Matching Price to Value: 3 Lessons in Monetization from Menlo Ventures

All right. Hi everybody. Thanks for joining me. I think I'm the last speaker between you and happy hour, so I appreciate you taking the time. We're going to talk about matching price to value and I'm going to share some lessons from my time as both an operator and an investor. And my goal when I do talks like this is to make sure that there's some insight, some tactic that each and every one of you can take back to your companies. So let's get started. I began my career in new product development and starting in 2011 got really excited about the high growth phase of a company. I joined Evernote when we were about 10 million users. I was on the product team and saw this opportunity for us to work more cross functionally across the company and focus heavily on retention and monetization. So I put together our first growth team focused on driving people to habit formation, migrating people from the free version to the premium version, and brought that to a company called Invoice to Go. This is a mobile solution for SMBs to send an invoice and get paid. Built out a bunch of the org across product data analytics to do much of the same work to drive retention and monetization. So I'm a nerd on this subject. I love it and I brought it now from my time as an advisor and an operator to the investing side, I like to not just be a source of capital, but an advisor so that companies can get their customers to stay and pay. So let's get started. What I love about SaaS as a product person is that it's a long term value exchange. You're creating value for your customers, but we have to make sure that you're also being compensated appropriately for that value. And I think monetization doesn't always get the airtime it deserves. This is an interesting study that was done by the folks from Price. Intelligently, they talked to over 500 SaaS companies and looked at what a 1% improvement in acquisition, retention and monetization did to the company's bottom line and the results were pretty staggering. Monetization had 4x the impact compared to acquisition. So all this talk about growth, growth, growth typically covers the acquisition side of the equation. But I'm shifting the discussion today to monetization. And because we're in this room to learn, we're all probably pretty goal oriented people. A nice target for a SaaS business is to achieve 130% revenue retention. So this means net of churn downgrades. Your existing customer base that sticks around is Actually paying you more overtime on the order of 130% revenue retention. So let that be the gold standard that we work towards today. So in my time as an advisor and now an investor, I often ask folks, how did you decide on your pricing? I think it's such an interesting window into the company's business model. And I bucket companies into one of three buckets. The first one is it was honestly pretty arbitrary. We just kind of picked something and you'd be shocked how often I hear this. It's pretty scary. These companies labor over engineering, architecture, pixel perfect design, and the pricing was an afterthought. The next camp, we priced it artificially low. We didn't want price to get in the way of people using it. So this can be referred to as a land and expand strategy. A lot of freemium businesses use this tactic. It's a land grab. You want to get as many users as possible to use your product. The challenge with this strategy is it takes a lot of heavy lifting to get a customer who's paid you nothing or a small amount to pay you a hell of a lot more over time. And that brings us to the third group. I heard this from a company recently, and I thought it perfectly captured my point. We wanted to position ourselves as premium. One prospective customer told us, you're the apple of your category. I have to save budget to afford you. And that was fine by us. So listen to the restraint from this company compared to the previous one. The other one was all about price low, get the customer. This is saying, I'm going to maintain my pricing power. I'm not going to use discounts to close a deal. They'll come, they'll buy eventually. So these are a window into the starting point for a company. Neither of them is a guaranteed recipe for success or failure. It's just the starting point that this monetization work needs to move from. So let's get to some of the core lessons here. The first one is know who you're building for. Segment your users by needs and willingness to pay. It's often I see companies do segmentation studies focused on very demographic or superficial demographics. Age, gender, business size. Those things aren't always correlated to the action of monetization. So this lesson is a nod to the book Monetizing Innovation. It was written by some of the partners from a premier pricing consultancy called Simon and Kutcher, and it's one of my favorite pricing books. And they go into depth about this strategy of using needs and willingness to pay to segment your user Base. I'm going to share an example to bring this to life and I'm going to highlight some stories from various portfolio companies at Menlo as well as companies that I've worked at. So we're investors in Uber and this is not a SaaS example, but I think it's a nice one to illustrate the point. They segment users by needs and willingness to pay. If you are a price sensitive customer, you need to get from A to B, but you're willing to walk a few blocks to go meet your driver, you're willing to share your ride with a passenger. It's going to take you a little longer to get to your destination, but you know you're getting the cheapest price. If you value speed, you might opt in for an Uber X, you're going to pay a little bit more. If you need more space, you've got a lot of people or luggage, you might want an suv. And if you're not price sensitive at all, you not only want to get there quickly but you want to