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🚨 Note: All standard Universal Analytics properties will stop processing new hits on July 1, 2023. 360 Universal Analytics properties will stop processing new hits on October 1, 2023. That’s why it’s recommended to do the GA4 migration.

When a user converts on your website, they often go through a journey of channels—and as a marketer, your job is to track this journey accurately.

Assisted conversions are a great way to understand the channels that support user conversions, even if those channels don’t directly participate in the final interaction. 

In this guide, we’ll learn how to use the assisted conversions report in Google Analytics to leverage our marketing efforts. 

An overview of what we’ll cover: 

So let’s dive in!

What Are Assisted Conversions, and Why Use Them?

Assisted conversions are interactions that lead to the conversion but aren’t the main source or conversion point for the audience.

Thus, all interactions on a conversion path (except the last interaction) are assisted conversions. 

Google Analytics provides a report on assisted conversions. To access it, you can go to Conversions → Multi-Channel Funnels → Top Conversion Paths in your account.

You can select the Path Length and type of transaction in this report. For our report, let’s select the Path Length of 6 and select Transaction under E-commerce. 

The results of this report show the path of the channels a user visited before converting.

To get a more granular view of these reports, we’ll shift our Primary dimension from the multi-channel funnel—the MCF Channel Grouping Path—to the Source Medium Path. 

This will give a path of all the channels visited by the user before converting. 

Note that all the reports of Google Analytics relating to Acquisition apply a standard attribution model. As per this model, the report gives conversion credit only to the last channel on the path and ignores all the previous sources. 

This means, in our example, the credit for this conversion will be given to chúng tôi , ignoring all the previous channels. 

But for a marketer, the other sources that facilitated the user to the final conversion are still important for tracking purposes. We can track these sources in two ways—using an Attribution Model or Assisted Conversions. 

The Assisted Conversion model is quite old in Google Analytics. It counts every conversion that was not the last conversion as an assisted conversion. 

Thus, in our example, all the other 5 channels out of the 6 will be considered assisted conversions. However, if a channel is occurring more than once, then it will only be counted once. 

In Short, Assisted Conversions Are Funnel Steps

To summarize, the last channel gets the credit for the conversion, whereas all the other unique channels before the last one get credit for the assisted conversion. Thus, every channel in the path gets some credit for the final conversion. 

Google Analytics has an entire report dedicated to assisted conversions. 

Let’s take a look!

The Assisted Conversions Report in Google Analytics

In your Google Analytics account, open the reports from Conversions → Multi-Channel Funnels → Assisted Conversions. 

Next, filter the type of conversion. For our example, we’ll select E-commerce  → Transaction. 

And again, select the Primary Dimension as Source/Medium. 

This report shows the number of  Assisted Conversions for each channel in the first data column. This includes any source that appeared at least once in a user’s conversion path.

Our main focus, however, will not be the volumes of these two conversions, but rather the ratio of these conversions. 

Let’s see how this ratio is significant for marketers!

The Assisted Conversions / Direct Conversions Ratio

The Assisted Conversions / Direct Conversions ratio tells us the position of the assisting channel in the conversion path.

We can classify this ratio into three categories—significantly less than 1, close to 1, and significantly more than 1. 

The first category (significantly less than 1) suggests that the assisting channel is towards the end of the conversion path. 

The second category includes the ratio which is almost 1. This suggests that the channel is equally an assisting channel and the last channel of conversion. 

The third category includes the ratio which is significantly more than 1. This suggests that the channel is mostly an assisting source for conversion and occurs at the beginning of the conversion path. 

Clearly, the ratio suggests the effectiveness of the channel in converting the user. Thus, it can help you boost your conversions—if you use it wisely.

How to Use The Assisted Conversions / Direct Conversions Ratio

With this ratio, you can decide the type of communication you want to present to the user based on the position of the channel in the conversion path. 

For example, if you have an extensive email campaign running from your channel, you’d want the call to action to be towards the end of the conversion path. Thus, the selected channel should have a ratio significantly less than 1. 

On the other hand, if you know that a source is typically at the beginning of the conversion path, you might want to change the message you convey on that channel. 

