Microsoft should take a page from the launch of Wolfram’s Alpha using social channels.

Wolfram Alpha – 1.6 million google search results

Microsoft Kumo – 624k google search results

wolfram-alpha-search-resultskumo-search-results

www.WolframAlpha.com is launched, but Microsoft Kumo.com is not even launched. So there is NO benefit from all the news coverage.

wolfram-compete

wolfram-referrals

wolfram-links

Search intensity and volume indicates interest of users — Wolfram Alpha is kicking Microsoft butt.

search-intensity

http://bits.blogs.nytimes.com/2009/04/28/wolfram-alpha-veil-lifted/

http://gizmodo.com/5240514/wolfram-alpha-and-google-tested-head+to+head

http://gizmodo.com/5236115/wolfram-alpha-search-engine-on-video

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Bed Bath and Beyond is blocking RetailMeNot — possibly because their blended average margin is relatively low and it cannot afford giving “expensive” coupons to everyone.

bbb

Macy’s is advertising on RetailMeNot — see “Featured Discounts”

macys

OldNavy is advertising AND shutting off user-submitted coupons


oldnavy

Retailmenot.com started later than Dealcatcher, but quickly overtook them and continues to increase — it is simply more useful because of real time consumer feedback on whether the coupons/codes worked or not.


retailmenot
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1. post your “viral” video, banner ad, etc.
2. tweet about it
3. see if any one of your followers re-tweets it
4. check twitt(url)y to see “twitter intensity” around you asset

this is a quick way to tell if what you think is viral is viral. If even your own circle of followers don’t retweet it, it probably isn’t viral.  What you think is cool may actually not be that cool.  And sticking it on YouTube and supporting it with a lot of paid media, doesn’t make it viral!

Agree with me?  Or tell me I’m stupid @acfou

using twitter intensity to determine if something is viral (or not). 

twitturly2

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Every year around SXSW, there’s a surge in interest about twitter. This time around people have even gone as far as to proclaim twitter to be “the next google” or “the future of search” etc.  Bullocks!

Here’s why:

1) distant from other social networks – While we are seeing a massive surge in interest and usage of twitter, it is still a long way off from the number of users of other social networks; it will take a long time to get to critical mass; and this is a prerequisite for twitter to assail the established habit of the majority of consumers to “google it.” — Google’s already a verb. 

2) no business model – It remains to be seen whether Twitter can come up with a business model to survive for the long haul. Ads with search are proven. Ads on social networks are not. And given the 140-character limit, there’s hardly any space to add ads. 

3) lead adopters’ perspective is skewed - Twitter is still mostly lead adopters and techies so far; so the perspectives on its potential may be skewed too positively. As more mainstream users start to use it, we’re likely to see more tweets about nose picking, waking up, making coffee, being bored, etc….  This will quickly make the collective mass of content far less specialized and useful (as it is now). 

4) too few friends to matter – Most people have too few friends. Not everyone is a Scott Monty ( @scottmonty ) with nearly 15,000 followers. So while a user’s own circle of friends would be useful for real-time searches like “what restaurant should I go to right now?” the circle is too small to know everything about everything they want to search on. And even if you take it out to a few concentric circles from the original user who asked, that depends on people retweeting your question to their followers and ultimately someone notifying you when the network has arrived at an answer — not likely to happen. 

5) topics only interesting to small circle of followers – Most topics tweeted are interesting to only a very small circle of followers, most likely not even to all the followers of a particular person. A great way to see this phenomenon is with twitt(url)y. It measures twitter intensity of a particular story and lists the most tweeted and retweeted stories.  Out of the millions of users and billions of tweets, the top most tweeted stories range in the 100 – 500 tweet range and recently these included March 18 – Apple’s iPhone OS 3.0 preview event; #skittles; and the shutdown of Denver’s Rocky Mountain News.  Most other tweets are simply not important enough to enough people for them to retweet. 

6) single purpose apps or social networks go away when other sites come along with more functionality or when big players simply add their functionality to their suite of services. 

twitter

twitturly

Am I missing something here, people?  Agree with me or tell me I’m stupid @acfou   :-)

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List of 2009 Superbowl spots on AdAge.com

http://adage.com/superbowl09/article?article_id=134136

Lift in search is a great indicator of interest. Modern consumers may be inspired by TV ads, but they usually go online to do more research for themselves, to inform their own purchase decision. The following examples show the lift in search after Superbowl commercials or for launch of products like Subway Footlongs. The use of unique, made-up words makes it easier to detect lift in search (see related post: made up words are great for tracking buzz and search volume ). There is now a correlation between offline paid advertising and online behaviors of modern consumers that can be tracked and ultimately related to sales. 

 

What is harder to do is track lift in search from smaller TV media buys or from terms which are generic — e.g. American Express OPEN, Proctor & Gamble’s TAG (men’s deoorant), etc. And furthermore, people may or may not remember the brand name itself and may type in a more general search query — e.g. “talking baby” instead of” e-Trade” or “dancing lizards” instead of “SoBe LifeWater.” And most people usually forget to type in special URLs specified in the ads. So the opportunity is to 1) use made-up words which can be used to detect lift in search and 2) search-optimize around other more generic terms that people may search for if they remembered the ad, but did not remember the brand name itself. 