ride in a high quality vehicle. You're going to gravitate towards UberSELECT or black. So this is a nice lesson in revenue maximization by aligning the offering of your product to customers needs and willingness to pay. But we are at Sasser so I'm going to bring it back to the world of SaaS. For those of you that have a self serve or bottom up business, you have a pricing page that probably looks like this and you probably have a good, better best model, maybe a Starter Pro advanced, different packages that have additional features and a higher price point. And what I often tell companies when we're dissecting their pricing and packaging is that value does not automatically translate to perceived value. What do I mean by that? Just because you have this long checklist of features does not mean you get credit for it. You can create paralysis for your customer. They're overwhelmed by the payment decision of which plan to buy. They might not understand the features or they might understand them and just not value them. So the idea here is for customers to actually know what to buy and know what they're getting. There's an effect. In the book Simon Kutcher, they call this feature shock. This is that reaction to I'm just overwhelmed by the features in front of me. And they tell a story that really stuck with me. There's a client of Simon Kutcher's that caters to the SMB segment and they weren't happy with their revenue growth so they dug in and saw that their plan had 27 features, 27 features, many of which weren't valued by the SMB segment. So what did they do? They cut that 27 feature list down to 8. They increased the price and drove a 25% lift in sales. So they actually reduced the value from 27 down to 8 and in effect increased the perceived value of what they were offering their customers. Pretty powerful thing to wrap your head around. So in the world of SaaS, we need to spend time on making sure we're building for the right person, knowing who we're building for, understanding why they pay you. The reason why somebody converts or upgrades is never evenly distributed. Across every feature down that list there's typically a small handful that are the main carrots, the main drivers of monetization. And we need to understand how much they're willing to pay. Is that price on the pricing page the right one or was it arbitrary? So how do we do this? I encourage you to establish a pricing process and I followed something like this in the past. First is assigning an owner. In self serve businesses like I've worked at, this was within my product growth team. Product managers, data scientists, analysts, user researchers. I had a cross functional team that owned pricing and packaging. If you have an upmarket customer, more mid market and enterprise sales obviously plays a role. So for your business, think about who are the stakeholders to be this pricing task force and give them ownership of this exercise. Next is talking to your customers. A key benefit of having a sales team, a success team, a support team, is that they hear the voice of the customer day in and day out. They hear the voice of the prospective customer, of the churn customer. These are rich insights that need to find their way back into the product monetization team. One of our biggest, most high impact experiments at Invoice2Go came from one of these sessions that I put into place with our customer operations team. We used to do a bi monthly lunch on a given topic and those folks that were listening to customers all day every day came and helped us build a narrative around what customers were thinking. There's other survey methods, things like Conjoint and Van Westendorp and I'll explain what those mean and finally be willing to take some risks. We're going to experiment a bit with our pricing. If that seems really scary, you can isolate a given geography. If you're a global company, try it in one location, test the results before rolling it out worldwide and know that this work is never done. Your product roadmap is never done. You're constantly developing new features and functionality. So why on earth wouldn't you revisit your pricing every 12 to 18 months, step back and say, we've built this new value, we've created all this value for our customers. Are we being compensated appropriately for that value? So I promised a little more detail around some of these research methods. So I've seen survey questions like the top left below is a list of features. Check off all the ones you want. The problem with asking a question that way is customers are just going to say they want everything. Another way to think about asking that question is 100 points. Give your customers 100 points and have them allocate those points against features. The more they value a feature, the more points they give it. An alternative is rating each feature as what's must have, nice to have and not necessary. Based on the nice to haves and the must haves, which ones would you group into an ideal package? So now that you know demand, it's the relative prioritization of these features where the real insights live. So now that you have a sense of that, ask the price question, it's kind of a taboo subject. You know, sales teams have these conversations all the time. But when you're a product driven bottom up SaaS business, this conversation's typically unsaid. So a way to ask this is once your survey respondents have given you their ideal package, ask people what's a reasonable price for this? Then take it a step further and say what's an expensive price? And then ask them what's a prohibitively expensive price. You'll start seeing these rages unfold with which you can expand your pricing. This is the Van Westendorp model. And the insights you get will be so interesting because any assumptions that you made going into your pricing may have just been plucked out of thin air and now will be rooted in real research. So we know where there's