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Value Metrics and Pricing Design for SaaS Companies

May 21, 2020
13 Mins
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Value Metrics and Pricing Design for SaaS Companies

Host
Dr. Ajay Sethi
Venture Partner, Accel
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Host
Dr. Ajay Sethi
Venture Partner, Accel
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Value Metrics and Pricing Design for SaaS Companies
 Mins Read
13 Mins
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Host
Dr. Ajay Sethi
Venture Partner, Accel
Guests
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Summary

Engagement Graph

Growth Spiral depends on a simple but fundamental insight: customers derive value not only based on how well a product or service caters to their functional needs but also their non-functional goals. However, customers have different expectations about these two (functional needs and non-functional goals) for different activities that they perform. The most important parameter that influences these expectations is the frequency of the activity. In most cases, higher the frequency of an activity, lower is the non-functional goal (or, the importance) attached with it.

We illustrate this in the Engagement graph below:

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For this, let’s start by observing that all activities done by people — whether in personal or work context — can be viewed from the perspective of the “Frequency of activity” and “Importance of activity”.

Tasks that correspond to daily (or a few times a week) use-cases are considered to have “high” frequency of activity; weekly (or a few times a month) use-cases have “medium” frequency of activity; all other use-cases have “low” frequency of activity. Tasks that have large implication and, therefore, require consultation with other stakeholders (such as multiple team members or corporate committees) can be classified to have “high” importance of activity; tasks that trigger users to diligently evaluate pros/cons amongst alternatives as “medium” importance; utility-like tasks that can be performed without much thought are “low” importance tasks.

Engagement Graph shows various business activities. Email marketing, ERP, marketing automation, billing & accounting are some of the high frequency & low importance activities. CRM, collections and business travel are examples of medium frequency & medium importance activities. Getting loans and signing contracts are examples of low frequency & high importance activities.

We have explained earlier that the Engagement Graph is a good abstraction to understand and uncover value creation and growth drivers that are most suitable to a company. [link, link]

Based on this, we can classify problems that are fairly frequent (with daily or weekly frequency) as “frequent problems” and problems that are important but not as frequent as “important problems”. These problems are shown in the Engagement Graph below:

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As an example, let’s consider Clevertap, which is a customer engagement and retention platform. Other companies with somewhat similar charter are Adobe Marketing Cloud, Amplitude, Braze, LeanPlum, MoEngage, etc.

Clevertap is classified under the “Important problem” bucket because Clevertap’s platform has a large impact on Clevertap’s clients because they rely on Clevertap platform to engage customers throughout their lifecycle with the client. As a result, not only does the marketing department rely on Clevertap’s platform but also the product and revenue teams (especially because the Clevertap provides a unified customer data platform by aggregating marketing data with engagement, revenue, etc. data).

Value metrics

What is a value metric? A value metric is the anchor that best correlates with value perceived by customers. Value metric is a quantitative measure that is directly correlated with the outcomes (value) that are understood and appreciated by customers. A clearly defined value metric helps companies to optimize the value exchanged with customers via the product.

Note that there can be a difference between the value delivered by the product and the value perceived (recognized) by customers. In other words, value perceived by customers might be lower than than the value delivered by the product. This challenge is often faced by startups because successful startups typically either create new categories or redefine existing categories. A startup, therefore, has to find mechanisms to bridge the gap between value delivered and value perceived by customers.

Once the company has identified a good value metric, it can be used to define the product’s pricing strategy (because the value metric helps to align pricing to the value derived by customers). Value metric, therefore, helps to define the pricing strategy that enables the company to achieve equilibrium between the value derived by the customers and the price of the product paid by customers (i.e., the value derived by the company).

Note that this is a dynamic equilibrium in the sense that the pricing is never static. There are many factors that influence pricing: market maturity, company’s growth plans, competitive pressures, etc. However, the value metric is relatively more stable: it doesn’t change based on market dynamics. Value metric changes if and when either customer’s value perception changes or when company’s value offering evolves. As a result, value metric evolves slowly.

Frequency metrics and Importance metrics

Let’s start by defining two sets of metrics, which we refer to as Frequency metrics and Importance metrics. Frequency metric is related to the functional needs of the customers (which is typically referred to as JTBD — Jobs To Be Done — in the product marketing literature). Importance metric, on the other hand, is related to the non-functional needs of the customers (which we refer to as GTBA — Goals To Be Achieved; see here for more details).

Frequency metric depends on the natural frequency of customer’s functional needs. Importance metric, on the other hand, depends on the outcomes (goals) that customers care about. Note that one can reach the “goals” from “needs” by asking “five whys”.

For example, Clevertap’s frequency metrics can be: number of events raised by the client. Or, it can be number of distinct segments created by the client. Or, number of messages sent out by the client.

Note that Clevertap’s frequency metric is not (for example) number of unique customers engaged by the client.

What are examples of importance metrics? To identify them, we should look at the outcomes that clients care about. Possible importance metrics are: number of users who clicked on the messages, number of users who visited the product after clicking on the messages, number of users who “converted” (i.e. performed the core activity that delivers value) after clicking on messages.

