Marketing Career

What skills are needed to work in marketing analytics? They might be a little different than what you think. Marketing professionals need analytical skills, such as data visualization and statistical analysis, but they also need more creative skills like storytelling, copywriting, and content strategy. In this post, we’ll tell you which key skills will help you get an analytics job!

Hands-on data tracking deployment

When I first got interested in web analytics, it was about 2005 when blogs were becoming popular, and Google Analytics became available for the public. Getting the tracking up and running is one thing, but getting actionable data from Google Analytics takes much work.

You need to make sure you have the right tracking on all your pages (and that this is done correctly), and then you also need to think about how you use that data in a practical sense.

When I started with web analytics, I couldn’t find many resources about using Google Analytics data. So my best advice is: get your hands dirty and start tracking!

My first few years of experience working in the agency world built my marketing foundation. First, I was reading and studying as much as possible about tracking and data integration. Then, most importantly, applying that knowledge in real-world business.

The hands-on deployment was probably the most important part of the job to build the experience and track record working with large enterprises’ websites. Eventually, those skills helped me get a better job on the client-side.

As you can imagine, the analytics world has changed a lot in the last decade. It’s no longer just about tracking visitor behavior and web traffic.

Enterprise-grade and fast growth business exposure

Most organizations need more than one person looking at the marketing analytics data, which means it’s important to work with others in a team environment.

As a marketer, you need to understand how the business works and what decisions are made at different levels of an organization. You can’t work in marketing analytics by yourself, so communicating with other teams is important!

Following the exposure to enterprise-grade web analytics tools, was getting the experience working with a large e-commerce site. Having that exposure to a business that grew tremendously was an important part of building the foundation for data-driven marketing. In addition, that experience to tie data back to business growth and to deploy marketing strategies with large marketing spend was valuable.

In addition to the above skills specifically related to marketing analytics (data visualization/statistical analysis), you also need broader business-related skills, such as project management, teamwork, interpersonal communication, and budgeting.

For example, you need to prioritize your team’s projects and meet deadlines for various stakeholders. It would help if you also understood how budgets work to know whether or not a proposed initiative is feasible within constraints like time and money.

Business Intelligence Experience

Having the ability to tell stories with data is essential for marketers who want to work in marketing analytics. We are all familiar with seeing pretty graphs or fancy infographics, but you must tell compelling stories about what your audience wants.

One phase in my analytics career that built a firm foundation is adopting BI tools like Tableau, which many companies and marketers use today to find insights from their data.

Learning Tableau and applying it in practice was a game-changer. It allowed me to be free from excel and, most importantly, do more with analytics and insights discovery.

Any analyst needs to be able to communicate your data and insights. Marketing analytics professionals have several ways of expressing their findings, including both verbal and visual presentations. You should know how to use visuals and statistics effectively to tell a compelling story that persuades the audience at hand.

In Business Intelligence, knowing how the data is collected, presented, and reported is important. Creating a data dictionary of metrics that you use regularly can help ensure everyone in your organization understands what certain terms mean. In addition, having the data dictionary or data definitions can be helpful when working with other departments or people outside of marketing.

Knowing about how ETL processes work is also important for marketers in current days in age. Once you have a certain metric, it’s often useful to break it into different segments (e.g., by product category or geographic area). You can’t do this if you don’t know how the data gets extracted, ingested, and transformed from its initial form into something more manageable and relevant for your analysis.

Data wrangling skills

First, you need to be able to understand the data. Marketing analytics is one of those professions that require knowledge in both Big Data and traditional marketing skills. You should know how to access your data, clean it up (i.e., remove duplicates), analyze it for insights, and report on what you’ve found with a clear explanation.

Data wrangling means being able to extract the data you need from your various sources, as well as being able to clean and organize it. You can use Excel, Google Sheets, Tableau, or other tools for this process.

Data wrangling is one of the most important skills for working with data. Unfortunately, it’s also one of those tasks that might be considered more tedious and frustrating than it sounds, so you need to make sure that you enjoy this part before committing to your career!

Suppose you’re a marketer and not able to work with SQL or just tired of data wrangling in Excel. In that case, there are tools like Tableau Prep or Google Trifecta that allow anyone to transform data into usable data to analyze.

Any analytics professional will tell you; data preparation is a big part of their day-to-day operation. I’ve read some articles that could take up 70% of analysts’ time for data prep related tasks. It shows how much data wrangling is an important skill set to have.

