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There’s a lot to know about search intent, from using deep learning to infer search intent by classifying text and breaking down SERP titles using Natural Language Processing (NLP) techniques, to clustering based on semantic relevance with the benefits explained.

Not only do we know the benefits of deciphering search intent – we have a number of techniques at our disposal for scale and automation, too.

But often, those involve building your own AI. What if you don’t have the time nor the knowledge for that?

In this column, you’ll learn a step-by-step process for automating keyword clustering by search intent using Python.

SERPs Contain Insights For Search Intent

Some methods require that you get all of the copy from titles of the ranking content for a given keyword, then feed it into a neural network model (which you have to then build and test), or maybe you’re using NLP to cluster keywords.

There is another method that enables you to use Google’s very own AI to do the work for you, without having to scrape all the SERPs content and build an AI model.

Let’s assume that Google ranks site URLs by the likelihood of the content satisfying the user query in descending order. It follows that if the intent for two keywords is the same, then the SERPs are likely to be similar.

For years, many SEO professionals compared SERP results for keywords to infer shared (or shared) search intent to stay on top of Core Updates, so this is nothing new.

The value-add here is the automation and scaling of this comparison, offering both speed and greater precision.

How To Cluster Keywords By Search Intent At Scale Using Python (With Code)

Begin with your SERPs results in a CSV download.

1. Import The List Into Your Python Notebook. import pandas as pd import numpy as np serps_input = pd.read_csv('data/sej_serps_input.csv') serps_input

Below is the SERPs file now imported into a Pandas dataframe.

2. Filter Data For Page 1

We want to compare the Page 1 results of each SERP between keywords.

We’ll split the dataframe into mini keyword dataframes to run the filtering function before recombining into a single dataframe, because we want to filter at keyword level:

# Split serps_grpby_keyword = serps_input.groupby("keyword") k_urls = 15 # Apply Combine def filter_k_urls(group_df): filtered_df = group_df.loc[group_df['url'].notnull()] filtered_df = filtered_df.loc[filtered_df['rank'] <= k_urls] return filtered_df filtered_serps = serps_grpby_keyword.apply(filter_k_urls) # Combine ## Add prefix to column names #normed = normed.add_prefix('normed_') # Concatenate with initial data frame filtered_serps_df = pd.concat([filtered_serps],axis=0) del filtered_serps_df['keyword'] filtered_serps_df = filtered_serps_df.reset_index() del filtered_serps_df['level_1'] filtered_serps_df 3. Convert Ranking URLs To A String

Because there are more SERP result URLs than keywords, we need to compress those URLs into a single line to represent the keyword’s SERP.

Here’s how:

# convert results to strings using Split Apply Combine filtserps_grpby_keyword = filtered_serps_df.groupby("keyword") def string_serps(df): df['serp_string'] = ''.join(df['url']) return df # Combine strung_serps = filtserps_grpby_keyword.apply(string_serps) # Concatenate with initial data frame and clean strung_serps = pd.concat([strung_serps],axis=0) strung_serps = strung_serps[['keyword', 'serp_string']]#.head(30) strung_serps = strung_serps.drop_duplicates() strung_serps 4. Compare SERP Similarity

To perform the comparison, we now need every combination of keyword SERP paired with other pairs:

# align serps def serps_align(k, df): prime_df = df.loc[df.keyword == k] prime_df = prime_df.rename(columns = {"serp_string" : "serp_string_a", 'keyword': 'keyword_a'}) comp_df = df.loc[df.keyword != k].reset_index(drop=True) prime_df = prime_df.loc[prime_df.index.repeat(len(comp_df.index))].reset_index(drop=True) prime_df = pd.concat([prime_df, comp_df], axis=1) prime_df = prime_df.rename(columns = {"serp_string" : "serp_string_b", 'keyword': 'keyword_b', "serp_string_a" : "serp_string", 'keyword_a': 'keyword'}) return prime_df columns = ['keyword', 'serp_string', 'keyword_b', 'serp_string_b'] matched_serps = pd.DataFrame(columns=columns) matched_serps = matched_serps.fillna(0) queries = strung_serps.keyword.to_list() for q in queries: temp_df = serps_align(q, strung_serps) matched_serps = matched_serps.append(temp_df) matched_serps

The above shows all of the keyword SERP pair combinations, making it ready for SERP string comparison.