demand, we know relative price sensitivity of our customers. We're going to put that into effect. Let's talk more about our pricing and packaging and business model and how do we align it with building accretive value for our customers? Okay, we did some of this work at Invoice2go. We researched what new features people wanted. We got a sense of willingness to pay and were thoughtful about our pricing and packaging evolution. And we were able to increase the upgrade rate by 108%. These were people who upgraded from our starter plan to a higher plan and we increased the price 33%. So we had a hell of a lot More people upgrading and paying us more along the way without any backlash. We matched price to value in a way that our customers accepted. So how do you do this? First is around increasing prices. We're going to talk a little bit more about that. Next is rebalancing your sticks and your carrots. I'm going to explain what I mean. And upselling and cross selling users from a cheaper plan to a higher priced plan. Okay, spoiler alert. You're probably underpriced. This is a story from one of our portfolio companies called Envoy. It's a visitor check in solution. You've probably used it when going to visit an office. And I was talking to the CEO and asking him about how he thought about pricing and we had a good laugh because he puts himself in the camp of artificially pricing low. He was just so afraid of getting customers. And so I asked him how he thought about pricing and he told this story and I thought it was so funny, but also such an aha moment for him. So I wanted to share it. Today he was at a major hospitality company and it was clear that they wanted to use the product. He's doing the sales pitch. They're really excited. Clear demand. And the conversation drifted to pricing. And at this point it was $20 a month. $20 a month. And so Larry in that moment decided to take a real risk. He said to the buyer, he said, It's $20. I mean it's $200 a month. He 10x'd his price mid conversation with the buyer and the buyer laughed and called him out on it and said, I see what you did there. But $200 is fine. Didn't even blink. Was ready to sign on the dotted line. So Larry thought he was really pushing the envelope, really stretching to capture more revenue. And he learned in that moment that he was still severely underpriced. 20 to 200. And it's gotten more expensive from. Next step is using upgrade levers. And I call these sticks and carrots. So your sticks are your quota limits. A user hits some threshold and in order to get more, they need to upgrade. This is different than carrots. Carrots are the features you dangle in front of a customer. If they upgrade, they unlock them. It's new value. Let's explain this further. So your sticks are your value metrics. I'm sure each and every one of you in this room has experienced them. It's running out of storage on Dropbox. It's wanting to read more articles on the Economist or the New York Times. Sending Messages in Slack or an invoice. With invoice to go, you hit some limit and you get one of these jarring red pop ups encouraging you to upgrade. And they're wildly successful. So the reason why sticks are so effective is twofold. One, you're catching users in a very high intent state. They want to add that file or read that article. You're catching them during the state of urgency. And the second thing is it doesn't require any additional education. This is the core utility of the product. They understand the concept of storage, the concept of articles or messages. So it's a really effective way to drive conversion. I'm going to walk you through an exercise with another one of our portfolio companies called Carta. Carta does it helps you manage your equity ownership. And I spoke with folks there about how they thought about their stick, their value metric, and the feature that's a big wedge for them that their customers love is this cap table management. So originally they thought, ok, well we'll charge by number of seats, the admins that need to use the tool will pay us on a per seat basis. And they thought about it and said that's not ideal because as a company scales, it's probably still just a small handful of people who are in the product every day, folks from the legal and finance teams. But the vast majority of users only need to sign in a couple times a year. So customers get more value. Carta doesn't get compensated more for that value. They could have done something like equity grants. Every time a company gives equity to a new employee, they could be charged. Problem with that one is it would disincentivize usage. Companies might actually pull back and reduce the cadence with which they dole out equity. So they landed on something I think is really smart, number of shareholders, number of lions on the cap table. Because the benefit of this feature is to make sense of the mess of having significant more employees who are equity holders, more investors over time who now have a piece of the pie. So as that number continues to grow, card is compensated appropriately. So that was their thought process in thinking about what's the right stick for their business. Now let's go to the carrot side, the premium features that unlock more value here and revenue. When Carta got started, they focused on early stage startups, things like company formation and 409A valuations. And companies loved these features. But what also happened is these companies grew up and they had more needs, they started having more complex board structures, they had liquidity events such as secondaries, and they Needed more solutions. So Carta began prioritizing their roadmap for revenue maximization. They thought about the needs of the enterprise, the public company, and they started building for them. So as they created more value during this evolution of a company growing up across life stages, they also started charging a lot more. So as customers