*I frequently get asked questions about Data Science, so in the interest of helping as many people as possible, I’ve started this blog to answer those questions as simply as possible. This is a robust topic, and if you want a more in-depth discussion, please revisit my blog, where we will be going into greater depth at another time.
Data analysis can be leveraged to develop more effective consumer marketing programs. Analyzing data shows marketers who their audience is and provides valuable insights to reach them. Find out what your data tells you to increase your industry staying power.
Data analysis is a valuable tool for marketers to learn more about their consumer base. It reveals the products and services they need and how demonstrates to market to them. Leveraging data analysis can yield precise metrics, leading to more effective marketing and sales. This can help increase a brand’s staying power in their respective industries.
Understanding data analysis is essential. What is it, and how does it work? In what ways can analyzing data help with consumer marketing programs? Are there any risks or rewards? Here is what you need to know.
Table of Contents
#1. Identify What You’re Looking For
#3. Clean and Prep for Analysis
Leveraging Data Analysis for Consumer Marketing
#3. Determining Preferred Communication
#4. Tracking Important Metrics
#5. Targeting the Right Audience
#6. Personalizing the Consumer Journey
#1. Machine Learning and Artificial Intelligence
#3. Improves Customer Experience
What is Data Analysis?
Data analysis is collecting and analyzing information to pull insights and support decision-making. There are varying methods and techniques to perform your analysis, depending on your industry. However, most plans are based on quantitative and qualitative research.
- Quantitative research is numbers-focused. Thus, it relies on statistics, counts, surveys, and mathematical or systematic observations.
- Qualitative research refers to nonnumerical data. This could include case studies, focus groups, and other methods that outline a person’s cause for certain behaviors.
Combining these two types of research helps provide well-rounded and efficient, actionable insights that can be utilized in countless ways.
How Does Data Analysis Work?
Generally speaking, there is a process to data analysis that is important to follow to reach solid conclusions. In most cases, the analysis process can be broken down into five steps:
- Identify what you’re looking for
- Collect data
- Clean and prep for analysis
- Analyze data
- Interpret data
#1. Identify What You’re Looking For
Before you start gathering data, you need to know what you’re looking for or what questions you’re seeking to answer. This could include questions like, “What do customers think of when they see your brand?” or “How many people from a specific group use your goods and services?”
#2. Collect the Data
There are several data collection methods to explore. The one you pick will depend on what sort of information you seek. It also matters whether you’re in a quantitative or qualitative scenario. Generally, data can come from internal or external sources. This might include surveys, interviews, focus groups, case studies, and questionnaires.
#3. Clean and Prep for Analysis
When you’re collecting data, not everything you gather is helpful. Therefore, this stage is essential. It is where you’ll clean up all your findings and prepare them for analysis. You can eliminate any duplicated or poorly formatted data here. Streamline your approach by not forgetting this step.
#4. Analyze the Data
Like data collection, there are also several data analysis techniques. These typically include statistical analysis, regressions, and neural networks. Regardless of the method you choose, this data analysis stage is where you’ll start extracting key insights and drawing conclusions. Look for trends, correlations, variations, patterns, and anomalies first.
#5. Interpret the Data
Last is interpreting the data. Researchers come up with actionable steps based on the findings at this stage. For example, say your original question was, “What do customers think when they see my brand?” Your data should help you answer that by now. Then, the answers can help your team work together on more impactful marketing plays and better industry perception.
Leveraging Data Analysis for Consumer Marketing
The marketing industry has always relied on data to measure campaign effectiveness and investment returns. In cases where data wasn’t accessible, marketers would often act based on their gut instinct and previous successful practices. However, the times and the marketing strategies have drastically changed.
Today, the information marketing teams need is widely available. Companies can access it from various platforms, with user-friendly customizing options to suit unique marketing plans. Not taking advantage of data analysis could make some business lose their competitive edge.
Data evaluation is also critical when learning about consumers. It allows marketers to gain precise insights that can be implemented later. Those insights ultimately make targeting and reaching specific demographics more accessible.
Companies can use free tools like Google Analytics to review popular goods and services from competitors or parallel industries. They can uncover hidden consumer needs or requests while developing affordable solutions. Some of the ways marketers can leverage data analysis to create better consumer marketing programs include:
- Determining a focal point
- Tracking interactions
- Determining preferred communication
- Tracking important metrics
- Targeting the right audience
- Personalizing the consumer journey
#1. Defining a Focal Point
By utilizing data analysis and leveraging consumer data, marketers will know exactly where they should be focusing their time to yield more returns. Analyzing the data and crunching numbers can be done via machine learning or artificial intelligence, making the process much easier for marketers.
Interpreting the data analysis to determine a focal point helps better use marketers’ time. It could also prevent companies from wasting money. For example, insights from data may show that a current campaign targeting customers on Facebook isn’t profiting as they hoped. So, they could end that campaign, regroup, and launch something more practical.
#2. Tracking Interactions
Digital marketing plays a tremendous role in today’s marketplace. It also typically yields more returns than traditional brick-and-mortar marketing. E-commerce customers enjoy interacting with brands across various platforms. Thus, competitive marketers must interact with them on each channel.
Leveraging data analysis allows your marketing team to reach customers more effectively. It helps companies show customers where and how to engage with the brand. Brands can also enhance communications across several channels, including social media.
#3. Determining Preferred Communication
Similarly to tracking where interactions are taking place, it’s essential to pay attention to what channels and methods of communication consumers prefer. Maybe your audience responds well to social media ads but tends to delete email ads without opening them. You can provide consumers options or target them on their preferred platforms.