It might be natural to think that the importance metric that really matters is the revenue generated by the activities (targeted messages sent out, in this case) enabled by company’s products. However, this is not always correct because revenue depends on several other factors. Company might have increased / reduced their prices; company might have offered discounts; company might not have the right landing pages; and so on. Therefore, it is important to only consider metrics that are directly correlated with company’s activities as importance metrics.

How does one identify value metric?

How does one identify value metric based on the frequency metrics and importance metrics?

This can be done in two steps:

Step 1: Using the frequency metric identified earlier:

  1. Map activities (product features) that correspond to various customer needs.
  2. Identify activities that have the most consistent natural frequency and sustained usage pattern across the year.
  3. Sort the activities from the highest to lowest usage frequency (while taking consistent and sustained usage into account).

Step 2: Using the importance metric identified earlier:

  1. Map activities (product features) that correspond to various customer goals.
  2. Classify activities based on their direct correlation to customer goals. In other words, activities can either have high or low correlation with the outcome customers care the most about.
  3. Sort the activities from the highest to lowest importance / priority.

Now, the value metric is nothing but the high frequency activity that is directly correlated with customer’s important goals.

To make this more concrete, let’s look at this again with Clevertap example.

For Clevertap, frequency metrics sorted from the highest to the lowest frequency are as follows:

  1. Number of events raised by the client: This depends on number of engagements client’s customers have with the client’s website/app; therefore, every interaction triggers new events. This, therefore, happens very frequently.
  2. Number of messages sent out by the client: This is typically done once a day or a few times in a week.
  3. Number of distinct segments created by the client: This is typically done once a week or less frequently.

For Clevertap, importance metrics sorted from the highest to the lowest importance are as follows:

  1. Number of users who performed the core action that delivers value: This depends on: users reading the message, clicking on the “call to action” within the message and performing the core activity.
  2. Number of users who visited the product: This depends on: users reading the message and, based on that, clicking on the “call to action” within the message.
  3. Number of users who read the message.

Note that #1 delivers the most value to the client; however, it depends on the success of #2 activity which, in turn, depends on the success of #3 activity.

Given this, what’s a good value metric for Clevertap? As we mentioned earlier, value metric should be the highest frequency activity that has the best correlation with customer’s important goals. In some sense, therefore, we have to intersect the frequency metrics with the importance metrics.

For the sake of illustration, we do this in a step-by-step manner as follows:

  • The highest frequency activity (= number of events raised) is not directly correlated with the importance metric. So, it is not a candidate for value metric.
  • The third highest frequency activity (= number of segments created) is not directly correlated with the importance metric — so, it is not a candidate for value metric. User segments (corresponding to user behavior) help to improve the message targeting and, therefore, help to increase the response rate; however, this is an indirect correlation.
  • The second highest frequency activity (= number of messages delivered) is directly correlated with the importance metric.

Let’s now look at the importance metrics:

  • The highest importance activity (= number of users who performed the core action that delivers value) is not solely dependent on frequent activities. This is because the core action depends on several parameters that are outside the purview of Clevertap product.
  • The other two (= number of users who visited the product and number of users who read the message) are both solely dependent on the frequent activities (specifically, number of messages delivered).

Based on the above analysis, we can assume that the “number of users who visited the product” is an appropriate value metric for Clevertap.

Note that the above analysis helped us do two things: it helped to identify the highest frequency activity that has a direct correlation with customer’s important goals. Moreover, since this activity has the highest usage frequency, we can expect it scale linearly with the value delivered to the client.

Pricing design

OK, so we have identified a set of good value metrics. Now, how does one derive pricing from a value metric?

Pricing, in fact, depends on several factors in addition to the value metric. First and foremost amongst these is customer’s Willingness To Pay (WTP) and, even before that, customer’s Ability To Pay (ATP). Customer’s WTP and ATP can be gauged by the pricing of the competitive or alternative products in the same category.

In order to determine the right pricing for the product, the company also needs to be mindful about the existing pricing anchors amongst their target personas (especially for the product’s category).

Now, it’s entirely possible that the innovative product built by the company has a value metric that no one else has used in the past. In this case, company can introduce new pricing based on the value metric while using industry’s pricing range as the initial anchor.

To summarize, pricing depends on the following:

  1. Identify customer’s WTP and ATP
  2. Identify which activities have well-defined pricing anchors. Explore and identify whether the established pricing anchors are aligned with company’s chosen value metric?
  3. Identify the existing price range within the product category (either at a per-activity level or at an aggregate level).

Let’s continue with the Clevertap example. First, what about “Willingness to Pay” and “Ability to Pay”. There are a lot of large companies in the space (such as Adobe Marketing Cloud, Amplitude, Braze, Mixpanel, MoEngage, etc.) who are generating a lot of revenue — so, clearly, WTP and ATP is not an issue.

