MEC: First Day!

My first day at MEC passed in a breeze. After a talking-through of the benefits I am entitled to and the regulations I must adhere to, I was introduced to the team I will be working with. I completed around 1.5 hours worth of actual work. Most of the day was spent on the lunch break, attending at company expense a gourmet restaurant with the other two starters, plus seven permanent workers. By the time lunch was over it was already nearing the end of the work day, being only a 10:00-15:30 schedule for new starters! A generous first day.

Tomorrow is a full day, 9:00-17:30 and by the end of the week, I'm probably going to be too tired to update! Thanks for reading anyway and hopefully I'll write up here again soon.

My New Employer

As I mentioned in my last update and at the end of my inSIDious short story I have recently been taken on at another media agency for the next 10-12 months. This year will be fully-paid I'm glad to say and -having met my line manager on Friday- I'm certainly looking forward to starting, and not just because of the perks!

MEC is a media-buying agency owned by GroupM, who are in-turn owned by Wire & Plastic Products (WPP,) the largest advertising, marketing and PR group in the world. We're prominent across the UK and Europe, having recently won new business interests from both the RAF and Comparethemarket.com, totalling over £100M worth of budgets. The Manchester office sits over the canal that runs through Deansgate, a snaking white bridge running over the waters next to the office area providing a calm and remote feel to the place, while the nearby luxury restaurant and bar provide a country aesthetic in the midst of concrete, where employees often go to enjoy beer or two in the sunshine that seldom glows over Manchester.

MEC: One of the world's most prominent media agencies

I'm working in analytics again, the A&I dept as it's called (Analytics & Insight,) where the trading desks are split in a way slightly different to Carat, though I haven't actually seen any of it in action yet. Interestingly though, the A&I team consists of both comms planners from a previous team and data managers from the original core, due to the new team being formed rather recently in order to capitalise on the growth of data and data trends/current market prominence. In all honesty I'm actually very excited that I was picked to tread this particular route at Carat (one of the reasons as to why I was hired, though my line manager comes from a logistics background, so it's still very diverse) because in recent years, the growth of data in marketing has seen increasing demand for 'data scientists' and people dedicated to managing and interpreting data, so I guess the future looks bright for full-time employment if the trend continues.

There's a very good reason why MEC has been voted into the Times Top 100 Companies to Work For too. Not only does it have high rankings for employee wellbeing, leadership and just under half the staff on £35000+ a year, but it's also got a host of other benefits in exchange for hard work. We get pushed into developing ourselves through an Inspiration Fund, small cash amounts to put towards a goal that would develop us as people and better employees. For example, current employees have claimed the cash incentive to contribute to foreign language lessons, skydiving and musical pursuits, and not only that, but at the bottom of email signatures there will be a statement along the lines of "X makes Y thrive," so everyone will know what your project is, encouraging each other like a family to succeed. We also get early finishes on Fridays and even earlier finishes (2 hours early instead of 1) on summer Fridays i.e. July and August, because -as I'm sure you can imagine- it gets quite unbearable in offices from time to time, certainly true if Carat was anything to go by. But aside from the extra cash, early finishes etc. there's also a vocational piece of work. Everyone gets to choose a project, a piece of individual research to be carried out over 90 days alongside regular workloads, designed to stretch our skills and bring in fresh ideas outside of what we normally do, or for markets that we don't currently work within. A brilliant way of gaining new business I think, the projects better preparing us for when we have to pitch ourselves to a prospective client and show our knowledge and skill.

Thank you for reading this piece and hopefully I will keep you posted as time goes on, but I doubt I'll be able to post a summary of every day I work! I'll see what I can do/post in the future but until then

Take Care!

I love these agency logo lists! 

Right to be Forgotten: Opinion

I'm sure I don't need to remind everyone on the EU's ruling over the Google-Gonzalez case. Basically put for those of you still unaware, a Spanish man (Mario Costeja Gonzalez) discovered a search result on Google over a decade old, showing his old house up for auction in order to pay his debts. As I'm sure you can understand, such a result can pose certain financial difficulties in securing finance for the future. This, he argued in the European Court, is in violation of a 'right to be forgotten' (a right that has been sought by others before him up to two years ago) and Google should respond to his request that the unwanted search result be delisted (see the BBC Article.)

