| Our process
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Our process



Always “PLAN” before you start to build.

Our process model may be called “Build-Measure-Learn” but, if you follow that sequence and jump in at the “Build” phase, you’ll be missing the mark. Instead, it’s essential to start with a planning stage to avoid overenthousiast ‘false starts’.

Your first task is to define the idea that you want to test and the metrics or info that you aim to learn. You do this by developing a hypothesis – your prediction of what will happen during the first experiment.

Your hypothesis could focus on anything from product features and customer service ideas to finding the best pricing strategies and distribution channels. You might, for example, hypothesise that “increasing the frequency of our newsletters from two to four per month will increase overall revenue.”

Next, decide what you’ll need to measure to test your hypothesis, and plan how you’ll collect your data. Interviews, surveys, website analytics and measured interactions are common methods for gathering valuable data to structure your study.


Your goal here is to create an iconic Minimum Viable Product  (MVP) – the smallest possible product that allows you to test your hypothesis.

It could be a working prototype, a basic advertisement or landing page. Other MVP’s consist of a concierge service, sleek presentation slideshow, a mock-up, paper brochure, a sample dataset, a storyboard, or a video that illustrates what you aim to offer. Whatever MVP you choose, it needs to show just enough core features to attract the interest of  early adopters– the people who’ll likely want to buy your product or service as soon as you make it available to them.


As you work through repeated iterations of Build-Measure-Learn, your MVP will become more complex. But your priority should be to prove the demand for your proposed product, not to build a fully functioning model that’s full of advanced features..


Measure the results that you obtained in your previous Build step. How does what actually happened compare with your hypothesis? Is there sufficient interest in your idea to continue developing it? Does the data show that you’ll be able to build a sustainable business around your product or service?

  • Data analysis. Analyze the data obtained from the experiment. What happened? What are the implications of the data to your hypothesis? Make a comparison on what you hypothesized to what actually happene
  • Data organization. Organize your data in a way that will make it easily understood, and for the whole scenario to be easily comprehended by whoever listens to it. Any format that conveys the finding goes.
  • Data Presentation. Make your presentation of the data as compelling as possible. You want the members of the organization or the company to be engaged, so make sure you present it in a way that will truly grab their attention and hook them.

In order to speed up measuring, Eric Ries suggested conducting activities such as split tests, real-time monitoring, funnel analysis, cohort analysis and search engine marketing..



By the time you reach this stage, you’ll be equipped to make sound, evidence-based business decisions about what to do next.

There are then three ways forward:

  • Kill your darling We believe ideas are cheap and you should not hesitate to move on after you have fallen deeply in love with your super idea, but turn out to be the lone believer.. We’ll hold your hand on the knife and provide counseling to overcome the entrepreneurial trauma. Remember you saved yourself a lot of future disillusionment, wasted time and money!
  • Persevere: Your hypothesis was correct, so you decide to press on with the same goals. You repeat the feedback loop to continuously improve and refine your idea.(Even though your idea has achieved sufficient initial success to persevere with it, bear in mind that your next iteration may not do so. Be prepared to pivot in the future.)
  • Pivot: The experiment has refuted your hypothesis, but you’ve still gained valuable knowledge about what doesn’t work. You can reset, or correct your course and repeat the loop, using what you’ve learned to test new hypotheses and carry out different experiments.You can pivot in various ways. For example, you could develop a single feature from your MVP (called “zoom-in pivoting”) or focus on a different type of customer (“customer segment pivoting”). Or, you could try delivering through a new channel (“channel pivoting”) or use a single feature as the basis of a different product (“zoom-out pivoting”).


We’ll improve your loopings based on facts and validated learnings to discipline you at implementing these learning in an iterative process.

We adhere to the Build-Measure-Learn process, which was pioneered by Eric Ries in his book, “The Lean Startup.” It is a learning and feedback loop for establishing how effective a product, service or idea is, and doing this as quickly and cheaply as possible.


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