Also yes your job as a web analyst is a very hard one, even if you overcome the data hurdles there is still the challenge of convincing people to listen to you and take action based on your analysis / insights.
Perhaps it is because we swim in “session_id’s” and “shopper_id’s” and “evar’s” and “parameters” and “strings” that often the analysis that comes out of all that excellent work is very "dry". One of the reasons our decision makers don’t jump up and take action is they have a hard time relating to our analysis due to its “dryness”.
Hence Tip #4: Make data “connectable”. Quite simply this means that we should take as much of the “technical” / “dry” stuff out of our analysis, or should I say recommendations, and try to make it easy to understand from the perspective of non-analysts / business users.
This could mean using “pretty pictures” (slides or graphs or colorful tables or click density report screen shots), it could also mean using language and terms that “hide” some of the dryness and make our data easy to understand and to relate to.
There are tons of pretty picture examples (see my post on data visualization for one such example of "connecting" via pretty pictures). Hence I’ll illustrate this analytics tip with a language example.
I am sure you have done reports on “site engagement”. There are many ways to define site engagement but a definition frequently used is “number of pages viewed” on the website.
Perhaps a normal report for you is Site Engagement and Purchasers.
So you crank up your favorite analytics tool and produce this report (after applying the rules that: 1. usually absolute numbers are sub optimal 2. trends are magnificent and 3. segmentation is God’s gift to analysis):
This is a very insightful report and it is extremely easy to read and understand for most of us. As we look at it potential actions jump out at us right away.
But to a business, non-analytical, user perhaps they can pick up a clue or two from this but I would hypothesize that they might not get as much out of this as you might want them to.
Now let us try to make this “connectable”:
Ahhhh simple sprinkling of the english language and the table, with exact same data, “speaks” a lot more to any kinds of data consumer.
Now when our business decision makers see this data they are able to internalize it much more. They can relate to the english terms we have used much more then they could understand segments as defined by page views. The numbers are the same but this table will suggest action to our decision makers.
In real life when this was done here is what happened :
- The users wanted to know the definitions, this meant they actually bothered to learn what each segment was in a way that did not happen before.
- They looked beyond the site conversion rate (represented by the one-off-wonders, loyalists) and from the numbers truly understood the opportunity landscape that the first three sets of numbers represent.
- Since the terms used are very reflective of the persona way of thinking the terms became ingrained in the company culture (especially marketing). People started talking about Flirters and how to convert them into Browsers and meetings where held to specifically target Browsers and get them to become buyers and special programs were created for Loyalists (because they were so rare).
You can’t underestimate the power of your customer segments becoming a part of decision making culture.
- Additional monthly analysis were created for each segment with someone assigned to “take care of the browsers and give them some love”, (translation: what action can we take?).
This is an extremely simple example of making data and analysis more “connectable” but hopefully it illustrates the power of stepping beyond our level of thinking and into the world of our decision makers and as a result doing something simple to have a big impact.
Choice of words and careful thinking can enliven the simplest or the most complex report helping us communicate our message more effectively, as well as in some cases atleast bring about some fundamental change in our organizations.
Agree? Does this make sense? Not buying this at all? Do you have examples where you did something different to make data more “connectable”? Please share your feedback and critique via comments.
Props : The original creative spark for this specific approach came from listening to a presentation by Tim Boughton of Holiday-Rentals.com & WVR Group at the eMetrics summit in London. I have evolved Tim’s suggestion a bit above. Suffice it to say the data and profiles above don’t remotely reflect that of Tim’s employer or mine, though if you find it useful please book a vacation at his employer’s website or buy some financial software from mine. : )
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