*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.
Drive sales and improve customer relations by implementing data science in your marketing campaigns. Analyzing metrics can help you learn more about customers. Reach people where they’ll interact the most. Start improving your marketing strategies with these simple steps.
Data science is a great tool to use when trying to understand your audience and launch effective marketing campaigns. Your marketing team will gain insight into your audience’s buyer behavior by leveraging metrics. More relevant information can drive sales to improve customer relationships and brand recognition.
The science of collecting data can be complicated. However, the concept is straightforward. What is data science, and how can it positively impact your company? What techniques help you leverage metrics to improve marketing strategies? Here is what you need to know.
Table of Contents
Data science is a complex field of study. It consists of extracting meaningful and actionable insights from dense data sets. Advanced AI technology also helps data scientists determine feasible goals from the statistics. Therefore, metrics provide relevant facts for real-world applications.
Leveraging data science is crucial for businesses that want to maintain a competitive edge. Today’s corporate landscape relies on accurate information to manage individuals, groups, and projects. Implementing a scientific approach to data collection can benefit companies in several ways. From marketing campaigns to research and development, general operations depend on precision data.
There are several ways to gather records and implement them into your company procedures. Use these six steps as a guide:
- Identify and frame your problem
- Gather raw data
- Process the data for analysis
- Analyze and explore the data
- Share results
- Deploy the process for future data sets
Before you start collecting data, you need to identify the problem you are trying to solve. If you don’t have a specific issue, you can also develop a hypothesis for testing and analysis. For example, maybe you want to gather records on what social media platform you connect most with your customers. The information can help you reach more people and delete ineffective strategies.
Say your business is looking to expand its audience to reach younger customers. You could start by first identifying your current target demographic. Then analyze the facts to determine why the desired group is missing.
From there, you can start asking questions to help frame your problem. For instance:
- “How much money could we lose from ignoring a different customer base?”
- “Why aren’t people in a specific age group interested in our product?
- “What are the competitors doing better?”
The goal is to gather all the information you need first. Then you can start collecting data and solving complex problems.
Raw data will give you the necessary insights to help direct teams and formulate solutions. In this stage, figure out what kind of information you need. You can tap into company databases and sales metrics for real-time analytics. Many companies store sales data in a customer relationship management system or CRM.
Sort out the files once you have them all. You should also proofread the records to ensure no gaps, errors, or omissions could corrupt the data set. Some of the most common mistakes in data science are:
- Missing values
- Corrupted values and invalid entries
- Time zone differences
- Date range errors
This step is cleaning up your data. Even the slightest mistake could throw off the whole set, so you want to ensure it’s error-free before moving forward. Otherwise, you may jeopardize your findings and risk missing the solution to your original problem.
One of the most crucial steps in the data science process is exploring the findings. At this phase, you should look for patterns and repetition. Maybe you’ll notice that sales for a particular product have significantly decreased. Or perhaps you’ll uncover the secret to why your company can’t compete with more prominent organizations.
You must leverage all your data science knowledge to crunch the numbers successfully. This step can require a lot of mathematical, statistical, and technical expertise. It may be helpful to have a predictive model to compare your data.
Predictive modeling is when you use machine learning and data mining to predict likely outcomes based on existing information. For example, you can use a predictive model to compare your average customer with an underperforming group. Based on the model, you may find that some customers are easier to reach via social media rather than email. Then you can adjust your marketing strategies to suit their preferences.
NOTE: After analyzing the data, you can combine your qualitative and quantitative findings to develop industry-leading tactics.
After gathering all the data and exploring and analyzing it, you’re ready to put your findings to work and share them with the rest of the company. The marketing and sales teams will be the most important departments to brief on the results. It’s essential to make sure the teams you’re sharing the data with people who understand its importance and how to act on it.
Without adequate dispersal, it’s unlikely that your findings will make a difference. Assertive communication will be key here. Make sure your team knows how to use the data and implement the proposed solutions.
Marketing teams are tasked with building brands, promoting sales, and securing long-term customers. Advertising is a vast industry with countless moving parts. By leveraging data science to better understand your customers and their needs, you can help boost your efforts and launch more successful, targeted campaigns.
