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An Alternative to SEM: Being Where Your Buyers Go After They Search Doug Olender

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What B2B marketing stat are you sick of hearing about more:  how 72% of buyers start with Search, or how your buyers go 70% through the buy-cycle before they engage with your sales rep?

Why does what happens after search matter?

For starters, it matters because the primary tactics for engaging with your buyers at this crucial time in the buy-cycle are limited.  Basically you have SEM/AdWords, and SEO.

SEM/AdWords can be costly and inefficient

So let’s be honest, how many of you can afford to have your website rank high enough?  Certainly there are exceptions, but by-in large content production and topical authority at the volume required to have a footprint in search is incredibly costly.  Some professional publishers spend everything they can on it and still struggle for 1st page ranking.  You, on the other hand,  are a marketer with only a portion of your budget to devote to content production.

It gets worse:

Even if you did well with 1st page results, Google research indicates that it takes people about 12 searches before they may land on an actual vendor website.

Your buyers are searching for an independent voice

Wouldn’t it be great if you influence more of them in these formative stages?  If you did, you’d not only get more buyers to come to you later, but you’d also get them talking your language when they come to you.

Reconsider how you evaluate marketing vendors/partners

Given these realities, shouldn’t you ask your suppliers how they’re getting an audience to come to them?  And to what extent is it organic vs. bought?  How often is it refreshed on the topics you care about?  Since you can’t be on all the Page 1 search results that matter to you – find a partner who delivers immense search power.  Remember, search results are a numbers game. You need to weed out those partners that aren’t outranking their competition.

Dig into the terms they rank well in.  Are they ones that your sales reps would drool over if you could deliver?

How do you capture the benefits of being first in search?

Over 17 years of doing this stuff, we’ve built an incredibly strong content footprint and unrivaled topical authority.  That’s why TechTarget is so good at getting you in front of your B2B technology buyer audience.

TechTarget publishes 75,000 new content pieces each year across more than 10,000 B2B technology topics.  We’re the #1 highest growth domain in B2B technology (SearchMetrics). This translates to over 2.7 million ranking keywords and more than 800,000 first-page organic results. Think about this: to replicate TechTarget’s search power would cost upwards of $36 million per month (Source: SEMRush).  We believe that by partnering with a company like us, one who has already developed search power with your target audience, you stand a much better chance of efficiently engaging buyers earlier in their process.  We believe that intercepting real purchase intent behavior before your competitors do is one of the best ways to ensure your products and solutions are top-of-mind throughout the buyer’s journey.

The value of an organic first-page ranking on Google is undeniable.  Unfortunately, getting your company there takes time, focus and lots of money. There are other ways to capture the benefits of search behavior.  If you want to learn how partnering with a search leader can put you front and center with your B2B tech buyers, reach out to us today.

Google, search, search engine marketing, search engine optimization, SEM, SEO, serach behavior, TechTarget

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Where To Go When Insta Bans You

Michelle Battersby on creating a safe space for dangerous nipples.

There was always a question in Michelle Battersby’s mind while she built the Australian business of the dating app Bumble. As the app went through a million Australian registrations, two million, then three million, the question remained:

“Am I really good at this?”

Or was it just that she had a great product?

She went further afield, heading the marketing as Bumble launched across Asia. “I could travel to different countries, and I seemed to be able to pull the right levers to make then it grow,” says Battersby. But there was always the question.

She ended up leaving Bumble and going to the fitness and wellness brand Keep It Cleaner. “It was a bit of a challenge to me, like, can I go to a different company and make that grow as well?”

Once again, the numbers all went the right way. “COVID definitely helped me, but I was building more confidence in myself and my ability, and I was feeling like I was ready to perhaps do something for myself.”

But what was the product? Then an email came out of the blue from Lucy Mort, the flatmate of a friend in New York. Mort had been a lead app designer at the dating app Hinge.

She was fascinated by the amount of money women were making putting their content on the subscriber-only site Only Fans.