There is no open source library that compares list objects by order, so the function has been written for you below.

The function ‘serp_compare’ compares the overlap of sites and the order of those sites between SERPs.

import py_stringmatching as sm ws_tok = sm.WhitespaceTokenizer() # Only compare the top k_urls results def serps_similarity(serps_str1, serps_str2, k=15): denom = k+1 norm = sum([2*(1/i - 1.0/(denom)) for i in range(1, denom)]) ws_tok = sm.WhitespaceTokenizer() serps_1 = ws_tok.tokenize(serps_str1)[:k] serps_2 = ws_tok.tokenize(serps_str2)[:k] match = lambda a, b: [b.index(x)+1 if x in b else None for x in a] pos_intersections = [(i+1,j) for i,j in enumerate(match(serps_1, serps_2)) if j is not None] pos_in1_not_in2 = [i+1 for i,j in enumerate(match(serps_1, serps_2)) if j is None] pos_in2_not_in1 = [i+1 for i,j in enumerate(match(serps_2, serps_1)) if j is None] a_sum = sum([abs(1/i -1/j) for i,j in pos_intersections]) b_sum = sum([abs(1/i -1/denom) for i in pos_in1_not_in2]) c_sum = sum([abs(1/i -1/denom) for i in pos_in2_not_in1]) intent_prime = a_sum + b_sum + c_sum intent_dist = 1 - (intent_prime/norm) return intent_dist # Apply the function matched_serps['si_simi'] = matched_serps.apply(lambda x: serps_similarity(x.serp_string, x.serp_string_b), axis=1) serps_compared = matched_serps[['keyword', 'keyword_b', 'si_simi']] serps_compared

Now that the comparisons have been executed, we can start clustering keywords.

We will be treating any keywords which have a weighted similarity of 40% or more.

# group keywords by search intent simi_lim = 0.4 # join search volume keysv_df = serps_input[['keyword', 'search_volume']].drop_duplicates() keysv_df.head() # append topic vols keywords_crossed_vols = serps_compared.merge(keysv_df, on = 'keyword', how = 'left') keywords_crossed_vols = keywords_crossed_vols.rename(columns = {'keyword': 'topic', 'keyword_b': 'keyword', 'search_volume': 'topic_volume'}) # sim si_simi keywords_crossed_vols.sort_values('topic_volume', ascending = False) # strip NANs keywords_filtered_nonnan = keywords_crossed_vols.dropna() keywords_filtered_nonnan

You’ll note that keyword and keyword_b have been renamed to topic and keyword, respectively.

Now we’re going to iterate over the columns in the dataframe using the lamdas technique.

The lamdas technique is an efficient way to iterate over rows in a Pandas dataframe because it converts rows to a list as opposed to the .iterrows() function.

Here goes:

queries_in_df = list(set(keywords_filtered_nonnan.topic.to_list())) topic_groups_numbered = {} topics_added = [] def find_topics(si, keyw, topc): i = 0 i += 1 topics_added.append(keyw) topics_added.append(topc) topic_groups_numbered[i] = [keyw, topc] j = [key for key, value in topic_groups_numbered.items() if keyw in value] topics_added.append(topc) topic_groups_numbered[j[0]].append(topc) j = [key for key, value in topic_groups_numbered.items() if topc in value] topics_added.append(keyw) topic_groups_numbered[j[0]].append(keyw) def apply_impl_ft(df): return df.apply( lambda row: find_topics(row.si_simi, row.keyword, row.topic), axis=1) apply_impl_ft(keywords_filtered_nonnan) topic_groups_numbered = {k:list(set(v)) for k, v in topic_groups_numbered.items()} topic_groups_numbered