Surprisingly, the EU Court ruled in favour of the man and forced the California-based search giant to honour such requests in the future. Naturally this has caused an explosion of opinion and has rekindled the fires of mistrust over who collects our data. The ruling is unprecedented, with Wikipedia co-founder Jimmy Wales calling it one of the most "...wide-sweeping Internet censorship rulings I've ever seen..." He doesn't believe that it will stand for very long and already there have been protests against the EU Court's decision over the implications that such a ruling will have.

Against the Ruling:

The concern over this ruling by freedom-supporters is that "outdated and irrelevant results" is far too broad a ruling. The obvious implications over this is that near-enough anyone could go to Google and claim a critical article of them -however factual or valid- is "irrelevant" and "outdated." For example, last year I wrote a critical article about Secretary of State for Work & Pensions Iain Duncan Smith, accusing him of wasting money on politically-motivated projects based on bigoted opinions. Using this ruling, he could claim that my article is irrelevant and outdated and -by going to Google- could request that the article be removed from search indexes. You can see how this is ultimately insidious, because it is not active censorship in the sense of articles being taken off the web completely, but rather only gatekeepers of the web i.e. search engines who drive the traffic, will be prevented from doing that, which means that the article I wrote will be lost to the thickening forests of data increasingly being indexed by Google daily. This will lead to my article ultimately becoming lost on those who should be reading it and thus, our hypothetical goal for IDS will be complete: Censorship of criticism.

As a small aside, Google have since received 'forget me' requests, including those from a disgraced politician and a paedophile, which serve to highlight the original concerns.

In Favour of the Ruling:

Those who support the ruling are primarily those pushing for greater ability to manage their individual data. This will include those for example who don't wish to see a prospective employer pulling up their data as an excuse to fire them. YouGov have commissioned a poll since, which shows British public support for the ruling (50% Yes; 23% No.) However, it should be noted that the vast majority of these people do not see any personal need to pursue such requests (73% said there wasn't any irrelevant etc. data online, only 9% said yes.)

As I am sure most of you will know, companies these days will refer to Google for information about prospective employees, primarily trawling through social media profiles. We were given a lecture last year on this subject and below are two images from the presentation showing statistics for rejection/hiring, based on results from social media searches (Goldstraw, 2013):

Why they were Rejected

Why they were Hired

These charts show that a great many people are not even invited for an interview because their social media profiles are filled with images of irresponsible behaviour, posts of an unsavoury character or just lies lies and more lies. This ruling will be able to inhibit such processes but -as was pointed out in  the original BBC article, it will do little to stop a determined party from accessing such data anyway.

So What is My Opinion?

While I dislike those who try to censor the world around them and can't stand it when politicians/courts try to meddle with others' affairs, I find it hard to be truly outraged at this ruling, a little annoyed, but not much beyond that. The main reason for this is quite simply that, firstly, Google's mission statement is to make the world's information accessible and available to whomever wishes to see it, which of course can't be done with this ruling in place. Google's team of lawyers I'm sure will fight tooth and nail over this ruling and will ensure that it remains difficult for such requests to be passed. Why is this? Because -quite simply- Google make money through advertising services placed on websites, adjacent to content etc., which will be inhibited if their search indexes are shrunk by this ruling, due to so many de-listings of search results from disgruntled parties. This will mean -naturally- that as few requests as possible are acknowledged in order to retain large profitability.

Now providing that my prediction is correct, this could engender a two-tier system of requests. Imagine two different people who wish to see "irrelevant" and "outdated" content removed from the web: The first is an average man who posted something unsavoury on Facebook and wants it removed, while another is a powerful celebrity wishing to see a magazine article taken down because it defames their character (assume that the article is not untrue but rather shows the celeb in question in an unflattering light, but as a result of their own reckless behaviour nevertheless.) It is natural to assume that the celebrity -who may threaten with lawsuit- is more likely to have a request passed than the average person, of whom there will infinitely more and will deliver a larger loss for Google collectively. They are currently implementing a request button of some kind that will be ready in a matter of weeks. From here one can only imagine what the process will be like, however, the interesting thing is that Google can counter requests, but only if the person in question is a public figure, which suggests that anyone else is immune to scrutiny from Google.

Given all of these facts, I personally have to stand against this decision on Google, because I ultimately place the right to know higher than the right to be forgotten. People should have a right to know if it will concern them in some way e.g. an employer wishing to check a person's background, because this can be costly to businesses who may hire what turns out to be the wrong person. While I respect privacy, sometimes it is a price worth paying I believe for a universal standard that covers everyone regards to the data they choose to put online etc. We already have defamation and libel laws that adequately cover falsehoods posted by others after all, so why the extra ruling I ask? 