Perhaps they opt out of emails but opt into texts. Different parts of your audience may also have other preferences. For example, younger consumers may prefer interacting on social media, while older consumers could prefer email ads.
#4. Tracking Important Metrics
Leveraging data analysis can also help marketers track metrics vital to making practical decisions. The key performance indicators (KPIs) marketers should be tracking are:
- ROAS
- ROI
ROAS stands for return on ad spend. This figure represents the revenue generated from ads divided by the cost to determine the return. Tracking ROAS is critical when determining how much revenue your paid advertising generates.
If your ROAs are low, you may need to investigate the effectiveness of your paid advertising. This would also be an excellent time to revamp your marketing strategy with data analysis. If your ROAS is high, invest in more paid advertising to continue driving sales.
ROI stands for return on investment. It helps determine your company’s profitability by comparing profits with costs. ROI refers to assets, like software and salaries, rather than company costs, like advertising campaigns. Consistently tracking and monitoring these KPIs can help you reach critical milestones.
NOTE: Use data analysis to track site traffic, keywords, click-throughs, and time spent on your platform.
#5. Targeting the Right Audience
Marketing to a specific audience is one of the primary benefits of data collection and analysis. Effective targeting is key to understanding the consumer and what they want. You also need to know how they feel about your brand and their usual purchasing preferences.
Many marketing teams use consumer profiles or avatars to help define these things. Consumer personas highlight the characteristics of those most likely to purchase your goods or services. These profiles can also give marketers better insights into delivering advertising best.
#6. Personalizing the Consumer Journey
Implementing findings from data analysis and referencing consumer profiles/personas allows marketers to personalize the consumer journey. From product recommendations to automated social ads, everything can be personalized.
This helps to maximize customer engagement. Engaging and tailored content or advertising helps encourage more meaningful interactions. Customers remember the companies who understood their needs the most. Data analysis allows brands to maintain their current customer base while building a new one.
Data Analysis Technology
Modern technological advancements have made the data analysis process more accurate, detailed, and easy to implement. Some of the leading technologies for conventional data analysis include:
- Machine learning and artificial intelligence
- Data management
- Data mining
- Predictive analytics
#1. Machine Learning and Artificial Intelligence
Artificial intelligence (AI) is the concept of using advanced computer systems that can mimic human intelligence and efficiently complete tasks. Machine learning is a subcategory of AI. It develops adaptable computer systems to learn and analyze data with various algorithms.
Machine learning enables applications to interpret data and predict outcomes. However, it doesn’t require someone to program the system because everything is automated manually. You have to train the machine to learn an algorithm on a sample data set. Yet, the system will continue learning on its own as it gathers more information.
#2. Data Management
Before analyzing any data, there must be procedures that manage data flow between systems and keep it all organized. Establish a central data management platform (DMP) to keep your marketing team on the same page handling data. A DMP will collect and manage all kinds of information, including first, second, and third-party data. It then uses that data to build detailed customer profiles for more effective advertising.
#3. Data Mining
Data mining is sorting through large data sets to identify valuable patterns or correlations. These tools can help predict future trends. Data mining allows marketers and digital analysts to sift through information and determine what’s relevant. Most data mining technologies do this quickly, making sorting more efficient.
#4. Predictive Analytics
Predictive analytics is a category of data analysis that focuses on making future predictions. It is based on historical data through statistical algorithms and machine learning. These predictions can help companies make decisions that position themselves for future success and maintain a competitive edge. It also helps to anticipate consumers’ needs and position accordingly.
Pros of Using Data Analysis
Leveraging data analysis is an effective tool to improve targeted advertising and overall marketing initiatives. Some other advantages of gathering data include:
- Increases productivity
- Potentially reduces costs
- Improves customer experience
#1. Increases Productivity
Data analysis can help your company process information faster, boosting overall productivity. The faster data is processed, the quicker you can integrate your findings into your marketing strategies.
#2. Potentially Reduces Costs
When your organization is more productive, it’s usually more profitable. Data analysis prevents your marketing team from having to scrap underperforming ad campaigns or waste money on new developments.
#3. Improves Customer Experience
Using data analysis to create customized marketing makes your audience feel seen by your brand. This could lead to more sales and customer conversions, building brand loyalty in the meantime.
Cons of Using Data Analysis
Despite the many benefits, there may be some disadvantages to data analysis to acknowledge. Two of those include:
- Data quality
- Security concerns
#1. Data Quality
You may encounter poor sets that don’t necessarily hold value when collecting data. Luckily, data analysis technology can help sift through anything that doesn’t pertain to the original problem. It’s crucial to clean up your data sets before analyzing them because insufficient data could harm your overall findings.
#2. Security Concerns
Privacy and security are significant concerns for many people. Some consumers may not be too keen on your organization gathering their data and sending them targeted advertisements.
Conclusion
Data analysis is a valuable tool for marketers to learn more about their consumer base. It reveals information about their favorite products and services. However, accurate data analytics also help marketing teams reach new customers while making the existing ones happier.
Collecting data is easier today than ever before. Good information is available on various platforms, including some untapped resources that analysis could uncover. Take advantage of timely information to avoid losing your competitive edge. Then perform an audit and update every 6-12 months.
About the Author
Tiffany Perkins-Munn orchestrates aggressive strategies to identify objectives, expose patterns, and implement game-changing solutions with the agility that transcends traditional marketing. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.
Dr. Perkins-Munn earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on
Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology,
statistics, and their real-world applications. As a published author, coursework developer, and
Dissertation Committee Chair Tiffany still finds time for family and hobbies. Her non-linear career path
has given her an exclusive skill set that is virtually impossible to reproduce in another individual.