Below shows a dictionary containing all the keywords clustered by search intent into numbered groups:

{1: ['fixed rate isa', 'isa rates', 'isa interest rates', 'best isa rates', 'cash isa', 'cash isa rates'], 2: ['child savings account', 'kids savings account'], 3: ['savings account', 'savings account interest rate', 'savings rates', 'fixed rate savings', 'easy access savings', 'fixed rate bonds', 'online savings account', 'easy access savings account', 'savings accounts uk'], 4: ['isa account', 'isa', 'isa savings']}

Let’s stick that into a dataframe:

topic_groups_lst = [] for k, l in topic_groups_numbered.items(): for v in l: topic_groups_lst.append([k, v]) topic_groups_dictdf = pd.DataFrame(topic_groups_lst, columns=['topic_group_no', 'keyword']) topic_groups_dictdf

The search intent groups above show a good approximation of the keywords inside them, something that an SEO expert would likely achieve.

Although we only used a small set of keywords, the method can obviously be scaled to thousands (if not more).

Activating The Outputs To Make Your Search Better

Of course, the above could be taken further using neural networks processing the ranking content for more accurate clusters and cluster group naming, as some of the commercial products out there already do.

For now, with this output you can:

Incorporate this into your own SEO dashboard systems to make your trends and SEO reporting more meaningful.

Build better paid search campaigns by structuring your Google Ads accounts by search intent for a higher Quality Score.

Merge redundant facet ecommerce search URLs.

Structure a shopping site’s taxonomy according to search intent instead of a typical product catalog.

In any case, your SEO keyword research just got that little bit more scalable, accurate, and quicker!

You're reading How To Automate Seo Keyword Clustering By Search Intent With Python

Automate Tasks By Writing Or Recording Macros

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Introduction to Automation in Excel

Automation in Excel generally involves coding in VBA (Visual Basic for Applications), a variation of Visual Basic Language to integrate with Microsoft Office applications. It is an Excel-based programming language that helps users automate tasks by writing or recording macros. A macro is a code that runs in an Excel environment and is used to automate repetitive tasks, such as when you must repeat a set of actions multiple times. Excel can record these actions and generate a macro containing code to repeat those steps. After recording the macro, the set of actions can be repeated by running the recorded macro. The macro produces VBA code users can view via VBE in a module.

Users employ Automation Add-Ins like XLTools and AutoMacro to automate their routine Excel tasks without relying on macros.

Examples of Excel Automation

Let us see below how a recorded macro can automate a daily report.

You can download this Automation Excel Template here – Automation Excel Template

Example #1 – Automation via Macro Recording

We have a dataset of some numbers and wish to have row-wise summary statistics (like sum, average, minimum, and maximum) for these. Also, we wish to apply some formatting styles to the dataset. We record these steps in a macro so that whenever we have a new dataset that requires the same operations, we can run this recorded macro to accomplish the task.

Let’s see what the dataset looks like:

Now to do the required operations and record it in a macro, we will follow the below process:

On doing this, a pop-up window opens as follows.

Now we can perform the required operations on the dataset as follows.

After using the formula, the result is shown below.

Applying the Average Formula in cell H2, the result is shown below.

Applying MIN Formula in cell I2, the result is shown below.

Applying the MAX formula in cell J2, the result is shown below.

Now drag these to get these statistics for all the rows.

Now when all these operations are performed, we stop recording the macro as below.

Example #2 – Using the Recorded Macros

Now let’s say we have another similar dataset like this in ‘Example #2’ of the Excel file, which requires the same operations and formatting. So to do this, we just run the above-recorded macro, and our task will be accomplished.

So we can see in the above screenshot that on running the recorded macro named ‘Marks_macro1’, we have automated and hence replicated the operations done on Sheet1 in Example1 to Sheet2. This is automation via recording a macro.