Carat Media Internship: Journal Entry #23

Admittedly this post is slightly late as I've been rather busy recently. Last week was very enjoyable albeit a little similar to the week before. I was involved with our DSPs using pixels again, but this time I only looked for the age of the average converting customer as opposed to examining more than one market. Apparently the digital executives manning the Irish desk were quite pleased last week I'm glad to report, so I guess I did right after all, despite my initial doubts. After completing around 9 different campaigns spread across three of our clients I moved onto a new task, using an online maps tool to discover postcode radii.

Pixels

I used the radius tool to focus on London and proceeded to select a radius of around 30Km I think. From here I examined the numbers appearing against the areas, showing population size, which I then cross-referenced with official statistics to discern the areas outside of London with the highest populations. These areas were then placed onto a simple list in Excel and sent off to my colleague, who used the list to select towns and cities from within another DSP. This was again an interesting experience, which taught me a little about the most populated areas of the country and their history as I browsed government reports and Wikipedia, making the task a rather fun one to complete.

My final task for the day was to complete another site list removal, examining campaigns for three more clients. Here I removed the obvious sites, those with unusual URLs, dead links, irrelevant pages and again, these were placed into a removal list within our DSP, which should hopefully funnel out options for targeting sites with our ads. After all, the more bad sites we remove, the less of them there will be and easier it will become to engage our audience.

Thanks for reading this post and I think that this week will be my last session on the internship, which is sad to say the least. The exams are beginning to loom and the year will come to a close in time. So I am afraid to say that the next post on this internship will be my last and before I go I will say that it has been a massive experience for me. Before arriving at Carat I had next to no knowledge of so much, whether that be Excel, digital analytics, databases etc. and after working here for some six months I can definitely say that, even though I am sure I have only scratched the surface, it feels like I've gained an entire repository of knowledge. It's been great fun and so we charge ahead into the final week, next week . . .

Carat Media Internship: Journal Entry #22

During this week I had to complete some administrative stuff for the unit itself, which involved a video that I had to have checked (for client confidentiality purposes.) After all this was done I was set a few different tasks which made the day quite a varied one!

The first task was a presentation that needed producing for an airport client: A keywords campaign. I had a few different line items to analyse so what I did was I used Excel to split the data as usual but instead of analysing and annotating the spreadsheets I produced graphs showing the top decile (best 10 keywords) and produced a table showing the top three contrasted with the worst three for conversions and clicks. This was finally accompanied by a commentary on overall performance for each strategy with recommendations for the future. It was an interesting task I thought.

A Decile Analysis

My next assignment was to a DSP task where I analysed a campaign for audience characteristics using what is called a "Pixel," which tracks users so that -in our case- the client could know exactly which group of people were clicking and what their income was -for example, and match this with age, gender or any other sub-group that you could care to mention. However, not all of these categories have information within them, as they may still be building up a picture which -ideally- will occur as time goes by and the data available grows. In my case, the data available to me was limited in one or two areas but I was still able to discover some interesting things about our audience, which was intriguing.

This was my final task for the day, but between tasks I participated in another meeting, in which I listened to what the good people at one of our search retargeting data partnerships had to say in their pitch to Carat, extolling the values of search retargeting technology for us as an ad agency. The presentation was very interesting, the process of retargeting ads to approximated consumer groups via cookie-tracking pixels innovative and displayed effectively through a simple, grid-like infographic showing how data informs the process, the pixel is placed within a publisher's website and how that pixel garners more engagement as the prospecting blends with the contextual (which was also demonstrated via an amended "conversion funnel," like the one below.) 

Conversion Funnel

As you can see above, search retargeting generally fits around the middle of the scale between discovering customers and driving awareness and moving in for the sell to generate conversions and business. During the meeting I also came across a strategy that I hadn't heard of: Yes, I'd heard about Prospecting and yes I'd heard about Remarketing and finally yes, I'd heard about Contextual. But today I learnt of another strategy: Conquesting. This is basically the intense competition between one company's display ads against a competitor's editorial content e.g. an ad for Ford against an actual article about Vauxhall. It's quite popular at the minute apparently, though it does radiate with '80s-style tactics and language that came with the whole "guerrilla marketing" revolution, when marketing directors and execs started reading books about war tactics as well as Jay Conrad Levinson's classic on the subject. But putting that aside, the meeting was most enjoyable and very informative.

