Google Attribution 360, improved campaign measurement

attribution 360 | DBi
Today we will discuss one of the heavyweights of this new suite: Google Attribution 360. Attribution 360 promises to be a turning point in digital marketing and become the tool that will help drive success of marketeers. Its aim is to help you evaluate the performance of different campaigns – both online and offline – and all contact points (when and where the ad appears, etc.) throughout the customer journey. In this way, advertisers can discover and identify the correlation between acquisition and media activity, such as television, or even external factors such as weather phenomena.   One of the main advantages of Attribution 360 is that it not only allows measurement through various channels, but also of different devices, obtaining a single multi-channel and multi-device funnel. The tool has an unlimited number of preliminary steps to conversion within 30 days compared to 4 steps now showing Google Analytics.   Strengths: Data visualization and analysis of “path to purchase”. Attribution 360 also takes into account the position of the channels in the path, rather than just the participation. It also offers a new powerful data visualisation capability that allows reporting on different channels and media, with the ability to propose create media and budgetary plans, all within the tool itself. The data-based attribution modelling in this tool means better investment decisions and deeper understanding of the real contribution (role and weight) of each of the channels in your digital strategy. Knowing what action opens the conversion cycle, what actions influence the user journey and what action closes the cycle facilitates analysis of all interactions en route, not simply attributing... Read more

Which countries really won at the Rio 2016 Olympics?

OLYMPICS DATA MEDALS
We posted a couple of weeks ago at the midway point of the Olympic Games 2016 with a slightly different view of who was winning the Olympics depending on what data points to you for comparison. So now the games are over, how did each country do? Medal haul: Similar to the last post, in terms of the haul, nothing has change. The US and the UK had record breaking years while China, although still in third had a bad year compared to previous games. (table based on a points system – Gold = 3, Silver = 2, Bronze = 1). Key points: – Size of population and economy might are clear determiners for large medal hauls – There is no country that has more than 25 medals that has less than 60m inhabitants – Out of the top 15 countries by medal haul, the only non-OECD countries are China and Russia (judged on GDP per capita). – The top 20 countries in terms of government expenditure represent 68% of all medals won. Proportion of Each Team to Win Medals: Two weeks ago North Korea, with a team of 35 managed to win seven medals, pulling ahead early on by a strong showing in weightlifting. That’s a whopping 20% of their athletes. They haven’t won anything since and as such they have been pushed down to fifth. The new kings are Azerbaijan, Ethiopia and the US. Azerbaijan and Ethiopia showing the importance of dominating a particular sport to push you up the standings. Azerbaijan with a good haul in wrestling and all of Ethiopia’s medals coming on the track.... Read more

Who’s really winning at the Olympics? – Update on the following post

thmb
Usain Bolt the 100m and 200m, Mo Farah takes the 5000 and 10,000, Michael Phelps to win…everything. Olympic results are predictable. Well that depends how you look at it. Everyone so far has been focussing on the medal haul of each country, but what does performance look like if we factor in a few more variables? At the halfway point of the games we’ve been playing around with some data to see which countries really have performed well in the games so far. You’ll be surprised at the results. THIS DATA IS AS OF AUGUST 16 2016 – Check then update we did at the end of the games Medal haul: In terms of just medals, things are quite predictable. You’ve all seen this table, the US way out in front, with the UK and China scrapping for second (table based on a points system – Gold = 3, Silver = 2, Bronze = 1). One thing we can say for sure looking at the medals table is: size matters. In order to have a large medal haul,  lets say over 25 medals in total, size of population and economy might are clearly a factor. There is no country that has more than 25 medals that has less than 60m inhabitants and out of the top 15 countries with, the only non-OECD countries are China and Russia (judged on GDP per capita). The top 20 countries in terms of government expenditure represent 68% of all medals won. Proportion of Each Team to Win Medals: What about the amount of medals compared to team size. North Korea, with a team of... Read more

Sentiment Analysis with Twitter

twitter sentiment analysis img
Recently, I’ve been learning the basics of performing sentiment analysis on social media data with R. In particular, I used the TwitteR library – written and generously shared by Jeff Gentry – to pull tweets mentioning companies competing in the digital environment out of the twitter API, analyse their content using text mining methodologies, and plot their sentiment against each other. This method can be helpful to benchmark the perception people have of a company against its competitors, and to understand what specific things do people like and dislike about them. The best part if it is that it all can be done for free. The aim of this post it not to provide a comprehensive guide about how to perform sentiment analysis on Twitter data, but to explain step by step one of the simplest methods to do so, and be used as a starting point to develop a more advanced analysis in line with your company strategy. Most of the code for this article has been taken from the Mining Twitter for Airline Consumer Sentiment. Result So, what is the expected result of a “sentiment analysis”? We’ll start showing an example output of the analysis, and then we’ll present the details of the process and the code used to get it. Sentiment Graphs The first and more visual result is a series of histograms that show, for each company in the analysis, the number of tweets for each level of sentiment. The tweets with a score lower than 0 are considered negative, the ones with a score equal to 0 are neutral and the ones with a score 1 or higher are deemed positive: On the... Read more

The latest enhancements in Adobe Analysis Workspace

Adobw analysis desktop | DBi
The latest addition to the Adobe Analytics family – Analysis Workspace has issued a new update last week. If Adobe keeps up with the enhancements very soon we might ditch Ad hoc and rely entirely on Analysis Workspace. For those of you who are not familiar with the product I highly recommend you go and try it out today! In this post I will cover the most recent features added to Analysis Workspace that has made it better, faster and even more user friendly.   1. Undo So, the first new addition and probably the most needed one is the UNDO functionality. You can either select it from the action bar or you can use the keyboard shortcut (Cmd+Z/Ctrl+Z). However, you should be aware that there are some cases when you cannot use the Undo function. For example: Changing the report suite ID in the report suite selector Resizing or moving panels and sub-panels Selecting and highlighting cells in a pivot table Running cohorts, except when you click Run. Changing the configuration (dragging metrics, changing values, and so on) is not undoable   2. Link to This Project To make our lives easier when sharing projects Adobe has also added “Link to This Project”. This way you can send your Analysis Workspace project to any of your colleagues by just sending them the URL. Then they can go straight to the project by clicking on the link. You don’t need to share it – recipients will be able to open it in a “Read only” mode. All admin recipients will still be able to edit and save the project.... Read more

6 quick wins to help you speed up reporting

Speed-up-reporting
We often come across cases where a business will have an analytics package in place, but the tool is hugely underused. Staff who are already busy with their own work either don’t have time to sit down and learn the tool or have trouble navigating their way round the warren of badly named report. Emails hit inboxes every Monday with reports that nobody looks at but can’t figure out how to stop, generated by a poor analyst who is supposed to decipher what should be in a report from with nothing but a cryptic 8 word subject header in an email. But it doesn’t have to be like that! Below I will briefly outline some very simple processes you can follow that can help encourage people to use the tool and to generate quicker more relevant reports that people actually look at. 1. Renaming analytics variables If you have any variables that are not completely self-evident as to what they refer them, rename then in a more intuitive way. The names you give these variables will be the names of the reports they correspond to, so put yourself in the shoes of someone who is new to analytics and has to decipher what these report titles mean. This is often hurdle number one in stopping people using the tool, so make sure it’s good. 2. Reorganise menus Not all analytics packages – Google Analytics for example – will let you do this, but in other tools like Adobe Analytics will, and is a quick and easy way to make the tool much more user friendly. We find it’s best to... Read more