Client facing experience

Lastly, you need to know how to tell a story. Although data is important in marketing analytics professions, there are times when it doesn’t give us all of the answers we need or want. This is where storytelling can help fill in those gaps – providing context for how we got to where we are now and what might happen next.

Marketing professionals need communication skills, both verbal and visual/aesthetic. You should be able to tell a story with your data or visuals as well as with words. Client-facing experience is important because you’ll be presenting your findings to people regularly.

Marketing analyst’s clients aren’t necessarily limited to external clients. Internal stakeholders within the company could also be your client. So, it’s important to communicate your findings in a way that is easy to understand, knowing that anyone you face or present with data is your client.

In Summary

If you are looking to get a job in marketing analytics, I covered these five skills that will help you be successful.

• Hands-on data tracking deployment
• Enterprise-grade and fast growth business exposure
• Business Intelligence Experience
• Data wrangling skills
• Client facing experience

In the end, what matters most when applying for jobs is that you have the right combination of experience and relevant skillset. We hope this article helps narrow down some possibilities so that you can use them with confidence! Happy hunting!

I started my career in a financial securities company working in data processing automation for securities trading for institutional clients. Around 2005 when the blog was taking off, Google Analytics was launched. It was a game-changing event for me.  

I had been thinking about what I wanted to do next with my career. One of the things that came up was wanting to get into marketing and analytics. I believe at that time, with server costs expected to decline, more data will become available for anyone. I truly thought that digital businesses would eventually take off, and data will become one of the most important parts of running such businesses.

Digital Analytics seemed like a perfect fit for me, given my expertise in data processing automation, data query language SQL, and writing scripts. It just so happened Google launched their new web analytics tool, “Google Analytics,” in 2005 and made it available for free to anyone that owned a website.

What is Digital Analytics

Digital Analytics is the process of measuring, collecting, and analyzing data about user behavior to improve engagement. There are many different types of analytics, but all share a common goal: understanding what’s happening on your site or app to make informed decisions that will positively impact user experience. Digital Marketing Analytics is an emerging discipline in marketing with its own set of tools and techniques.

In 2005, the common terminology that described tracking and measuring the website was referred to as web analytics. However, it was also considered a subset of the larger discipline, referred to as Digital Analytics.

Web analytics consists of many different types of tracking and analysis around:

• Content engagement tracking: Understand what content visitors are viewed
• Traffic source and campaign tracking: Where visitors came from to access your website and landing pages.
• Conversion tracking: Understand where the visitor came from and purchased a specific product at a certain price.

Digital Analytics has evolved since the early 2000s. Today, it is the primary discipline of marketing and management that focuses on understanding user behavior online to improve engagement with your company or product.

Digital analytics has helped businesses of all sizes grow. For example, by using technology to collect data from our online marketing activity, we can better understand what resonates with customers and how they engage with content through the different devices they use throughout their day. 

The goal here is to grow customer experience and convert more consumers into paying customers by understanding which digital channels work best for them and when (morning vs. night) or where (mobile vs. desktop).

How I got started with analytics for marketing

I started my marketing career in 2006 as a marketing analyst at a digital agency, where I got the chance to learn about analytics and the optimization process on websites for clients in the CPG industry.

At that time, there weren’t many job openings for marketing analytics. Instead, it was geared towards IT or some technical role that supports marketers enabling tracking and building reports.

Many enterprise companies are now investing heavily in the digital marketing space, but only a few large companies have invested in enterprise-grade web analytics platforms. Having Google Analytics experience on the resume didn’t help much, as many marketing managers were looking for specific experience with certain tools on a resume.

What helped me get a marketing job was making a strong case around my data engineering skill-set and tying it back to my projects building blogs. Getting my foot into the door on the agency side was a great choice when I had zero experience in marketing.

The foundation of skills that help me get analytics jobs

My marketing foundation was built in the first few years of my career in the agency world, when I was reading and studying as much as possible about tracking and data integration.

The hands-on deployment was probably the most important part of the job to build the experience and track record working with large enterprises’ websites. Eventually, those skills helped me get a better job on the client-side.

It’s been an amazing ride since then!

As you can imagine, the analytics world has changed a lot in the last decade. It’s no longer just about tracking visitor behavior and web traffic, but also audience growth, social media marketing with advertising for performance-driven campaigns on Facebook, and many other SNS and ad channels.

Following the exposure to enterprise-grade web analytics tools, was getting the experience working with a large e-commerce site. Having that exposure to a business that grew tremendously was an important part of building the foundation for data-driven marketing. In addition, that experience to tie data back to business growth and to deploy marketing strategies with large marketing spend was valuable.

