Good web analysts love producing 'actionable insights' - it's the way we add value to the business we're in; it makes our managers happy - it's like finding hidden treasure. But what are actionable insights (five years ago I asked who makes them actionable - the analyst or the manager) and how can get better at finding them and sharing them?
Web analytics starts with data - this could be various kinds of data depending on the business model you're following. So, in order to keep things industry-neutral, I'm going to focus on an unrelated area, and see what we can learn from it. Yes, I'm going back to my old favourite: reporting and analysing the weather.
In meteorology, scientists gather all kinds of data from the atmosphere. They are interested in collecting multiple types of data - or "data points" - from multiple sources in various ways. And the good news is that this data is quantitative (it can be given a number).
A thermometer will tell you the air and ground temperature - how comfortable things are at the moment
A barometer - the air pressure where you are at the moment
A hygrometer will measure humidity (or possibly rainfall) - depending on conditions
An anemometer - is used to measure wind speed and direction , and will tell you which way things are going to change and where to look for what's coming next.
Each instrument will tell you different things about how things are at the moment. The anemometer can tell you which way things are going to change, and to some extent, which way to look to see what's going to happen next. The data that these devices will give you will almost always be numerical (or partly numerical), and certainly abstract. Each one individually will give you a partial picture of the current situation. None of them by themselves will actually tell you anything meaningful: is it raining? Yes, but has it started, has it stopped, is it getting heavier or lighter? The temperature may be 16o C but what time of day is it; what time of year is it and is the temperature going up or down?
What is needed here is some analysis.
Analysis is the art of combining the data sources to tell you something more meaningful, with a wider view, and painting a better picture.
One of the easiest forms of analysis is comparison. It's hot today, but is it hotter than yesterday, this time last week, or this time last year? Meteorologists typically compare year on year - there's little benefit in comparing sunny May with rainy April (in the UK, in theory). But comparing May 2016 with May 2015 will tell you if we're having a good spring season.
And comparison leads naturally to trending. It might be raining more today than it was yesterday, but how does that pattern compare over a longer time period? And if you want to present this data to a wider audience, you'll either compile a table of data, or produce a graph of your data. And the analysis is already starting - comparing two forms of data (typically time and another measurement) and producing comparable data (and possibly even trends).
Another form of analysis is statistical analysis - comparing averages, ranges, populations and so on. Providing you and your audience are agreed on which average you're taking, what it means and what its potential drawbacks are, this can be a very useful form of analysis.
A note: analysis is not just plotting graphs. No, really, it isn't. A spreadsheet can plot graphs, but analysis requires brainpower. Therefore, plotting graphs (by itself) isn't analysis. It can help to direct your analysis, and tell you what the data is saying, but a graph is just a set of lines on a page. Plotting good, meaningful graphs is an exercise by itself, and data visualization is a whole subject of its own. And a sidenote to this note: there are times when a simple, basic bar chart will be more informative and drive more action than any trendy visualisation with arrows, flowcharts and nodes. Good doesn't mean visually impressive.
Good analysis breaks down the data into meaningful and relevant sections that will start to tell you more than just the individual data points. Analysis will combine data points: for example, imagine combining temperature data combined with geographic data, compared to average data:
Web analytics starts with data - this could be various kinds of data depending on the business model you're following. So, in order to keep things industry-neutral, I'm going to focus on an unrelated area, and see what we can learn from it. Yes, I'm going back to my old favourite: reporting and analysing the weather.
In meteorology, scientists gather all kinds of data from the atmosphere. They are interested in collecting multiple types of data - or "data points" - from multiple sources in various ways. And the good news is that this data is quantitative (it can be given a number).
A thermometer will tell you the air and ground temperature - how comfortable things are at the moment
A barometer - the air pressure where you are at the moment
A hygrometer will measure humidity (or possibly rainfall) - depending on conditions
An anemometer - is used to measure wind speed and direction , and will tell you which way things are going to change and where to look for what's coming next.
Each instrument will tell you different things about how things are at the moment. The anemometer can tell you which way things are going to change, and to some extent, which way to look to see what's going to happen next. The data that these devices will give you will almost always be numerical (or partly numerical), and certainly abstract. Each one individually will give you a partial picture of the current situation. None of them by themselves will actually tell you anything meaningful: is it raining? Yes, but has it started, has it stopped, is it getting heavier or lighter? The temperature may be 16o C but what time of day is it; what time of year is it and is the temperature going up or down?
What is needed here is some analysis.
Analysis is the art of combining the data sources to tell you something more meaningful, with a wider view, and painting a better picture.
One of the easiest forms of analysis is comparison. It's hot today, but is it hotter than yesterday, this time last week, or this time last year? Meteorologists typically compare year on year - there's little benefit in comparing sunny May with rainy April (in the UK, in theory). But comparing May 2016 with May 2015 will tell you if we're having a good spring season.
And comparison leads naturally to trending. It might be raining more today than it was yesterday, but how does that pattern compare over a longer time period? And if you want to present this data to a wider audience, you'll either compile a table of data, or produce a graph of your data. And the analysis is already starting - comparing two forms of data (typically time and another measurement) and producing comparable data (and possibly even trends).
