Traversing the Traction Gap

by Bruce Cleveland

  To avoid those “Slide 29” issues I discussed in the first chapter, your initial product- and market-engineering tasks must be completed before you declare MVP. Trust me, no miracles are likely to occur, only results (traction) or the lack thereof based upon adequate preparation at this stage.

  In this chapter, I share with you some of the key tasks you need to complete to generate traction and begin to scale, once you decide to declare MVP. These tasks include a discussion about pricing strategy (as mentioned above), measuring user engagement, the importance of implementing a customer experience function, capturing NPS scores and usage rates, the value of developing a community now versus later, and finalizing your category definition.

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  ESTABLISHING THE VALUE OF YOUR PRODUCT

  MVP traditionally has been defined as the minimum number of features for which a customer will pay.

  “The minimum viable product is that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort.”

  ERIC RIES, CEO, Long-Term Stock Exchange

  I take a slightly different view. I like what Bryan Stolle, my Wildcat partner, says of B2B applications and infrastructure: “I want to see companies pay to use Beta software.”

  A Beta product typically has not yet reached MVP. What Bryan means is that businesses will only pay for products from which they can derive value. And if they can be induced to pay you for Beta software, you can be sure early on that you are solving an important problem and delivering value. The fact is that until you can get a consumer or a business to fork over cash for your product or service, according to the IRS you’re a hobby.

  For consumers, the acid test is how often they use your product. This is why Daily Active Usage (DAU) rates are so important, especially for consumer applications, which generate revenue via advertising.

  For business products, DAU rates matter, but revenue is the standard by which you will ultimately be evaluated. This implies that you should begin experimenting with your pricing model during the IPR-to-MVP phase. Don’t worry, you don’t have to have price locked in at this point, but now is the time to experiment with some “straw” pricing to test the market.

  I recommend that you consider trying a smoke test (referred to below and an example of which is described at the end of Chapter 4, “Getting to Initial Product Release,” in the Hacks section) to try out different offers and pricing before committing to them with formal public announcements. The same reasoning holds for customer proposals.

  Pricing is a complex topic that involves many different factors, such as the market(s) you’re competing in, competitive dynamics, and business model. I will make a few brief comments here about pricing a product or service.

  I always encourage teams to focus on value-based pricing:

  How valuable is the product to a company or consumer?

  How much time does the product save, and what is that time worth?

  How much expense does it eliminate?

  How much revenue can it help generate?

  What does the product let you do that you can’t live without, and what is that worth to the business or the consumer?

  Many companies attempt to use return on investment (ROI) calculations for business products to develop their pricing and discount models. This approach is fine; however, it must be balanced against other products that may already exist in the market and the business models/pricing associated with those products. You can’t determine price in a vacuum.

  For example, let’s say that through your ROI calculations you determine that your business application could save a company in your target market $1M per year. So you set your price to $250K, generating a strong ROI argument. But, if other companies are already in the market with similar products—even if those products aren’t as good as yours—and they only charge $50K, then you will have a tough slog persuading prospective buyers to pay your price, no matter what your ROI calculations produce.

  If you believe there are no competitors in your category, you are wrong. Trust me: if nothing else, you will be competing for part of a budget, and the end user’s or company’s decision to purchase or use a product or service will be based on the availability of time, cash, or both. If a consumer or business buys your product or service, they will have less to spend on another product. With businesses, there is typically not much, if any, unallocated budget just lying around. So a purchase decision for one solution always takes budget away from another group, product, project, or ability to hire the next employee.

  In a market-first approach, you can evaluate your different price points using a smoke test to identify value propositions and potential pricing. A landing page attached to a digital ad in LinkedIn can act as a “one-question survey” that asks “What would you pay?” for this product.

  Similarly, the other terms and conditions you attach to the purchase of your product (e.g., reimbursement policy or service levels) must be balanced against similar terms and conditions competitors may offer.

  I remember suggesting to one of my B2B SaaS portfolio companies—which was wrestling with what terms it should offer in its first agreements—to initially adopt the same terms and conditions Salesforce used in its agreements. I happened to know that those terms had been well-vetted by Salesforce, accepted by the industry, and would establish a starting point that could be amended or revised later. This approach got the company off the ground and on the path to success.

  Knowing what other companies in or near your category are offering with respect to their product capabilities, pricing, and other terms is a mandatory best practice if you intend to be a market-first company. Today, there are great products available, such as Skyword, AirPR, or even Google Alerts, that enable you to automatically track your competitors, and I highly recommend that you invest in one or more of them. The days of performing competitive research manually—or not at all—are long gone. Knowing what your competitors or potential competitors are doing at all times is not a “nice to have” business practice, it’s mission-critical.