 

key learnings include:

1. only the superbowl TV ads generates enough awareness to drive lift in search volume detectable above the noise or normal levels

2. made up words are useful in correlating paid advertising and subsequent online actions (e.g. search) because most users forget or are too lazy to type special URLs

3. is is always better to have real analytics from the site to see when paid campaigns hit; site analytics will also reveal more information about users including demographic information, what they are looking for, and even whether they “convert” to a sale or a desired action — like print off a coupon, etc.

 

Notice the January spikes for several of the examples below — these are their Superbowl ads in action. But also notice how sharp the spikes are — most of them go back to prior levels within 1 – 3 days (see related post: the ephemerality of the Superbowl halo )

Source: Google Insights for Search

footlongs

jackinthebox

dennys

ecoimagination

godaddy1

lifewater

drinkability

etrade

cash4gold

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Comparing the 2008 and 2005 Google Heat Map Studies:

Source: Think Eyetracking, September 2008

In 2005, they would look down the page at the results. By 2008, users glance at the first 3 – 4 results and then refine their search. They’d sooner type in a “long tail” search than go to page 2 of the results.

Source: http://www.searchenginejournal.com/long-tail-page-one-rankings/

Excerpt

 

The Powerful Long Tail of SEO: By Glenn Gabe

I think many people in Search understand the importance of ranking highly in Google, but I think too many people outside of Search are hung up on ranking for just a few target keywords. As mentioned earlier, I’ve written about the long tail of SEO on my blog, and it’s hard to overlook the power of the long tail when heavily analyzing search traffic across websites and verticals. I’m constantly talking about the long tail during client meetings, internal brainstorms, and to random people on the subway. Don’t worry, I’m in New York, so most people are used to this type of strange behavior. :)

To quickly review, the long tail of SEO includes longer queries, typically including three or more keywords. These longer queries derive from your target keywords (or your head terms). For example, a head term might be Nintendo Wii, but a long tail keyword might be what are the best Nintendo Wii games. Although many people focus on head terms, the long tail might generate more quality visitors in aggregate (taking into account all long tail keywords versus just head terms). Anyone tracking SEO for a living has probably seen the impact of the long tail.

continue reading about long tail SEO by Glenn Gabe  ….

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evidence that people who type long-tail keywords are more engaged and spend more time on site…

Compare “head” keywords which drive traffic to Apple.com (e.g. iTunes drives 7.1% of the site’s traffic) versus “long tail” keywords which drive traffic to MobilOil.com (e.g. “mobil1 turbo diesel truck”).  The time index of these long tail keywords are far higher than the time index of the head terms.

Apple.com  (Source: Compete.com)

 Top 25 Search Keywords driving MOST VOLUME to Apple.com

Volume Rank

Keyword

% of Site’s Search Traffic

Average Time Index

1

itunes

7.1

3.1

 

2

apple

4.2

3.4

 

3

apple store

2.4

3.5

 

4

ipod touch

1.6

2.7

 

5

itunes download

1.6

3.5

 

6

iphone

1.5

2.1

 

7

ipod

1.5

2.9

 

8

quicktime

1.1

3.4

 

9

apple.com

1.1

4.0

 

10

itunes store

0.9

3.9

 

11

ipod nano

0.7

2.5

 

12

itunes.com

0.7

4.0

 

13

i tunes

0.6

3.1

 

14

apple ipod

0.6

4.5

 

15

apple trailers

0.5

3.7

 

16

www.itunes.com

0.4

5.5

 

17

ipod shuffle

0.4

2.2

 

18

mac

0.4

2.3

 

19

movie trailers

0.4

3.6

 

20

itouch

0.4

1.9

 

21

download itunes

0.3

2.3

 

22

www.apple.com/ipod/start

0.3

4.0

 

23

apple computers

0.3

4.4

 

24

www.apple.com

0.3

4.7

 

25

safari

0.2

1.5

 

Mobiloil.com  (Source: Compete.com)

Top 25 long tail terms which lead to HIGHEST TIME INDEX (people spending time on the site)