What are some of the well-defined pricing anchors in the field? Let’s take a look at their pricing pages:

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We can notice that companies such as Amplitude and Mixpanel use the following anchors:

  • Number of user seats (i.e., number of licenses)
  • Number of actions (events) tracked
  • Number of unique users tracked
  • Duration for data history (i.e., size of the data repository)
  • Analytics and Behavioral reports
  • And so on

Based on our analysis, none of these are aligned with the value metrics that we have identified. Given this, we can’t really use them as anchors.

But what about pricing? Amplitude doesn’t provide pricing for the “Growth” and “Enterprise” slabs. Likewise, Mixpanel mentions “Custom pricing” for the “Enterprise” slab. Interestingly, Mixpanel mentions (starting at) $89 per 1000 MTUs (monthly tracked users) for the Growth slab. If we assume that a growth-stage company would get 100k unique users, Mixpanel bill would be $8,900 per month (or, more than $100k annually). Clevertap’s pricing can, therefore, work with $100k / year range while devising their pricing scheme based on the “number of users who visited the product” value metric and come up with relevant pricing slabs.

In addition to these, pricing design involves more decisions:

  • Should the company leverage freemium or free-trial mechanism?
  • How should the company price one-time activities (such as custom integrations or data migration)? Should the professional services be priced separately or bundled into the recurring pricing?
  • What parts of the products should be subsidized? What aspects of the product mix should be given away for free?

Each of these requires deep understanding of buyer persona. Buyer persona concept has become popular amongst the marketing teams. We, however, believe that it is better to build a holistic persona (what we refer to as 3d persona; see here for details) in order to avoid creating silos of customer requirements and goals.

To avoid making this article even longer, we defer some of these questions. (If you are interested to find out more about this, please let us know in the comments section below.) However, let’s consider the question about “what parts of the products should be subsidized”. This is an important question that has not been addressed by anyone so far.

We use the Engagement Matrix to answer this question:

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The Engagement Matrix is just a 3x3 matrix corresponding to the Engagement Graph discussed earlier

Here’s an important insight: it is important to ensure that the highest frequency activities are not priced high. This is because high-frequency products typically edge out lower-frequency products. For example, if you use smartphones frequently for calls and internet browsing, you are likely to use smartphone instead of digital cameras for taking occasional photographs — even if digital cameras help capture better quality photos. In the marketing world, if you use Mailchimp for sending emails and running campaigns frequently, it is convenient to use Mailchimp for less frequent activities (such as audience analysis, brand-related work, etc.) as well.

Therefore, low pricing of frequent activities is an important way to prevent competitors from upstaging you by undercutting you on pricing. It is better to leave the money on the table and, in fact, even subsidize high-frequency activities (and the corresponding features) to avoid getting beaten on price for such activities.

Also, really high importance activities should not be monetized too heavily. It is better, instead, to leverage them for strengthening brand and building strong connect with customers.

The monetization sweet spot, therefore, is in the middle: medium frequency activities with medium importance are the ideal candidates for building scalable and defensible monetization strategy.

Revenue metrics

The efficacy of the pricing design can be measured via revenue metrics. For SaaS companies, revenue expansion has been identified to be a very important metric that demonstrates company’s ability to continually deliver more value to customers. Successful SaaS businesses have demonstrated that they are able to generate more than 20% revenue expansion from their existing customer base.

Note that revenue expansion can happen due to one of the two factors:

  • Revenue expansion due to increasing value delivered by the products
  • Revenue expansion due to wider product penetration

Note that the first revenue expansion occurs naturally as the product usage increases. In other words, as the customer derives more value from the product, they naturally pay more for the product. This is typically due to well-designed pricing strategy that relies on the value metric. As a result, when either the frequency of product usage increases or when the importance of product usage increases (for example, when customer users “advanced features” that deliver more value), the customer naturally pays more for the product.

The second revenue expansion occurs due to a well-designed “land-and-grow” strategy and relies on the customer success and customer account management team. In other words, it is not product-led growth and, therefore, a little less efficient. (Even in this case, the revenue expansion can be product-enabled; account management team can leverage existing data and metadata to signup more department with the customer’s organization or cross-sell / up-sell more products to customers.)

Related topics

Question: The frequency metrics and importance metrics do not include some of the metrics used commonly by SaaS companies such as customer churn, activation rate, free trial to paid conversion rate, etc. Why is this?

These are important metrics that help understand whether or not the product is delivering value to customers. These are not considered in the discussion above because they don’t directly play a role in identifying the value metric.

Question: What about the metrics related to marketing activities (such as traffic, unique visitors, product-qualified leads, marketing/sales-qualified leads, etc.)?

These can be treated as input metrics — they help the company to generate sufficient number of users / customers who can experience the product. It can be observed that the value metric does not depend on these.

Question: How are frequency metrics and importance metrics related to output metrics and outcome metrics?

Frequency metrics are similar to the output metrics while importance metrics are similar to the outcome metrics.

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Summary

Value metrics are important because they play a critical role in company’s pricing strategy because they help find the most suitable pricing anchors and come with the best-suited pricing design. Good value metrics can be identified in a systematic manner using the Engagement Graph (frequency metrics and important metrics). Finally, even though the value metrics change slowly, it is important to constantly experiment with pricing to stay abreast with the changing marketing dynamics.

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