- What We Solve
Frustration, and mixed results associated with matching online and offline data is common in the marketing ecosystem. To this day, numerous databases that originate from household level data are still heavily used, creating major headaches for businesses when they need to match user data back to offline data. As mobility and the Internet of Things related expansion continues this issue will only get worse.
Through linkage-identification, digital footprint databases are the keystone to mapping: IP, email, device ID, user agent string, social media credentials, landline phone and mobile phone numbers that tie back to their original full consumer record. This type of data is also proven to drive better onboarding match rates in the first place. We have constructed a near-census, individually compiled data asset, with all channel data needed to map data from offline to online. We have wide ranging experience helping businesses leverage this to attack identify related issues. The list that follows are the most frequent use cases we help address.
An identity graph, or ID graph, is a database that houses all the known identifiers that correlate with individual consumers. At a minimum, an ID graph has one identifier that can be used to track a consumer’s journey but the best ID graph’s will have many personal identifiers associated to an individual. For instance, email addresses (plain text and hashed), first and last name plus a physical address, mobile phone numbers, IP addresses, User agent strings, cookies, device ID’s and social media handles. Different ID graphs offer different types of accuracy, methodologies and content. Individual or Household as well as Deterministic or Probabilistic.
At Audience Acuity, our Super Identity Graph is based 100% on deterministic matching. It uses known customer information, such as a name and address or email addresses, to match and recognize individuals on whatever device they may be using. As the data is known to be accurate the match is made with near 100% certainty. These IDs are continually validated via account log-ins, transactions and email response activity making them more reliable than cookie-based data that works only on the Web and gets deleted. The ID graph collects these identifiers and connects them to an Individual ID, plus any related data points, including demographics, behavioral data or purchase history.
As such, we are able to help clients address the challenge of matching partial data from both the offline and online world together.
Our experience suggests that Audience Acuity can help an end-user client identify 30 – 50% of all the anonymous consumers that engage with their brand. Given the significantly higher conversions that typically result from re-targeting to people that have recently engaged with the brand, this can result in as high as a 15% conversion of these previously anonymous consumers.
We are able to take large data sets of people that visit company websites as a result of the company’s SEM efforts but fail to convert and turn an otherwise lost marketing investment into a multi-channel retargeting audience with substantially higher conversion using our Super Identify Graph and advanced data science routines.
Audience Acuity’s data can be used as a foundational layer for customer support operations as the number or IP of person seeking support can be identified and matched with other data and then used for expedited validation to recognize inbound callers within milliseconds of the call initiation, drawing details from a 240 million record individual database that is nearly a complete census of U.S. adults. Name and address information as well as any additional relevant data points on the caller are delivered instantly into the call center platform which enables the agents to verify the identity of the caller as opposed to capturing data, resulting in faster handle times which improve customer satisfaction while also leading to reduced data entry errors.
Audience Acuity enables clients to identify and separate their target audience from the masses.
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