Volume Rank

Keyword

% of Site’s Search Traffic

Average Time Index

49

engine run in +luricants

0.58

100.0

188

power steering bubbles and growls

0.15

88.1

201

synthetic transmission fluids

0.13

85.7

152

ram enterprises

0.19

78.7

112

mobil 1 turbo diesel truck 5w-40

0.30

75.5

53

half axle replacement

0.56

69.0

135

mobil one oil filters miles

0.24

59.6

136

mobil synthetic

0.24

59.6

153

how to change a cv joint

0.19

57.6

113

cleaning throttle body

0.30

55.6

19

www.mobiloil.com

0.91

55.0

202

vin devers sylvania oh

0.13

47.7

203

mobile 1 oil company .com

0.13

47.7

204

2000 s500 mercedes transmission fluid rating

0.13

47.7

91

is it bad to mix oil with synthetic oil

0.38

46.4

16

mobil 1 online rebate

0.98

45.3

29

mobile 1 oil

0.77

43.9

168

recommend synthetic oil change

0.17

43.1

94

mobil 1 5w30

0.37

41.1

98

extended life, mobil 1

0.35

38.2

122

mobile one oil vs regular oil

0.27

36.7

123

mobile one oil

0.27

36.7

59

where to buy mobil one 0w 30 synthetic

0.52

35.3

15

how do i change differential oil

1.00

35.3

169

mobil 1 racing oil

0.17

35.0

166

long wearing tires

0.18

32.9

183

mobile 1 mx4t motorcycle oil

0.16

30.8

78

mobile one oil change interval

0.42

28.4

127

what is a throttle body

0.26

27.6

103

what to use to clean valve cover

0.34

27.5

88

mobile oil rebates

0.38

26.8

63

mobile one

0.50

26.4

104

mobiloil.com

0.34

26.4

14

mobil oil filters

1.04

25.9

150

zddp mobil 1

0.19

25.9

83

how to replace shocks

0.41

25.7

143

www.mobil1.com

0.21

25.3

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during the customer’s journey down the “purchase funnel” from awareness through consideration to the purchase, there can be many moments of truth. For modern consumers, who spend a large portion of their day online, or at least “connected” via a mobile device, many of these moments of truth are “electronic” – in other words, electronic moments of truth – EMOTs. Understanding EMOTs through the purchase funnel can lead to greater efficiencies in advertisers’ marketing programs to drive customers expeditiously through the funnel. 

For example, when a customer goes online to do research – an EMOT electronic moment of truth — if they can’t find you, you don’t exist.  Many many factors contribute to being “findable” online. Proper search engine optimization (SEO) can ensure a brand has web pages that show up near the top of the results on the first page of search engine results. Also, using brand names that are not common words like “open” (american express small business brand) or “tag” (male deodorant from P&G) means they can more easily be found. For example GE’s “ecoimagination” or Subway’s “footlongs.”  (see “made-up words” post) are easily found. 

Other EMOTs could include a person standing on the street (in New York City) and needing a restaurant recommendation in the vicinity, immediately! They use their mobile device and search for restaurants in the area around their current GPS location. When they search on their mobile device, if the website is not mobile compliant and does not display properly or cannot be used by a primitive mobile browser (no graphics, no javascript, no flash, etc.) the user would not be able to find what they are looking for. So this EMOT was a FAIL for the customer. 

The brands  that will be the most successful are the ones who can deliver value at every EMOT of their target customer. If the customer goes online to search and research, the brand’s information should be findable – even better, the specific bit of information being sought should be findable. If the customer needs recipe help while standing in the store (“what ingredient or how much should I buy?”) the information should be findable, specifically through a mobile device.  Ultimately by delivering value at each EMOT, brands can answer customers’ missing links and thus efficiently move the customer down the purchase funnel towards the purchase.

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how do we judge the relative merit and effectiveness of different types of advertising? By finding a common parameter that can be used to compare “apples to apples.” We argue that cost of customer acquisition is a great candidate for such a parameter.

For example, if television advertising cost $50 million to produce and air, and 1,000 people came to the acquisition website, and 10 people applied for and received credit cards then the CCA — cost of customer acquisition would be $5 million ($50 million / 10 people who got the credit card). Of course television advertisers would claim that the “impressions” from TV would have “branded” millions more people and they would eventually get a credit card from the company. That’s possible. But for the purposes of this exercise, if there is no absolute end-to-end tracking, we don’t count it. Because, for example, many other possible scenarios can also occur, like the person saw this ad for a credit card but ended up getting a card from a different bank, they saw and remembered the ad but they already had several credit cards from the company, etc.

With “online” we can easily see lift in search activity around the time that brand/awareness advertising is in-flight. This is one of the best indicators of interest — the person saw the TV ad, and was inspired enough to go online to do more research to inform their own purchase decision. Modern consumers will typically search and then click through. In rare instances, they will type the URL, but it is usually the domain name, not the special URL — domain_name.com/special_url — just because of pure laziness or simply because they forgot the /special_url portion.

Now let’s look at a print example: a print ad cost $5 million to produce and traffic in targeted magazines. About 1,000 people came to the website and 10 people ended up purchasing the advertised product. So the CCA is $500,000 per customer acquired.  There may be more people who saw the ad and eventually came in to buy a product. But again, there is a problem of attribution. 

Now a final example from “online” marketing.  Search ads were run using Google Adwords and a $1 CPC (cost per click) was paid. Of those people who clicked through 1 in 20 purchased a product. So it took 20 clicks at $1 each to achieve 1 sale – so the cost of customer acquisition is $20. 

OK, so what about prodycts not sold online? We can use a proxy which has a known conversion to sales. For example, once a coupon is printed from the website, from historic data the advertiser knows that 30% end up using the coupon – i.e. redeeming with a purchase. So, again, if we used a $1 CPC and 1 in 20 ended up printing the coupon and 30% of those “converted” to an offline sale, the CCA would be $66.67  ($20/0.30).  

So to recap

Television – $5 million CCA

Print – $500,000 CCA

Paid Search – $20 CCA

Paid Search + Offline Sale – $67 CCA

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