A lot of that was porn.

Mort thought she could build a better platform for women to put their stuff on without the stigma of being next to so much explicit content.

There’d be no porn, but sexualised content would be acceptable. They wouldn’t be censoring the occasional rogue nipple. It would only be for women and non-binary creators who could post without fear of being censored, shadow-banned or de-platformed, as they risked on Instagram and Tik Tok.

 It would all be behind a paywall, and the creators would set the price. If anybody was going to troll them, they could pay for the privilege.

“Only Fans was the original inspiration, but there are some big challenges with Only Fans,” recalls Battersby. “So that’s kind of what Sunroom was born of: How can we build something like this, but in a slightly adjacent space serving a different kind of creator?”

Mort and Battersby raised almost $5 million, more than half of which was from female angel investors and including Blackbird, Li Jin (Atelier Ventures), Cyan Bannister, Sarah Downey, Peanut CEO and Cofounder Michelle Kennedy and Brud Cofounder Trevor McFedries.

They both moved to LA. They launched their app, Sunroom, in February 2023, and a year later, claim a month-on-month revenue growth of 20%.  

Consistently Punished

“We appeal to, like, sexologists, sex educators, people that speak a lot about pleasure, dating, intimacy … We have kinky history classes, sex toy reviews, dating ideas, dating profile reviews are a big one. It’s tough to talk about some of those topics on Instagram and Tiktok because they are censored.

 “We allow those creators a space to monetise that content and it not be turned into something it’s not. We drive home that on Sunroom, and we celebrate women being able to put a price on their time, money, intimacy, creativity.”

  One such creator is Eleanor Hadley, who says she is “consistently punished” for simply existing as a sex educator and a sex-positive woman” on other platforms. “On Sunroom, I feel celebrated for exactly that.”

Her Instagram account was recently disabled for more than a week for what was alleged to be “sexual solicitation”, Hadley says, “despite literally never soliciting sex in my life”.

 It was reinstated, but the stress of losing her business was immense. “Sunroom is my saviour since it provides a way for me to monetise my content and doesn’t force me to water myself down in the process.”

  She makes around US$2,500 a month from Sunroom.

Battersby says there are creators making US$20,000 a month on the app.

 “I make up to US$2,500 a month on my Sunroom account,” says Battersby. “I share a career-confessions series, as well as the highs and lows of running a startup. I also mentor women via 30-minute calls that can be booked through the app.” 

Michelle Battersby will be speaking on the Power of Product at the inaugural Forbes Australia Women’s Summit on the 22nd of March, presented by NAB Private Wealth. She’ll be joined by other influential women, including Miranda Kerr, Christine Holgate and Natasha Oakley, discussing how to break barriers in business, build wealth and make industry connections. You can see the full lineup and get your tickets at Women’s Summit 2023 – Forbes Australia.

Ubuntu: Where Did The Love Go?

When Ubuntu first appeared, the free and open source software (FOSS) community was delighted. Suddenly, here was a distribution with the definite goal of usability, headed by a former space tourist who not only understood computer programming but had the money to throw at problems.

The only objections were that Ubuntu was ripping off Debian, the source of most of its packages. For everyone else, Ubuntu and its parent company Canonical seemed everything FOSS had been waiting for.

Now, in 2011, that honeymoon is long past. Although Ubuntu remains the dominant distro, criticisms of its relationship with the rest of FOSS seem to be coming every other month.

What happened? Ubuntu supporters sometimes dismiss the change as jealousy of Ubuntu’s success.

But, although that may be an element, the change in attitude is probably due chiefly to the gap between the expectations created by Ubuntu and Canonical in their early days and their increasing tendency to focus on commercial concerns.

Instead of being the model corporate member of the community that it first appeared, today Ubuntu/ Canonical increasingly seems concerned with its own interests rather than those of FOSS as a whole. No doubt there are sound business reasons for the change, but many interpret it as proof of hypocrisy. Added to the suspicion towards the corporate world that lingers in many parts of the FOSS community, the change looks damning, especially when it is so clearly documented in Canonical’s corporate history.

