6 Reasons Marketers Struggle to Get Results From Data Analytics for Digital Marketing?

Data Analytics Digital Marketing

Marketers Struggle to Get Results From Data Analytics and Digital Marketing Campaigns

Data Analytics for digital marketing in any industry is simple if you have the right tools.

Data analytics is the process of collecting, organizing, and analyzing data. Data can be anything from numbers to text or images.

The goal of data analytics is to extract information to gain insights that lead to improved decision-making.

Marketing campaigns use data analytics to understand their customers better and improve marketing efforts accordingly.

However, some marketers struggle with getting results from this type of analysis due to poor implementation or no understanding of how it works specifically for digital marketing purposes.

To help you understand why marketers might struggle with getting results from data analytics for digital marketing, we’ve compiled a list of 6 reasons below:

  1. Data collection needs more time than expected
  2. The wrong metrics were looked at during the analysis
  3. Data was interpreted incorrectly by the marketer
  4. some variables were not measured or considered
  5. Analysis is done too late in the process after a campaign has already been launched
  6. The analysis did not include taking an omnichannel approach to marketing campaigns.

The importance of data and analytics to modern businesses, especially marketing departments, cannot be questioned.

However, as businesses deal with the ever-increasing volumes of data, they struggle to figure out how to manage data in a way that ensures it is accurate and readily available when needed. 

This is because it isn’t as simple as dealing with one data repository.

Large volumes of information run through data systems, making it difficult to find the right information and produce useful insights from the collected data. 

58% of business intelligence work is spent locating the correct data to integrate for analysis, from Forester Research. That said, below are some challenges that marketers face when it comes to data analytics.

Data Access Challenge

Data Access Challenge

Access to data is the first and most important step when working with big data. Good data access enables companies and digital marketers to use information from various sources in real-time. 

Therefore, marketers need immediate access to data generated and stored in systems within or outside the organization. To mention some sources of data include;

With the above and many more sources, you should not be surprised that more than 46% of marketers find data to access their main challenge. Besides multiple data sources, these two factors also hinder data access:

  • Monolithic systems that focus on specific tasks thus don’t guarantee easy data access. For instance, legacy systems are designed to function as contained units that support episodic and infrequent extraction of information instead of a continuous model.
  • Strict organizational processes support close data security/guarding and limited access instead of sharing information. In most companies, IT departments are gatekeepers and guardians that restrict data access. So say, marketers given direct access to data used 1.6 times more customer data than those who acquired data through IT departments.

All these factors contribute to balkanizing data marketers cannot access in real-time. Similarly, initial solutions, such as creating data lakes and warehouses, are perfect for a “store and analyze” system, which is ineffective in the current environment.

Data Unification Challenge

Data Unification b2b b2c dtc

Data unification is the process of aggregating data from multiple resources together. 

A unified approach to customer data is beneficial as it enables digital marketers to engage with customers better and offer personalized experiences that suit not only their profiles (age, gender, and preferences) but also customer behavior (phone activity, website visits, current location, social media activity, and more)

Marketers should have a 3D-360-degree customer view, according to McKinsey & Company.

This includes data from multiple sources that should be updated regularly and in real-time to provide value. Unfortunately, 41% of digital marketers consider this as their top challenge.

Ignoring Statistically Significant Timeframes and Seasonality

Statistically Significant Timeframes Seasonality

Most marketers generalize an ebb or increase in lead volume over a week or months. However, data gathered within a short period cannot yield accurate reflection that can be used long-term. 

For instance, starting businesses cannot judge their performance based on their first weeks. Therefore, marketers shouldn’t be tied to daily/weekly numbers but rather make regular analyses.

On the other hand, eCommerce businesses will likely register the most sales during Black Friday, while B2B businesses will have the most traffic during the holidays. 

While this data cannot be used to make general conclusions, marketers can use data from previous years to predict months with the highest and lowest sales volumes.

Understating the Importance of Offline Activity

Another significant data analysis challenge is failing to consider the impacts of offline activity. Marketers should be aware of unprecedented events that can affect businesses, either locally or globally. 

For instance, graphs for most businesses in 2021 didn’t align with their normal patterns. Unfortunately, most marketers ignore or don’t consider outside factors, leading to inaccurate conclusions and predictions.