Another phase in my analytics career that built a firm foundation is adopting Tableau, a BI tool that many companies and marketers use today to find insights from their data. Learning Tableau and applying it in practice was a game-changer. It allowed me to be free from excel and, most importantly, do more with analytics and insights discovery.

Three important lessons I learned to become a data-driven marketer

1) Set the right expectations by interpreting the data according to the stakeholder

This means asking, “What is this data telling me?” and what would the key stakeholders who would see the data or reporting would take away from the insight?

As a marketer working with data, you will find many bad results or marketing outcomes that aren’t living up to the goal. It’s up to you how you tell the story, but it is important to set the right expectations for stakeholders, so your data don’t mislead them.

2) Avoid vanity metrics

There are many KPIs that marketers obsess over but don’t correlate with customer acquisition or retention. A great example of this is social media likes and followers. As a marketer who will need to bring results to the business, it is important to focus on metrics that bring value.

Use data trends and analysis to determine how that impacts the KPI and the business’s bottom line. And know what the objective of the data asks is since it is very common for management to ask marketers with different intent, depending on the business they’re in.

On one day, asking for conversion rate means a CEO is trying to prepare a comment for a board meeting, and on another day, the question’s intent could be to learn what the team should be working on to improve the sales. But if you as a marketer understand the difference between vanity metrics and KPIs, you can prepare yourself to answer questions quickly and efficiently.

3) Understand how the data is tracked and avoid bad data

I’m sure you’ve heard “Garbage In Garbage Out,” which is the idea that bad data will result in a distorted analysis. With analytics, it’s true!

You have to be very careful when choosing what data you want to collect. It is also important to know how the information you’re tracking and reporting is being collected and processed within the data tool from which you pull the data.

I’ve come across many situations where marketers refer to the term conversion rates, but the numbers and math used to define the conversion rate make it a very different metric.

It would be best to be extremely careful, especially when you encounter a situation where the collection and methodology change for data representing the business’s KPI. Executives never enjoy the case when their KPI’s definition varies because the last thing they want is an unpredictable measure of their success or progress.

What you can learn from my path to become a data-driven marketer

I learned that data is a powerful weapon in the marketing arsenal, but it also requires experts who understand how and when to use it for different purposes. It’s easy to be seduced by vanity metrics or without understanding what stakeholders ask about KPIs. However, you can become a valuable asset in any company when you know how to interpret the data and what tools are better for certain KPIs.

A successfully seasoned marketer who is data-savvy could reap the benefit of a higher salary, promotions, etc.

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It is important to have a skill set that will keep one ahead of the game in any career. This is especially true in marketing. One of the most valuable skills for marketers today is data skills. Data has become an integral part of many companies, and having these skills can help you stand out from other candidates. In this blog post, we will look at why data skills are so important for marketers today and how they can benefit your career!

Marketing data is widely available now.

Company data can be found in more places than ever before. The internet has made finding company info easier and making the connections you need for a successful marketing campaign. In contrast, previously, companies did not have such open access to information about their competitors. In addition, data is now available as part of your everyday work life- tools like Google Analytics are the norm.

Many marketing technology tools have improved in recent years, with more and more data sets available to a marketer. There are plenty of free tools on the internet that allows you to do your analysis, but there is also an array of expensive options for those who want access to even more information.

The average number of tools marketers use daily is 12. It ranges from social media tools to more complex analytical software suites.

Marketers could not have any more excuses for not knowing what is going on in the domain they’re responsible for.

The availability of data has increased, but not all marketers are good at using it.

Most people still don’t know how to use data, and they don’t know how to analyze it.

What’s important is that the data skills gap between marketers and other professionals in their field isn’t as wide as you would expect. Of course, data availability does not mean all marketers are good at using them, but having these skills could impact your career.

According to the Forrester research, over half (57%) of marketers worldwide feel overwhelmed by the incoming data. Additionally, research indicates nearly 36% of companies don’t use all the data they possess.

It’s important to know how to use data and interpret it to feel confident and be successful.

In this current time, I think there are various scenarios I can think of how marketers are using data and analytics to run their business.

• Marketers have to put reports together by using data from their day-to-day tools. That could mean exporting their marketing technology tool data into a spreadsheet and writing frequent reports for management updates.
• Automated dashboards created by the BI team and marketers are using their tools for ad hoc reporting. This is a case where a more analytical approach is taken. Data engineering and support resources are allocated to support the marketer. Which helps marketers do their job effectively and report consistent KPIs to the management. In this scenario, a lot of the heavy lifting is offloaded to the data team (IT, BI team, Data Engineers).
• Almost all KPIs and dashboards are automated, and even ad hoc reports are created by the marketers on the fly with BI tools like Tableau and publishing it where key stakeholders would have access. If your company and marketers are in this environment, you’re in a pretty advanced company.

