About Benjamin Blachère

French Online Marketer previously based in Sydney. Launched a Web Marketing Agency in Paris, SLAP digital where we help our clients to increase their traffic, the size of their membership databases as well as their revenues. Passionate about web marketing, I have previously launched my own website, a community for people in Long Distance Relationships.

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    My blog is carbon neutral

    I have now added a ‘Carbon Neutral’ icon in the sidebar of this blog. I did this as part of the “My blog is carbon neutral” initiative. It was originally started in Germany by the “Make it Green” programme, that has the goal to reduce carbon dioxide emissions.

    They plant a tree for your blog and thereby neutralise your blog’s carbon footprint for the next 50 years!

    Is your blog carbon neutral?

    Everyone can make a small contribution to the environment. Every tree counts!

    So why don’t you add it to your blog too? Click Here to learn how.


    When will a Shazam for images be available?

    I have wondered for a few years when we were going to reach the (slightly scary) age of ‘search by images’ search engines – aka visual search engines. Let me explain, at the moment you can type in pretty much any word or sentence in Google, Bing, Yahoo… and you will end up with either a list of websites, blogs, videos, images etc.

    So why not be able to do the reverse for images: upload someone or something’s image and google will tell you what this image is about and what it contains.

    Alright, the idea seems simple enough, however this is technologically and ethically challenging.

    After doing some reading on the subject, I inevitably ended up on Wikipedia, its page about Computer Vision is very informative. Computer vision is the science and technology of machines that see. It seems to be a very broad subject, but for the purpose of this post, I won’t focus too much on how image recognition works but rather on how this could be applied to search, who’s working on it and what could be the business outcomes of such a technology.

    A database of the world’s images?
    Once an object within an image is identified it will need to be ‘cross-referenced’ against a database of other images and their descriptions. And no one else other than the photographer should be able to accurately describe the photo. A company called ALIPR has developed a machine-assisted image tagging and searching service which aims to assist to reference the images online. But realistically I don’t think they will be able to reference all images on the web so there is a need to incentivise photographers/drawers to properly do so when uploading their images. Some incentives exist currently such as appearing in search results within the various photo websites, tagging people on Facebook to let them know you have uploaded photos, W3C compliance, SEO benefits, etc. But for the purpose of this exercise a set of rules should be written in order to ensure consistency in image descriptions… a whole challenge in itself.

    So where is visual search at currently?
    A few companies have been working on visual search in the last few years. But at this stage most of them such as Gazopa- owned by Hitachi- and TinEye enable users to search for a similar image from characteristics such as a color or a shape extracted from an image itself either uploaded or from the URL. There are abundant quantities of images on the web, however many of these simply cannot be described by keywords. Since GazoPa uses image features to search other similar images, a vast range of images can be retrieved from the web. Tineye is a reverse image search engine. It finds out where an image came from, how it is being used, if modified versions of the image exist, or if there is a higher resolution version. It has officially referenced over 1.5 billion images to date. But none of these solutions can identify people or objects in an image at this stage.

    Example of Google Landmark Recognition

    Example of Google Landmark Recognition

    The one company that seems (based on my modest research) to be the closest to achieve what I am talking about is…surprisingly Google! Google purchased a startup called Neven Vision back in 2006. Neven Vision had at the time deep technology and expertise around automatically extracting information from a photo, one of the best face recognition software, but also a client for mobile phones that searches for relevant content related to an image. They have since started to implement some interesting features in Google Images search and Picasa such as adding face recognition abilities to its image search. While currently unofficial and unannounced, users can now search for images that only contain faces by appending a query string onto the end of a search URL. For example, a general image search for “Washington” produces a variety of image results, but when appending “&imgtype=face” to the end of the URL, all new results contain photos of people.  (Source Arstechnica)

    But last year, some Google employees released a paper that takes us a step closer to our goal. Basically, a user can upload an image, then Google uses this new technology to analyze it and automatically identify if it’s a photo of a landmark, and if so which one. Google says it created a list of landmarks using tagged photos in Picasa, Panoramia, and online tour websites, then pulled the best images of each landmark from those sites plus Google Image Search, and finally developed “a highly efficient indexing system for fast image recognition. (Source: Venturebeat)

    You can download their paper here or view the video below (turn off the sound!)

