The term data analytics is too wide in its scope and application to throw around without specifying the particular use case scenario. However, therein lies the importance of a data scientist or analyst, because similar to how data analysis is extremely broad in its applicability and usefulness, data analysts themselves are, consequently, just as important and valid across multiple industries. Their honed ability to format, categorize and interpret data according to the interests of the particular project is highly valued in this digital age of business, given how crucial those inputs are for understanding the customer, the competition and the overall market.
The Connection between Digital Marketing and Data Science
In order to become a marketing data analyst, or even to be able to use the information to good effect, it is necessary to understand how data science helps businesses through every aspect of planning and executing their marketing campaigns. Stressing on the main highlights below, we will keep it straight to the point.
What Exactly Does a Data Analyst Do?
As already mentioned, the scopes of the subject and its applications are far too broad to discuss without a specified field, therefore, in the interest of the subject here, we will often be referencing data analysts who work closely with digital marketers, but they won’t always be the sole focus.
A data analyst collects and transforms raw data, which mostly consists of numbers and statistical figures representing parameters such as sales, revenue, profit, expenses, market research findings, supply chain logistics, information on the competition, market demands, etc. into more coherent and usable sets of interpreted reports. The purpose of the reports will vary in accordance with the needs of their client/employer. Specifically speaking, the following are the main highlights of the work that a data analyst does to help their clients/employer take profitable business decisions.
- Detecting, predicting and preventing unnecessary or fruitless expenses
- Predicting market patterns for the coming quarter, in respect to the target audience and the competition
- Figuring out how to reduce expenses and/or redirect cash flow towards better investments
- Helping with the pricing details of a product launch; the magic number which will encourage sales, without cutting down on the bottom line beyond practicality
- A marketing analyst will decipher the most important and relevant channels of a campaign, in respect to the goal of the project
- Marketing analysts will also help marketers determine the most valid avenues of channeling the available budget for the particular project
An analytics expert will always communicate to their clients or company executives via reports, which represent their findings, interpretations, insights, suggestions, predictions, and conclusions in such a way that they can then help the right people make informed decisions in the concerned business’s best interests.
Since these reports are essentially the summations of all their work, it’s extremely important that the analyst, irrespective of whether we are discussing financial analytics or marketing analytics, is actually capable of creating proper business reports. This is also part of the reason why it helps immensely if the digital marketer and the marketing analyst have a minimum knowledge gap between them, or at least if each has a basic understanding of the other’s work. It’s a consideration that we will discuss in detail next.
A Seamless Connection: Minimizing the Gap in Communication with a DMDA Program
What if the role of the data analyst and the digital marketer could be played by the same person? There would be no communication or knowledge gaps that might impede any of the processes, since there will be just one person in charge, with intricate knowledge about both digital marketing and data analysis (DMDA). Naturally, employers would prefer to have both skills in one person, because it will allow them to get faster, more accurate results, without having to hire two separate workers.
Therefore, the most prospective future of an aspiring marketer or marketing data analyst lies in their ability to work seamlessly in both fields. Visit the Emerson College Online website if you are interested in knowing whether a DMDA course would be in line with your own career goals and if so, which one would be right for you to pursue with your present qualifications.
Optimizing the Campaign Budget
Marketers always have to work within a budget, and unless we are discussing the likes of Comcast Corp or Amazon, that budget never feels like enough to do everything that needs to be done. Even if we were to include multibillion-dollar corporations, the massive scale of their marketing campaigns would also have to be taken into account. As a result, the principle ideas at work in planning most marketing campaigns are always along the following lines:
- Cost-effectiveness: Excellent return-on-investment from whatever budget they have access to
- Proper allocation of the resources via intelligent distribution
- Faster results, especially in case of time-sensitive campaigns, such as a product launch
Unfortunately, it’s not as easy as it sounds since unforeseen impedances are just that, unforeseen. This is precisely where data science comes in, using which the marketing analyst provides invaluable foresight. To provide a model example, the following chain of steps should be useful:
- The client’s or company’s expenses vs conversion data from past marketing campaigns should be analyzed
- Relevant data from multiple similar campaigns, run by other companies should be taken into account
- Data research is needed to provide the KPIs that would be the most relevant for the current project
- Based on their findings, the analyst needs to construct a spending model, one that’s most likely to extract the maximum ROI
- Marketers should then use that blueprint to compartmentalize the available budget accordingly, across multiple channels of digital marketing
Finding the Right Crowd
We hear so much about target audience identification because in order for a business to succeed, they must channel their resources towards marketing to the kind of audience who could potentially become their customers/clients. While this is not a new process by any means, and it has been around since the beginning of business itself, big data analysis has made the entire process infinitely more accurate and productive.
Before the intervention of data analytics, marketers only had vague estimates regarding factors of the most important relevance to work with, such as location, buying capacity, business/consumer classifications, etc. This meant that problems, such as the ones below, were far too common.
- Spending the entire budget catering to the wrong kind of audience
- Overspending/exceeding the budget
- Exceeding the budget with minimum ROI
Analysts solved most of these problems and minimized associated risks by:
- Determining the target demographics with data-backed accuracy
- Further refining the data to determine a spending model, which would enable marketers to derive the maximum possible ROI from each marketing investment
- Helping to identify the most relevant and productive channels of digial marketing, with respect to the specific brand and/or product in question
- Providing knowledge about both the right channels and the right audience, giving marketers the ability to reduce expenses, while boosting conversions simultaneously
Building Customer Specific Models for Marketing and Retention
Personalized ads are just as important for B2Cs, as they are for B2Bs. In the B2B sector, longtime clients are preferable, and depending on the sector we are discussing, retention can be crucial. Analysts help in both acquiring and maintaining long-term customers by putting the following steps in motion:
- They design and construct longtime/lifetime value models for companies to maintain/renew relationships with important clients, for as long as possible
- By analyzing the data from last year, a data analyst is often able to predict which customers are most likely to leave them this year
- Analysts can create a list of priority, in regard to the clients who are most likely to be lost in the coming year
- They can then build data-backed, personalized retention plans, which are most likely to succeed for each specific client
If the budget doesn’t allow for multiple retention plans in their maximum capacity, the marketer should be able to use the list of priority to determine which of the clients that are about to leave them, would be the biggest blow to the company, and channel the limited resources to retaining them only.
Data-Backed Lead Targeting And Informed Lead Scoring
Lead targeting and accurate scoring is an intricate part of ensuring good sales performance, and data analysis plays a crucial role here as well.
- Identification and consequent classification of leads, in accordance with their online behavior
- Identification of the lead’s present business associates to determine their needs and expectations
- Higher conversion rates are inevitable when the lead targeting and scoring is based on hard facts
- Analysts can construct a predictive lead scoring algorithm with the ability to forecast conversion probabilities in real-time
Marketing, and digital marketing in particular, alongside sales is highly dependent on data analytics today, which makes perfect sense, given everything we just discussed. From finding a target customer base and lead scoring, to customer retention and budget maximization, the scopes of data science are practically indispensable at this point of time.