As technologies that capture and analyze data proliferate, so, too, do businesses' abilities to contextualize data and draw new insights from it. Artificial intelligence is a critical tool for data capture, analysis, and collection of information that many businesses are using for a range of purposes, including better understanding day-to-day operations, making more informed business decisions and learning about their customers.
Customer data is a focus area all its own. From consumer behavior to predictive analytics, companies regularly capture, store, and analyze large amounts of quantitative and qualitative data on their consumer base every day. Some companies have built an entire business model around consumer data, whether they're companies selling personal information to a third party or creating targeted ads. Customer data is big business.
Here's a look at some of the ways companies capture consumer data, what exactly they do with that information, and how you can use the same techniques for your own business purposes.
Types of consumer data businesses collect
The consumer data that businesses collect can be broken down into four categories:
- Personal data. This category includes personally identifiable information such as Social Security numbers and gender as well as nonpersonally identifiable information, including your IP address, web browser cookies, and device IDs (which both your laptop and mobile device have).
- Engagement data. This type of data details how consumers interact with a business's website, mobile apps, text messages, social media pages, emails, paid ads and customer service routes.
- Behavioral data. This category includes transactional details such as purchase histories, product usage information (e.g., repeated actions), and qualitative data (e.g., mouse movement information).
- Attitudinal data. This data type encompasses metrics on consumer satisfaction, purchase criteria, product desirability and more.
How do businesses collect your data?
Companies capture data in many ways from many sources. Some collection methods are highly technical in nature, while others are more deductive (although these processes often employ sophisticated software).
The bottom line, though, is that companies are using a cornucopia of collection methods and sources to capture and process customer data on metrics, with interest in types of data ranging from demographic data to behavioral data, said Liam Hanham, data science manager at Workday.
"Customer data can be collected in three ways: by directly asking customers, by indirectly tracking customers, and by appending other sources of customer data to your own," said Hanham. "A robust business strategy needs all three."
Businesses are adept at pulling in all types of data from nearly every nook and cranny. The most obvious places are from consumer activity on their websites, social media pages, through customer phone calls and live chats, but there are some more interesting methods at work as well. [Make sure your company has the best business phone system.]
One example is location-based advertising, which utilizes tracking technologies such as an internet-connected device's IP address (and the other devices it interacts with – your laptop may interact with your mobile device and vice versa) to build a personalized data profile. This information is then used to target users' devices with hyperpersonalized, relevant advertising.
Companies also dig deep into their customer service records to see how customers have interacted with their sales and support departments in the past. Here, they are incorporating direct feedback about what worked and what didn't, what a customer liked and disliked, on a grand scale.
Besides collecting information for business purposes, companies that sell personal information and other data to third-party sources have become commonplace. Once captured, this information is regularly changing hands in a data marketplace of its own.
Turning data into knowledge
Capturing large amounts of data creates the problem of how to sort through and analyze all that data. No human can reasonably sit down and read through line after line of customer data all day long, and even if they could, they probably wouldn't make much of a dent. Computers, however, sift through this data more quickly and efficiently than humans, and they can operate 24/7/365 without taking a break.
As machine learning algorithms and other forms of AI proliferate and improve, data analytics becomes an even more powerful field for breaking down the sea of data into manageable tidbits of actionable insights. Some AI programs will flag anomalies or offer recommendations to decision-makers within an organization based on the contextualized data. Without programs like these, all the data captured in the world would be utterly useless.
How do businesses use your data?
There are several ways companies use the consumer data they collect and the insights they draw from that data.
1. To improve the customer experience
For many companies, consumer data offers a way to better understand and meet their customers' demands. By analyzing customer behavior, as well as vast troves of reviews and feedback, companies can nimbly modify their digital presence, goods, or services to better suit the current marketplace. [Check out our reviews of CRM systems to help you manage your customer data.]
Not only do companies use consumer data to improve consumer experiences as a whole, but they use data to make decisions on an individualized level, said Brandon Chopp, digital manager for iHeartRaves.
"Our most important source of marketing intelligence comes from understanding customer data and using it to improve our website functionality," Chopp said. "Our team has improved the customer experience by creating customized promotions and special offers based on customer data. Since each customer is going to have their own individual preferences, personalization is key."
2. To refine a company's marketing strategy
Contextualized data can help companies understand how consumers are engaging with and responding to their marketing campaigns, and adjust accordingly. This highly predictive use case gives businesses an idea of what consumers want based on what they have already done. Like other aspects of consumer data analysis, marketing is becoming more about personalization, said Brett Downes, SEO manager at Ghost Marketing.
"Mapping users' journeys and personalizing their journey, not just through your website but further onto platforms like YouTube, LinkedIn, Facebook, or on to any other website, is now essential," Downes said. "Segmenting data effectively allows you to market to only the people you know are most likely to engage. These have opened up new opportunities in industries previously very hard to market to."
3. To transform the data into cash flow
Companies that capture data stand to profit from it. Data brokers, or data service providers that buy and sell information on customers, have risen as a new industry alongside big data. For businesses that capture large amounts of data, collecting information and then selling it represent opportunities for new revenue streams.
For advertisers, having this information available for purchase is immensely valuable, so the demand for more and more data is ever increasing. That means the more disparate data sources data brokers can pull from to package more thorough data profiles, the more money they can make by selling this information to one another and advertisers.
4. To secure more data
Some businesses even use consumer data as a means of securing more sensitive information. For example, banking institutions sometimes use voice recognition data to authorize a user to access their financial information or protect them for fraudulent attempts to steal their information.
These systems work by marrying data from a customer's interaction with a call center, machine learning algorithms, and tracking technologies that can identify and flag potentially fraudulent attempts to access a customer's account. This takes some of the guesswork and human error out of catching a con.
As data capture and analytics technologies become more sophisticated, companies will find new and more effective ways to collect and contextualize data on everything, including consumers. For businesses, doing so is essential to remain competitive well into the future; failing to do so, on the other hand, is like running a race with your legs tied together. Insight is king, and insight in the modern business environment is gleaned from contextualized data.
Data privacy regulations
So much consumer data has been captured and analyzed that governments are crafting strict data and consumer privacy regulations designed to give individuals a modicum of control over how their data is used. The European Union's General Data Protection Requirements (GDPR) lays out the rules of data capture, storage, usage, and sharing for companies, and GDPR regulation and compliance doesn't just matter for European countries – it's a law applicable to any business that targets or collects the personal data of EU citizens.
Data privacy has made it to the U.S. in the form of the California Consumer Privacy Act (CCPA). The CCPA is, in some ways, similar to GDPR regulation but differs in that it requires consumers to opt out of data collection rather than putting the onus on service providers. It also names the state as the entity to develop applicable data law rather than a company's internal decision-makers.
Data privacy regulations are changing the way businesses capture, store, share and analyze consumer data. Businesses that are so far untouched by data privacy regulations can expect to have a greater legal obligation to protect consumers' data as more consumers demand privacy rights. Data collection by private companies, though, is unlikely to go away; it will merely change in form as businesses adapt to new laws and regulations.
Adam Uzialko also contributed to the reporting and writing in this article. Some source interviews were conducted for a previous version of this article.