This kind of automation is use for simple repetitive operations such as daily report formatting, database communication, file merging, document creation, data manipulation, and more.

How Macros are Saved as VBA Code Procedures in VBE

Access the Visual Basic Editor by pressing Alt+F11 to open the Visual Basic Editor window.

On doing this, a window opens. Under the ‘Project-VBAProject’ pane, we will store the macro in one module.

With VBA Macro Recorder, we do not need to code the macro; instead, we just record it.

Users can store the macros in a personal workbook, a hidden workbook that opens in the background whenever Excel is started. Saving the macros in a Personal workbook makes the macro always available since the Personal Workbook is not system or file specified.

Excel always produces a sub-procedure (not a function procedure) when recording macros. So, recorded codes are only useful for simple macros. More complex macros can be generated by writing VBA codes by enabling the “Developer’ menu in Excel.

Recording macros to automate tasks can have some limitations. It may not always be possible to record macros that work exactly as we wish. The macro code often requires some manual updates, so in that case, AutoMacro can be used, which is an Add-In that directly installs into VBE. It requires very little knowledge of VBA coding.

Things to Remember about Excel Automation

Some tools used for Excel automation without coding knowledge are Power Query, VBA Macro Recorder, and Automation Add-Ins like Auto Macro and XLTools. XLTools is an automation Add-In that can integrate Excel with other applications and tools like SQL and Python. It can use to write commands in simple Excel tables.

Users can add macros to the Excel function menu with a button available, similar to Excel’s built-in functions.

Recording a macro helps us perform simple repetitive tasks and also when we are writing complex macro codes by editing the recorded codes.

Recommended Articles

This is a guide to Excel Automation. Here we discuss how to automate tasks by writing or recording macros, practical examples, and a downloadable Excel template. You can also go through our other suggested articles –

How To Schedule And Automate Tasks With Crontab In Ubuntu

Tired of having to manually handle certain tasks on your computer by yourself? If you are using Linux or Ubuntu, these manual tasks shouldn’t be a problem, as you can easily schedule tasks.

In this article we discuss the use of Crontab to schedule and automate tasks in Ubuntu. Do note that while we are using Ubuntu as an example here, the steps below will work for any Linux distribution.

Using Crontab to Schedule and Automate Tasks in Ubuntu

The Cron daemon performs the same function as Task scheduler on Windows. With this application, you can choose a preferred time for any process you want to start, whether it is a backup or maintenance task. With this utility, you can schedule a task without manual intervention.

However, before delving into the use of Crontab, it is important to understand the structure and arrangement for configuring jobs on it.

Crontab Job Arrangement Basics

└───────────────────────── min (0 – 59)

m – represents minute and can be any number from 0 to 59.

h – represents hour and can be any number from 0 to 23.

dom – represents the day of the month and can be any number between 1 to 31.

mon – represents months. You can explicitly set the month you want a task to run. The range is 1 through 12.

dow – do you want a task to run on a specific day of the week? You can choose a number between 0 and 6.

user – if you have more than one user on Ubuntu, you can specify which one is responsible for the task.

command – after choosing the time and user account, you need to write out a command for the task itself.

How to Use Crontab to Schedule a Backup on Ubuntu

Having explained the tools you will be using to automate tasks in Ubuntu, here’s how to schedule a backup task:

1. Launch a terminal, either from the Applications menu or by pressing Ctrl + Alt + T.

2. Type the command:



3. If you are running it for the first time, it will ask you to choose the editor for opening the file. You can press 2 for nano.

4. In the crontab file that opened, scroll down to the end of the file with the Down arrow key. To add a task to run at a specific time, add your task in the following format:

m h dom mon dow









For example, to run a backup script at 5am every Monday:

5. When you are done, press Ctrl + O to save the file in the nano editor. Press Ctrl + X to exit the nano editor.

That’s it.


