Garnering customer data is the single most important key to your marketing strategy. Without data, your customers’ understanding suffers and so does your ability to deliver value.
Many companies aren’t aware of the impact of data collection methods on their business. Improper data collection (that encompasses email sign-ups, feedback forms, purchase orders, and more) can impact insight extraction and ultimately decision-making.
If data collection isn’t your priority, it’s time to switch gears. Why base your business efforts on what you think customers want when you could give them exactly what they want?
Data collection empowers businesses learn about their audience and identify ways to target them. The collected customer data feeds must be properly analyzed to comprehend demographics better – know what their target audience is interested in, what they need, what are their top concerns, and more. Faulty data collection methods can disturb the entire process, compromising decision-making and making organizations difficult to do business with.
A lot of problems arise because businesses find it hard to gain details about a single customer. It is a good practice to identify customers as a group and then use the information accordingly. Meaning, segmenting your customers into groups according to their needs can simplify the data collection process. The data gathered can be utilized, buyer's personas can be easily understood, and better experiences can be delivered. One method to do this effectively is data mapping.
Data mapping is a process of organizing various bits of data and converting them into a manageable and easy-to-comprehend system. Primarily, data mapping is a mechanism that helps users match source data fields with target data fields during storage. In simple terms, not all data goes by the same standards. For example, common data such as a phone number can have different formats as you can think of. A data mapping tool can go through these phone numbers, recognize them for what they are, and place them all in the same destination field rather than having them drift by other names.
With a data mapping strategy in place, you can organize data better, in turn, streamline data collection. You can tap into target audience’ behavior, identify commonalities and differences, and even decipher contradictions that you must be wary of. With access to this information, your business can meet customer needs faster to gain a competitive edge.
While regular data mapping proves beneficial for your business growth, it has several limitations too. At times, it can jeopardize the integrity of results. Artificial learning techniques can improve the quality of data mapping by leaps and bounds. Find out how artificial intelligence can improve data mapping to deliver better outcomes.
Artificial intelligence uses machine learning algorithms to deliver predictions from the existing library of tested and validated data maps that dramatically reduces the effort and time required to create intelligent data mappings. In the earlier example, artificial intelligence/ machine learning can be employed to recognize phone numbers and assign them to its relevant category to carry out various operations. Plus, errors such as duplicities, missing values, etc. can be easily recognized.
So, artificial intelligence-powered data mapping not only accurately maps data from myriad data sources to the target fields, but also maintains data integrity to improve decision-making and completely change the way you do business with your customers or partners. Not to mention, a lot of time is saved in the data cleaning and integration processes due to reduced manual intervention.
The cleansed data can then be displayed in a form an organization needs to see it. Suppose, geospatial data is one route machine learning can automatically pursue and deliver. Geospatial data is information gathered by translating data into a map and positioning it on the respective routes and locations that your end-consumer takes each day. AI-powered data mapping can enable your business to do this in a better and faster way.
Simply put, artificial intelligence infusion in data mapping can actually improve its quality, making it more precise and accurate. It uses machine learning algorithms to map data faster to speed up the data transformation step, allowing even non-technical users employ the information to create data integrations with a few clicks.
Data mapping can not only simplify data collection but also retain data integrity – enabling users to make better decisions without compromise.