Anshid Babu

Hi. I work as a senior SEO and web analytics strategist at Webtoon Technologies. I have around 5.8 years of experience in the same domain, especially in SEO and analytics. I got an opportunity to work with various companies, including a multinational company. So, mostly, I have a good expertise in tools like Google Analytics, Google Tag Manager, and AB testing tools and visualization tools like Looker Studio, and that includes analysis tools like BigQuery as well.

Yeah. Mostly, I do follow a lot of blogs, especially Simo Ahava's blogs, which I think is the pioneer of digital analytics and digital marketing. And so I follow Julius' analytics mania for updating my skill sets and other blogs like Measure School by Julius. And yeah. So these are the main blogs I'm following for updating my digital analytics skills. But also to understand other things like Google SEO, I mostly follow Search Engine Land and Search Engine Journal. Mostly, I follow those blogs for the latest Google updates, Google app core updates, and the latest news about the Google algorithm updates, everything. Yeah.

Yeah. So, we have to analyze data from multiple sources to understand the trends and we all know that no data is 100% accurate. Sometimes we have to analyze. For example, if you ask me, I would say that if I have two analytics tools, like Amplitude or Google Analytics. So, what I want to do is know that both tools are performing well, but to make sure, I have to track website data using both tools. So, then we understand the discrepancy, and then we can later analyze why it is happening and what is the reason behind that. So yeah. Mostly, we have to analyze data from multiple sources. So, I mostly use tools like Fluke Studio, which allows us to connect various data sources, and we can even blend the data and analyze how it is performing. You know, for example, if you ask me about the organic level of marketing, then we can connect Google Search Console to Fluke Studio. And at the same time, we can connect Google Analytics. And we can analyze how data is performing, including the number of users or sessions we are getting through both platforms, and we can blend the data and analyze it. So yeah. Mostly, you can use Fluke Studio for that. And along with Fluke Studio, if we need a deep analysis of raw data, we can use BigQuery as well. Yeah.

To understand audience insights, like I said, mostly, we are using Google Analytics to measure and track the performance of users. And, along with Google Analytics, we can use a tag management tool like TDM or Google Tag Manager to track all the interactions and the journey of the users, and send data to Oracle Analytics. And after that, we can do a visualization using tools like Fluke Studio or Power BI. Once we understand the pattern or the journey or behavior of the user, we can create various audience groups based around their interaction, and we can later use it for retargeting or remarketing for campaigns through social media or paid campaigns. And also, the most useful insights, I would say, if we found good insights about the users and their interest in our product or service based on their interaction. And based on their interaction, we can create customized banners or customized headings using AB testing tools. So later on, we can target them based around these personalized messages, like personalized, once they are coming to our web page, and they can see a personalized heading, a personalized call to action, compared to regular users. So these are the main tools we can use to get audience insights.

Yeah. The main segmentation approach is, like, the general segmentation approach. We can segment users, like, by default, tools like Google Analytics automatically segment users, for example, organic, social, paid, etcetera. But, also, we can segment each journey of the users as separate segments. For example, if a user has viewed the list of items, we can create a segment for that. If a user has interacted with a particular item, we can create a segment based on that. Once a user has added an item to the cart, we can create a segment, how many users have added items to the cart, and also, how many users have removed items from the cart, and how many people have added billing information. So, we can create separate segments based on the user journey. These are examples for ecommerce. And later on, we can target these segments for retargeting purposes, like AB testing. We can show personalized messages to these segmented users. Mostly, I would segment users into separate audiences. That's the main segmentation approach I'm creating. Also, I would create separate reports in Google Analytics for Explorer. We can understand the behavior of the users based on these segments. These are the main strategies.

So, the term market analysis is quite broad. I don't think I can answer it quickly because market analysis can be defended in various ways. We can define it in one way. Market analysis basically includes a lot of things, mainly competition analysis. Before launching any project, we analyze and understand the latest trends they are using, how updated they are compared to us, in terms of settings. That is one approach, especially in SEO. We have to do market analysis, like competitive analysis, and understand the gap between our website and the competitors. We can identify our keyword gap, content gap, backlink gap, and other things in terms of SEO. Another thing is SWOT analysis. We can do a SWOT analysis and understand the strength, weakness, and opportunities compared to the common details. That is another approach, SWOT analysis. So, these are the main approaches.

To determine which channels are most effective for a client campaign, we can analyze it based on key events, such as the number of conversions. Once we set up goals before launching any campaign, and we can track the conversion. And based on the conversion, we can understand which campaigns are performing well. First, we have to set important goals that we want to target from any campaign, whether it's a paid campaign or an organic campaign. After launching the campaign, we have to analyze the results after 30 days or 45 days. Based on this number, based on the number of conversions, we can understand which channel is performing well, and we can do a comparison of how they are performing over one month, over a year, like that. Also, if it is a paid campaign, we can analyze the pricing and connect it with the GFO. We can understand which keywords are performing well, which keywords are generating more revenue, and which campaigns are not performing well. All this data can be analyzed in a GFO. So, mostly, based on the conversion, we can understand which campaigns are performing well. And if it is an ecommerce website, based on the return on investment, we can understand which channels are not performing well and where we have to invest more money in or more budget in the coming quarter based on that. I determine all these channels using GA4 reports.

Yeah. So, the main challenging digital campaign that we are facing is, sometimes the tools are not brilliant enough to track or identify, or attribute the credit. Basically, most of the tools are not intelligent enough to attribute the credit of conversion, even GA4 also. So I would say, one example is that we have a lot of different attribution models for campaigns. So based on that, the tool gives the credit to the user. For example, if we have set it up, it's a last click or first click attribution model setup. Then, once a user comes to our website through a campaign, based on the last click, the tool understands that the user is coming from a particular channel. For example, if it's a paid channel, the user coming from a paid channel, the tool attributes the credit to the paid channel campaign because the user is coming from a paid scan campaign. But there's also a chance that if the user had come to our website initially through organic search, and after two days, the user came using paid campaigns. So, still, the credit is given to the last interactive channel. That is one thing. On the other hand, if we set it up on the first click attribution model, no matter from where the user has converted, the credit will be given to the first channel the user interacted with the website. So, there are different attribution models out there. Even Google recommends the driven attribution model, which uses its AI intelligence to give the credit based on user interaction. So, still, for example, if a user has initially interacted with our website through our social media page, but later on, the user came to the website through any paid campaign or organic channel and made the first conversion. We give the conversion to either the paid campaign or organic channel, but the user actually interacted with our website or came to know about our website through our social media page. So, as of now, we don't have enough features to pack that. I think that's the main challenge.

Multi-territory digital index project. A multi-territory digital interest project. I don't understand the question. So, multi-directory digitization project names. Oh, I'm sorry. I didn't get it this way.

So, yeah. We have a lot of traditional data sources to gain consumer insight, such as UTM parameters and various tracking tools like Google Tag Manager, Google Analytics, Adobe Analytics Pro. If you ask me, non-traditional data sources, such as JAR, are also available, but it is a traditional data source. Non-traditional data sources I have used to gain consumer insight are more, you know, SaaS-based projects or websites. In that case, I would say Mixpanel. I use Mixpanel. It has a lot of wide features to analyze user behavior compared to Google Analytics or Payvig Pro. So, that is a good option for SaaS. Also, I would say Amplitude. Amplitude is a good option. It also delivers a lot of data, a lot of insights. I will come back to the regular tools. So, yeah, I would say Mixpanel and Amplitude.