grew, they derived more value from Carta and Carta commanded more money. And what has it got them? Really fast revenue growth. So this is a chart of how long it's taken a few companies to go from 0 to 100 million in ARR. Slack really breaks the mold here, having done that in three years. And Carta, we plotted them from when they shifted from a pay as you go model to more of a recurring revenue model. And they're here in good company. They have also achieved this gold standard we talked about. By year two, they exceed 140% revenue retention. So by being very thoughtful about their monetization, their sticks and their carrots, they've been able to put themselves in a really healthy position. And that brings us to lesson three, a topic that I don't think is getting enough attention. Personalizing your customer's monetization path by using predictive data. So we had these hypotheses at Invoice2Go. We thought to ourselves, we did this research around what people want and have a willingness to pay for. We used that to overhaul our pricing and packaging. That's sort of our base business model. But there's probably data around how people use or don't use our product that could help us predict whether they're going to stick around. So we hired a data scientist and started building models. We input all the variables that we thought would be correlated to upgrades or retention. We found out which ones were indeed the most predictive of these downstream actions. And we used that to create a scoring system. We bucketed customers into very high, high, low and very low propensity to renew. And based on those segments, we met customers where they were in the product experience. This is all automated, no human touch through sales success or support. We had personalized experiences for users to better match price to value. So for those that had a very low propensity to renew, we tried things like reinforcing the value of the product, educating them. Maybe they didn't quite understand what the features could do for them. We tried targeted offers and discounts. Maybe they just needed a lower price point. And for those on the other end of the spectrum who had a high likelihood of to renew, we looked at the ones that we thought were the most upgradeable, we suggested higher price plans so that they didn't just renew at their current plan, but unlocked more value and we unlocked more revenue. In just our first few experiments, by personalizing the conversion, upgrade and renewal path, we drove an increase of our expansion revenue by 10%. So now shifting to the investing side, I joined Menlo Ventures and I meet this company called Clearbrain and this became my first investment. And the reason why I had so much conviction around this company was because they offer out of the box what we had just built in house and invoice to go. This is AI driven marketing. They help you with just a single line of code predict which audiences are going to perform an action based on previous actions. They let you personalize your messaging and do targeted offers based on someone's propensity to convert, propensity to upgrade, or propensity to renew this powerful intelligence that meets a user where they are and optimizes the funnel. They were kind enough to share a case study with one of their customers, InVision as a platform for product design and they were using clearbrain to do an upgrade campaign much like we had done. This is the dialogue. They did both a retargeting campaign on Facebook targeting free users that they thought had a propensity to convert to a subscription and their conversion went up from 4% to 10% more than doubled by getting the right user at the right time with the right message. They also did an in product campaign where they showed this dialogue contextually to those who they thought were upgradable within the product. They used a tool called Braze and the clearbrain intelligence told them who was upgradable and they enjoyed this double digit lift in cta. So you're seeing compounding double and triple digit benefits by using predictive data to enhance your monetization. And this isn't always done just in an automated way. Those happen to be self serve examples. But I think Slack is a nice example for how to use predictive data in more of these handoffs between teams. So something they've done in the past is have a self service team that's dedicated to the self service version of the product. And that team is focused on the SMB segment customers, 250 employees or less. But the other segment they see in the self service version is smaller teams that might be a part of a much larger enterprise. This might be the engineering team from a Fortune 500 company that's just kicking the tires trying out Slack and deciding whether it's something that should be rolled out company wide and so what I've heard these guys do is use the data around who they think these teams are, look at how they use the product and do qualified lead gen to the sales team handing off these accounts to the mid market enterprise sales folks to provide that human touch and help migrate these customers to a true enterprise solution and expand ACV along the way. So a few ideas on how you can use predictive data to get someone to convert, upgrade, renew or hand them off to that human touch that's required to continue the sale and I hope these were helpful lessons to you. I want to wrap real quick. Know who you're building for. Segment your customers by their needs and their willingness to pay. Align pricing and packaging with building accretive value. Pick the right stick and keep building the right carrots. But you should probably raise your prices too to make sure you're compensated appropriately. And finally, look for opportunities to use data, predictive data to tell the future, meet a user where they are and continue to drive revenue. Thank you for coming and now I'll let you guys go to the happy hour outside. Thank you.