You’d only try to persuade the user to move forward on the conversion path, rather than constantly sending a hard-selling message. Such sources will have a ratio significantly more than 1. 

You can also use the volume of assisted conversions to decide the marketing message. 

A higher volume of assisted conversions tells us that a particular channel is trying to bring a greater audience to our website for the first or second time.

This channel may not convert the audience, but it will help to increase our reach.

FAQ How do I access the assisted conversions report in Google Analytics?

To access the assisted conversions report in Google Analytics, follow these steps:

What information does the Assisted Conversions report provide?

The Assisted Conversions report shows:

Can I track assisted conversions using an attribution model other than the Assisted Conversions report?

Yes, you can use different attribution models in Google Analytics to track and analyze assisted conversions. The Assisted Conversions report is just one way to gain insights into the contribution of various channels.


So that’s everything you need to know about the assisted conversions report in Google Analytics!

Assisted conversions are the channels that facilitate user conversion. They are one of the important factors to measure the effectiveness of a marketing campaign. The metrics on the Assisted Conversions report in Google Analytics tell us about the contribution of each channel in the final conversion.

Once you set this up, you can also learn some other tracking techniques in Google Analytics to measure the success of your campaigns. 

You're reading How To Use The Assisted Conversions Report In Google Analytics

How To Use Ecommerce Analytics For Better Conversions

When you operate an ecommerce store; you’re either losing or making money. Sales are either up or down. Whatever your situation, you can always increase your store’s conversions to stop the bleeding or make even more money

One of the most accurate methods to help you do this is by digging deeply into your store’s analytics. Analytics can help you find out what on your web pages isn’t working, or can still be improved further.

Unfortunately, the stats on how ecommerce stores use their analytics is rather dismal. 80% of online stores don’t use Google Analytics properly, which breaks down to:

Just half of all ecommerce stores even bother tracking their main conversion points

67% of stores haven’t integrated social-media tracking with their analytics

73% don’t bother to track micro conversions like newsletter signups or new registrations

When you closely monitor your analytics and understand them, however, you can fix any problems and see better results.

What Ecommerce Analytics Look Like

It’ll help us to define specifically what analytics are in an ecommerce context. Essentially, they’re any piece of data that gives you more detailed information about the user behavior of your customers.

Going by this definition, analytics can cover a broad range of factors, just some of which are:


traffic to your site

is coming from (organic searches, social media, etc.)

How long customers stay on a particular page

Where customers look first on a page

What page elements customers interact with

The bounce rate of a page

The conversion rate of a page (both mini conversions and actual purchases)

The user flows on any given page

What specific keywords bring in traffic

As you can see, there’s a lot of data that you can track in your store.

For our purposes here, we’ll focus on three, significant pieces of analytics data in ecommerce stores and thoroughly analyze them:

Abandoned shopping cart data

Landing or product page data

Heatmap data

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Abandoned Cart Data and What to Do With It

Stats say that, on average, 15% of those who abandoned their carts will come back to buy from you. Sometimes, that percentage is as high as 30%.

Whatever percentage or rate of abandonment your analytics are showing you, take heart that you can do something about it.

It goes without saying that you shouldn’t give up on turning some of those abandoners into outright conversions. Sometimes, all it takes is a small nudge. However, the best way to prevent this in the first place is optimizing your checkout process.

Here are a few things you can do from the get go:

Address checkout fears by prominently displaying trust seals or symbols

(BBB, McAfee, VeriSign, etc.) Up to

61% of shoppers won’t buy

when trust symbols aren’t present in the checkout process.

Encourage conversion completions by providing exclusive discounts

at the point of checkout.

Ensure that “free shipping” messages are prominently displayed

throughout the checkout process since various case studies indicate that

free shipping boosts conversion rates.

These preemptive tactics won’t bring your abandonment rate all the way down to 0%. So here are some approaches to reduce your store’s abandonment rate in addition to the above:

Include a special discount or coupon in your follow-up emails to sweeten the deal further.

Landing/Product Page Data and What to Do About It

Check your analytics to see which pages are performing poorly relative to the rest, from a conversion and revenue standpoint. All told, this can cost your store a lot of money in the long run. You can stop the leaking conversions and lost revenue by implementing a few, sound fixes.