With these instructions it will be quite easy for you to schedule tasks in Ubuntu. Would you like to check out more tips on using Ubuntu? Check out our guide on how to boot Ubuntu in recovery mode.

Sarah Adedun

Sarah Adedun is a technology enthusiast. When she is not reviewing tech products, you can find her sharing personal thoughts on Medium and researching ways to merge Finance and Technology together.

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Google Publishes Image Search Seo Tips

Google has updated their Image Publishing Guidelines and it’s a vast improvement.

The new version offers actionable information that could make your images rank better, position them to appear in rich results, make them voice assistant friendly, and bring more traffic to your website.

This updated support page has a lot to offer.

Image Search SEO Tips

Google has provided useful image search SEO tips. Here are the actionable takeaways:

Provide good context: Make sure images are contextually relevant because the text of the content is going to influence how Google interprets what the image is about.

Optimize Image Placement: This means to place images so that text that is relevant to the image is nearby. The surrounding text will be picked up by Google to help it understand what the image is about. Adding a caption is an additional good way to do this. Adding an important image close to the top of the page is another tip.

Create informative and High Quality Sites: Google considers the quality of the content as part of the image ranking process. The content is also used to generate a meaningful snippet for the content. So it helps for the page content to relate with the image content.

Create Device-Friendly Sites: Google revealed that users search for images on mobile more than they do on desktop. This means it’s important to be mobile friendly in order to rank better and capitalize on the traffic.

Create Good URL Structure for Your Images: This is an interesting SEO tip. Google revealed that they use the file path and the file name in order to better understand and rank images. This is an actual ranking factor Google is sharing. This is a very useful tip for ranking images. For example, if your images are of different kinds of products, instead of dumping them all into a generic “/assets/” or “/images/” folder, you could in theory create a more organized structure to organize the images into something more meaningful like, /mens/ and /womens/ in order to organize images of men’s and women’s clothing.

Technical SEO Tips for Image Search Check Your Page Title & Description

While it was previously understood that title tags were important, it’s an interesting revelation that the meta description tag plays a role.  Here is what the new Google Image Search Support page says:

“Google Images automatically generates a title and snippet to best explain each result and how it relates to the user query… We use a number of different sources for this information, including descriptive information in the title, and meta tags “

Add Structured Data

This is a very interesting addition to the new support page. Structured data plays a role in rich results and although it’s not mentioned in this support page, we also know that structured data plays a role in voice assisted search, which is an important area of search. Google’s Voice Assistant is now embedded in over 5,000 devices including in automobiles.

Here is what Google’s support page states:

If you include structured data, Google Images can display your images as rich results, including a prominent badge, which gives users relevant information about your page and can drive better targeted traffic to your site.

From the Google Support Page about Badges:

“If you’re publishing recipes, add Recipe markup on your page, for products, add Product markup, and for videos, add Video markup.”

More SEO Advice for Image Search

Google ends the support page by highlighting the following SEO tips for image search:

Optimize image size for speed

Optimize photos for sharpness

Use descriptive titles, captions, descriptive alt text, file names, and surrounding text.

Use an XML Image Site Map

Safe Search SEO: Segregate adult content into it’s own image folder

The Google support pages Danny Sullivan is credited with feature a similar usefulness as this support page. The previous Image Publishing Guidelines didn’t have as many actionable SEO tips.

I don’t know if Sullivan had a hand in crafting this new image publishing guideline, but it needs to be noted that this is an excellent and useful page.  Read: Google’s new Image Publishing Guidelines.

Images by Shutterstock, Modified by Author

How To 3X Your Blog Traffic With Technical Seo

Blogging has become an industry in itself with many people now making full-time careers from it and companies exploiting blogs as a key way of attracting new business.

As the influence of blogs has increased, it has naturally become more competitive and more difficult to stand out from the crowd. Bloggers are investing huge amounts of time and money into their content, so simply sitting down and publishing some words you wrote in your kitchen is unlikely to cut it these days.

