The term analytics is rooted in the ancient Greek word for ‘breaking up’ or ‘setting free’ and was intended to indicate a transformation of something complex into something simple which everyone could understand.
With that in mind, it’s funny that analytics itself is now widely regarded as complex, difficult and something best left to the experts.
When it is broken down into its various practices, however, analytics is much more approachable and certainly something just about anyone could handle.
In this series, we are going to cover four of the most-used analytic approaches and provide details on what distinguishes them, in what circumstances they should be used, and how marketers can use each of them more effectively.
Once marketers can distinguish different types of analytics and know how best to use them, they will hopefully gain confidence that they truly understand ‘analytics’. To start off, we’ll look at an overview of descriptive analytics.
Before we begin, though, we’d like to let you know that Econsultancy are running an Advanced Mastering Analytics session in Singapore on Tuesday, August 15th. Click here to see more details and book your spot.
So what is ‘analytics’?
Before we describe a type of analytics, it’s best to define exactly what we mean by the term.
First off, analytics is the practice of converting existing data and information into new data and information which can support decision making. Analytics turns data into actionable insight.
That is, when you have ‘done analytics’ you should have easier-to-read data than you had previously and it should help people make better decisions.
Also, analytics is a process which involves a number of steps including:
- acquiring data,
- applying domain knowledge,
- performing mathematical functions on the data,
- using statistics where appropriate, and
- reporting results in an easy-to-understand format
Finally, analytics is a discipline which crosses IT, business intelligence and marketing as well as executive decision makers. So learning the best practices for how to process and present data is a useful skill for just about anyone.
Descriptive analytics overview
In this post, we’re starting with one type of analytics which is probably the simplest of all the types, descriptive analytics.
Descriptive analytics exists to highlight the features and characteristics of a data set by using a summary. It is typically used to convert a large amount data into a small amount of information which is easier to understand.
An example
For example, a business which sells cars may have a long list of all of the cars it has sold in a year. That list is too hard for people to use for decision making, and so an analyst would summarize the data using descriptive analytics.
The resulting report may include the number of cars sold each month, an average of how many cars were sold per day, or simply the sum of cars sold in a year.
All of these figures describe the data in simpler terms than the list as a whole. Because it is easier to digest a summary than a list, the descriptive report will be more suitable for those trying to understand what happened and decide what to do in the future.
Dashboards are a feature of analytics software
The distinguishing features of descriptive analytics
So with the above definition and example in mind, what makes analytics ‘descriptive’ as opposed to something else?
For a start, descriptive analytics only uses facts and real data. Descriptive analytics should not include assumptions or derived data which cloud the description. For example, the report described above should not include estimates and any missing data should be clearly noted.
Descriptive analytics is also only about the past. Future estimates and predictions belong to another type of analytics with different best practices.
And finally, calculations made for a descriptive analytics report should be marked clearly. Analysts should indicate if a data point is a sum, average, or an aggregation. Probability and statistics, like predictions, belong to another sort of analytics and should be omitted.
When to use descriptive analytics
Descriptive analytics can be presented either as a real-time dashboard or a report depending on the urgency of the data.
KPI reports are a particularly popular example of descriptive analytics as they include real numbers from the past which require little or no further calculations to make sense.
How to do descriptive analytics
1) Start by collecting relevant data
For marketers, there is typically a short list of metrics which are relevant to other departments:
- Ad clicks
- Web page views
- Conversions
- Revenue
So to get started, these figures need to be collected into a single database or spreadsheet so that they are ready to analyse.
2) Do the analysis
When analyzing data, decide first what people really need to know. Do they want to see trends over time, or just want to know whether targets are hit?
If in doubt, leave it out and see whether anyone asks for it.
Also, be conscious that what you are doing is descriptive analysis and stick to the key principles listed above.
3) Present data clearly
Unlike other types of analytics, descriptive analytics leaves the interpretation of the data to the reader.
The analyst can influence perceptions of the data through scaling, but this is discouraged.
4) Aim for consistency
Having the same report every week helps decision makers compare results over long time frames, so descriptive analytic reports should be regular and consistent.
Dashboards should also be consistent and changes should be versioned so that it is simple to revert to a previous one.
Best practices for descriptive analytics
While there are many sizable tomes and lengthy blog posts about how best to present descriptive data, there are a few general principles which are fairly straightforward.
1) If you can use a single number instead of a chart, do so
Too often, descriptive reports are filled with useless, distracting charts which would have been better delivered as a number.
For example, web traffic is typically reported as a line chart, with each day’s figure creating a data point. What might be easier to understand, though, is the week’s daily average and how it has moved since last week.
2) Only include what is necessary
Additionally, descriptive analytics should concentrate on necessary figures and charts for the decisions being made. Extra data or superfluous charts and graphs go against the purpose of analytics!
3) Know your charts and when to use them
If you are going to use charts, be sure to use them correctly. A few best practices include:
- Only use line graphs when the items on the x-axis are continuous, e.g. a period of time.
- Use horizontal bars when the categories have lengthy descriptions.
- Pie charts are best when there are less than 4 major ‘slices’ and be sure to order them according to their size.
Refer to one of the many posts on the subject for more info on this topic.
4) Remove chart junk
Finally, remove many of the chart elements which Excel includes as standard. Y-axis numbers, data points, and grid lines can usually be omitted.
The result is a clear presentation of data which still gets your point across without confusing your reader.
So…
For those new to analytics, descriptive analytics is the best way to start as very little data manipulation is required to deliver high-quality, useful reports.
There are some best practices to follow, but overall descriptive reports and dashboards should deliver what the consumer requires, and nothing more.
Keeping things simple makes life easier for the decision maker, and for you, the analyst, as well!
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This article first appeared in www.econsultancy.com
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