Poorly performing landing or product pages can be the result of various problems. It could be anything from hard-to-see call to action buttons to page goals or user flows that are poorly defined and unclear. The good news is that the fix for something like this is very straightforward.

Research shows that product pages that feature well-defined storytelling, as opposed to just rote product descriptions, boast higher conversion rates.

One case study revealed that a product page for wine bottles with rich storytelling boosted conversions by 5% compared to the control pages that featured only product descriptions.

In this case, having a story as part of the product page helped to persuade more people to convert, so instead of just writing dry copy to describe your product, involve your customers by telling them a story about your product.

Something as simple as putting your call to action button above the fold can also have dramatic, positive effects on landing-page conversion rates. One case study for a premium WordPress editor tool revealed that including a big, easy-to-see call to action button in the new page helped to boost conversions by an impressive 47%.

Heatmap Data and What to Do About It

Heatmaps are used to display user behavior on specific pages. As a result, they provide extremely detailed insight into how your customers engage with a specific page in your store.

In any case, heatmaps are also easier to analyze because they’re extremely visual. This also makes them ideal if you don’t like to pore through a lot of statistical data in, say, Google Analytics. The beauty of heatmaps is that they provide direct answers or at least indications of what to fix.

Another case study involving heatmaps conducted by a UX-research firm revealed that users tend to only look at images on pages that are relevant while ignoring gaudy images like stock photos.

Armed with information like this, you can ensure that your store’s product pages only show relevant images of your items. You should also eliminate unnecessary stock photos that are filler and placeholders.

Another case study involving heatmaps performed by an optimization split tester found that the size, color, and placement of your call to action buttons will have a profound impact on your conversions.

On a landing page with three, competing calls to action, the CTA that had the highest conversion rate had the following features:

It stood out the most due to size and color

It had the most persuasive and easy-to-read copy

It was positioned to be one of the page’s main focal points

Armed with this information, you can boost your page’s conversions, too, by ensuring your CTA shares these three characteristics in the case study.

Accurate Data Always Helps Conversions

Think of your store’s analytics data as the key to boosting your conversions and increasing your sales. Taking a deep look at it is the difference between continuing to leak money (or not make as much as you could be) and enjoying an increase in revenue.

If you’ve ever wondered why a certain page of your store is performing the way it is, your analytics will have the answer. While it might seem overwhelming at first to pore through the data, it’s well worth it to make the adjustments you need to operate a more successful store.

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Explore the top 10 ecommerce mistakes and how to avoid them. Ecommerce is a fast-moving environment and we actively keep our mistakes up to date so you’re always making the most of your marketing

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How To Filter Out Referral Spam In Google Analytics

Referral spammers have been making their way into our Google Analytics (GA) data without ever actually visiting our websites since around 2013.

Referral spam may show up to administrators as either a fake traffic referral, a search term, or a direct visit.

Referral spambots hijack the referrer that displays in your GA referral traffic, indicating a page visit from their preferred site even though a user has not viewed the page.

The problem is that marketers have to manually decipher and filter this type of traffic out of their GA data to make proper sense of it.

Since we rely on GA to make major ongoing marketing decisions, clean data means everything to us.

Without knowing about referral spam and how to filter it, marketers could be making weighted conclusions based on bogus bot traffic.

In this column, marketers will learn how to clean their Google Analytics data by filtering referral spam.

If you’ve recently migrated to Google Analytics 4, we’ve got a section in here for you too.

For the Love of Filters, What Is Referral Spam?

Referral spam, also known as referrer spam or ghost spam, is created by spam bots that are made to visit websites and artificially trigger a page view.

It sounds sketchy, but bots are just pieces of programmed script that are designed to complete a task automatically online.

It’s estimated that 37% of website activity is created by bots, and less than half of this bot activity is legit.

Desirable bots include:

Search crawlers creating search engine results pages.

Checkers monitoring the health of your website.

Feed fetchers converting content to a mobile format.

The other half of bots aren’t so noble.

Some are designed specifically to spam our referral reports by sending false HTTP requests to our websites with the ability to create non-human traffic otherwise known as bot traffic.