For instance, HVAC businesses should expect more inquiries with changing weather trends. SaaS businesses will also experience a surge in interest when their competitors prices increase. 

How does this apply to me and my industry?

  • Contractors
  • Electrical
  • Fire Protection
  • Flooring
  • Franchises
  • Solar
  • Garage Door
  • Home Builders
  • HVAC
  • Landscaping
  • Moving
  • Storage
  • Painting
  • Pest Control
  • Plumbing
  • Remodeling
  • Roofing
  • Windows & Doors

Therefore, keeping tabs on external events can help marketers predict the potential for increased or decreased business. Any industry can benefit from data analytics for digital marketing.

Not Accounting For Multi-Channel Engagement

Not Accounting Multi-Channel Engagement omnichannel

Interestingly, most marketers get tied to analyzing data from a specific channel, be it organic search, Facebook Advertising, LinkedIn advertising, or paid search, and are obsessed over getting results from the platform.

However, these channels don’t operate in a silo since web users do not strictly utilize one channel.

Such marketers have ad and analytic platforms that attribute to last-click, which exacerbates this challenge. 

Marketers focus solely on the final traffic source or a campaign that drove the lead without considering that the user may have started with an organic, non-branded search before clicking on a Facebook ad and many other possible steps before converting.

That said, marketers should pay close attention to assisted conversions and possible conversion paths. Fortunately, all these can be traced from the “Multi-Channel Funnels” section in your Google Analytics platform.

Data analytics for digital marketing get a little easier once you have captured and organized your data. Now get ready for paid search.

Data Analysis Challenge and Data Analytics for Digital Marketing

Data analysis is probably the hardest step when it comes to data analytics. It isn’t surprising that 54% of all marketers find this a top challenge to getting accurate results. However, marketing experts attribute data analysis challenges to three main things, namely;

  • Lack of a strategic approach to data analysis – in most cases, marketing teams jump and start working on data analysis without a solid strategy, hoping to find answers magically. However, “dumpster diving” into data analytics requires effort and doesn’t assure accurate results.
  • Missing data analysis skills in marketing teams – though discussed below, a few people can do data-driven marketing domains successfully. Organizations struggle to find good talent, leading to confusing reports.
  • Deployment of wrong technology – marketers should use the right technology to solve data analysis problems. Instead, most marketers prefer the latest technology that may not provide accurate results. Marketers should correctly set the data analytic problem, identify use cases, and select appropriate technology.

The Data Analyst Challenge

As mentioned above, lack of skilled talent is another reason marketers struggle to get reliable results from data analytics. 

A survey found that 1.9% of marketing leaders attest that their organizations have the right talent to use data analytics tools. Just like good data, good data analysts are hard to find.

Unfortunately, this is a persistent problem bound to affect businesses for a long time. For instance, the overall rating of data analysts on a 7-point scale, with 7 denoting the right talent and one absence of talent, did not improve from 3.7 from 2013 to 2017. 

Therefore, organizations should align their data strategy with their data analyst talent to maximize the potential of data analytics for digital marketing.

Without the right talent, good data remains unexploited, preventing organizations from taking maximum advantage of data. What characteristics should companies and marketers look for when hiring a data analyst?

Data analytics for digital marketing words if you have a problem, which may be a marketing problem to a business issue. Run a gap analysis.

Define Business Problems

Data analysts who can define business problems and know how to solve business problems are very valuable.

For instance, while a marketer may be focused on driving conversions, they may not realize the importance of pertinent data gathered from the business’ purchase funnel, which is beneficial in generating long-term sales.

Map Algorithms and Data to Business Problems

Organizations will benefit more from data analytics if data analysts understand the company’s objectives, are informed of the marketing strategies, are exposed to customers, and know the company structure.

However, to understand this, data analysts should spend some time outside their data analytics realm, visit customers to understand market requirements, attend planning meetings to appreciate company goals, and ensure that marketing goals and data analytics needs are aligned.

Data analytics for digital marketing must align with your company goals. If not, everyone will be on board.

Understand Company Goals and Data Analytics for Digital Marketing

Regardless of the size of an organization, data analysts have multiple requests from various departments. However, a clear understanding of company goals will enable data analysts to prioritize easily and allocate more time to the most important projects with high marginal value.

