I’m pleased to share that I’m going to run a session at the UK’s largest affiliate expo – a4u– in October.

This will be a one-hour hands-on session to get started with and optimize  Google’s latest Display innovations;  retargeting and behavioural / interest based targeting. It will cover both direct response and brand building exercises.

I’ve got a tough slot though which sits just before the first party off day 1 (and there are also some other seemingly great presentations run in parallel), but hopefully I’ll be able to tempt a few people to hold off the beers for just a few moments more.

So, if you’re an affiliate who haven’t tried retargeting yet or looking to improve your results; come along!

The full agenda is available on the A4U Expo website.

Related posts:

1. Speaking at SMX Advanced London
2. SES London – Affiliate Marketing Doesn’t Equal Search
3. Google AdWords PPC Personalisation through Session-Based Broadmatch

In a recent post I mentioned that I’ve read three books on my iPhone 4. I did however not reveal which they were.

The selected material was:

• Purple Cow – a classic Seth Godin piece about the importance of finding a USP to stand out in an increasingly competitive market place. Recommended for inspiration.
• Freakonomics - Some popular economics and statistics that while at times a bit too sensationalist for my taste, is still an enjoyable read with fascinating ideas that at least gets you thinking about stats in a different way.
• Crossing the Chasm – an updated edition of the classic 90’s book on technology marketing and how to attract the majority markets. A must read for anyone working in technology and b2b marketing.

All available on the iBook store.

No related posts.

Since Google launched site links for all search keywords, paid search affiliates have been quick to jump on this opportunity. Without naming names and putting anyone on the spot, I’ve noticed that the site links feature is increasingly being abused. Apparently it is technically allowing discrepancies in display URL and destination URL, which is clearly against the Adwords policies.

The examples I’ve seen have been in a couple of different industries and various affiliates, but the technique is the same: use the own affiliate website as display URL and destination URL for the main ad copy, but add direct-to-merchant site links (but of course with the crucial affiliate tracking code intact).

From an affiliate and possibly merchant point of view, this might be a good thing, but it’s basically double serving (assuming there’s a merchant ad) and doesn’t add any value to the end-user.

Related posts:

1. Google AdWords Comparison Ads – Big Impact on Finance Marketing
2. Does Google Website Translation Improve Site Stickiness?
3. Should you take the Google Adwords and Analytics Exams?

In recent sessions with Google, it has become apparent that as the use of search as an acquisition tool has matured, the company is looking to broaden paid search’s appeal as a branding tool (and vice-versa can be noticed with display now being pushed for its direct response credentials).

I’ve previously written about the inherent challenge of branding through paid search due to the quality score factor. This post won’t help you improve the quality score, but instead make sure you maximise exposure by keeping an eye on the impression share and active bid management.

The search branding tool kit

The first port of call is to check the Adwords impression share report. This is done by pulling a campaign performance report and adding the columns “Impression Share” and “Exact Match IS”.

The impression share column will show for what percentage of legible searches that your keyword adverts was shown. If you’re using various match types in the campaign, this could be any number of searches stemming from variations as well. This is why we also want to analyse the column “Exact match IS”, as this percentage is considering your keywords as exact matches and thus gives you an idea of how you’re performing on your most strategic branding keywords.

For example, looking at the chart below you can see that most campaigns have very high exposure for exact match searches, meaning that they’re showing for the most strategic keywords.  However, Account 4 needs a deeper dive as it’s showing for less than 50% of the exact matches. Also Accounts 9 and 12 are of particular interest, as they have the lowest regular impression share figures, indicating that there’s possibly a long tail of searches that the campaigns are not tapping into.

The second vital tool is the Google Adwords bid simulator. The simulator can be viewed by clicking the little graph symbol that is shown against select keywords (it will only show for keywords with enough historical data) in the Max CPC column.

While it’s not trying to predict the future, it’s simulating what would have happened in the last seven days if the bid had been at a different level, but all other factors constant (such as quality score, competitor activity and search volumes). Depending on the amount of historical information it will show estimated impressions, clicks and cost. For lower volume keywords you’re restricted to impression volumes.

Since the Ad Rank in Google is determined be your bid and Quality Score, for particular branding keywords it might be very difficult to improve the quality score, so you will need to qualify by bidding higher. Worth noting is that if the quality score falls down to 2/10 or lower, it will simply not qualify for all impression regardless of bid levels and will need to be optimised.

How are you using the impression share report and bid simulator?

Related posts:

1. Brand Building through Paid Search Marketing
2. Top Tools and Techniques for Capturing Hot Search Keywords
3. Google Product Ads Lowers the Barriers of Entry to Paid Search

If you’re determined, you can get your statistics to tell you any story you want. In the metric-obsessed world of online marketing this is particularly dangerous as it’s easy to make the wrong decisions by being biased from preconceptions about what the conclusions should be.

Here are five common pitfalls in stats:

1) Correlation doesn’t imply causation

Anyone who have ever studied a statistical subject or opened a book on the topic will have come across this expression. It basically tells you that just because two events are correlating; it doesn’t prove that the one factor is causing the other to behave in a certain way.

For example: let’s say you are experiencing a correlation in paid search average position and conversion rate. Based on this you might assume that the position is directly impacting the conversion rate. However, by looking beyond the figures, you can see that there are other factors impacting the conversion rate, which brings us to the next point…

2) Looking at stats in isolation

By further interrogating the above example, it could well be discovered that the average campaign position increased due to competitors dropping of in the space. Thus the increase in conversion rate was not due to the average position change, but simply down to the fact that there was less competition.

3) Lack of benchmarking

A 20% increase in site traffic from a particular campaign might sound great at first, but it doesn’t actually tell you much unless you know the point of reference or can benchmark against other activity. Look for industry benchmarks, and historical performance of similar campaigns to better understand what to expect.

4) Small sample sizes

A very common pitfall is that of using small sample sizes and not achieving statistical significance, before judging performance. As the lead in to this article states – you can prove anything with statistics.

For example: It’s easy to get excited about a 20% increase in site conversion rate. However, if that 20% increase is measured by a split test where 100 visitors are leading to 6 sales compared to 100 visitors leading to 5 sales, the results are not statistically significant (in this instance probably have to wait for another 5,000 or so visits…).

5) Graphs don’t lie…

…they are just very easy to misinterpret and/or distort. Consider the three graphs below that are all based on the same data. Chart A and C shows exactly the same amount of data over time, but there’s a vast difference in what story they tell – chart A shows dramatic ups and downs, whereas chart B shows a fairly steady scenario. Chart B is zooming in on the last few data points, and is thus showing a tremendous upswing that would look good in any pitch.

What common misuse of statistics have you come across?

Related posts:

1. Ad Critique: Dixons Getting Online Right Offline
2. First Direct’s Consumer Feedback Online Campaign
3. Mobile Paid Search Marketing – The Wait is Over