Web Analytics: Who determines an actionable insight?

Who determines what an actionable insight is? 
I ask the question, because I carry out a range of analysis in my current role, analysing click paths and click streams; conversion rates and  attrition; segmenting and forecasting, with the aim of producing actionable insights for the developers in my team, so that we can work  towards improving our website.  But what makes an insight actionable?  I've discovered that it's not just crunching the numbers, asking the right questions and segmenting the  data until you've found something useful, and based a recommendation on it - the recommendation is usually a sentence or two in English, with a few numbers to support it.  

However, even a recommendation of this sort may not become actionable.  

For example, you might recommend  changing a call to action to include the words 'bonus', 'exclusive' or somesuch.  You might have carried out your testing and determined that  the call to action needs to be a red triangle or a green circle.  Unfortunately, if the main focus of the sales and marketing teams is not to sell  green circles, and there's a cross-channel push to sell blue squares, then you'll have to optimise your own work to determine how best to sell  blue squares.  

Sometimes, actionable insights have to include a wider view of the business you're in.  In a situation where you're  recommending how to achive a goal, and the goalposts have moved, then the position of the new goalposts has to become a factor in your  analysis.  It's true that your proposed course of action would score goals in the old goal, but if the target has different, then you need to  readjust.  Use your existing analysis to help you - don't throw it away.  For example, you might do some keyword analysis, and find that 'budget shapes' converts at a better rate than 'cheap triangles' and 'coloured  shapes'.  So, using conversion rates (and, if you can get them, costs per click and so on) you write a recommendation that says, "The  conversion rate for 'budget shapes' is much better than 'cheap triangles' and I can confirm this with statistical confidence, and I therefore  propose that we change our spending accordingly."  However, if paid search isn't on your marketing team's list of priorities (or they've already  reached target for shapes sales this year) because they're focusing on the next display campaign, then you'll need to readjust.  Take account of the learning you've made - keep a note of it - and in particular how you reached your recommendation so that you can use the tools again  next time, and move to the next target.  

On the other hand, you might be presented with a request to analyse a particular campaign.  Perhaps the marketing team want to understand  how their display campaign is performing, or the web content team want to know which shape to promote on the home page.  This is your  opportunity to go out and hit an actionable insight.  It helps, in these terms, to know what's possible - what can be changed in the campaign, or  on the home page, or wherever.  If the promotions team has decided that they want to sell green triangles, then work within those constraints.  If the message on the home page needs to say, "Exclusive shapes for sale here," then make sure this is included in your recommendations.  It  might not be the optimal solution - there may be better options available, and certainly include these in your recommendations - but if it's better than the present version of the site, then it's certainly a valid recommendation, and an actionable one too!  

It's rare that a colleague will come to you with a blank slate and ask what the data shows is the best answer; he or she is more likely to ask for  your input into a decision that's already being made, but in any case, do your best to show what's possible and what's better.  By working  within the constraints that you're set, and with your colleague's agenda already in place, you're much more likely to achieve an actionable  insight that will actually result in action being taken.  This leads to a positive result for you, and for your colleague.  

I liken the situation to batting in a game of cricket.  Sometimes, a batsman will get to take a large stride towards the ball, and play off the front  foot in an expansive style, hitting out and scoring big runs.  Given a clear definition of the area for research on a website, and the ability to test ideas, make larger changes and follow the data where it leads, it's possible to really hit some big wins.

At other times, the batsman has to stand up straight, bat in front of body, and play  off the back foot - in a more defensive way, still hitting the ball but working to the bowler's agenda, and almost having the ball hit the bat, rather than the other way round.  Asked how much traffic a website has had in a given week, day or month, there are few ways of responding to the question without given the short, direct answer.  It's still possible to play big, expansive  strokes off the back foot - the big, bat-swinging strokes that score big runs, when the batsman adjusts his agenda to the bowler's, and reacts  in the most positive way possible.  It's not always possible, and the defensive shots are often easier to make.  In other ways, it often comes down to Mark Twain's remark that, "Most people use statistics the way a drunk uses a lamp post; more for  support than illumination."