    What could it mean in terms of business?
    I believe that the Police and Militaries already have access to similar technologies not yet available to the public. Obvious uses of such a technology could be for the detection of enemies and identification of surroundings of hostage taking situation (based on photo/video) for example.

    Such a poweful visual search engine could be used in many different ways by ‘civilians’ too.

    • Some of the uses could be considered ‘stalking’ such as a private investigators looking for compromising photos online. Or a recruiter checking on a candidate. In terms of dating, someone could take a picture of someone and then be able to contact them online….scary I know.
    • Holiday websites could use to help users identify the destination of a photo they mught have found online but also use the similar images search engine to suggest new holidays to their customers “loved your last holiday, upload your photos and we will recommend you similar destinations’.
    • Art and Antique fans could identify the author of paintings and photos they find online.
    • In the medical field, doctors, as well as patients, could upload photos of an injury or other body symptom to see if anyone had this in the past and what it could be. Doctors could use it for the purpose of making a medical diagnosis of a patient.
    • Shopping: People could use it to do some comparison shopping such as currently on Like.com which already uses visual search technology to help shoppers find the purchases they’re looking for. People could also find similar cars, properties… that they are seeing on the go and photographing with their mobile.

    If someone can master this technology they will no doubt find ways to make it a successful business venture.


    Black Friday and Cyber Monday 2009

    Translation please? Yes I wasn’t sure what this meant not so long ago either.

    Black Friday

    Black Friday is the Friday following Thanksgiving Day in the United States, traditionally the beginning of the Christmas shopping season. According to Reuters, in 2007 135 million people participated in the Black Friday shopping rush. Black Friday is not an official holiday, but as many workers have the day off as part of the Thanksgiving holiday, this increases the number of potential shoppers.

    The term “Black Friday” refers to the beginning of the period in which retailers go from being in the red (i.e., posting a loss on the books) to being in the black (i.e., turning a profit). (Wikipedia)

    The U.S. online retail sector delivered a strong comeback on Black Friday 2009 compared to the same period last year, according to Coremetrics:
    - The average dollar value that consumers spent per online order rose 35.0 percent year over year, led by apparel retailers.
    - Consumers are buying more items per order than they did last year—by 18.3 percent.
    - Consumers are spending considerably less time browsing retailers’ sites, suggesting they had done their research prior to Black Friday and that they are shopping from lists.
    o Browsing sessions were down by 5.4 percent
    o The number of people who left a site after viewing only one page (also known as a “bounce” rate) was up by 39.4 percent
    o Page views per session declined by 30.4 percent

    U.S. Retail
    Session Traffic Summary Metrics Black Friday
    Black Friday
    % Change
    Nov 20, 09
    % Change
    Bounce (One Page) Rate1 30.86% 22.13% 39.43% 32.79% -5.88%
    Multi-Page Session Percent2 69.14% 77.87% -11.20% 67.21% 2.87%
    Browsing Sessions Percent 3 48.99% 51.80% -5.42% 47.85% 2.37%
    Shopping Cart Sessions Percent4 10.95% 10.80% 1.41% 9.61% 13.99%
    Order Sessions Percent5 4.50% 3.49% 29.07% 4.04% 11.60%
    Visitor Experience Summary Metrics Black Friday
    Black Friday
    % Change
    Nov 20, 09
    % Change
    Page Views Per Session6 8.79 12.63 -30.36% 8.09 8.70%
    Product Views Per Session7 2.17 2.86 -23.98% 1.98 10.01%
    Average Session Length (in
    7:28 8:37 -13.32% 7:06 5.37%
    Transaction Summary Metrics Black Friday
    Black Friday
    % Change
    Nov 20, 09
    % Change
    Items Pre Order9 5.4 4.56 18.33% 6.19 -12.76%
    Average Order Value10 $170.19 $126.04 35.02% $176.92 -3.80%
    Shopping Cart Conversion Rate11 34.60% 33.56% 3.08% 32.89% 5.19%
    Shopping Cart Abandonment Rate12 65.40% 66.44% -1.55% 67.11% -2.54%
    New Visitor Conversion Percent13 3.71% 2.27% 63.36% 3.19% 16.16%
    On-Site Search Summary Black Friday
    Black Friday
    % Change
    Nov 20, 09
    % Change
    On-Site Searches Per Session14 18.32% 18.44% -0.62% 18.57% -1.34%

