Customers provide you with information about themselves whenever they interact with your brand, but there are three common challenges to deal with:
First, the information provided is sparse or inaccurate. Customers transact with your business and they forget to present their loyalty card, have multiple loyalty cards, or simply pay with their credit card without providing additional information about themselves. So, how do you relate these sparse transactional data points to individual customers?
Second, the Information provided is trapped in different data silos. Different customer touchpoints are fronted by different IT systems and are also typically provided by different vendors. When customers interact with a brand at its physical locations, website, mobile app, customer care channels, etc., each system captures and stores data separately. These different technologies all have different customer data models and have no common keys that connect the disparate data. So, how do you bring all this data together cost-effectively and quickly relate the data to individual customers?
Thirdly, the information provided is inconsistent across different customer channels. Customers interact across a brand’s channels and often provide inconsistent information. Whether they make a mistake with the data entry, their lives change (e.g. getting married, relocating, etc.), or they want to use different contact information for the different channels they interact in — all these behaviors make it challenging to create an accurate customer profile. So, how do you make sense of this inconsistent information?
The answer to all three questions lies in using a first-party customer data identity resolution solution that is able to cost-effectively bring together all the disparate sources of customer data and leave no stone unturned in extracting the available signals, no matter how sparse they may be, from ALL the data, and then intelligently resolving customer identities within that data.