A Brief History of Canonical and Ubuntu

After Ubuntu’s first release in October 2004, Ubuntu/Canonical seemed in many ways a model FOSS entity. Nor was there much reason to doubt that initial sincerity. Shuttleworth, in particular, who was then the main speaker for both Ubuntu and Canonical, made considerable efforts to express support for other aspects of FOSS.

For example, Shuttleworth emphasized that “we all win, when Red Hat has a win.” He made a special point of attending DebConf, Debian’s annual conference, and of insisting that “Every Debian developer is also an Ubuntu developer” at a time when relations between Debian and Ubuntu were strained.

However, even in the first years there were signs of isolationism. Ubuntu/

Canonical insisted on using the proprietaryLaunchpad for development rather than existing free tools. Launchpad components did not begin to be released under free licenses until 2007, and the entire code was only released under the Affero GNU General Public License in 2009.

Similarly, in November 2006, Shuttleworth himself created controversy when he invited openSUSE developers to join Ubuntu. Although Shuttleworth later claimed that the offer was a response to Microsoft and Novell’s cooperative agreements (Novell being openSUSE’s corporate sponsor), it was widely condemned as an effort at corporate raiding unprecedented in the FOSS world, and Shuttleworth apologized a few days later.

However, the real turning point in Ubuntu/ Canonical policy appears to have been Shuttleworth’s failure to convince other FOSS projects to coordinate their release cycles.

Shuttleworth first made the case in December 2006 that “it would be nice at the beginning of an Ubuntu release cycle to have a really confident picture of which projects will produce stable releases during those few months when we can incorporate new upstream versions. It would be even better if, during the release cycle, we knew immediately if there was a *change* in what was going to be released.”

The FOSS response, though, showed a distinct lack of interest. Many, including KDE’s Aaron Seigo, saw the suggestion as squeezing projects into a uniformity that might not fit their needs.

What Is The Best Search Alternative To Google?

Google has dominated the search engine market for most of its 20-year existence. Today, most SEO efforts mainly revolve around the popular search engine.

Google holds a massive 92.74 percent search engine market share worldwide, according to StatCounter, as of October.

While Google is truly a force to be reckoned with, some view its dominance in the internet search space as problematic.

The company, with its large network of Internet-related services and products, owns a vast wealth of information on its users and we don’t exactly know all the ways they are using it.

Privacy concerns are among the top reasons why some people prefer using other search engines instead of Google.

We wanted to know which Google search alternative is favored by marketers, so we asked our Twitter community.

What Is Your Favorite Google Search Alternative?

Here are the results from this #SEJSurveySays poll question.

According to SEJ’s Twitter audience:

36 percent chose DuckDuckGo as their favorite Google search alternative.

32 percent said their top pick is Twitter.

30 percent their favorite alternative search engine is Bing.

2 percent favor Yandex as a Google search alternative.

Here Are a Few Comments from Our Twitter Followers

A few followers explained the reason behind their vote:

DDG hands down, it respects your privacy which is why I use it.

— Denpafighter978VGCP (@DAXISAWINNER) October 29, 2023

But in number of search queries @YouTube is on 2nd position. 🙂

— Digital Prem (@DigitalPrem1) November 1, 2023

For me, Bing is as good as Google. I have started using Bing a lot from last 4 months.

However, I am looking forward to install DuckDuckGo (after seeing the poll result). It’s not prominent in India, so it will be interesting to see what results it gives for Indian search terms.

— Mihir Vedpathak🚀 (@VedpathakMihir) October 29, 2023

I actually don’t use anything other than google

— Imtanan Tech Tips (@ImtananTech) October 30, 2023

Other followers also shared a few other Google search alternatives such as:

Qwant.

Ecosia.

Startpage.

Mojeek.

Which Search Engine Is Right for You?

Whatever your reason is for deciding not to use Google, you have plenty of other search engine options.