You Cannot Weigh Your Gold with Garbage on the Scale

Referral spam artificially inflates your Google Analytics data.

The level of artificial inflation depends on the amount of referral spam your website is getting, which can vary depending on your industry.

Similarly, the threat this traffic poses to the integrity of your data is directly proportional to the amount of legitimate traffic your website receives on a normal day.

For example, if you receive thousands or even tens of thousands of visits every month, your data won’t be significantly skewed by a couple of hundred spam referral sessions.

However, if you only receive 50-100 visits every month, a couple of hundred spam referrals would throw off your GA data completely, effectively suffocating legitimate traffic.

If you aren’t aware of this problem, it can be very dangerous to your marketing strategy.

How to Filter Referral Spam in Universal Analytics

It’s a nuisance to have bots spamming our websites.

The good news is that it has historically been pretty straightforward to filter this type of traffic.

However, the plot thickened in October 2023, when Google launched Google Analytics 4.

We’ll discuss referral spam in this new version of GA in the next section.

For now, let’s see how to achieve this important task inside your Universal Analytics account.

Make sure that you have the necessary permissions to make changes in your Google Analytics account at the Admin level and then navigate there.

To get started, first create a new view.

It’s a best practice in GA to test new configurations like filters in a new view, instead of in your default raw data view since changes can be permanent and mistakes can be made along the way.

Select the type of view you are creating, either Website or Mobile app.

Then give it a name, and select the same regions and time zone as your main view to make sure you’re comparing apples to apples:

Google will do the bulk of the referral spam filtering work for you automatically.

Navigate to your test view View Settings and ensure that the option to Exclude all hits from known bots and spiders is selected:

By checking this off, you’ll automatically and easily be able to filter out about 75-80% of bot traffic.

Another best practice is to add an annotation to mark the date you started filtering bot traffic.

Annotations act as a helpful reference to remember significant changes over time and can help teams keep a record of these types of changes.

Next, you’ll have to do a bit of manual work to weed out any remaining spam making it through Google’s filter.

But before you can do that, you need to know which spam sites are getting in.

How Do You Identify Spam Referral Traffic in GA?

If you want to see if the websites that you suspect to be spam in your Referrals report actually are, first check if they’re on this list or this list of known spam websites.

Other indicators are a bounce rate of either 0 or 100%, a session time of 0 seconds (it’s easy to see how data could become skewed with outliers like these), and a hostname referral that’s not set.

With the list of “bad referrers,” you can block them manually.

Head over to your Referrals report, and filter by descending bounce rate.

That number can vary according to your traffic volume.

In the example below 50 was used.

To identify suspected spam referral sites, use the pointers above.

It is important to roll out filtering in your test view account first.

Once these sites are filtered, they’re gone for good (so you better be damn sure that it truly is spam!).

Once you’re sure, create your list in Notepad or Text Editor so you can paste it back into GA.

Cut down all the URLs to their top-level domain (TLD).

For example, chúng tôi is an affiliate of chúng tôi so it’s better to just add chúng tôi to your potential referral exclusion list.

Now create a regular expression with your list of URLs, so it looks like the example below from Moz:

Be careful to separate websites with a pipe bar, and to add a backslash in front of the domain extension.

This will allow for other subdomains belonging to that TLD to be excluded, as well.

Now, you’re finally ready to create your filter!

Give your new filter a descriptive name like Referral Spam for easy identification later on.

Change your Filter Type to Custom, and change the Exclude Filter Field to Campaign Source (not the Referral field).

Finally, paste your pre-made list of referral spam URLs:

Once you start filtering referral spam, you can start to see how much it was and is affecting your traffic.

It could account for a fair portion of your website traffic if left unchecked, so it’s easy to see why search marketers get annoyed by it.

Blocking Referral Spam Using Data Filters in Google Analytics 4

If you’ve recently started using Google Analytics, or actively migrated your Universal Analytics account, you should have a Google Analytics 4 (GA 4) property (which is now the default).

While digital marketers are going to love the new engagement tab, setting up filters for spam referrers looks different now.

Most prominently is the fact that in the new Google Analytics 4 Admin interface, the View column is no longer present.

Instead, GA 4 uses Data Streams, which does not have its own column.