I’m sure there are many other scenarios I didn’t mention. But, the bottom line is that data skills are a valuable skill set for marketers in this current time and age, especially when it comes to their careers.

I say this because, in today’s world, data is being used in various ways for marketing. For example, a lot of marketers are using it to make decisions and understand their audiences better. As a result, dashboards will automate marketing data in the new feature, and less manual spreadsheet work and slides building will be required.

Data skills marketers could start learning.

• How marketing’s data tracking works.
• How API’s work.
• Business Intelligence, especially around data environments.
• Data visualization.
• Basic SQL.

As more marketers adopt and advance in data-driven marketing, it is important to understand how the data knowledge mentioned above can be a valuable asset for your career.

When working with data in any form, knowing how your data gets tracked is important, especially when privacy concerns are taking notice in the market and technology changes occur as third-party cookies get replaced.

API’s are important to understand because they can be accessed by marketers and data scientists for deeper insights into co helpful when it comes to an understanding what drives the business and how each department interacts with one another. Every marketer uses tools that provide API somehow, and it is important to own that knowledge and domain.

BI and data visualization will also be important in the future as data is becoming more and more pervasive and important in everyday lives. BI, when practiced correctly, can provide marketers with an understanding of how effective their marketing campaigns are through visualizing marketing data. More tools make it easier for marketers to run ad hoc reports, and BI will no longer be a thing for IT, data engineers, analysts, etc.

Lastly, we will talk about SQL, which stands for Structured Query Language. Basic knowledge of SQL will align your mindset with many of the standards on how data experts access data. More companies will adopt BI and custom data environments, and knowing SQL will give you an advantage, so you know how to work with marketing data and engage well with data analysts.

In Summary

When we talk about marketing automation, it’s not just a buzzword. It is the new normal for most marketers to have dashboards and automated reports made by their team members on the fly with BI tools like Tableau or PowerBI and published where key stakeholders would have access. If your company has already adopted these practices, then you’re in pretty good shape if your management is forward-thinking enough (and they should be).

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I’d like to share some of my lessons learned from my career working with data in marketing, focusing around the things that I tried which I believe added value to the business.

• Share what you have learned

In this field of analytics and marketing, it is pretty common to find people who have learned things keep those knowledge and insights for themselves as if that belongs to that person. This is not unique to people in analytics, but that is a pretty lame move in general. Your team and people working in the company are trying to perform, and analytics are important function of this entire business process. The biggest part of analytics people’s job is to support solving business problems through data, evangelize data and insights, support the business move forward achieving it’s goals. Without sharing the data and insights, there is no evangelizing.

Also when you assume what you have shared is being translated to others, don’t assume they have shared with others. Many companies have great internal resources and capabilities that allows employees to share what’s going on.  If not, then I’m sorry to hear that. In tools like Jive, it is important to create your own area and start publishing your learnings. You can always reference that source, and communicate to the wider audience. This is obviously not the only way, but the idea is try and find ways to share more than that one point of contact, boss, CMO, etc. What good it is to not share your awesome work and let it sit and decay.

• Be curious, build a habit to find answers and apply

Every job, great jobs come with opportunities to learn. Sometimes it may come in a form of unexpected things you have to deal with, or things encountered through new projects, or even come by terminologies encountered through work place, experts, school, and peers. Never be shy to ask, there are no dumb questions. There is Google for us to search wealth of knowledge online as well, and we should take every advantage of it. Up until this point, I hope people do search for their own answers when encountered with questions that you want answers to. Curiosity in a dictionary states the meaning ‘a strong desire to know or learn something’, and for me, this is one of the must have behavior in analytics expert.

Another key thing is to ‘apply’. One of the common characteristics I see in successful people in any industry is that they don’t just consume things, but they apply their learnings and practice what is in their mind to help drive tangible or real impact. Example could be… don’t just talk about tools and wish it get’s purchased or implemented, find ways to bring in a proof of concept deal, and prove the value of the tool.

