In 2017, The Economist published an article stating that the world’s most valuable resource is no longer oil, but data. Data leads to information, and information leads to knowledge, meaning that without data, we couldn’t keep learning everything we need to.
Data is the foundation for data science and machine learning, and machine learning builds its algorithms and models from data. And the use of data is only increasing!
This year, due to the COVID-19 pandemic, we saw an increase in digital data generation. According to Domo’s infographic, Data Never Sleeps 8.0, the internet reaches 59% of the world’s population and now represents 4.57 billion people in April 2020. As per EMC, the data produced by humans in 2020 reached 44 zettabytes.
So what does all this data mean, and how can you get access to it? Let’s look at the different types of data, the current state of data collection, and how you can collect data for your business.
The Four Types of Consumer Data
In today’s modern digital era, gathering large amounts of data about existing and potential consumers is a common goal for many businesses. Consumer data is essential for predictive analysis, sentiment analysis, or targeting potential customers.
For example, Facebook acquired WhatsApp for a whopping $19 billion, mainly for its data. By purchasing the messaging app, Facebook received access to user information that they didn’t have before. WhatsApp’s data now belongs to Facebook and is used to improve its services.
We can divide consumer data into four main categories: personal data, engagement data, behavioral data, and attitudinal data.
1. Personal Data
As its name suggests, personal data includes personal information such as names, genders, birthdays, social security numbers, and some online information like IP addresses, devices, web browser cookies, and more.
2. Engagement Data
Engagement data refers to the way consumers interact with emails, websites, mobile apps, social media pages, paid ads, and customer service.
3. Behavioral Data
Behavioral data shows the behavior and buying patterns of consumers. It includes transactional details such as purchase histories, browsing patterns, product usage, brand affinity, or repeated purchases.
4. Attitudinal Data
Attitudinal data is collected based on customers’ attitudes toward products, such as purchase criteria, product desirability, customer satisfaction, and more.
The State of Data Collection
There are many ways to collect data, and the two primary sources of data are current customers and potential customers.
Here are a few of the most common ways companies collect data:
Asking consumers for their data
The simplest way to collect data is to ask consumers through forms, filling out details on websites, providing credential details, collecting subscription data, sending out surveys, and more. All of this data is derived directly from the consumer.
Indirectly tracking customers
Many indirect tracking methods assist in gathering information about customers. These methods include tracking their social accounts, tracking purchase history, transactional data tracking, and online tracking. You can track online activities like sites they visit, products they are looking for, or the average amount of money the customer spends. These activities are all relatively easy to monitor.
Other sources of customer data
Other customer data sources include location-based advertising, which utilizes tracking technologies such as an internet-connected device’s IP address. Their IP address interacts with other devices like mobile phones, laptops, etc., to build a personalized data profile. Marketers and brands may use this information for personalized advertisements or landing pages.
Types of Data Sources
Now that you know how to collect data let’s discuss the different types of data sources. Traditional data sources include:
Customers visit various websites, and many ask for a subscription or form fill. These data submissions allow companies to, directly and indirectly, fetch data. The types of sites a user regularly visits are critical data points for any business. Optimize your website to collect accurate user data.
Social Media Profiles
Consumers use various social media platforms. These platforms gather personal information from users and collect data based on their posts, likes, follows, etc. Marketers can use data collected from social media to build buyer personas and predict customer journeys.
Location-Based Advertising (IP addresses)
Many apps and other advertisers are utilizing tracking based technology. Location-based advertising can be helpful to determine where your customers live and travel.
Customer Service Records
When users subscribe or register on a site, they provide a lot of personal information, such as their email, birthday, and full name. This information becomes consumer data for companies.
E-commerce sites are gaining popularity every day. With every online shopping purchase, transaction data is automatically collected by companies.
Third-Party Data Collection
Companies that sell personal information and other consumer data to third-party sources are regularities in today’s business world. Once information is collected, it gets passed around in a data marketplace of its own.
Here are some examples:
- You are looking for a property to buy or rent and contact a broker. Suddenly, you start getting many leads from different real estate companies. It may seem like a coincidence, but it is third-party data sharing.
