“What is the difference between marketing analyst and data scientist?”
The main two roles in marketing data
The key difference between Marketing Analyst and Data Scientist
- Marketing analyst should have a solid experience with marketing metrics while it is not required in data scientist role.
- The data scientist is expected to formulate the critical questions that will help the business and then use the data to solve it, while a marketing analyst is given questions by the marketing team and pursues a solution with that guidance.
- The marketing analyst not required to be advanced in programming side while the data scientist should be professional in writing queries. Yet, both roles should work with IT teams to source the right data.
- The data scientist role requires a strong data visualization skills and the ability to convert data into a business story. A marketing analyst is more focused on analyzing the marketing metrics.
- The data scientist usually work in a multidirectional and free form in order to extract better insights, while marketing analyst usually has a specific direction to work on.
Defining the Marketing Analyst Role
- Measure the effectiveness of marketing activities and the online ROI, of various marketing channels used to position a product or service. Given the increasing variety and complexity of marketing channelsreaching this objective is a serious challenge.
- Bring the data analytics into the heart of all marketing campaigns and tools while setting up the most effective metrics to measure and trends to manage.
- Turn insights and data patterns into clear indicators and tactics for growth hacking, budget allocation, and performance management.
- Maintain a reliable and effective connection between the marketing specialists needs and data scientist reports.
Who is the best Marketing Analyst?
- A native marketer who knows how to play professionally with marketing technology tools and marketing metrics.
- A scientifically minded person with an appreciation for design. He needs to know the effect of messaging and design on the consumer experience.
- Analysts by the heart who dominate the dashboards and he have charts ready even for his grocery shopping habits and his girlfriend mood swing.
- He knows that insights are more important than figures. He loves the data in front of him but he is more in love with knowing the consumer.
- He is the honest guy who never takes any sides. Neither marketing performance team nor data team.
Technical skills for marketing analyst
- Strong analytical, conceptual and reasoning skills
- Professional skills in Web Analytics, Marketing clouds, AdTech, and Automation
- Experience with Statistical Software, Business Intelligence Platforms, and Data Visualization
- Intermediate experience with programming language and database querying
- Experience with market research, segment analysis, consumer behavior and marketing channels
Defining the Data Scientist Role in Marketing Department
Business acumen is the main asset desired in a marketing data scientists, after technical skill. It’s so critical because a lot of quantitative candidates I’ve seen are getting so wrapped up in the elegance of the analytics that they forget that they’re hired to answer business problems.
Working with marketing team is somehow challenging for data scientists. The marketing ever-changing periodical strategies can be a roller coaster for data team and they have to adapt and survive quickly. Unlike the majority of businesses where the top element of the data science job is the ability to use computing power to acquire the data, marketing needs could be problematic and tactically challenging over the time.
Who is the best Data Scientist?
- Tech-savvy with different programming languages and statistics capabilities.
- A scientist who applies statistical tools, economic tools, and different disciplines is another facet.
- A coder who aggregate and clean data in the most efficient possible ways with ability to invent new algorithms to solve problems and build new tools to automate work or provide real-time reporting system
- He is an expert in interpreting the visual display of complex data sets and tells a story.
- He is sophisticated with analytics programs, machine learning, and statistical methods and quick with preparing data for use in predictive and prescriptive modeling
- Without asking he is always busy with conducting undirected research, exploring and examining data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
- He speaks the language of IT and able to communicate requirements and predictions to IT departments through effective data visualizations and reports
- Expert in Math (linear algebra, calculus, and probability), Statistics (hypothesis testing and summary statistics), Data visualization (Tableau, Power BI, SAP Analytic Cloud) and reporting techniques
- Professional with Software engineering skills, Data mining, Data cleaning and munging
- Professional skills in programming (R, SQL databases, Python or C/C++)
- Professional with BigQuery, DynamoDB and cloud computing tools
- Experience with ML tools and techniques (k-nearest neighbors, random forests, ensemble methods)
Collaboration between Marketing Analyst and Data Scientist
Your marketing analyst should deliver the clear results in marketing language while the data scientist should work on doing the math (statically and technically). Technically, a marketing analyst is solid at creating relations between data and marketing needs while data scientist is the true advocate in bringing the data and advanced statistics and bring the most reliable, clean, fastest results to the table.
You have known knowns, known unknowns, and unknown unknowns. Just be careful if both get a conflict. I have seen some violent fights at the office!
Finally, becareful with Data
There are many times where the underlying data that is the basis for what people have calculated is actually wrong. If you make a mistake with the underlying data, that could be a big problem while you analyze.
The premium on being able to understand what data you have, to understand what types of questions can be answered with it, and to make smart decisions is really, really high.
However, there are places where pure data science functions can fall short of what’s required to boost success in the marketplace. This is where marketers thrive.
Looking for your opinion on this and how do you see the difference between the two roles. Contact me if you are looking for marketing analytics consultant.