Learning more about your customers and achieving a single customer view is a seemingly elusive goal for many companies. With the right tools, however, you can append your existing information with additional data to get a more holistic view of your customers. Prospect IQ (PIQ) is one of Experian’s data enrichment solutions that enables you to do so. PIQ is used most often by companies to enhance their marketing through greater customer insight and personalization. Whether that means better approaches to getting repeat customers, new avenues for attracting new leads, or better targeting existing leads, enriched data provides additional information that helps you approach your goal more strategically. Beyond marketing, other customers use PIQ to better understand their customers’ wants and needs, to expand product lines, and to model the likelihood of a sale.
In order to provide such robust use cases, PIQ has over 1700 attributes available, like whether a customer is a coffee drinker, how many vehicles are in their household, and so on. With such a large selection available, it is vital in regard to both time and cost that you choose the right attributes for the job. To do this, let’s first take a look at how attributes differ from each other (besides the obvious). Each attribute contains information on a match level defined as either individual, household, or area. This match level is indicative of whether the attribute shows information on the person, information on the living unit of the person, or aggregated information on the area where the person lives.
Additionally, different attributes will have different levels of granularity. There may be two attributes that contain the same general information but one is a yes/no and the other is a 1-99 propensity scoring. Further, some attributes are confirmed information, while others are modeled or a mixture of both. It’s important to take note of the match-level of the attribute in addition to the granularity. Some attributes are definitive, while others are less defined, modeled estimates.
Let's say, for example, that the goal of a project is to better connect with existing customers. In that case, individual and some household level attributes would be far preferred over area/zip level. With these attributes, it will likely be best to work with definitive, confirmed info or a mixture of modeled and confirmed. On the other hand, if the project goal is to broaden the customer base in an unengaged area, area/zip level attributes would likely resonate with more people as they are often the averages of the area. With this goal, it’s likely better to aim at modeled and mixture of modeled and confirmed, because models take many things--including area average--into account.
Let’s start the dive into some of the individual attributes that tend to be useful for most circumstances. Attributes like “Median Age”, “Household Income”, “Gender”, and “Mosaic Global Household” can be applied to most use cases as a way to broadly stratify records. The Experian “Mosaic” attributes, in particular, are some of Experian’s premier attributes that are used to group people across the US based on demographic characteristics, lifestyles, and behavior. For better insight into individuals, more specific attributes should be applied. Attributes like “Education” or “Education Model”, “Occupation” or “Occupation Model”, “Homeowner/Renter”, Experian’s various “Truetouch” attributes, and “Mosaic Household” are potentials for better insight into individuals.
For very specific attributes, Experian offers information as granular as the sports a person is interested in, the type of music an individual listens to, whether the person is a new parent, or even how much money the household is estimated to spend on alcohol in a year. With such specificity, you can really get a sense of who your customers are and start to build out a complete record to inform your strategy of how best to communicate with that individual, and what messages will resonate with them most. With your enriched data, you can make smart decisions based on a deep understanding of your customer base.
Have you been looking to learn more about your customers? Our data enrichment solutions can help you gain the insight you are looking for.
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