That said, organizations should centralize requests for data analytics teams to prioritize if;

  • The analytics findings can change how things are done in the business
  • If these changes bring economic consequences

To achieve this, most businesses develop standardized request forms, which ensure that all requests are assessed equally.

The main benefit of developing such standardized procedures is that it eliminates the potential of using business analysts for opportunistic research.

For instance, someone may approach data analysts to analyze a preconceived strategy for personal gains instead of focusing on the company’s best interests.

Identify the Best Tools

Data analytics for digital marketing requires good tools to help with the heavy lifting. Tools like HubSpot CRM, Moz, SEMrush, and RankMath.

A good data analyst should also identify the best data analytics platform. With many options available, your analyst should help you choose a tool that makes sense to the business context and can produce the desired results.

You need technology for data analytics for digital marketing. I guess you could do it manually.

Have Extensive Skills in Data Analytics for Digital Marketing

While some marketing data analysts are good at coding and math, others handle framing issues, developing data explanations, and connecting them to business issues. However, only a few are great at both skills.

You should find someone with a great understanding of both skills to leverage your data.

Drawing Conclusion from Faulty Data

Despite advances in data acquisition technologies, most marketers still conclude faulty or inaccurate data.

Therefore, before using any data, you should ensure that your ad conversion tracking, configurations on Google Analytics, and other tools used to gather data are configured properly.

Otherwise, you might underreport your conversions if a Pixel on your “Thank you page” is misfiring.

Similarly, you can easily overreport if the conversion rule is configured to the wrong page. Therefore, you should establish a system that regularly checks to ensure that data flows properly.

Poor Data Visualization

Poor Data Visualization

Data visualization is essentially a graphic representation of analyzed data. Instead of presenting data in rows and columns where discerning a meaning is difficult, data analysts should display information in charts, line plots, and other visual infographics that give a story of your data.

Such visuals are used to create data dashboards, enabling users to easily view and understand crucial data.

While this may seem easy, marketers are not data experts and find it difficult to choose a good data visualization method. Without proper background knowledge, choosing the best data visualization for your data becomes overwhelming. 

Most people look for aesthetically looking visuals, which misrepresent their data. For instance, some may pick charts even before understanding their data.

Therefore, marketers should understand their data before selecting a visualization method. 

For instance, a scatter plot might lead to more confusion if you want a visual to compare profits between various months. For this, opt for a bar chart as users can easily see months with the highest or lowest profits.

Data analytics for digital marketing works hand in hand.

Conclusion and Data Analytics for Digital Marketing

Data-driven marketing campaigns support several areas in a business, including understanding your target customers, finding new markets, customer and product segmentation, and more. 

Data analytics and digital marketing go hand in hand. But don’t get lost in the data.

However, the foundation to achieving all these is finding the right data and analyzing it properly. Unfortunately, even as data becomes the “fuel of the modern economy,” marketers still face challenges making good use of it.

Data analytics for digital marketing get simple once your KPIs, objectives, and goals are defined.

Let us know if you need help with data analytics and digital marketing.

General FAQs for data analytics and digital marketing

​​What is a Data Analytics Specialist?

This person has deep knowledge and experience in data analytics for an organization. They can analyze and interpret the data provided through technology and social media to reveal insights about customers.

What Are Data Analytics for Digital Marketing?

Data Analytics for Digital Marketing is a term that describes the type of analytics software and hardware tools, such as virtualization and artificial intelligence, that allow customers to more conveniently engage with businesses by providing companies with more customer data about their preferences.

How Can We Get Results From Data Analytics for Digital Marketing?

It is necessary to collect data through a process called “data analytics.” The term typically refers to using statistics and other mathematical techniques to extract data insights. Data analysts often work with large numbers to glean information, such as how many people visit a site or the purchase rates of different marketing campaigns.

What industries use data analytics?

Organizations in any field can use data analytics to understand their customers better and optimize productivity.
A handful of industries that use data analytics regularly:
(1) Banking and insurance; and (2) Healthcare; Marketing. Companies can conduct large consumer surveys and utilize statistical analysis to identify “market segments” or groups of people who share common traits. Marketing companies can then use this information to develop tailored campaigns for specific groups, such as millennials aged 18-24 with disposable income and a preference for digital media platforms like Snapchat.

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