Thank you for reading and see you next week!

Carat Media Internship: Journal Entry #21

Last week was an eventful one at Carat to say the least, as I carried out a new set of tasks revolving around Sitelists. The day primarily focused on spreadsheets that showed all of our website placements from which I had to decide which ones to exclude. After analysing every site in every strategy, I had to compile a Sitelist Removal list that was later inserted into Doubleclick. As long and arduous as it may sound, analysing the sites wasn't all too bad, mainly because the only sites that I had to analyse were the ones with irrelevant URL names, e.g. in a campaign for a camping site I found sites for boating and island purchasing!

Once I'd completed the site lists I showed them to a colleague who placed them into an Exclude list on the DSP in order to remove funding from adverts there in future revisions. He explained the difference between the include and exclude functions and when they would likely be used. For example, if one were engaging in a prospecting campaign, then once many ads had been distributed to as many sites as possible to drum up business, the trial and error method of revision would be applied afterwards. This of course will mean excluding grossly under-performing sites, but for other campaign types (such as retargeting ones,) an include-lead approach may be more efficient because you know where the customers have been before and will want to include a possibly narrower site list. A very enjoyable experience that bestowed upon me new understanding of Doubleclick, which should hopefully benefit my future work.

Now moving on a second set of sheets, were produced for the same client: Day-Part analyses for keyword lists tailored to national campaigns, tackling both Scotland and Northern Ireland. These keyword lists were basically split into the various line items we were running for our client, with each list separated into hourly performance. After organising the tables I discovered the key performance for our client to be across many hours, with some lists performing in the mornings and some in the evenings. This was of course noted in the analysis.

Dayparts

My final task also revolved around keywords for a ticket site, for which I had to discern the performers from the under-performers and list them as before. This was again followed by exclusion on a basis of CPCs, click numbers and CPAs to discern the few that were performing, which in all made the day a rather successful one.

 Keyword Groups

An Essay on Analysis

HOW TO ANALYSE DATA

This essay is a compilation of pointers detailing how to think and –where appropriate- how to best use Excel to implement desires to achieve a result. Before we begin however, I feel that I should provide you with a list of abbreviations and definitions:

-       AUDIENCE REACH: Traditionally the number of people to simply see an advertisement

-       CLICK: When a display advert (e.g. a banner) is clicked on by the user

-       CONVERSION: When a desired action is completed on the part of the user AFTER clicking the advert e.g. making a purchase.

-       IMPRESSION: Every time a person simply sees the advert on a webpage (See AUDIENCE REACH)

-       LEAD: A direct marketing term referring to communications seeking SIGN-UPS of some kind for future communications, as opposed to simply making a purchase for the present.

-       CPC (Cost per Click)

-       CTR (Click-through Rate)

-       CPA (Cost per Acquisition)

-       CPL (Cost per Lead)

-       CPM (Cost per Mille or Thousand)

-       DSP (Demand-side Platform)

PART I: THE ANALYTICAL MIND

The mind of the analytical is concerned with questions. These questions can be summed up in rather simple lexis such as “Why” and “What,” these chief two words being crucial to reaching conclusions and implementing reactions to them because they, A) Discover the reasons for a particular event and B) Discover the implications for its consequences, whereas other questions such as “How” don’t really tell us much about implications or reason, but rather about technicalities, explaining the conditions that caused the event, which is useful of course, but it is not the chief interrogative that gives us the path to solving a problem because it is chiefly concerned with the past or present and not the future (OUP, 2014: Online.)

So how do you think in those terms? Well I’m not going to patronise you and give you baby steps with which I’m sure you’re accustomed, but below is a simple flow chart that I’ve devised in order to visualise an analytical thought path.

As you can see, the “How” node takes us towards the causes of a particular occurrence but it is not the reasons behind the occurrence, for instance, let’s say that I want to know how a CPC became so high and the reason or CAUSE of such an event might be that fewer people are clicking the advert. Fair enough, but why aren’t they clicking, is the question that we need to ask and the answer might be that the creative is off-key, or the timing is wrong and from this information, we can think of solutions to the crux of the issue. But this is just the introduction to our study of analysis. How do we implement this into a concrete example, where we deduce a problem and think of a solution therefrom?