The data gathered can often be extensive. It usually covers various critical topics the marketing team may want to consider. However, there are some specific areas where data science and analysis may be crucial to making successful marketing decisions. Those areas include:
- Sales data
- Customer data
- Competitor analysis
- Market research
- Product data
Sales data covers how and why your products and services sell. It includes sales growth, net revenue, and average profit margin metrics. Sales data also helps the marketing team improve forecasting and consumer relations.
The marketing team needs to know who the customers are if they’re going to reach them effectively. Consumer data can help improve targeted marketing by personalizing to create compelling sales propositions for the target audience.
There are four different groups of customer data:
- Descriptive data includes demographics like age, income, and location.
- Behavioral data includes customer patterns like website browsing and preferred purchase times.
- Interactive data covers how customers interact with different advertising.
- Attitudinal data reveals what branding customers gravitate towards, their thoughts on products and services, and their perceived value. This data is typically captured in focus groups.
When you know your competitor’s strengths and weaknesses, you can identify any gaps in the market that you may be able to fill. Plus, if you know what your competitor is doing, you can upgrade your efforts to perform better in your industry.
While it sounds similar to competitor analysis, market research is quite different. Conducting market research shows your company’s position compared to similar organizations in your industry. It also reveals the feasibility of your marketing campaigns.
The primary purpose is to understand the environment associated with a product or service. Market research is used to help define objectives, create promotions, and strengthen public relations during product development. Surveys, interviews, focus groups, and customer observation are the most common methods.
Product data helps businesses cultivate brand loyalty and increase the consumer’s lifetime value. This helps drive sales and produce positive customer responses. These metrics can vary but ultimately track real-time engagement and behavior to help businesses optimize the approach.
One of the key performance indicators to keep track of is the product adoption rate. This metric measures how new products and features perform under specific circumstances. You can’t put a value on such helpful insights.
Collecting and analyzing information specific to your business and audience helps you develop targeted campaigns and accurately track results. This helps drive sales, a net positive for your business and profitability. Improving your marketing tactics isn’t the only data science benefit, however.
Some of the other advantages can include:
- Getting to know your customers better
- Increasing customer retention
- Segmenting your audience
- Automating tasks
- Better performing campaigns
Customer data will help you better understand who your audience is (and isn’t). You can address their needs better than other companies when you know them better. You’ll be able to meet there where they’re at, whether on social media, through email, or otherwise. This also provides you with critical feedback for future data analysis.
When you provide your customers with a personalized experience, they’re more likely to return for repeat business. You want your customers to feel special when shopping for your goods or services. The idea is to make them happy to choose you over the competitors. After all, satisfied customers develop brand loyalty, and they prefer doing business with an intuitive organization.
Once you start analyzing the data, you’ll realize that you can’t send the same marketing campaign to every customer. Each customer may be at a different stage in the buying process. Some might also have specific concerns that make them more or less likely to respond to targeted marketing tactics.
Data science can help you learn customers’ behaviors, habits, and demographics. With that information, you can segment your audience into different groups. For example, say you have one group of customers that are 40+, and another group that is 30 and under. Segmenting your audience will allow you to develop specialized promotional methods for maximum impact.
Artificial intelligence (AI) and machine learning are valuable and essential for data scientists and their analyses. This technology can be beneficial when dealing with time-consuming tasks, specifically those involved with SEO and digital marketing.
You can use AI and machine learning to automate tedious tasks. Make keyword searches more efficient or create automated email campaigns for different projects and groups. You can even send messages using the customer’s name or other personal information. Using technology allows you to leverage data and reach your audience while saving time and resources.
Ultimately, all of the benefits of data science equal better-performing campaigns with increased competitiveness and more intuitive results. It is crucial to know who your audience is, how to target them, and how to automate the marketing process.
Leveraging data science, especially with AI and machine learning, helps businesses and entrepreneurs improve tactics and create actionable strategies for long-term use. To successfully reach your customers, you need to know who they are and where they shop. Data science is the tool to help reveal that.
Data science helps companies and executives understand their audience and launch effective marketing campaigns. It is the scientific way to target specific groups and give them what they need. By leveraging data and analytics, organizations gain valuable insights into audience behavior, preferences, and concerns. More information means better sales and enhanced relationships with customers.
Modern data science typically involves artificial intelligence and machine learning to help streamline the analysis process. Using accurate and uncorrupted information helps startups find their niche and makes it easier for established companies to remain relevant. What is your data saying about business operations and profitability? Find out as soon as you can, then create a master plan.
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.