With the new GA 4, marketers can create up to 10 data filters per property.

Internal traffic filters are suggested and somewhat pre-configured.

However, currently, there are only two types of filters available:

Developer Traffic

Internal Traffic

Neither of these seems appropriate for filtering external referral spam.

What’s more, if you turn to Google support for help, you find yourself in an endless loop between Google’s top-drawer banner that tells you to navigate to Google Analytics 4 support and the search bar on that page that takes you back to the Universal Analytics results for filtering referral domains.

We’ve reached out to Google to clarify exactly how to do this in GA 4, and they confirmed that it isn’t yet possible (current at time of publication).

Google said:

“…since GA4 is a new upgraded product in Analytics, thus the feature i.e “Referral Exclusions” are yet to be launched in GA. Different resources have different timelines, so we cannot assure a specific date for the launch. However, I would like to inform you that the feature is being worked upon…”

While we wait for the ability to exclude referral spam in GA4, I recommend creating an old and new version of Google Analytics:

One in your legacy Universal Analytics mode.

And a new one in Google Analytics 4 mode.

Follow the instructions from the previous section in Universal Analytics to filter referral spam from your GA reports for now.

The good news is that this new iteration of Google Analytics has testing built-in, so it won’t be necessary to create new views for implementing new configurations:

The Benefits of Weeding Out Referrer Spam

Clean data is everything when it comes to making meaningful and actionable conclusions based on it.

With these powerful tactics behind you, you’ll be able to filter referral spam so you can make decisions based on facts.

Since referral spam can hit lower-traffic websites even harder than larger sites, it’s important that marketing teams of all sizes stay on top of it.

That means checking for new referral spam websites regularly and adding them to your exclusion list.

Remember to keep your Universal Analytics view alive for now, until we know more about how to exclude referral spam in Google Analytics 4.

More Resources:

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How To Create A Custom Dashboard In Google Analytics

Home Tab

I don’t know about you, but I’m really enjoying the new features Google Analytics added in the last upgrade. We’re all familiar with the standard dashboard where you can see at a glance all metrics for your website, but the default settings might not be exactly what you need to measure for your particular website. Here we will show you how to create custom dashboards for your exact analytic needs so you’re measuring and tracking only what is important to you.

Log into your Google Analytics account and open the Home tab. The Home tab is where you can see several new analytic options, including Real-Time reporting and Custom Dashboards. As you can see in the image below, the default dashboard has sections for measuring things I don’t use-like Conversions and Alerts. That real estate could be better utilized with metrics I need to see and measure. The same is probably true for you as well, so let’s see how we can make this a little more user friendly.

Dashboard Options

In the left sidebar, select the New Dashboards tab. A window will pop up allowing you to choose a Blank Canvas to create truly custom dashboards or you can choose a Starter Dashboard which will give you pointers to get started with customizing your dashboard.

Create A Blank Canvas Dashboard

A Blank Canvas dashboard is an excellent place to setup metrics to track your ad campaigns and other marketing strategies. The custom dashboards feature allows you to create up to 20 different custom dashboards so you can create as many or as few as your site requires. You’ll be able to see the results in a glance and all of your dashboards are accessible in the left sidebar for quick reference. To get started, select the Blank Canvas option and you’ll see the widgets window pop up allowing you to create specific widget sections for your dashboard. Starting with the Metrics tab, you can select one of many metric options to target specific goals for your site. Basically anything you can measure in Google Analytics can be set as a metric.

Once you have all the widgets you want on your dashboard, you might need to move them around to place them in order of importance or make comparing two metrics easier in a glance. all you need to do is simply drag them where you want them placed for better viewing.

What If I Don’t See A Metric I Want To Track?

Create A Starter Dashboard

If you’re new to Google Analytics or aren’t sure what metrics you need to track, using a Starter Dashboard will help you get off on the right foot. The Starter Dashboard is pretty much the same as the default dashboard with the added feature of creating widgets to make it semi-custom. If you like the default settings but want to add specific widgets to it, this template is for you. You can keep the default page the same and add specific dashboards showing just visitor metrics, and just content metrics for easy data analysis.