    Cyber Monday

    Cyber Monday is a marketing term for the Monday immediately following Black Friday. The term was created by the National Retail Federation and announced in 2005 conjunction with the deployment of their own website CyberMonday.com designed to serve as a portal for Cyber Monday deals and offers. (Wikipedia)

    The U.S. online retail sector reported strong sales results on Cyber Monday (Nov. 30) 2009 compared to the same period last year according to Coremetrics:
    - Cyber Monday continued the momentum set by Black Friday. Sales were up 24.1 percent compared to Black Friday 2009.
    - Consumers spent more per online order ($180.03 versus $170.19 for an increase of 5.8 percent) compared to Black Friday 2009.
    - Sales were up 13.7 percent compared to Cyber Monday 2008.
    - The average dollar amount consumers spent per online order rose 38.2 percent from Cyber Monday 2008 ($180.03 versus $130.24), led by apparel retailers.
    - Consumers bought nearly 10 percent more items per order on Cyber Monday 2009 compared to Black Friday 2009 and nearly 30 percent more compared to Cyber Monday 2008.
    - Consumer shopping hit its peak from 9-10 a.m. PST, but maintained stronger momentum throughout the day than on Cyber Monday 2008.

    U.S. Retail
    Session Traffic Summary Metrics Cyber Monday 09 Cyber Monday 08 % Change
    Black Friday 09 % Change (cm/bf)
    Bounce (One Page) Rate1 28.86% 24.51% 17.75% 30.86% -6.48%
    Multi-Page Session Percent2 71.14% 75.49% -5.76% 69.14% 2.89%
    Browsing Sessions Percent 3 48.88% 51.30% -4.72% 48.99% -0.22%
    Shopping Cart Sessions Percent4 12.13% 10.91% 11.18% 10.95% 10.78%
    Order Sessions Percent5 5.26% 3.99% 31.83% 4.50% 16.89%
    Visitor Experience Summary Metrics Cyber Monday 09 Cyber Monday 08 % Change (y/y) Black Friday 09 % Change (cm/bf)
    Page Views per Session6 8.8 11.59 -24.07% 8.79 0.11%
    Product Views Per Session7 2.14 2.63 -18.63% 2.17 -1.38%
    Average Session Length (in
    7:32 8:23 -10.14% 7:28 0.89%
    Transaction Summary Metrics Cyber Monday 09 Cyber Monday 08 % Change (y/y) Black Friday 09 % Change (cm/bf)
    Items per Order9 5.92 4.56 29.82% 5.4 9.63%
    Average Order Value10 $180.03 $130.24 38.23% $170.19 5.78%
    Shopping Cart Conversion Rate11 36.81% 37.99% -3.11% 34.60% 6.39%
    Shopping Cart Abandonment Rate12 63.19% 62.01% 1.90% 65.40% -3.38%
    New Visitor Conversion Percent13 4.28% 2.91% 47.08% 3.71% 15.36%
    On site Search Summary Cyber Monday 09 Cyber Monday 08 % Change (y/y) Black Friday 09 % Change (cm/bf)
    On-Site Searches per Session14 19.49% 17.86% 9.13% 18.32% 6.39%

    Smiths “Do us a flavour!” a history of missed opportunities

    Smiths, which is part of Pepsico has launched a few months ago the “Do us a flavour!” competition – a straight replica of what it’s sister company Walker did in the UK.

