The Constant Observer

Analytics have resulted in the optimization of blogs, shopping sites, and other e- commerce sites alike. While the benefits of analytics have been enjoyed over the Internet, similar kinds of analytics have failed to really percolate into the real World.

True – there may be what one may call ‘retail scanners’ where cameras may be strategically placed, maybe even infrared counters coupled with observational researchers. They however, have very little scope in terms of scale and identifying mega trends in terms of the retail environment simultaneously.

Path Intelligence is a UK based company that could possibly provide some answers to this problem. The model hopes to monitor foot traffic in a rather indigenous way – through people’s mobile phones.

Our cell phones often work in a way where they ping the nearest tower and inform them about their location. So when I am in Worli, Mumbai – BPL Mobile has 2 towers. 1 in Worli Naka and one on Worli Sea Face. So depending on which corner of Worli I am in, one of the two towers will get pinged. Now in terms of identifying location of user – its not really accurate because the area each tower covers is quite huge so it does not really tell the data analyst anything. Path Intelligence has built receivers that detect these signals and triangulate the owner’s location with accuracy of up to a meter – and as a result has brought accuracy into the concept of tracking through mobile.

Each ping is identifiable with each single user. It’s almost like an IP address. IP addresses have their share of problems (think dynamic IP’s) – with mobiles the ping query will not change. So data trackers and analysts can effectively monitor movements in stores (or any other places for that matter – it depends on there the Path Intelligence Towers are put). They can get data such as time spent at locations, “bounce rate” for locations, roadmap for shopping (where do they go first – and subsequently what follows).

If Path Intelligence can somehow co relate the data of users across different retail outlets then they have a winner because they effectively percolate a person’s life. For example, if they can build systems where my data can be tracked in Big Bazaar, Wal Mart, Reliance Fresh, Shubhiksha, Pataloons, LifeStyle etc, and then all that can be combined – then imagine the data analysis potential.

The major disadvantage with this kind of a model is that one can track user movements but there is nothing that Path Intelligence can really know about the customer. Since everything is tracked through mobile phones, only mobile companies will know that.

In my post on the future of VAS I said that Mobile Distributors i.e. the service providers will hold the key to many forms of revenue. This again is one such example where the service providers can milk revenue.

I see an opportunity for cross media conglomerates such as Reliance where they can co relate such data tracking in their stores and super markets with their Infocomm divisions. The only problem is that Retail is being promoted by Mukesh Ambani and Infocomm by Anil Ambani.

Path Intelligence also plans to let users subscribe to services where they can tie their phones to their signal ID’s and receive contextual offers based on their location. The danger obviously is that of spamming.

The company is currently only launched in the U.K but has plans to expand into other countries if the research and business model are viable. If they don’t, expect other companies to develop similar products and monetize on them especially in countries like India where today, the possibilities of data collection through mobile are enormous.

How exactly does one calculate the Return on Investment (ROI) for internet advertising?

Lets first look at why web metrics are important.
Imagine you are an advertiser who is looking to put up ads on the internet for various purposes such as branding, sales etc. You would need to examine which site is most visited, how long people stay on the site, and how often they visit. That will enable you to do your math and figure out how much you need to pay for your ad on one medium as opposed to another.

You can thus get an idea that there already seem to be various measures of web metrics and analytics. Before we get into the specifics of the various measures of web metrics, lets get an overview of web metrics with respect to other mass media metrics.

In the good old days of broadcast television and radio, firms such as Neilsen or Arbitron gave gadgets to samples of volunteers who measured what their televisions or radios were tuned to; or they asked people to fill out diaries describing their reading, listening and viewing habits. Both methods produced results that were almost criminal in nature in terms of market research.

Even today, TRP ratings are generated through only a representative sample of 500 TV Sets by TAM India! True – it’s a representative sample which means that those 500 TV viewers are further subdivided in terms of income and socio economic classifications (so if 50% of India is SEC C, then 50% of those 500 TV sets monitored will be in SEC C households); despite that, I still feel that 500 TV sets representing 120 million TV households is too small a figure. Hardly ‘representative’.

Newspapers also have had their fair share of controversy with prestigious brands such as Indian Express opting out of circulation and readership analysis studies with the allegation that such studies are rigged by those who have deep pockets.

So in a sense web metrics are much better off (by miles) because pages have access to tools such as Google Analytics, Site Meter, and Stat Counter or better where they can track users by their Ip’s and get a good idea of new users vs repeat users and all the other required information that an advertiser would actually need (maybe more than required).