Jessica Prouty

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How To Link Google Ads To Google Analytics Step

🚨 Note: All standard Universal Analytics properties will stop processing new hits on July 1, 2023. 360 Universal Analytics properties will stop processing new hits on October 1, 2023. That’s why it is recommended to do the GA4 migration. We’ve also created a GA4 version of this post.

Google Ads and Google Analytics are both powerful marketing tools on their own—but what if you could get the best of both worlds by connecting them?

In this guide, you’ll learn why you should link Google Ads to Google Analytics, how to do it, and how to make sense of the collected data. 

An overview of what we’ll cover: 

So let’s start!

Why Connect Your Google Ads and Google Analytics Accounts?

Linking your Google Analytics account to your Google Ads account has two major benefits that you wouldn’t be able to leverage from these tools separately. 

Observe the Behavior of Google Ad Traffic

Firstly, you’ll be able to track the behavior of the users that visit your website from a Google Ad.

For example, did the user visits other pages on the website? Or did they leave immediately? Are they more likely to convert than users who arrived from other sources?

You can answer all of these questions by importing Google Analytics metrics like Bounce Rate, Pages/Session, and Average Session Duration into your Google Ads account.

Thus, linking these two accounts extends your ability to track traffic and user behavior. It also tells you about the quality of traffic that you’re buying with Google Ads.

Google Analytics Retargeting Audience

Secondly, you can retarget an audience from your Google Analytics account using Google Ads. 

Depending on your requirements, you can create different types of audiences in Google Analytics and target them using Google Ads.

Apart from this, you can also import Analytics goals and Ecommerce transactions into your Google Ads account for better goal tracking. Similarly, you can import cross-device conversions into your Google Ads account when you activate Google signals.

So let’s see how to connect these accounts!

Log In with the Same Email Address on Both Accounts

We’ll start by logging into both of our accounts.

🚨 Note: Make sure you are logged in with the same email address on your Google Ads account that you are logged in with your Google Analytics account.

First, find your Google Ads email address at the top right-hand side of the screen.

Your Google Analytics email address will be found under your account name.

Next, we’ll need to check whether we have the correct account permissions set for connecting. 

Check That You Have the Right Account Permissions

One major thing we need to take care of is to grant correct permissions. 

Let’s see how!

Google Ads Permissions

Then, check your access under Access level. You need to have Admin access level set up with your email address.

Google Analytics Permissions

Go over to the Admin section at the lower left-hand side of the platform.

Under User Management, you need to have edit access to the account.

Link Your Accounts Together

Check the compatibility of your Google Ads IDs.

Choose and input an account name in the Link group title field. This way, if you have multiple accounts that you connect to your Google Ads account, you can determine where this is coming from. 

Choose where you want to pull data from. You are allowed to choose multiple views. 

Enable auto-tagging to automatically pull data from your Google Ads account into Google Analytics. 

You may also want to leave auto-tagging settings as they are, especially if you are utilizing UTM tags and you want to avoid mixing it up with the auto-tagging feature.

You may also want to try to link Google Ads and Google Analytics through Google Ads’ linking wizard.

So let’s go ahead and see how the data will look once the two accounts are linked! 

Looking at Live Data

Open the homepage of your Google Analytics account. You’ll be able to see all the campaigns and reports under Acquisition → Google Ads → Campaigns. 

On the top of the screen, you’ll see the sales charts. It will show the number of Users vs. Transactions report of a particular timeframe for your campaign.

Going further down on the Campaigns page, you’ll see the different metrics of your campaigns. 

For example, you’ll find the Cost and Revenue in this report. You’ll also see the Ecommerce Conversion Rate, Bounce Rate, Sessions, etc. for your campaigns. 

Similarly, you can analyze and compare the results of different campaigns to increase their effectiveness. 

For example, the bounce rate of a smart campaign can be considered good even if it’s around 80%, but the bounce rate of a shopping campaign will be considered good only if it’s really low.

You can definitely obtain revenue-related information from your Google Ads account. But when you analyze the reports with your Google Analytics account, you can make more informed decisions as you have a holistic view of data. 

FAQ What account permissions do I need to connect my accounts? What data can I see once my accounts are linked?