Check out the post that inspired our poll, by Chuck Price: 14 Great Search Engines You Can Use Instead of Google.

Learn more about the most popular search engines worldwide with these posts from our SEJ contributors:

Have Your Say

What is your favorite Google search alternative? Tag us on social media to let us know.

Be sure to have your say in the next survey – check out the #SEJSurveySays hashtag on Twitter for future polls and data.

Image Credit

Chart created by Shayne Zalameda

Search Engine Updates : Spot Them Before They Happen

Search Engine Updates : Spot Them Before They Happen

Over the past few weeks we’ve seen all the major engines update in some form. From major Google updates to minor MSN And Yahoo updates. Sometimes these updates catch website owners off guard.

It wasn’t too long ago that you could set your watch (almost) by a search engine update. And at that time Google was the one most watched. For a few days during the end of the month and spilling into the next month Google would do a complete update of it’s index. Those days are long gone in favor of a constantly updating index, however they do throw in major updates here and there to keep us on our toes.

Similarly, Yahoo! Also does that all inclusive update occasionally. Before I go into the indicators for these two lets take a look at the third of the big 3:

MSN – MSN launched their own crawler based search engine earlier this year. They claim to use a neural network to help index pages and improve results over time.

They also subscribe to the belief of an perpetual update where new content is added as its found, and results are tweaked on the fly.

However, in monitoring MSN since its inception I’ve found occasions where there have been updates which are noticeable. I think I’ve even discovered how you too can watch for them.

Usually, just before a shakeup of MSN’s results, there’s a dramatic change in either pages indexed or back links for a bunch of sites.

Usually I check these factors across all the engines for my clients on Monday mornings, first thing.

And almost without fail, before a major MSN change I will see pages or back links indexed drop from a high number to a low number or even 0. It isn’t the usual change you see in MSN – where new pages or links are added a few at a time. This is a major change in the numbers.

In every case I can think of this precedes a major update by a few days to a week.

In other words, if my client had 15,434 pages indexed, it would drop to 27. This was a precursor to an MSN update. Conversely if the client site went from 15,434 to 15,994 I wouldn’t consider that as an update indication.

Yahoo! – There are a couple indicators for Yahoo! That I’ve noticed in the past.

For one thing, they do something similar to MSN. That is you will see a change in the days leading up to the update. For example, for one of my clients, their back links had remained steady at just under 1 million for the past few weeks. Then in the weeks leading up to the most recent update they began to drop. Now they are at about 760,000 – a 25% loss in back links.

Similarly, the indexed page count followed a similar pattern. For this same client the indexed page count sat around 2.3 million for the past few weeks. 2 weeks before the update it dropped to 1.8 million then to 1.5 million the next week. Now it’s back up to just under 2.3 million.

One other thing I’ve noticed with Yahoo! is that they seem to plan major updates every 3-6 months and these updates take place either just before or just after their earnings report.

I’ve noticed this for the past couple years now and for a few updates you could almost gauge how drastic an update it was going to be based on their earnings report : if Yahoo! had a good quarter then the impact on search engine rankings as a result of the index shift was greater.

These days, however, Yahoo! only seems to have one or two major shakeups per year, with a series of smaller ones throughout the year.

Google – Google, of course, is the engine most people watch.

And while Google too has moved to a perpetually updating index, much like MSN, they do have major updates when algorithms are tweaked.

In fact, we have just completed one such update. And as with previous major updates, I was expecting this one.

There were similar indicators as with MSN And Yahoo! In that there was a subtle shift in the number of back links indexed, but the largest indicator for me was the recent doubling of the index.

You see, back when Yahoo! proclaimed ‘Ours is bigger’ Google retaliated with a massive crawl and index. In almost every case I’ve seen the number of indexed pages roughly doubled.

But since many of the sites in question didn’t actually have that many pages I new an adjustment was coming.