  “It is not good enough anymore to use ‘gut-feel’ and best guesses to build and grow your sales team, especially when there are tools to help you make solid decisions based on predictable results.”

  LORI RICHARDSON, Founding Member, Sales Enablement Society, B2B Sales Growth Strategist

  Sean Ellis, CEO of Growthhacker.com, has a straightforward approach to pricing. In an interview, he told us:

  So, finding the right price. This was something I did both with Dropbox and initially with LogMeIn, where I essentially tried to actually map a demand curve at different price points for a product.

  I divided people [into groups] who were using the products. We took people who were on the free product and then laid out the benefit of what the paid product would be. We then told the first group that the cost of the product was going to be $50 a month. We told the next group the price was going to be $40 a month . . . all the way down. I don’t remember exactly what the price differentials were, but basically I took ten groups and gave them ten different prices and then asked them what the likelihood was that they would buy a product that does X, Y, and Z and costs this price.

  Then we gave each group a multiple choice: Definitely I’d buy it, maybe I’d buy it, I definitely wouldn’t buy it, and then discounted each of those prices. If they said they’d definitely buy it, they might probably be a 50 percent conversion rate. If they said they’d probably buy it, maybe that would be a 20 percent conversion rate; and then we just discounted everybody else to zero. We were able to get a pretty smooth demand curve that helped us zoom in on a price where we could see what was the max yield price per 100 free users or 1000 free users; [we knew] this would be the price that was going to drive the most premium upgrades.

  Dale Sakai, the cofounder of Obo and a thought leader in the product process, has stated: “Best-practice pricing requires conjoint,
cluster analysis and market simulation. The simplified methods generally do not yield valid pricing levels.” As with any relationship, with pricing “it’s complicated.”

  Finally, when it comes to price, keep in mind that there is always a price the market will and won’t bear. Determining what that price should be is considered an “art.” It’s something we work on at Wildcat with our early-stage portfolio companies. Each company is different, and therefore pricing is always a bespoke process.

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  USER FEEDBACK

  You must design your Beta program so you can quickly and easily capture, process, and share data-driven feedback from customer and consumer product use.

  The great news for software startups is that products on the market, such as MixPanel or Pendo, can give you daily product usage data and analytics that you can use to track user behavior.

  During this phase, whatever usage/analytics application you choose, making sure you have your team focused on the quantitative and qualitative feedback is mission critical. You can’t rely solely upon metrics; you also need qualitative feedback. Qualitative feedback comes only from meeting face-to-face with end-users and watching how they use the product and where they get stuck, and being ruthless about the user experience (UX).

  “That’s actually one of the most disappointing things about doing user interviews and user feedback, which is why I think . . . people don’t do it. You’re going to get negative news about your favorite pet feature most of the time.”

  EMMETT SHEAR, CEO, Twitch

  Investing in face-to-face time enables your customers and consumers to provide you with information not just about how often they use your product, but how they “feel” about using it. You need this feedback because how users feel will determine whether they will or will not recommend your product to other potential customers.

  Engagement Analysis

  We all know the excitement that comes from releasing a new product or feature. There is much celebration and many high-fives. But now you must ask the really important question: How are we doing?

  During this phase, you need to have your engagement analysis strategy up and running.

  “Get closer than ever to your customers. So close that you tell them what they need well before they realize it themselves.”

  STEVE JOBS, Cofounder & CEO, Apple Inc.

  Engagement is critical in the extreme: if no one uses your product or feature, then you don’t have a successful product or feature regardless of how many people elected to try or purchase it at the start.

  Industry-accepted metrics determine engagement for software applications. Venture investors will want you to apply them to know how your product stacks up in the real world. These metrics include:

  Monthly Active Users (MAU)

  Weekly Active Users (WAU)

  Daily Active Users (DAU)

  The ratio of DAU/MAU

  For B2C software startups, Sequoia Ventures—a Silicon Valley venture capital icon for the past four decades—has stated that the standard DAU/MAU ratio is typically 10 to 20 percent, with only a handful of companies greater than 50 percent. According to Sequoia, Whatsapp has an astounding 70 percent DAU/MAU ratio!

  For SaaS startups, the DAU/MAU ratio, according to MixPanel, is a proxy for stickiness; and the stickier your product, the more likely you’ll generate a Customer Lifetime Value (CLTV) of more than 3x your Customer Acquisition Cost (CAC), the minimum multiple required to create a successful SaaS business.