After linking your accounts, you’ll be able to see more data in both Google Ads and Google Analytics. In Google Analytics, go to Acquisition → Google Ads → Campaigns to view campaigns and reports. You’ll see sales charts, metrics like Cost, Revenue, Ecommerce Conversion Rate, Bounce Rate, and Sessions. You can analyze and compare the results of different campaigns to optimize their effectiveness.

How does linking Google Ads and Google Analytics help with decision-making?

Linking the two accounts provides a holistic view of data, allowing for more informed decision-making. While revenue-related information can be obtained from Google Ads, analyzing reports in Google Analytics provides additional insights and a comprehensive understanding of user behavior, enabling better decision-making for ad campaigns.


So that’s all you need to know about linking your Google Analytics account with your Google Ads account. 

Have you started doing keyword research for your Google Ads campaign? Check out our handy guide on how to use Google Keyword Planner for SEO keyword research.

How To View The Report Company View In Tallyprime Server


Company View in TallyPrime Server displays the list of companies. For each company, the number of Currently Logged in Users, the number of times the reports have been viewed, printed, exported, and edited will be displayed. The number of entries (Transactions and Masters) created, altered and deleted, and the number of imported entries created, altered and deleted will be displayed if the option Show Entries is enabled.

The companies that are currently online will appear in blue, whereas the offline companies will appear in black.

If TallyPrime freezes for ten minutes or longer, the Company open will be displayed in red. The activity will be displayed as Long Hold, accompanied by the name of the user for whom the Long Hold has occurred. A Long Hold can happen when a report is being processed for voluminous company data.

Users A, B, and C are accessing ABC Company in concurrence. Users B and C are recording voucher entries, while User A has opened the Day Book for the entire year, with the voucher count running to lakhs. Due to the huge amount of data that has to be processed, User A’s TallyPrime becomes non-responsive and remains that way for more than ten minutes, while users B and C continue recording voucher entries. In TallyPrime Server Monitor,

ABC Company will display the activity as Long Hold in red, accompanied by the field By: A, who is the user experiencing the Long Hold. This helps the Administrator identify the problems and take necessary action.

Note: To view the last activity details of one or more companies, select the required companies and press Shift+Enter.

Press F2 to change the period of the report.

Press Ctrl+H (Change View) to change to User View. To toggle back to Company View, use the same button.

If multiple TallyPrime Servers are being used, then press F3 (Select Server) to change from one server to another.

Press F4 (Change User) to log in as a different user.

You can also perform operations like Export, Backup, Restore, Rewrite, and Disconnect Users.

Administrators can customise the look of the report based on the requirements.

Time format for the reports: Time spent in accessing reports can be viewed in H:M (Hours:Minutes), H:M:S (Hours:Minutes:Seconds), Hrs, Mins, or Secs formats.

Interval for auto refresh in minutes: You can set the interval for auto-refreshing the report. This time will be in minutes. By default, it is set to 5 minutes. The minimum time that can be set is 0.10 minutes. To disable Auto Refresh, enter 0 (zero) in this field.

Sorting Method: The Company Name can be sorted either in ascending or descending order. By default, ascending order is selected.

You can drill down from the following fields:

Company Name

Reports Viewed/Printed/Exported/Edited

Entries Created/Altered/Deleted

Import Entries Created/Altered/Deleted

This report gives the details of all the users connected to that particular company. For each User, details of the Current Activity, number of time Reports have been Viewed, Printed, Exported, and Edited will be displayed. The number of Entries (Transactions and Masters) Created, Altered and Deleted, and number of Imported Entries Created, Altered, and Deleted will be displayed if the option Show Entries is enabled.

Even in this report, the Administrator can drill down from Reports Viewed/Printed/Exported/Edited, Entries Created/Altered/Deleted, and Import Entries Created/Altered/Deleted.

This report will display the number of users accessing (Editing, Viewing, Printing, and Exporting) reports, against the report name, Number of times a report has been accessed (including User-wise break up), and the Time spent in the report (in Seconds).

This report gives the details of Transactions and Masters Created, Altered and Deleted. A user-wise break up the number of Transactions and Masters created, altered, and deleted can be viewed in this report.

Press F1 (Detailed) to view the user-wise breakup for each company.

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