Customer Discovery, first phase of Customer Development

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As part of my research about the Steve Blank´s  (@sgblank) Customer Development methodology, now I´m reading the book The Startup Owner’s Manual: The Step-By-Step Guide for Building a Great Company (http://www.amazon.com/The-Startup-Owners-Manual-Step-By-Step/dp/0984999302) which is very good resource for every entrepreneur.

In this post, I would like to share my notes and insights about the first phase in the Customer Development method: the Customer Discovery. The goal of Customer Discovery is to be sure that a specific product solves known problem for an identifiable customer segment. This phase is executed by the founders.

A startup begins with the vision of its founders: a vision of a new product or service that solves a customers´ problem. The goal of customer discovery is to turn the founders´ initial hypothesis (guesses) about customers, market and solution (product/service) into facts in order to search for the problem/solution fit. Facts exist only outside the building, where customer live, so we need to get out of the building in front of the customers (days, months and even years). It´s done by the founders, so they can know if it´s a vision or just hallucination, so the value proposition matches the customer segment it plans to target. Remember that most business model fails because we waste money, effort and time in building the wrong product.

It´s remarkable to say that there may be multiple value propositions and multiple customer segments, but the problem/solution fit is only achieved when the revenue and pricing model, value proposition, and customer acquisition efforts all match up with customers´ needs.

In a startup, at the first day, we don´t have plenty of customer knowledge, so the first product (minimum viable product – MVP) is not designed to satisfy a mainstream customers but small group of early customers who have bought the startup´s vision. They will give feedback to startups necessary to add features to the product over time and tell others about the product to the world. The idea is to put the MVP in front of customers to find out whether we have understood the customer problem to define the key features of the solution. Then, we iteratively refine the solution.

The business model canvas from Alex Osterwalder (@AlexOsterwalder) is the scorecard used in the customer discovery step to organizing our thinking by specifing the hypotheses (guesses) and experiments as well as a medium to record the result (pass/fail) of the experiments for the validation of hypotheses in searching for the business model.

The business model canvas (http://en.wikipedia.org/wiki/Business_Model_Canvas) enables specifying how the company expects to make money in nine blocks: value proposition, customer segment, channels, customer relationship and demand creation, revenue and pricing model, partners, activities and costs structure.

The business model canvas can´t be static snapshot but dynamic. We need to update the canvas to reflect any pivot and iterations in time period (let´s say a week). After the time period, we need to agree on the changes to the business model and integrate them in a new view of the canvas to work on for the next time period. In any precise moment of the time, we have the current canvas and a stack of previous canvases.

We need to do a market research using Total Available Market (TAM) and Served Available Market (SAM). TAM covers every way a customer can currently meet a need, and SAM is the portion of the TAM that our product covers. TAM answers the question: How big is the market (the total of all unit sales of all the competing products)? In short, TAM is the total potential market. TAM is expressed using dollar value. Identifying the TAM and SAM can help to understand the target customers. We can use several tools such as Google Insights, Google Trends and Facebook ads, industry-analyst reports, market-research reports and competitor´s  press.

We also have to launch a landing page (with call-to-action) as product concept, traffic analysis and medium for validating hypothesis as well as to contact the target customers using a contact list in order to conduct survey, get insights and receive feedback. After that, we need to launch a low fidelity MVP.

Customer Discovery phase has four sub-phases.

  • Phase 1 deconstructs the founders´ vision into the nine parts of the business model canvas
    • Goal: To sketch out the possible problems we´re solving and what product we´re building and how we believe this will create value for the customers, in other words we´re stating our hypothesis
    • Description: We need to describe the jobs the customers are trying to get done and outline their pains and gains as well as to list the products/services we´re trying to offer to alleviate pains and create gains. The team specifies the hypothesis for each part of the business model (value proposition, customer segment, channels, market type, customer relationship and demand creation, revenue and pricing model, partners, activities and costs structure) including the list of experiments to conduct to prove or disprove each one
    • Tools:
  • Phase 2 enables conducting experiments to test the problem-related hypotheses in the business model canvas
    • Goal: To understand the problem/solution fit by turning hypotheses into facts or discarding them if they´re wrong and replacing them with new hypotheses
    • Description: We do so by hearing our customers and testing the most important elements in the business model including the customer problems, value proposition, pricing, channel strategy and sales process in order to understand how important the problem is and how big it can become (start getting out of the building to talk as many potential customers as possible). Building a landing page is hard because we need customer insights and iterating without talking directly to customers is slow. So, talking directly to customers has more learning validation than any other method. In other to structure the problem presentation, we can use the following checklist:
    • State the top 3 problems
    • Ask the customer to prioritize the problems
    • Ask the customer how he works today and his pains and gains today
    • Ask the customer how he solves the problems today
    • Very briefly, describe how we might solve the problem
    • Would the customer use the solution if it were free?
    • Would the customer pay $X per year?
    • Ask for referrals to others

Phase 3 enables testing the solution by presenting the value proposition and low fidelity MVP

Phase 4 enables stopping and assessing the results of the experiments we´ve conducted and verified

  • Goal: To verify, if we need whether to pivot or to start selling the product because we´ve achieved the problem/solution fit
  • Description: We can have a full understanding of customers´ problems and needs, confirmed the value proposition solves real problems, determined a sizable volume of customers, learn the customers will pay for the product and finally made certain the revenue will deliver a profitable business
  • Tools:

According to the Ash Maurya (@ashmaurya), there are 3 rules to actionable metrics derived from Lean Startup principles.

Rule 1: Measure the right macro

Eric Ries recommends focusing on the macro effect of an experiment such as sign-ups versus button clicks.