• Communicate and be clear

Communicate your findings, but reserve for the insights.  Don’t communicate for the sake of communication.  When you communicate, you want to make sure you’re respecting every moment of people’s valuable time. What that means is, be clear as much as possible. Whatever you find through data, you don’t want that to be miss interpreted or taken action in a wrong way from what you originally recommended. If you have found something interesting, but not necessarily gives an answer to the business question, steer the communication to drive testing, or excite others to ‘test it out’.  A/B testing tools are so cheap now a days and it is no longer something we should be wait for. Clear communication needs to have a direction, and expected action or outcome that is lead by you.

• Always be clear on the problem you’re trying to help solve.

Unless you have some spare time and just playing around with data, don’t waste your time solving and analyzing problems your team, department, and company is not solving for. If a data practice you’re involved isn’t involved in the problem business is not trying to solve, that is a super low priority thing to work on. When analyzing data and concluding your findings, make sure to go back, and ask if the finding helps solve the core business problem or not.

It doesn’t help to be confused on the problem business is trying to solve either. If you’re trying to analyze data for some problem that is way off from the real problem, then that’ll be a huge waste of time. Make sure the problem is clear and you really understand it.

In a knut shell…

I might have learned the hard way to understand the key behavior points listed here (which is not limited to), but hope you find this list of key acumens required in marketing analytics helpful.

My apologies if this came out to be me puking out things many people already know, but I thought it was important for me to put this out and see if this helps for others. Being in this field of data for over 12 years, there is definitely more to it, but I guess I’ll stop here and write more in near future.

Digital Analytics Role and Responsibilities include turning data into an asset and data into insights 

Despite the shortage in digital marketing analytics talent, the demand for digital analytics role is exploding.  Many companies hire that one web analytics analyst, get great value out of them.  However, they need to keep in mind that that digital analytics discipline is getting more deeper and wider in its subject matter, and certainly more complex.

I strongly agree, super agree!,  with Avinash’s 90-10 rule where 90% should be about people/analyst than tools where it should be 10%. This post is not about that, but focusing on that 90% and really understand why we need smart engineers and analysts behind digital data.

Businesses who are new into web analytics or digital marketing analytics need to start thinking about analytics from end to end planning, and NOT just from hiring someone who can look at data coming out of Google Analytics, Webtrends, SiteCatalysts, etc.  Don’t hire people just because they know the tool.

What are the common digital analytics competencies companies should look out for?  Let’s come from an angle where the company is new to digital marketing.

Digital analyst’s disciplines and potential expertises require:

• Web Analytics ( experience measuring outcomes rather than traffic and engagement, or the so called the vanity metrics)
• Knowledge on how data and trackings are enabled
• Media planning and buys.  Including but not limited to SEM, eMail, Banner Ads.
• CRM.  Many companies are finding value and investing a lot in areas to onboard web analytics behavior data into marketing automation that ties data to user based data.
• A/B testing.  Common knowledge on the process and assessing A/B test results.
• Modeling (marketing mix, forecasting or predictive modeling)
• VOC (voice of customer) or Consumer Insights.  This is important in marketing, as great marketers are great listeners.  Digital analytics person will need to know how to turn VOC into insights.  It is a gold mine.
• Market analytics or Competitive insights.  Digital Analytics role will need to have a wide vision to take into account of market dynamics to explain any possible hypothesis based signals coming from the macro environment.

Imagine the engineering side of the digital analytics role. The engineering side needs the following acumen to help turn these data sources into usable data for these analysts.

• Data integration: engineering skills required to bring various data into company’s environment.
• Translation of data: Bad data in, Bad data out. You can still have “Good Data In and Bad Data Out” if the folks who are working on the logical data layer messing up the translation after data comes from the database. This is a critical piece to making sure analysts and businesses have good data to analyze.
• QA and governance: experts who understand the business needs and making sure the implemented solutions are accurate, not breaking things, and checking to make sure business has good data on hand.
• Expertise in infrastructure: Yes, all these stuff around data needs to be processed, stored, and reported. Where and how? Engineers need to understand the technical environment surrounding the data, and someone needs to own maintaining it.

Even if you’re not thinking about one head count for all of these disciplines, let’s look at where these data and where it comes from and ask you how many levels of expertises or resources are needed to “manage” all of these.

Web Site Analytics // Web Analytics Implementation Engineer:

Data typically comes from analytics platform’s given JavaScripts embedded on the website, and the analytics service provider would process the received data within a day to aggregate necessary data to report on visitors, sessions, page views, and tie all of these to user behaviors or action events.

These could be implemented by web engineers, but in most of the case, pure engineers would not understand what tracker means what to business. Dedicated implementation engineers usually require a strong business acumen so they can connect the dots between javascript to business needs.