At the end of October, the week before Halloween I noticed that the back link counts for all my clients changed significantly. To me this was the beginning of the update, yet no formal announcement from the “traditional” sources happened until a few days later. That’s when Webmasterworld began calling it Jagger. It was still a few days after that that Matt Cutts acknowledged the update on his blog.

And this isn’t the first time I’ve noticed updates before official announcements, this is about the third Google ‘major’ update that has been preceded by either a page or back link count change.

So what does this mean (if anything?)

As you can see, in all three cases there are good indicators preceding an update.

Either you see major changes in links or pages indexed, or even a positive earnings report after which an update happens.

Granted this is by no means scientific. This is just observations I’ve made on a couple dozen client’s websites over the past few quarters.

But when I see the same things happening to these clients at roughly the same time, this to me is an indicator. One which I will be sure to pay attention to in the future.

One final note, I’ve also noticed in Google that updates seem to follow themes. In other words, the update doesn’t happen all over at once. Sure different data centers update at different times, but the update seems to hit different parts of the index at different time.

For example, I don’t see rankings changes on legal sites at the same time as rankings changes on rental sites. They always happen a week or 2 apart.

These changes don’t seem PageRank or authority based but do seem to revolve around the theme or topic of the sites.

Therefore, if you do see changes happening with your site, check your competitors and you may also see changes affecting them. But if you don’t see changes happening with non-related sites, that doesn’t necessarily mean that there isn’t an update, it could just mean that it’s already affected those other sites or hasn’t hit them yet.

Columnist Rob Sullivan is an SEO Specialist and Internet Marketing Consultant at Text Link Brokers

Python Program To Search An Element In An Array

In Python, there are mainly two searching algorithms that are majorly used. Out of those, the first one is Linear Search and the second one is Binary Search.

These two techniques are majorly used in order to search an element from the given array or from the given list also. While searching an element, there are two methodologies that can be followed in any kind of algorithm. One of those is recursive approach and the other is iterative approach. Let us discuss both algorithms in both approaches and solve similar problems.

Linear Search

The Linear Search technique is also known as Sequential search. The meaning of the name “ Sequential search ” is definitely justified by the process followed by this search algorithm. It is a method or technique which is used in order to find the elements within an array or a list in Python.

It is known to be the most simplest and easiest of all the other searching algorithms. But, the only drawback of this algorithm is that it is not so efficient. That is the main reason for not using Linear search very frequently.

Algorithm

Step 1 − It searches for an element in a sequential order just by comparing the desired element with each element present in the given array.

Step 2 − If the desired element is found, then the index or position of the element will be displayed to the user.

Step 3 − If the element is not present within the array, then the user will be informed that the element is not found. In this way, the algorithm is processed.

In general, Linear search algorithm is comparatively suitable and efficient for small arrays or small lists which has a size less than or equal to 100 as it checks and compares with each element.

More time will be consumed if the desired element is present in the last position of the array.

The Time complexity of Linear Search algorithm in best case is “ O( 1 ) ”. In this case, the element will be present in the first position of the array, i.e., with the index “ 0 ”.

The Time complexity of Linear Search algorithm in average case is “ O( n ) ”. In this case, the element will be present in the middle position of the array, i.e., with the index “ ( n – 1 ) / 2 ” or “ (( n – 1 ) / 2 )+ 1 ”.

The Time complexity of Linear Search algorithm in worst case is “ O( n ) ”. In this case, the element will be present in the last position of the array, i.e., with the index “ n-1 ”.

