Data analytics and marketing as a whole are intricately connected today, but only a handful of marketers are able to utilize that connection to its full extent. This is largely because marketing experts usually do not have a background in data science. Consequently, they must rely on secondhand interpretations and data analysis reports to design target-oriented campaigns with high ROI potentials. Before elaborating more on this knowledge gap and how it affects ROI, it is necessary to first discuss how and why data analysis is so crucially connected to marketing.

Marketing Analytics Data: Learning and Improving ROI on Marketing Strategies

For the sake of clarity, it should be noted that target market data analytics and marketing analytics are not the same concepts. Market data includes all information related to the target market(s), which can be used for creating new marketing campaigns, or to update existing strategies.

Marketing analytics is the section where information from past and/or present marketing efforts made by the company is collected, organized, categorized, measured, and compared with the goal of optimizing present and future campaign performances. Similar data from previous or ongoing marketing campaigns conducted by competitors, or other relevant companies may also be considered for comparative analysis. The resulting reports are then used to identify and eliminate:

• Long-term, repetitive, low performing strategies

• Outdated or irrelevant steps with no potential

• Conflicting steps: an ad that conflicts with the brand's image, or a campaign which goes against the strongest industry trends

• Specific steps that failed to garner positive attention from the target audience

Marketers will use the remaining info to identify, reuse and/or modify:

• Previous and ongoing strategies that managed to deliver the highest ROIs

• Individual steps that delivered the most value

• Key marketing strategies across multiple campaigns that gained the highest popularity within the target audience

• Present and/or previous marketing efforts that are most in-line with the current industry trends

The core idea is to learn and improve from past experiences, but without marketing analysts sorting the vast data into readable insights, it would be impossible to do so for marketers.

Customer Data Analytics: Focusing on the Customer

Customer data analytics is one of the most important aspects of data analysis in marketing. Although it is closely related to market data analysis and at times the two can overlap, there are a few differences between them. Market data analytics concerns itself mostly with target customer group identification, new lead generation, popular trend identification, and several other aspects related to data about the target market.

On the other hand, customer data analysis focuses more on finding important patterns within the existing customer data. This data is organized, analyzed and reformatted into readable reports by data analysts, including insights such as:

• A well categorized list of repeat/subscribed customers, based on their respective value to the company

• An organized and prioritized list of top issues faced by clients/customers, as well as how each issue has affected the business

• Info on customers lost within a certain time period, as well as the (confirmed/estimated) cause behind each customer loss

• Info on acquisition of new customers (conversions), within a specified time period

• Info on monitored customer behavior and predictive estimations based accordingly

These, along with several others, are crucial insights for the core business, as they allow for making corrections, changes, replacements and redirecting future business processes with a more customer-centric approach. Depending on those changes, a marketer will devise marketing strategies to highlight those key, customer-centric business improvements to their target audience, customers and/or clients.                                                           

Target Market Data Analytics: Getting to Know All Relevant Parties Better

Target market data is not to be confused with trade market data, as these are two different concepts. Trade market data, or simply market data, refers to information about the most recent price of stocks, currencies, commodities, derivatives and other financial instruments. Most commonly, it is supplied by a trading exchange and used by share traders and investors.

Target market data on the other hand, refers to all information directly or indirectly related to the relevant sector's target market. This may include, but is not limited to:

• Identification of the target market(s) in respect to location, age, gender, lifestyle, preferences, buying patterns, etc.

• Understanding, predicting and even inspiring customer intent

• Identifying relevant industry trends

• Competitive intelligence: competitor identification, market share, pricing strategy, average profit estimation, etc.

• Product information: What is selling and how?

• Local information: social customs, local views, political stands, applicable laws and regulations, etc.

After analysts crunch through all related information and present their reports to marketing experts, they utilize that information to:

• Get to know their potential customers and create customized content for an increased rate of conversion

• Create targeted, personalized content that has a high chance of converting

• Generate a shorter, but more efficient list of leads with high intent

• Create strong lead scoring models, designed for scoring leads exclusively from the target market

• Change marketing strategies based on market conditions and customer behavior

• Comply with local laws and regulations related to marketing research and customer monitoring

• Manage expectations by aligning marketing strategies with the actual product and customer preferences

There can still be a continuity gap if the marketing team does not have a technical understanding of how data analysis works.

Mending the Knowledge Gap

Now that some of the important relationships between data analytics and marketing have been discussed, it should be more evident as to why marketing experts have a lot to gain from learning about data analytics. The aforementioned knowledge gap creates situations where a marketer has little choice but to follow the suggestions laid out in front of them by analytics reports.

Without sufficient knowledge about data analytics, they can neither verify, nor add anything new to further improve the effectiveness of their campaigns. Then there is also the continuity gap to consider. As mentioned, if there is an absence of technical understanding on the marketing end, there will always be a chance that the reports will not be maximized to their full potential. Employers are also aware of the same, which is why there is a growing demand for data-savvy digital marketers today.

The solution to this problem is fairly straightforward; marketers need to increase their understanding and knowledge about data analytics through further education. However, instead of wasting time, money and effort in a separate course, you could consider pursuing a master's degree in marketing analytics, data analytics and digital marketing from an institution like Emerson College. The graduation course can be pursued online, and it takes only about a year to complete. Post certification, you should be able to make better, more accurate, data-backed decisions while designing campaigns with high ROI potentials.

The Role of Automation: Making Data Analytics More Approachable for Marketers

Automation in data analytics has made it possible for analysts to crunch massive sets of data with improved accuracy, and within a shorter span of time. The same tools have also made the subject more approachable for marketers, who often do not have a strong background in statistics, math and data science. Let's go through a few important tasks that Analytic Process Automation (APA) helps with first.

• When augmented with a proper logic-interpretation base, APA can discover insights regarding various aspects of marketing data on its own

• APA can auto-sort findings and rank them in accordance with their importance or relevance, as per how the analyst has set it up

• APA is used often to autogenerate and auto-conduct customer surveys, which boosts the speed and volume of gathering customer data for marketing analysis

• Post conducting surveys, smart automation software can also categorize the various responses for easier interpretations

• Smart APA tools are extensively used to "clean" data, which is the process of correcting common mistakes and formatting errors

• APA can create visual data representations (analytical charts and graphs) in real time, based on the data available to it

• Advanced, AI-powered automation tools can also create visual representations for competitive analysis

Automation helps analysts save an immeasurable number of manual work hours every year. As long as any marketing expert has the basic knowledge and training in data analysis, they can make use of marketing automation to handle complex and repetitive tasks. Without the necessary knowledge base however, they will not be able to guide, change, correct and update the base parameters. Automated results can only be as accurate as the principles which are guiding the software.

Marketers from previous generations are inclined to consider their profession as being separate from that of the data analytics department. By now, it should be evident why that is not true, and how that line of thinking can compromise a campaign's effectiveness, appeal and ROI. Expertise over data empowers an experienced marketer to become better at what they do. It enables them to maximize all that information available to them, while increasing their own professional importance in the process. The demand for data-savvy marketers can only grow, so failing to be in line with the changing trends of the industry will eventually lead to loss of professional value.