Having the ability to identify your customers accurately is crucial to be able to target them effectively. Identity resolution is a process that links your customers’ behaviors and transactions across different touchpoints. It also helps you improve the quality of your customer data and ensure that you reach the right audience at the right time. An Identity Resolution strategy is essential for any digital ad targeting campaign. It ensures that your ads are shown to those most likely interested.
Customer lifecycle risk management
Identity resolution allows organizations to improve customer experience by providing accurate, high-quality reporting and suspicious activity detection. This approach also increases the effectiveness of financial crime and compliance programs. It provides a unified customer view by building relationships across multiple data sets. Identity resolution is essential for customer lifecycle risk management because customers interact across multiple channels. They are generating new data and experimenting with new vendors and media. This creates a constant stream of further data. Identity resolution connects people with the right people at the right time, enabling personalized experiences. When customer information is accurately matched, brands can more effectively deliver personalized experiences and increase customer retention. It also helps organizations improve database management. Identifying people is essential for any business. However, companies have different needs and approaches. For example, large retailers with frequent touchpoints may have more first-party data than infrequent touchpoint businesses. They may also have a more significant number of identity resolution solutions to choose from. In addition to connecting to customers, identity resolution can also be used for ad targeting and customer service. It can also enhance internal data with external data. For example, it can be used to follow customers across the enterprise. In addition, it can also connect with social media platforms, ad networks, and cookies in a data management platform. It also helps marketers reach the right people in real-time. While identity resolution is essential, it is not the silver bullet that will solve all customer experiences. Creating a unified customer view is a challenging task. Implementing identity resolution solutions that are privacy-compliant and manage large data sets is essential. Companies not tightly couple identity resolution with event collection risk breaking privacy laws. They risk losing customers to their competitors. And they may need to take advantage of an opportunity to improve customer experiences. This can result in a lifetime of lost dollars.
Using probabilistic matching to identify a customer is a great way to enhance the personalization. It can also help increase reach. However, there is a risk of false positives and inaccuracies. The key is identifying and understanding your use cases before deploying probabilistic matching. One of the marketers’ biggest challenges is the fragmented device-based customer data footprint. This creates challenges for ad tech systems to match identities. Probabilistic matching can be valuable if your organization needs access to deterministic data. Probabilistic algorithms can find matches at scale. This can also increase your reach, especially if you’re trying to target anonymous users. For example, probabilistic matching can be used to identify duplicate profiles. It can also be used to enhance omnichannel marketing efforts. When combining probabilistic and deterministic algorithms, marketers can make more accurate matches. As a result, they can also improve their customer’s experience. However, probabilistic methods need to provide more accuracy to identify a single person. Probabilistic matching tends to be more effective at targeting segments of an audience. Consequently, it’s helpful when you need to create industry-specific content. However, it’s only sometimes appropriate for one-to-one journey mapping or anonymous personalization. Deterministic methods are more accurate and can rule out large volumes of data. However, they can also be challenging to deploy due to customer touchpoints. This means that deterministic approaches can break down when the data needs to be cleaned. Some companies use deterministic approaches to match consumers with proxies in a privacy-conscious way. Other companies use probabilistic algorithms to find matches. This can increase your match rate but also introduce more false positives. The bottom line is that there is a tradeoff between reach and precision. For higher target campaigns, it’s better to sacrifice a little accuracy.
Improvements in customer data quality
Having high-quality data is essential to successful data-driven business strategies. In addition, good data quality leads to digital transformations and will help companies develop new data-centric products and services. When working on improving customer data quality, it’s essential to define the scope and goals of your data quality program. This will allow you to understand the impact of data quality on your business and develop an appropriate plan. If you’re looking for ways to improve customer data quality, consider implementing a Customer Data Platform (CDP) or other data management solution. A CDP can help you optimize data quality, increase conversion rates, and target the right customers. CDP can also help you build customer loyalty and segment your audience accurately.