There are too many ways of making a blog successful to cover in one blog post, but there are certainly a more manageable set of things you can do to improve your blog’s performance in search specifically that a surprising amount of bloggers overlook.

If you have a blog that has been built off the back of a great brand and fantastic social media presence, but you haven’t paid too much attention to SEO, then this post is for you.

I’m going to share exactly what we did to more than triple a travel blog’s search traffic over a 12-month period and take them from the tens of thousands of visits per month to the hundreds of thousands.

Our work has focused on technical SEO activity rather than content production or off-site work.

It’s important to highlight that this blog already had a very good presence and lots of good things already going for it, so I wanted to break down the starting points in a bit more detail first before we get into the nitty-gritty of what accelerated the growth.


The site already had a very good link profile, with a wide variety of links on strong, top-tier publications like CNN and the Independent, along with lots of links on other blogs they had built relationships with.

I’m not going to go into much detail on how to do this as it warrants its own post, but the key approaches were:

Guest Writing: Writing posts for other blogs or getting featured via interviews etc. This is very easy for bloggers with non-commercial sites and is a very scalable way to develop a good link profile.

PR: Building relationships with journalists or pitching stories to big publications that can gain you links and mentions on very powerful sites.

Other great resources on getting links for your blog:


The site has been around a long time so it had accumulated lots of content which was well written and had been edited and targeted with SEO in mind.

As a result, a lot of it was ranking well and bringing traffic to the site and seemingly performing very well.

If you’re just getting started on your blogging journey then populating the site with really good, quality content should be a high priority for you.

You can read more about that at the links below:

So, as I highlighted originally, the key part of our activity that took the site from tens of thousands of visits per month, to hundreds of thousands of visits per month, was technical SEO work.

I’m going to break down all the key elements we addressed below, so if you’re sat with a blog in a similar position to what I’ve described above you can implement these actions to help unleash your blog’s traffic, too.

I’ve prioritized these in a way that I believe has had the biggest impact (with the largest impact first), but this is obviously up for discussion and we can’t be sure what influence each individual action had as these were all implemented on the same timeline.

Indexation Issues

A common issue for blogs, especially those that have been around a long time, is having lots of URLs indexed by Google that are not genuine pages and offer no value to users.

These included regular offenders for WordPress sites, such as:

Category pages.

Tag pages.

Author pages.

Archive pages.

But also:

Attachment URLs.

Strange parameter pages getting crawled and indexed.

We crawled the site and identified the patterns behind the key offenders in this area and either noindexed them or updated Search Console to stop Google crawling them.

Thin Content

The site had a huge amount of pages with extremely thin content present.

These were basically category pages with a small intro added that were clearly created with SEO in mind to target long-tail phrases.

However, it was done to such a degree that the pages were of extremely low quality and added very little value for a user landing on it.

The potential upside of this kind of page wasn’t enough to warrant the time required to add content to them, so these were either removed or noindexed.

Page Speed

When we started work the site’s page speed was extremely poor due to various fonts, large images, caching, and various other issues.

We used some plugins to help improve this, which isn’t the dream solution (building a site more efficiently from the ground up is preferable).

But for bloggers on a tight budget and with limited resources and knowledge, you can still make some significant steps forward.


For some of the site’s key money phrases, there were multiple pages present that were targeting the same topic.

Google was chopping and changing between which of the pages was ranking so it was clear it was unsure which the best choice was. This is usually a good sign of content cannibalization and suggests you should merge those pages into one top quality page.

We did just that, and soon saw the ranking page settle down and ranking performance jump forward significantly and stay there consistently.

XML Sitemap

The site had a variety of sitemaps submitted in Search Console, many of which listed URLs which we did not want to be crawled, let alone indexed.

We trimmed this so the sitemaps present only listed URLs with good quality content present so it was much clearer what should be indexed and which content was most important on the site.

