An identity graph (ID graph) is a single database that collects datapoints from across multiple channels and matches them to an individual customer. Businesses from across many industries from retail to financial services use ID graphs as a platform to provide a clear picture about which products customers are interested in, what their behaviors are, and what their paths to purchase look like.
ID graphs are a powerful tool to build a 360-degree, omnichannel view of the consumer, but they are only as effective as the data they’re powered by. Gaps and inaccuracies in the data set can create inaccurate ID graphs, ultimately leading to wasted marketing spend. Data serves as the bedrock of an ID graph, and if the data is corrupt, the graph will not provide actionable insights.
Within an ever-changing landscape of emerging technologies, marketers are struggling with outdated legacy approaches to data management and identity resolution and many organizations lack the infrastructure, resources, or know-how to resolve data gaps and inconsistencies. This can result in false data matches and scalability issues, both of which plague the integrity of ID graphs. While many companies have a patchwork system in place to combat this, a unified and forward-facing strategy is crucial to success.
In order to combat inaccurate ID graphs and build a wholistic profile of each consumer, marketers must have a solid data management and identity resolution strategy supported by accurate and complete data (without any gaps!). Here are two ways companies can boost the accuracy of ID graphs and improve identity resolution.
Respondents in a recent Forrester study complained that low data quality and inaccurate clusters and linkages were the top two issues keeping ID graphs from being accurate. Companies can partner with reputable data providers to build solid data foundation for the graph. Third-party data partners can help boost identity resolution by:
Probabilistic data – Data that makes inferences based on consumers’ web behaviors and interactions
Deterministic data – Data that confirms an explicit link between identifiers based on information collected from users. For example: an email address that correlates with a tracking cookie or mobile ad ID.
A Forrester study found that 61% of respondents said their organizations are using probabilistic and deterministic data independently of each other, impeding their ability to effectively tie data back to individual customers. Using these two forms of data together can help you identify and track the same user across different devices with reasonable accuracy, but older legacy systems with limited interoperability often prevent marketers from combining these data sets.
Having the ability to track users across devices is an elemental step towards successful identity resolution, so companies must invest in solutions that help them use deterministic and probabilistic data together to supplement and enhance the power of an ID graph. Something to keep in mind – even if deterministic data and probabilistic data are used together successfully, without quality underlying data, the ID graph gap will not be solved.
B2C Link is a specially designed dataset of over 100 million records that blends business and consumer databases to allow marketers to link their audience’s consumer and business data into a single, 360-degree profile.
Using B2C Link, companies can link Bill Smith’s personal and work details, giving themselves the opportunity to reach him with the right contextual message wherever he is – whether at his office or at his couch. Using this information, a B2B marketer or salesperson could identify new ways to connect with Bill (like knowing that he loves sailing and is a Seattle Seahawks fan). Alternatively, a B2C retailer could personalize their apparel recommendations based on their understanding of Bill’s office life.
Cobb EMC, a non-profit electric utility company, needed to tweak a marketing strategy that wasn’t working – they were sending their database all communications they deemed noteworthy, without taking the time to figure out what messaging would actually resonate with different segments of their audience. Using an ID graph to segment their audience, Cobb EMC was able to figure out which types of communications would interest each group. For example, one group had an interest in electronic vehicles. Cobb EMC put together tools, resources and programs that would engage this particular segment. By tailoring their messaging and abandoning a “one-size-fits-all” approach, Cobb EMC increased open rates from 34% to 56.7%, and drove click-thru rates as high as 9%.
Identity graphs are a powerful tool when companies have a strong identity resolution protocol that combines quality data with proper data management and robust technology capabilities. Companies can leverage ID graphs to reach people in any environment throughout the customer journey.
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