PART II: AN EXCEL EXERCISE

Using Excel we can split data and make it sing and dance as we please in order to get beneath the surface. The following example is taken from a coursework task, courtesy of my academic institution. Please note that none of these names and associated data are based on a real company or client list; all information is fictitious:

Our customer has a business that offers services to multiple clients across multiple sectors. They made £5405 in 2013, but £5710 in 2012, indicating a loss of business in terms of sales. This would be evaluated as a business weakness to the untrained eye, a blanket statement that any fool would be able to spot with ease as the image below demonstrates:

The problem with this logic is that it has failed to analyse the problem, instead just pointing out the obvious. What we need to do is look deeper, so let’s examine more about this client list: Who are the people in it and where do they operate? Well, by organising the list vis-à-vis sector and grouping the sales total for each one in which our client has contracts, we can see a very different picture that changes our influences. The following graph details the sales totals in column clusters, detailing the sales value per year between 2012-2013 for each individual sector, and I can tell you that the picture looks very different indeed!

Now let’s have a closer look at those categories. The Leisure and Sports Stadia sectors are clearly increasing in sales value, which gives us a good picture with which to solve our financial problems. Not only have we identified why the problem came into being (declining sales in councils and office refurbishment,) but also what we can do about it in response: Shift focus to those growing sectors like Leisure. In completing this minor analysis we already have an idea of what we can do with the future; if we analyse further into whom those clients are and what they need from us, in addition to discovering how we can deliver that promise and we can ultimately get from this:

To this . . .

And so we can see that as we learn to present more facts as numbers alongside one another, they can be most revealing, but the ways in which we just presented those figures is only a single way of interpretation. Data –like anything made of numbers- can be expressed differently, which is why we must also consider

PART III: THE KEY EXPRESSIONS

Expressions contain not only raw numbers, but fractions, percentages and a plethora of graphs and charts used to not only demonstrate a particular conclusion, but also try to express it in a way that is the easiest to understand because whenever data is needed, those who seek it want it set before them in a way that doesn’t make the brain hurt with confusion, but rather throb with excitement!

This means that not only does the analyst need to reach the conclusion but show it, which means performing Excel functions in many cases. I’ve already shown via my Excel 101 Tutorial on the VLOOKUP function, but I haven’t demonstrated the use of Pivot Tables, a highly necessary function of the Excel software and a staple in the toolkit of the analyst.

PART III: PIVOT TABLES

Firstly, I expect that you have a whole bunch of data like this:

Of course all that’s ugly and inconclusive, just raw data in other words, data that must be broken down and reassembled properly with the hopes of reaching a more satisfactory conclusion. But first what does this sheet show?

Taken from offline as a practice Pivot Table sheet, it shows numbers of people working and earning across the United States, showing their sex, race and earnings amongst other variables: Now onto the Pivot.

Click on the “Pivot Table” function found under the DATA tab (bear in mind that I am using Excel for Mac 2011,) and you should come across a screen like this one below, (though if this does not happen first time, you may have to manually select the relevant cells via highlighting their columns detailing what is displayed their by using the Command, Shift and Direction functions. Highlighting all that you want to include and then pivoting should do the trick.)

The “Builder” will appear alongside a –most likely- filled in Pivot and now you may play with it to your heart’s desire. Usually I deselect all of the stuff Excel’s already selected for me and begin again, so un-tick all the Builder’s boxes and reselect them. You may find that you get some variables appearing on certain axes that render the table awkward to work with, which is why it is important to be wary of these:

In order to make the table work for you, you will need to change the placement of variables by dragging and dropping them into the different categories to ensure that they work for you. For example, if one of my variables is “Website,” then I don’t want every website dealt with appearing across the top of the sheet, because there’s likely going to be large numbers of individual URLs, which is why they are better placed on the Y axis running lengthways, because it makes it all easier to see. This rule generally applies to any situation where one deals with metrics via which to judge (X axis) and the subject being judged (Y axis) which in my example was a URL. Please note also that in those individual boxes, you can also place them above and below one another so that –for example- you can see every URL on a particular day of the week and judge performance against another time period etc.

In any case, what you should be left with is something akin to this:

But this is of course insufficient because all the numbers are the same and are littered with decimals, which is why you must then adjust every new column to its own type of measurement.

This can be done very simply with a column edit, so click on the tab where the column is e.g. Column E containing –in my case- a column showing each stratum’s earnings for the average year and every cell should be highlighted like so.