- You are browsing an online store for a particular product and start to see ads related to the product pop-up. Nothing remains under the curtain nowadays. Most of your online data can be easily tracked and shared by companies.
How Companies Use Consumer Data
Companies collect so much data from their current and potential customers that it must provide a high return-on-investment (ROI). Otherwise, why would anyone invest in the price data collection? Companies use their consumer data to:
1. To improve user experience (UX)
Companies analyze customer data by exploring consumer feedback and their experiences with their product. Based on the data, companies can resolve any glitches and undesirable outcomes for their customer experience.
Here’s an example:
A user is exploring an e-commerce site but abandoned their cart when a discount coupon did not apply to their order. Tracking the buyer’s journey can help brands resolve issues like this by monitoring where they exited their site.
2. To refine marketing strategy
Based on consumer data, likes, and interests, companies can refine their marketing strategy. Marketing nowadays is more personalized based on consumer data analysis.
Social media plays a critical role in personalized marketing. A user’s journey is mapped through various social platforms like LinkedIn, Facebook, Instagram, Snapchat, Twitter, YouTube, and other websites. Mapping this journey is essential, as it is imperative to engage the user—personalized marketing channels the user’s sentiments for their marketing strategy.
3. To sell products
Every business is hoping to sell its product or service to the consumer. It’s how a business grows and generates revenue. Selling the right product to the right customer is simple with analysis of consumer data and a user experience that attracts returning customers.
4. To predict future trends
Future trends and patterns continue to change and adapt to the customer’s wants and needs. Companies use consumer data to predict what consumers will like or dislike. This data points to the product they are most likely to buy in the future and what they are likely to spend money on.
Data security is fundamental to the user. Generally, users won’t submit their information to a site that does not seem trustworthy. Once trust is lost, it is hard to earn it back. So, data security is a significant concern. Only collect as much data as you can keep secure, or you may end up losing more customers than you gain.
Now it’s time to determine if you want to collect data or buy a large database. Let’s see the pros and cons of both.
Data collection is the process of gathering and collecting information from countless sources. It is a major bottleneck in machine learning and deep learning activities. Sometimes, there is a lack of cleaned and properly labeled data. When a large amount of data is required for deep learning systems, collecting your information is the only solution.
But, data collection itself is a tedious task. Data collection mainly consists of data cleaning, data acquisition, and the data labeling process. Only an expert should attempt these processes. However, data collection and data management combined with machine learning and artificial intelligence can provide information that makes the hard work pay off.
- Collecting data can be more accurate as you know the data source.
- Collecting data ensures that you are the first party to use the set of data.
- Collecting data guarantees the availability of adequately cleaned and labeled data.
- Data collection can be time-consuming.
- Data formatting like data cleaning, data labeling, and data acquisition adds to the data collection process’s length.
- Companies will need to hire workers who can use machine learning to analyze it correctly.
Purchasing a Large Database
Purchasing a large dataset is also an option. Collecting data requires expertise and analytical insights. Companies often make common mistakes in data analysis without an extensive background in data science. Also, the internal data collected by companies is often not enough when a large dataset is required.
Aggregating data is a time taking process and needs resources as well. For companies who want access to information quickly without hiring or training employees, purchasing data is an easy way to generate leads.
Clean and labeled datasets may be necessary for specific applications. There are many quality sources for large datasets, training data science models, and machine learning models like DataStock, Kaggle, and KDnuggests.
- Purchasing data provides massive amounts of data points.
- Purchased data can provide new insights that you might not be able to get otherwise.
- Purchasing data saves time and resources.
- Many data privacy regulations may interfere with the data you can purchase.
- The quality of purchased data varies based on the source and price of the data.
- Finding a quality database provider can be difficult.
- Purchasing data can be expensive.
The Final Verdict
If you have time and expertise, collecting data on your own will ensure it is unique and valuable. If you have less time and are not an expert in data collection, buying a dataset from trusted sources may be a better solution.
Remember, the dataset you’re purchasing must suit your requirements and be compatible with your organization’s technology. Both methods are practical and equally utilized, and it’s up to you to determine the best solution for your company. Let smartboost help; contact us today!