Ash Maurya, author of Running Lean, writes about lean startup methods, customer development, business model hypotheses and actionable metrics.
Commenters praise his book and blog, and cite his article on actionable metrics when arguing that most data is noise.
I'm going to take a guess because I'm obviously not part of your core team so I have no inside knowledge to how or what your doing.
As other have posted in classic purple cow speak [1], you build a better product and remember "your product is not THE product"[2]. Part of Ash's lean canvas[3] even has a spot for alternatives which may even include competitors. The focal point is you need to start testing everything so you can:
create a customer feedback loop of _learning_[4][4b].
Ash's model is a variant of the original "Business Model Canvas" which is also very good. I would urge you and everyone to LOOK at the examples and iterations of these models on the main site[5].
Some of the questions you start asking in this exercise are:
- What is the problem your solving, and what are the possible solutions?
- How will you reach customers, who are the early adopters?
Its never too late, and like I said actually the beginning of the exercise.
Even if you 'know this already', I would urge you to re-test and re-focus on your previous lean canvases/models. Success is in the finer details. Remember, you never stop learning about the customer. You want a 'specific' customer? You develop a strategy - focused on the problem.
So there you go, a 'teaser' on building a better product. Now go read Eric Ries's[6] book[7], Steve Blank's book[8] for overall strategy. See the business model generation book and site[9] (there is an ipad app!) for more finer details. There is also a…
I have a love-hate relationship with data. Data is great for helping drive product, messaging, engagement, etc. However, most data ends up as noise. It's noise because it isn't actionable.
Ash Maurya wrote this great article on actionable metrics:
http://www.ashmaurya.com/2010/07/3-rules-to-actionable-metrics/
In the end I think it depends on how you want to use the data.
Here are a few things that I find useful:
- calculating your ROI on customers by correlating marketing efforts with key metrics. Google Analytics is good for this as you can basically identify the customer origin with a marketing campaign and how they convert for key metrics.
- full funnel perspective and cohort charts for AARRR metrics (http://500hats.typepad.com/500blogs/2007/09/startup-metrics.html). KISS Metrics and Mixpanel are great for this, but I still haven't found anything that does the Cohort diagrams that well.
- experimenting with messaging using A/B testing tools like Visual Web Optimizer (http://visualwebsiteoptimizer.com/) which has it's own analytics.
- retention email marketing is great with Customer.io (http://customer.io/) but I have fairly limited experience with that.
Other tools include Statsmix (which I think is under-represented); it is pretty good for collecting custom event data.
Once you have the money for it, tools like Pardot are great for marketing overall. We just implemented this so I still don't have much experience with it personally. It's a full marketing…
It's not the customer's job to know what they want.
- Steve Jobs
Customers are great at articulating problems but whenever they start suggesting solutions, I try and get to the root problems whether it's before Version 1.0 or a feature request after.
Before launch I do talk to customers - the first is an interview, second is for feedback. The interview is to learn about problems and how they solve them today (existing alternatives).
Armed with that knowledge, I then build a "demo" (prototype, mockup, etc.) that I show to them once more to validate this "will" solve the problem before I build the real product/feature.
For a more detailed write-up: http://www.ashmaurya.com/2011/07/how-we-build-features/
- Ash
I think the issue is that you're thinking of the customer as being a person, who you move along the sales process.
think of "Customer" in this context as being Customer with a Capital "C". The General idea of customers.
As in, "Who's your Customer". In this case it's almost interchangable with "Market".
What you're developing is who the customer of your product is. You're making a model of what the customer is like, what they want, and why. And you're manipulating that model as you learn more things about real people who might actually be a customer of your company later on.
Think of it as Developing the Customer section of your [business plan](http://www.ashmaurya.com/2010/08/businessmodelcanvas/)
It's not a very big pain point. If I want to share links with friends, I tweet at them or send a DM, or use an IM client. So I would only need this app when sending a link to a friend that's not on Twitter or IM, which is like once a week. Not a big enough need to install a bookmarklet.
That said, it does appear pretty well done. I would consider taking the code and pivoting to an adjacent concept - one that you've validated and proved product-market fit.
http://www.ashmaurya.com/2009/11/achievingproductmarketfit/
http://blog.clarity.fm/the-journey-to-product-market-fit/
http://www.slideshare.net/ashmaurya/10-steps-to-productmarket-fit
good luck
He gives examples in various contexts, rather than advice, but it looks to me like there's a whole series of "Lean Startup" books coming up (e.g. this one: http://www.ashmaurya.com/2012/01/the-new-and-updated-running-lean-book/ ), and some of them will inevitably focus more on practice. I think the point of Ries' book (which I think is reasonable) is to lay out the theoretical basis and start a movement.