  According to a 2017 MixPanel Products Report, which studied more than 572 products, 1.3 billion unique users, and 50 billion transactional events, the median SaaS DAU/MAU ratio is 9.4 percent. This ratio suggests that the typical application is used only about three days per month per user. According to the same report, a SaaS DAU/MAU ratio of 20 percent is good, while 25 percent is exceptional.

  That said, the type of SaaS product you offer will have a dramatic impact on your DAU/MAU ratio. A B2B collaboration application (e.g., Slack) should enjoy daily usage, while a back-office application may be used only occasionally.

  All of this underscores the fact that not all users are the same. For example, execs may need only occasional access to your product, while the operations staff might use it steadily. So you may want to set separate DAU/MAU ratio targets based on user type.

  Other metrics, used primarily by B2C software companies, help to determine higher-order levels of engagement. One of these is the cohort retention curve, which is the percentage of users that tries a product or feature on a given day, then visits or uses it one week, two weeks, three weeks, one month, two months, . . . later.

  It is typical with this measure to see a plateau at some level (such as 20 percent) several months out. You can measure how the retention curves change (improve or worsen) as you add new features or go after different customer segments with the same product or feature. For example, you may want to experiment by adding a new level to a gaming application (new feature) or target women 45 and older in the UK (new customer segment) and then evaluate the response rates.

  In my experience, the type of product you offer influences the type of engagement you need to analyze. I’ve also discovered, to my dismay, that some startups—typically B2Bs—don’t gather any of this data at all. Not gathering data can be a deadly mistake.

  On the other end of the spectrum, some startups generate cohort retention curves for entire products or features and gather a ridiculous amount of clickstream data, only some fraction of which is directly actionable. Too little data isn’t good, and too much is simply overkill. It’s up to you to decide the amount and type of data you need to base your product decisions on.

  The two internal business functions that typically have direct contact with users are your customer experience and customer support teams. Let’s take a closer look at both.

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  CUSTOMER EXPERIENCE VERSUS CUSTOMER SUPPORT

  Customer Experience

  We all know customer support is a key business function; it is critical to take care of your customers and keep them happy and loyal. But a relatively new function has emerged designated “customer experience” (CX), which is equally critical but with an entirely different set of objectives.

  Customer experience is concerned with how your product and company are perceived by your customers or from the consumer’s perspective. In other words, customer experience is all about how your users feel and what the market thinks about your startup and its products.

  Customer experience management (CEM) should be staffed separately from your other business functions, so that these dedicated employees can focus exclusively on the interactions between a customer and the organization throughout the customer life cycle.

  The CEM role is specifically different from customer support, because:

  It takes the customer’s perspective and point of view;

  It forces the company to evaluate and respond to customer interactions;

  It is responsible for capturing customers’ responses, expectations, and market signals and translating that data into actionable information.

  Customer Support

  Many startup teams assume, since their customers have an opportunity to express their opinions to the customer support group, that those customers represent the market and are a strong source of market signals. It’s true that customer support is essential to provide customers with a channel through which to express frustrations—and sometimes even jubilation—about their experiences with a feature or product. And customer support is also excellent for helping to identify bugs and getting to an engineering fix.

  However, customer support represents a very selective set of feedback from people with problems. And not just any people with problems, but the subset of those people who are motivated enough to reach out to express their frustrations.

  “Your most unhappy customers are your greatest source of learning.”

  BILL GATES, Co
founder, Microsoft

  Finally, customer support is great at helping to identify the symptoms of a problem. There are plenty of metrics that can be used to figure out just how big a problem something is, based upon the quantity and types of customer responses.

  The problem is that customer support is not chartered to generate and capture new ideas. This group only reflects the perspectives of your current customers, specifically only those current customers who are motivated enough to speak up. In practice, this can be as little as 1 to 5 percent of your overall customer base—and they may not share all the characteristics of the other 90+ percent of your customers.

  Perhaps more importantly, this cohort does not include any of your noncustomers, the largest part of your potential market and a critical source of signals. The feedback into your customer support group represents a very narrow and selective group of people, which, by definition, introduces bias into your metrics.

  The market-first process requires you to find all the constituencies that make up the entire market, including your noncustomers, the largest part of the market.

  It was precisely to fulfill this unmet need that the idea of customer experience teams was born. Simply put, their job is to capture that valuable data (from both customers and noncustomers), data that customer support is not chartered to engage with.

  Time for a reality check: let’s say that you are a startup that is only at IPR and working your way to MVP. You are unlikely to have both a customer support group and a customer experience team. In fact, you probably don’t have either. Frankly, if you are like most startups at this stage, it is highly unlikely that you have the capital to staff these functions, or the bandwidth to manage them. Instead, you most likely will have your engineers and product managers doing double or triple duty. That’s why the days and weeks are long and seemingly never-ending.