-free manner without bothering higher-priced customers.
Most SaaS organizations need a lot of historical data about their current plans. Running a new price test on every new registration and comparing the new measurement with a previous base measurement is the easiest and cleanest way to test a new price and offer. Make sure you strictly follow all the required indicators.
These tactics have been proven to work in a variety of industries. Implementing these tactics will help you avoid price wars and maximize profits.