From here we must alter the numerical value measured within the column, which means right-clicking and –from the drop-down menu, selecting “Format Cells.” From here you must click the “Number” tab if not already highlighted and select the most relevant category, which, for me, is the “Currency” category. Now under Currency, you must select both the currency used (US$ for me) and the decimal place that you want to apply (2 decimal places is usually sufficient,) after which you should be able to see something like this:

And that should be that. You’re ready to go and analyse that data, but in order to do that we must move onto another tutorial, so once you’ve set up all your Pivot Tables and have their numbers sorted out in the columns as appropriate, read on for the next tutorial, dealing with data manipulation.

PART IV: DATA MANIPULATION

To do this I’d advise inserting what is known as a filter. You can to this simply by highlighting the columns again and clicking onto your DATA tab. Under this there should be an option to “Insert Filter,” depicting a symbol like a silver funnel. If you’ve performed this correctly you should see arrows appear at the top of each column, which basically means you’ve just inserted your filter.

So once the arrows appear, you can play with the tool. In order to do this, click the arrow and examine the black box that drops down. In the image below you can see an ascending/descending function, which –obviously- organises numerical values into the highest and lowest orders. This function also applies to lexis with alphabetical organisation. Ultimately this is known as sorting the data however, what can often be more relevant is to filter unwanted variables. Given that tables featuring variables such as individual URLs tend to possess repetitive data, it makes sense to disregard some options entirely, to shrink a table of 10,000 rows to just 10. To do this, you can check and uncheck different boxes to select and deselect certain options found within a particular column.

To demonstrate the power of a simple filter in the world of analytics, here is the pre-filtered table:

Here is the table filtered by years in education to determine who exactly has spent under 100 years there (please remember this is aggregate averages by state, not individuals!) and you can see a clear difference.

In summary, if you combine these two powerful tools you can deduce the most and least positive results, the abnormalities within a body of data, with astounding ease, which is how analysts get through spreadsheets with potentially a million rows of data in them, within as little as a few hours sometimes.

PART V: ANALYTICS WITHIN AD AGENCIES

In my quite limited experience working within an ad agency I’ve obviously had to work extensively with these tools. But in terms of marketing and advertising performance, what kinds of formulas do we use and what expressions benefit certain situations?

-       Cost per Click (CPC) is the bog-standard cost for display ads and tends to be a primary KPI (Key Performance Indicator) for clients. It is calculated via: TOTAL COST / CLICKS

-       Click-through Rate (CTR) is another popular performance metric measuring the actual marketing performance of the ad, showing engagement via the number of people who actually clicked, expressed as a percentage. It is calculated via: CLICKS / IMPRESSIONS

-       Cost per Acquisition (CPA) is another costing metric used to discern the average cost for each conversion garnered by a particular advertisement, calculated by: TOTAL COST / CONVERSIONS

-       Cost per Thousand Impressions (CPM) is a less-common metric yet still important to some clients, detailing the average cost for every one thousand impressions the ad receives. This is calculated via: TOTAL COST / IMPRESSIONS * 1000. Please note that this is also used for traditional media such as Television and Radio, Impressions of course being merely a digital term for audience reach.

-       Cost per Lead (CPL) is a similar formula to CPA but with a very crucial difference: While CPA measures the average cost for each conversion, a LEAD is an interested party to whom the communication is sent, being a form of direct marketing with a response component and a more future opportunities-centric focus, as opposed to general display marketing with an awareness-centric approach, designed for simply making a single conversion without any kind of sign-up. This of course has implications for results expected, because it may be harder –depending on target markets- for some to part with money for online purchases, whereas others may be reluctant/inert when requested to sign up to something. Here is the re-modelled calculation: TOTAL COST / LEADS GENERATED

These are the most prominent of display calculations used in industry today as I have experienced them in my quite brief time at Carat. The experience has been a riveting one it has to be said, with varied days and many tasks that –in my rather long essay above- have only been explained in the slightest detail. I will be continuing my internship series of posts but the internship itself with be ending after March I think, meaning that I’ve only got another two or so weeks to work. On the whole it’s been great!

I hope that you enjoyed this essay and/or any of my weekly blog posts you’ve read and if you, too, found it all riveting, please, let me know!

Thank You,

Jake

BIBLIOGRAPHY

Oxford University Press (2014) How http://www.oxforddictionaries.com/definition/english/how?q=how [Online] [accessed January 18^th 2014]

Russel, B. (1912) Problems of philosophy OUP Paperback Ed. [Reprint: 1971-1972]