There are only a handful of macro metrics (only 5 – AARRR) that really matter. These are the metrics for pirates from Dave McClure (@davemcclure) organized depending on the customer lifecycle as shown below:

  • Acquisition: Users come to the site from several channels
    • Key question:
      • How do users find you?
    • Examples:
      • X number of clicks
      • Y number of page views
      • Z time on the site
  • Activation: Users have a happy first experience with the product
    • Key question:
      • Do users have a great first experience?
    • Examples:
      • Conversion rate
      • Number of users that sign-up
      • Number of users that watch product demonstration
  • Retention: Users come several times to the site to use the product
    • Key question:
      • Do users come back?
    • Examples:
      • Number of users using the product per month
      • Number of email click throughts
      • Number of feedback
      • Retention rate
  • Referral: Users like the product enough to refer it to others
    • Key question:
      • Do users tell others?
    • Examples:
      • Number of referrals
      • Number of activations
      • Viral factor > 1 (it means that every customer gets more than one another customer, therefore your product will grow virally)
      • Conversion rate
  • Revenue: Users engage on monetization activities (purchase, subscription, etc), so to find out how much profit they make for every customer and scale the number of customers
    • Key question:
      • How do you make money?
    • Examples:
      • How much money do you make for every customer you acquire
      • Minimum revenue
      • Cancellation rate. Number of customer who cancel in any given month compared to total (paying) customers

Of the 5 metrics, only 2 metrics matter before the product/market fit (activation and retention). Before the product/market fit, we´re building some product that people want by providing a great first experience (activation) and most important customer engagement (retention).

Rule 2: Create simple reports

Reports that are hard to understand, simply won´t get used. Funnel reports are a great way to summarize key metrics. Funnel reports are simple and map well to Dave McClure´s AARRR startup metrics.

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Figure 1

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Figure 2

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Figure 3

David Cancel´s Funnel is shown below:

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Figure 4

Funnel reports have a key drawback: because we´re constantly changing the product, it´s impossible to tie back observed results to specific actions taken a month ago; so, it´s used a reporting period where events generated in that period are aggregated across all users

Funnel reports work well for micro-optimization experiments (such as landing page conversion) but fall short for macro-pivot experiments, so we need to combine them with cohorts.

A cohort analysis is a form of study design. For example, when the development team makes design choices, then they go back to review traffic data to evaluate the success of their choices. A cohort is a group of people who share a common characteristic or experience and we wish to track this property, for example, bucket users into the month they join. The most common cohort attribute is “join date”.

Let´s illustrate the concept of cohort analysis with two figures:

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Figure 5

The previous report is used for Retention. It´s generated using monthly cohort by join date and tracking key activities over time.

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Figure 6

A good report combining funnel and cohort is “Weekly Cohort Report by Join Date” as shown below:

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Figure 7

In the previous report, we group users by the week in the year they signed-up and then we track all their events over time. We can see visible changes in the metrics which can be tied back to specific activities done in a particular week.

Apart from reactive monitoring the funnel, cohorts can also be used to proactively measure A/B test experiments. For example, a report shows the experiment about a cohort measuring the “plan type” attribute for the Freemium versus Free Trials.

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Figure 8

Rule 3: Metrics are people

Metrics can only tell you what users did. To make them actionable, we need to tie them to actual people. This is important before the product/market fit when we don´t have a huge number of users and we rely on qualitative versus quantitative validation.

For example, a list of people which failed in the download step in the funnel.

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Figure 9

Finally, there are some techniques to getting to actionable metrics:

  • Split tests metrics. Produce the most actionable of all metrics, because it can confirm or refute a specific hypothesis. The real value is when we integrate them into the decision loop: putting the ideas into practice, seeing what happens, and learning. A good rule of thumb is to ask: if this test turns out differently from how I expect, will that cast serious doubts on what I think I know about my customers? If not, try something bigger. For example, let´s say we add a new feature, we´re using A/B tests in which 50% of customers (group A) see the new feature and the other 50% (group B) not. After some days, we measure the revenue and noticed that group B has 20% of revenue higher. After that, we roll out the feature to 100% (group A + group B) customers and keep on doing experiments/tests with more features in the same way
  • Per-customer metrics. It means that metrics are people too. For example, instead of looking at the total number of page views in a given month, consider looking at the number of page views per new and returning customer (most conversion). Those metrics should be relatively constant
  • Funnel metrics and cohort analysis. It´s a kind of per-customer metrics. For example, consider an ecommerce product that has a couple of key customer lifecycle events: registering for the product, signing for a free trial, using the product, and becoming a paying customer. We can create a simple report that shows these metrics for subsequent cohort (groups) over time. If the report says what percentage of customers who registered subsequently went on to take each lifecycle action. If these numbers are holding steady from cohort to cohort, then we have feedback telling that nothing is changing. If one cohort suddenly shifts up and down, we get into investigation

After, the customer discovery phase is successfully finished; we can proceed to customer validation in order to try to validate the sales roadmap, so we can be sure that a market is saleable and large enough that a viable business might be built.

What do you think about customer discovery phase? Please, feel free to tell your experience and comments.

4 thoughts on “Customer Discovery, first phase of Customer Development

  1. Pingback: Customer Development or Customer Happiness? « proAM#1

  2. Nice compilation, I got esp. excited at seeing tools to manage hypotheses. We do it via a Github wiki and the process is far from perfect. Thank you.

    Angle you might want to consider is getting feedback about your MVP/landing page using tools such as Criticue.com (shameless plug). They let you brainstorm with other startup founders and you can get tons of feedback esp. regarding readability and how intuitive your proposition is. if your landing page is not clear in terms of the value you offer, you may get a false impression (based on the metrics) that your offer itself has little or no value.

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