Another form of analysis is statistical analysis - comparing averages, ranges, populations and so on. Providing you and your audience are agreed on which average you're taking, what it means and what its potential drawbacks are, this can be a very useful form of analysis.
A note: analysis is not just plotting graphs. No, really, it isn't. A spreadsheet can plot graphs, but analysis requires brainpower. Therefore, plotting graphs (by itself) isn't analysis. It can help to direct your analysis, and tell you what the data is saying, but a graph is just a set of lines on a page. Plotting good, meaningful graphs is an exercise by itself, and data visualization is a whole subject of its own. And a sidenote to this note: there are times when a simple, basic bar chart will be more informative and drive more action than any trendy visualisation with arrows, flowcharts and nodes. Good doesn't mean visually impressive.
Good analysis breaks down the data into meaningful and relevant sections that will start to tell you more than just the individual data points. Analysis will combine data points: for example, imagine combining temperature data combined with geographic data, compared to average data:
This data has been presented in a very accessible way, and you can see at a glance that the southern half of the UK had a slightly-wetter-than-average January, whereas the northern half of the UK, and especially Scotland, was much drier than average.
This is analysis clearly presented. However, it isn't insight: I haven't explained why the rainfall varied so much. And if you're looking to explain why the rainfall in January was less than in June (for example), then you can easily point to annual trends: the rain in January is always less than in June.
Insight
Insight is the next step from analysis, and insight will often show you WHY something is happening. Yes, I know you won't fully answer "why" visitors behave in the way they do just by consulting quantitative data, but it's a start - and additionally, you'll be able to answer why a number went up, down or sideways. You'll know you're beginning to show insight when you've stopped drawing graphs and tables, and started writing in sentences. And not just describing what the data is saying, either, but explaining what's actually happening and the underlying causes. "Total sales this week fell from 100k to 74k" isn't analysis. "Total sales fell from 100k to 74k due to the conclusion of the summer sale and a drop in men's shoe sales; last year we continued the sale for an extra month and consistently achieved 100k+ sales for an additional three weeks with no loss of profit."Or, to keep within the weather theme: "Rainfall in the south and east was above average throughout June due to a series of Atlantic storms which passed over continental Europe; in previous years these storms have tracked much further south."
Insight is about using the data to tell you about something that's happened before, and what happened next. For example, we don't watch the weather channel to see what the weather was like yesterday or earlier this morning. We may watch the weather channel to see what the weather's like now in another part of the country (or the world), but more often we want to know what the weather's going to be like tomorrow. Good analysis will enable you to generate insights, extrapolate data and forecast future performance.
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| The regions of the UK Shipping Forecast, for which the BBC produces regular weather forecasts. |
Should I buy (or pack) an umbrella or sunblock? Which way do I point my windmill? How do I trim my sails? Do I go fishing tonight or wait until dawn? When do I gather my crops? How you use the data and then generate the insight depends on the audience. This is life or death for some people.
Online, there's a clearer connection between actions and consequences - if you increase your online advertising spend, you should see more traffic coming to your site (and if you don't, start analysing and find out why, and what you should do about it). With the weather - you can't make it rain, but you can work out why it rained (or didn't), when it's going to rain again (because you know it will), and what steps to take in order to make the best of the weather. If you work in a team or a situation where the brand, marketing and advertising decisions for the online channel are made by an offline team with TV, radio and press expertise, you may find yourself in this kind of situation: do not despair!
Wisdom
Some insights can be demonstrated repeatedly, and described succinctly so that they eventually become gathered wisdom:"The north wind doth blow, and we shall have snow."
"Red sky at night, shepherd's delight; red sky at morning, shepherd's warning"
In online marketing, it could be something like, "Always show the discounted price in red", (honestly, I wrote that before I discovered that somebody genuinely thought it was a good idea) or "Never show a banner with two different products" (I'm making this stuff up).
No amount of data will automatically produce wisdom. Big data (however big that might mean) will not spontaneously transform into insight and wisdom when it reaches a critical mass, in the same way that no amount of charcoal will produce diamonds even though they're made of the same stuff.
Actionable
Data, analysis and insight are useful tools and worthwhile aims - providing that they are actionable. In my examples, I've been talking about using weather data to inform decisions, such as whether to wear a sunhat or a raincoat. In this case, the data on temperature and rainfall are critical. In online analysis (or in any kind of data analysis) it's vital that the analysis and insight are focused on the key performance indicators - that's what will make it actionable. Talking about traffic to the landing page or the product information page will be trivial unless you can connect that data point to a key data point which drives the business - such as conversion, margin or revenue. When you gather the data which enables you to tie your analysis and insight to a KPI, then your insight is far more likely to be actionable (I say this as your recommendation may be profitable but not feasible)."My analysis shows that if we direct traffic from the landing page to page B instead of page A, then we will see an increase in conversion because 65% of people who see page B add an item to cart, compared to 43% for page A." You can almost hear the sound of the checkout ringing.
"If we change our call to action from 'Buy Now' to 'Find out more', then the click through rate will go up." Yes... and then what? The click-through rate is probably a good data point to start with, but how much will it go up by, and what will the impact be on the website's KPIs?
(no proof given, although my data confirms it).
