Customer Data is now the main priority for all types and sizes of businesses. With the proliferation of technologies capturing and analyzing data, there is also an improvement in the abilities of businesses to contextualize data then acquire new insights from this. Artificial intelligence and IoT are two crucial tools for companies to capture and analyze data, from an improved understanding of daily operations, learning more about customers, and making decisions for the business.
Focus on consumer data
Customer data is one focus area on its own. From predictive analytics to consumer behavior, companies regularly capture, analyze, and store huge amounts of data on their consumer base daily. There are several companies that even established an entire business model around the consumer data, whether they sell to third parties or create some targeted ads. Customer data is no doubt a big business.
Here is a quick overview of how companies are capturing data of their customers, what they do with this information, and more.
How Businesses Collect Data
There are different ways that companies can capture data from numerous sources. There are processes that are naturally very technical while some are more deductive even though such methods usually make use of sophisticated software.
However, the bottom line is that companies use a profusion of sources for capturing and processing data of customers on metrics, from behavioral data to demographic data.
There are three ways to collect customer data. First is through asking customers directly, second is through indirect tracking of customers, and third is through appending other customer data sources to your own. A robust and solid business strategy requires all these three.
Data Collecting Methods
Businesses are experts when it comes to pulling data in from almost all nooks and crannies. The most obvious areas are from consumer activities on their social media pages and websites. However, there are several more interesting work methods as well.
A good example of this is location-based advertising that uses the IP address of a device connected to the internet and other devices this interacts with to create a personalized data profile. The information is being used then to target devices of users with relevant and hyper-personalized advertising.
Companies are also digging deep into their own records of customer service to check how customers previously interacted with their support and sales departments. This is where they incorporate direct feedback about the things that worked and those that didn’t and the likes and dislikes of customers on a grander scale.
Aside from data collection, companies can also sell it to or buy it from third party sources. The moment it is captured, the information regularly changes hands in the data marketplace by itself.
Here is a great explaining YouTube video on how companies are gathering and sharing your data:
Data Turned into Knowledge
Capturing huge amounts of data leads to the problem of sorting through and analyzing all the data. No human being can just sit down and read through lines of customer data throughout the day and even if one could, there is a chance that they wouldn’t make that much dent.
Good thing that computers are now much better in doing this kind of work compared to humans, not to mention that they can also operate 24 hours a day, 7 days a week, and 365 days a year without taking breaks.
As machine learning algorithm and some AI forms improve and proliferate, data analytics becomes a more powerful field to break down data into a manageable tidbit of actionable insight. Several AI programs would flag any anomalies or provide recommendations to every decision-maker within organizations based on contextualized data. Without such programs, all data capture in this world will be useless.
Are Companies Protecting Your Data? How Do They Use Your Data?
There are many ways companies use consumer data collected. Some of these include the following:
1. Refining Marketing Strategy
Contextualized data may help companies know how consumers engage with and respond to each of their marketing campaigns and adjust to them accordingly. This predictive use case provides businesses the idea of what consumers would want based on what they have done already. Like some consumer data analysis aspects, marketing becomes more about personalization.
Mapping the journeys of users and personalizing them, not only through your site but other platforms including Facebook, LinkedIn, YouTube, and to some websites is essential. Data segmentation allows you to market effectively not only to the ones you know. These can open up new opportunities in the industry that is previously difficult to market to.
2. Turning Customer Data to Cash Flow
Businesses that capture consumer data stand to profit from this. Companies that sell and buy information on customers or data brokers have become a new industry with big data. For companies that capture a huge amount of data, it represents a chance for new streams of revenues.
For some advertisers, having this available information for purchase is valuable. Therefore, the demand for data is increasing. It only means that more desperate data sources that data brokers may pull from package more thorough data profile, thee more cash they could make through selling this information to advertisers.
3. Improving the Customer Experience
For a lot of businesses, consumer data provides a way to meet and understand the demands of customers. Through analyzing customer behavior and troves of feedback and reviews, companies may modify their digital presence, services or goods to suit the current marketplace better. Not only companies utilize consumer data to boost consumer experience as a whole, but also they use data making decisions on individualized levels.
4. Using Data for Securing Data
Other businesses use consumer data as a way to secure sensitive information. For instance, banking institutions would use voice recognition data sometimes to authorize users to access financial information and protect them from any fraudulent attempt to steal their data.
Such systems work through marrying data from the interaction of customers with call centers as well as machine learning algorithm, which can flag and determine fraudulent attempts for accessing the account of customers. It takes guesswork and human errors out of catching cons.
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