    I am not sure what their total budget was but it must have been pretty massive considering the amount of TV ads there has been as well as the appointment of celebrity chef Matt Moran as one of the judges.

    The campaign definitely succeeded in raising awareness about the competition and therefore the brand.

    The offer was extremely appealing:

    * The ability to see your product idea actually produced and sold in store (recognition)
    * $10,000 for the top 4 entries
    * $30,000 prize for the winner and a guaranteed one percent of all future sales of the flavour.

    Thanks to the heavy promotion and a user friendly website over Smiths claims to have received over 315,000 entries (not sure how many of these are unique entrants).

    What I mean by missed opportunities is that Smith’s failed to capitalise on the amount of traffic they received and the database of people with a genuine interest for their product they built.

    I actually entered a few ideas, and I did not receive any follow up emails ‘thank you for entering’ after posting my entries – I have litterally not heard of Smiths until a couple of months later to ask me to vote for 4 other ideas!

    This shows a clear lack of appreciation for the time that all entrants spent thinking about the idea, the visual and going through the process of entering it…

    Also I feel that Smith’s missed a great opportunity to get users to do their marketing for free. It seems crazy that they didn’t include a rating mechanism for users to vote on the flavour of their choice. They didn’t necessarily need to select the most voted idea as the winner but could have included it as part of the top 4 ideas. By doing this and providing them a URL to drive their friends to vote for their flavour they would not only have multiplied the traffic but also the number entries and the brand awareness.

    Here is what I would have recommended to Smith’s if asked:
    - Send a thank you for entering message for all entries (limited to 1 per day) and providing them the URL of their entry and some ‘send a friend’ and ‘share’ options. As everyone knows open rates on communications following an interaction from a user are much higher then when sending a standard ‘push’ email a few months later.
    - In this message I would have promoted a special offer / coupon only available to entrants to reward them for their input.
    - A couple of weeks later I would send them a follow up email letting them know that the entries were still being reviewed and that the 4 winners will be announced on X date (to create a bit of buzz) and would have asked them in the meantime to answer a few survey questions about the current products (potentially with a minor incentive).
    - Once the 4 finalists have been selected I would have push the competition between the supporters of each flavour and maybe created a Facebook Fan Page for each.

    And the ideal would be if they were sending a coupon for a free pack of the winning flavour to all entrants!

    Anyway, I will still taste the winner’s, I hope it’ll be good.


    Web Trend Map – they have done it again

    For the 4th time Information Architects Japan mapped the 333 most influential Web domains and the 111 most influential internet people onto the Tokyo Metro map.

    Each domain is evaluated based on traffic, revenue, age and the company that owns it. Websites are grouped based on their topic and are on a subway line with a consistent theme:entertainment, money, broadcasting…

    IA also associated specific websites with specific stations (the most trafficked for Google, the trendy one for Twitter…)

    Google owns a huge section of the map, while other key players on the web (Yahoo!, Microsoft, Amazon, Apple…) on major intersections.

    Some websites are accross 2 lines: like Adsense on advertisement and publishing and Pirate Bay at the mix of Sharing and Filter.

    I love how most chinese websites were placed on the ‘filter line’.

    There is also a top 50 trend ranking with Google, Yahoo! and Microsoft in the top 3,

    This map is complete and completely confusing – unless you spend some time looking at it in detail and then you realise the amount of thought that has gone into it.

    I love the concept, even though I guess it’d be more fun if I knew Tokyo and its underground!

    My request for next version: make it interactive and searchable.

    Click here to see what the real map of Tokyo’s underground looks like.


    Fantastic video about the evolution of the world and Internet

    This video is mind blowing and reminds me that our main challenge as online marketers (and any other job related to internet and new technologies) will be to stay up to speed with the latest changes in technology as well as demographics and user behaviours.

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