Let’s now take a look at all the different measures that are used to analyse ROI for the web.

Broadly Speaking there are 4 measures to calculate ROI.
1) Page Views
2) User sessions
3) Unique Visitors
4) Time Spent on a Site

Page views is a metric that has been around for a decade or so. It is the number of times web surfers call up web pages on a given site. Thus if a visitor comes on to FoxyMoron and visits the main page, then visits the about page, then goes back to the main page, and visits another article – FoxyMoron will generate 4 page views from this user. Page views became popular in the late 1990s, because they were far superior to the existing measure of “hits”, also known as “file requests”. Hits are confusing because every graphic on a page, as well as the page itself, counts as a hit. If a site owner puts more graphics on his pages, he gets more hits, even if visitors, clicks and everything else stay the same.

Old timers in a relatively young internet advertising industry have gone on record to say: “We produced hits numbers because we could, not because it was useful,”

Page views also became popular because media planners at the time were obsessed about pages and the revenue generated from each page (because they were working on newspapers and magazines with a relatively closed mind).

Another important thing to note about page views is that when a page automatically updates itself by reloading, which counts as another ‘page view’. All was well and media planners were solely concerned with page views till something odd happened. Certain web sites which seemed perfectly healthy saw declines in terms of their page views (and the got worked up).

The explanation has to do with “Web 2.0”, and more specifically with a constituent technology called “asynchronous JavaScript and XML”, or AJAX. This is a method that lets web pages update parts of themselves—a news update, a share price ticker, a new post on your blog or an e-mail inbox, say—without having to reload and redraw the rest of the page. The result is that web pages now that behave less like documents and more like pieces of software. Only small amounts of data are exchanged with the server for a web page and thus the page becomes more responsive and also saves the owner of the web page a lot of bandwidth. But this means that a user of an AJAX page, such as Yahoo! Mail or Yahoo! Finance, can spend the entire day working on the same page, and this activity counts as only a single page view!

Perhaps advertisers should therefore ditch page views in favour of “user sessions”, since that promises to count actual people, and show how many of them use a site. Except that it doesn't, because this measure counts browsers rather than humans. So 2m sessions could mean, theoretically, that 2m people visited a site once, that 1m people visited twice, or that one astonishing individual visited 2m times. People tend to check their favourite pages in the office, at home, and even from their mobile phones, which leads to an overestimate of the number of users. Conversely, sometimes several people watch YouTube clips when gathered around the same screen, which leads to an underestimate of the number of users. Nobody looking at user sessions would ever know.

To negate the defects of user sessions, web metric experts are now propogating the concept of Unique Visitors – which is the same as the concept of user sessions except that the Unique Visitors format tries to filter out repeat visitors by not counting IP adresses that have already been stored as cookies in the sites analytical tools. This metric too is fundamentally flawed because most ISP’s have dynamic IP adresses allocated so on any given day, if people login to their networks 10 times and log out 10 times, they would probably have had 10 different IP adresses during the course of the day. So if they visited a page 10 times, they would technically be “unique visitors” each time when technically they are not! In certain other cases such as in large offices, the entire office is provided a static IP which is then distributed among all the computers in the office. Thus if even 10 people click on the same site it will register as 1 “unique visitor”. So in this case as well there is either an over estimation or under estimation of people visiting the page and thus the metric of “unique visitors” does not really do anything new!

As websites, and especially those migrating to the Web 2.0 platform, become more interactive, advertisers are therefore interested in other measures (because of the flaws of Page Views). “Duration” and “time spent”, for instance, suggest how long one or more people are interacting with a page, which in turn hints at how “engaged” users visiting the website are. Using these criteria, social-networking sites such as Facebook, Orkut, and Myspace become extremely attractive on the advertising front. (Read here about Facebook’s advertising strategy).

(Which is also what we specialize in; check out FoxyMoron)

So the best thing an advertiser can do is take into account a combination of time spent and unique visits and think about how they want to engage the viewer in accordance with their communication objectives. New developments such as placing ads in social conversations is also becoming extremely popular due to the sheer impact of such ads on recall.

Having examined all the criticisms of web metrics one still has to conclude by saying that for advertisers, the web is like an open book. For text links, they pay only per click or per thousand click, or even per sale generated. In terms of Flash ads, advertisers pay for how many people see the ad as opposed to television where a lump sum is paid on speculative measures such as TRP’s. Advertisers still need to look at web metrics carefully but they must remember that no other medium will give them as much control over their communication strategies.