Background
To provoke a change in traditional industry, as nowadays the tradition industry like the word tradition is fading away and being replaced by innovation and disruption.
Goal
- using a data-driven mindset to explore opportunity
- focus on the key metrics and draw a line towards it.
- how to disrupt the nowadays big company for this transformation
Part 1 stop lying to yourself
Since the people may color their way of viewing the world, story about Airbnb may tell us how we could do to validate the idea in a small way.
CONCIERGE MVP it recognizes that even building a MVP is not worth the investment.
in the early days of the internet development, some of the metrics which looks silly today were measured and gave a great importance. Why? because the data is easy to acquire and do not need in-depth of knowledge of the business operates. This means how from one hit to first deal and continous engagement of customers.
By analyze the data to dig deep you could probably find something new and hidden opportunities that you could never thought of. For example, once I have download a full set of deal info from a shop website. Though the price of the shop seems higher than TaoBao, the customer keep coming. Why? I do not know exactly, but their location reveal most of these buyers live in remote villages and since this application also provide comparing price services maybe they hold the belief that what sells at the shop are the cheapest with the quality. If I have not got the data, I would not believe the shop could sell things out at such a high price.
When the data reveals a pattern, do not make the final call, but validate assumption gained from the data. There are a several ways of doing it in business, like A/B test. Define a workflow to these kind of test and set the criteria from the insight from the data.
| Problem | Solution | Unique value proposition | unfair advantage | customer segments |
|---|---|---|---|---|
| 1 | 4 | 3 | 9 | 2 |
| _ | key metrics | _ | Channels | _ |
| _ | 8 | _ | 5 | _ |
| Cost structure | _ | _ | revenue stream | |
| 7 | _ | _ | 6 |
identify the sweat spot that you could be paid and doing what you want and good at.
in order to bring the sense out of the data, you need to characterize the data and measure what matters.