Media Planners & Buyers:

Web Analytics data captures great post-click data, but not on the buy side. There are many companies specialize and are dedicated to the planning, buying, and executing the ads. In digital space, these data are planned in Ad Serve tools that aren’t web analytics.

In addition, recently, with YouTube gaining momentum replacing TV in gaining eyeballs at low cost, data around these platforms are becoming important as well. YoutTube impressions are replacing the TV GRP’s.

CRM // CRM manager or analysts or CRM data engineer:

In most cases, CRM data are stored in a form of database. In some cases, those in-house data are accessible by 3rd party depending on the company’s relationship with the agency. In other cases, the database is hosted and managed by third party service providers (sounds like a bad idea.. but you’ll hear it time to time).

Data needs are pushed into the database from somewhere. These managers should be the expert in the data management aspect as well as work with analysts to understand the lead process and operations.  This discipline has two required aspects, both technical side as well as the management side.

That is why it is really hard to find one person with both talents because database engineering and managements are two different skill sets.

Social Media Monitoring // Social Media Analysts or Operations Engineer:

Most of the social media monitoring tools enable the data through paid services (e.g. Radian 6, CrimsonHexagon, etc.) to collect certain phrases people are talking about and less about tagging. From a data standpoint, you’ll need operations engineer to bring the data into the data warehouse. Otherwise, you’ll be sitting on 3rd party tool collecting and hosting the data for you. Good luck to you when that company goes out of business.

Anyways, Social Data has many text inputs from the users, and to mine those data, it will be challenging and hard to bring in all those data into a data warehouse. But not a problem in an era where Big Data is a commodity right? Well, think about it, you still need data in-house if you really want to treat Social Data as your asset.

Conversion Rate Optimization Marketer // Growth Marketer // AB testing analyst: Any website testing involves planning, and that means you need to have a good planner. Once a great plan is locked in or approved, assuming all creative assets are final, then you’ll need to have a technical person who can enable the test by setting it up the event tracker on site. Then test needs to be set up in the Testing Tool as well.

Any website testing involves planning, and that means you need to have a good planner. Once a great plan is locked in or approved, assuming all creative assets are final, then you’ll need to have a technical person who can enable the test by setting up the event tracker on site. Then test needs to be set up in the Testing Tool as well.

This is a lot of work, again, planning and engineering are two different skill sets. If you really want to test multiple times in a month, then business really needs a strong commitment to resources. Learnings from a test and the culture built will need to be an asset for the company, so make sure to have those knowledge and learnings stay (perhaps in an intranet, or corporate’s wiki site, etc.).

Learnings from a test and the culture built will need to be an asset for the company, so make sure to have those knowledge and learnings stay (perhaps in an intranet, or corporate’s wiki site, etc.).

VOC (or Voice of Customer) // Consumer Insights: Quantitative data from analytics alone gives you an insight if you synthesize massive amounts of data into something actionable and insightful learning. However, you can not ignore or argue with what your customer directly tells you.

When that VOC data marries with quantitative data, it becomes super valuable. That means the data sources would come from these survey tracker implemented on the website while making sure you have well-designed survey questions to answer important business questions.

Someone needs to be able to plan the end to end aspect of the discipline. One analyst can not do that especially if you’re going to take VOC to the next level to best learn your customer’s pain points.

Market Analytics // Competitive insights:

Typically these data sources come from 3rd party paid services like ComScore, Compete, Builtwith, SimilarWeb, etc. You may think, no heavy requirements needed as it sounds easy.

Yes, if you end there, then web analyst could take on the rest. However, if you’re in a business like buying data from POS (point of sales) or analyzing market share, then it is a whole another level of manpower needed to make sense of it.

Web site analyst who looks at owned media alone may be missing the big picture when the market share of your company is increasing/decreasing. If you ever saw those data directly from NPD or GFK, and not just Compete.com stuff, then you’re going to say ‘wow’ I need more time to analyze all these data and see how that is impacting consumer behavior online.  Market data is a very powerful data to have.

My intent of this article is to make sure analysts are thinking about the data beyond your one segment of your digital discipline.

Data experts are involved with many experts outside of marketers. Management who gets it and transformed businesses from Web Analytics 1.0 to 2.0, now needs to think about how to turn data into an asset, better govern data practice, privacy, security, the technicality of data warehousing, etc.  Very overwhelming…

Not to mention growing number of data and sources CMO thinks marketers already have, so take action before business demands it. Start by looking at the resource on hand and the reality of the data ecosystem.