Example

In the following example, we are going to learn about the process of searching an element in an array using Linear search.

def iterative_linear( arr, n, key_element): for x in range(n): if(arr[x] == key_element): return x return -1 arr = [2, 3, 5, 7, 9, 1, 4, 6, 8, 10] max_size = len(arr) key = 8 result = iterative_linear(arr, max_size - 1, key) if result != -1: print ("The element", key," is found at the index " ,(result), "and in the ", (result+1), "position") else: print ("The element %d is not present in the given array" %(key)) Output

The output for the above program is as follows −

The element 8  is found at the index  8 and in the  9 position Example (Recursive)

In the following example, we are going to learn about the process of searching an element in an array using Linear search in recursive approach.

def recursive_linear( arr, first_index, last_index, key_element): if last_index < first_index: return -1 if arr[first_index] == key_element: return first_index if arr[last_index] == key_element: return last_index return recursive_linear(arr, first_index + 1, last_index - 1, key_element) arr = [2, 3, 5, 7, 9, 1, 4, 6, 8, 10] max_size = len(arr) key = 8 result = recursive_linear(arr, 0, max_size - 1, key) if result != -1: print ("The element", key," is found at the index " ,(result), "and in the ", (result+1), "position") else: print ("The element %d is not present in the given array" %(key)) Output

The output for the above program is as follows −

The element 8  is found at the index  8 and in the  9 position Binary Search

The Binary search algorithm is quite different from the Linear search algorithm. It follows completely different procedure in order to search an element from the array. It only considers sorted arrays generally.

If the array is not sorted in some cases, the array is sorted and then the procedure of the Binary search algorithm starts. As soon as the array is considered by the Binary search algorithm, it is sorted first and then the algorithm is applied on the array.

Algorithm

Step 1 − The process of sorting the array is the first step followed.

Step 2 − After the array is sorted, the array is considered as two halves. One half is starting from the first element to the middle element of the sorted array and the second half is starting from the element after the middle element to the last element of the sorted array.

Step 3 − The key element (the element that is supposed to be searched is known as key element) is compared with the middle element of the sorted array.

Step 4 − If the key element is less than or equal to the middle element of the sorted array, the second half elements are ignored further as the key element is smaller than the middle element. So, definitely, the element must be present in between the first element and the middle element.

Step 6 − If the key element is greater than the middle element, then the first half of the sorted array is ignored and the elements from the middle element to the last element are considered.

Step 7 − Out of those elements, the key element is again compared with the middle element of the halved array and repeats the same procedure. If the key element is greater than the middle element of the halved array, then the first half is neglected.

Step 8 − If the key element is less than or equal to the middle element of the halved array, the second half of the halved array will be neglected. In this way, the elements are searched in any half of the array accordingly.

So, when compared to the Linear search, the complexity is reduced by half or more than half as half of the elements will be removed or not considered in the first step itself. The best case time complexity of Binary search is “ O(1) ”. The worst case time complexity of Binary search is “ O(logn) ”. This is how the algorithm of binary search works out. Let us consider an example and apply the Binary search algorithm to find out the key element out of the elements present in the array.

Example

In this example, we are going to learn about the process of searching an element in an array using Binary search in recursive approach.

def recursive_binary(arr, first, last, key_element): if first <= last: mid = (first + last) if arr[mid] == key_element: return mid return recursive_binary(arr, first, mid - 1, key_element) elif arr[mid] < key_element: return recursive_binary(arr, mid + 1, last, key_element) else: return -1 arr = [20, 40, 60, 80, 100] key = 80 max_size = len(arr) result = recursive_binary(arr, 0, max_size - 1, key) if result != -1: print("The element", key, "is present at index", (result), "in the position", (result + 1)) else: print("The element is not present in the array") Output

The output for the above program is as follows −

The element 80  is found at the index 3 and in the position 4 Example

In this example, we are going to learn about the process of searching an element in an array using Binary search in iterative approach.

def iterative_binary(arr, last, key_element): first = 0 mid = 0 while first <= last: mid = (first + last) if arr[mid] < key_element: first = mid + 1 last = mid - 1 else: return mid return -1 arr = [20, 40, 60, 80, 100] key = 80 max_size = len(arr) result = iterative_binary(arr, max_size - 1, key) if result != -1: print("The element", key, "is present at index", (result), "in the position", (result + 1)) else: print("The element is not present in the array") Output

The output for the above program is as follows −

The element 80  is found at the index 3 and in the position 4

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