Aggressive Ads

Advertising is the way most bloggers make their money, so telling them to cut it down is not a popular conversation.

Page Structure

An issue we see regularly with blogs and websites in general is that header tags are used for styling rather than structure.

H1, H2, and H3 tags should be used to clearly illustrate the structure of your page so Google can map it on to the phrases it would expect to see mentioned on the topic being covered.

If your site’s headers are being used for styling then get on to your developer and get it changed so you use these elements in a more tactical and optimized way.

Internal Linking

We worked closely with the client to clean up internal links and improve how they were being used. This included:

Fixing any dead internal links that were linking to broken pages.

Fixing internal links that took users and bots through redirect chains, so the link pointed directly to the correct destination.

Adding more links to important pages throughout the site.

Link Updates

As I mentioned initially, the site had some excellent links that it had established over the years through natural approaches and some more direct efforts.

Some of these more direct efforts involved getting optimized anchor text to key money pages which had been a bit overzealous at times. We believed it was potentially causing the site to be held back from ranking for phrases in that area and for that page in particular.

There were other elements involved too, but those above were the key issues that were wide reaching and causing significant performance issues.

It’s rare to find one silver bullet with technical SEO, but if you chip away at the wide variety of issues that can impact you then you can see some serious improvements.

We also haven’t yet implemented all the recommendations we’ve suggested. One key outstanding one is implementing “hub pages” that guide people into all the key content on a topic.

In travel, this is very much destination focused, and there is a lot of search interest to gain if you create high quality pages for those hubs. This is the key focus to move on to next to help accelerate this site’s progress further, and there is a huge amount of potential in it once implemented.

So if you’re a blogger with lots of great content and links, but you haven’t yet paid any attention to your technical SEO, do it now!

Make sure you aren’t leaving significant amounts of traffic on the table – you may be sitting on huge growth potential. Time to kick into gear!

More Resources:

Image Credits

In-post Images: Created by author, January 2023

Msn Search Toolbar With Windows Desktop Search Launched

MSN Search Toolbar with Windows Desktop Search Launched

To address some privacy concerns, you can control which files the software indexes and how often. But are there enough reasons for me to switch from Copernic to MSN ? No, not yet.

1. MSN Desktop Search is only available on machines running Microsoft Windows XP/Server 2003/2000 & Microsoft Internet Explorer 5.01 or later. If you are a Firefox fan like me, stay away from it.

2. MSN added a preview pane similar to the one in Microsoft Outlook. This is a useful enhancement but according to SEW, Microsoft’s preview is painfully sluggish compared to Yahoo’s, to the point of being virtually unusable.

3. There is a toolbar everywhere. (See the image on the right) The suite includes three toolbars, one for Microsoft Outlook, a toolbar for Windows and Internet Explorer, and a toolbar for the Windows taskbar. Why not a standalone program ? I can’t live without the Google Toolbar and installing another toolbar decreases my browser preview area further. Why would I want to look at a toolbar all the time even though I would use only 2% of my time.

4. MSN doesn’t automatically index PDF files – Most of my official documentation is in PDF format but MSN requires you to download a separate plugin for indexing PDF files. I see some rivalry here. Microsoft is expected to release its own Metro document format which is being touted as Adobe’s PDF killer. Maybe that could be the reason for Microsoft not adding native support for PDF in their search suite. When MSN DS can index 200 file types, why not 201 ?

5. Microsoft plays some hide n’ seek – One of the non-public betas reviewed by PCMag actually added tabbed browsing to IE, a useful feature that has, unfortunately, been held back for further security testing. Hopes dashed.

6. Can I search for files in any language? – Not yet. MSN Toolbar Suite and Deskbar Search supportU.S. English only.

7. Do I really need MS Desktop Search ? No, It’s already there.

“MSN – You could not win my heart, nor a place on my desktop.” , Amit Agarwal

Amit Agarwal is a Desktop Search enthusiast and dedicated blogger – read his personal blog, The Indian Blogger

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