When building business forecasts and predictive models, it is important to understand everything you can about your customer. Incorporating customer demographics into these efforts will further enhance the predictability of your models. These data can include a wide variety of demographic indicators ranging from population counts, household income, and even behavioral data indicators like home ownership vs. rental. Demographic data can be found most recently in the 2020 Census.
Now is one of the most beneficial times in history to take advantage of this. Detailed results from the 2020 Census are scheduled to be released in August 2021. Combining this updated view of the population distribution with data aggregation platforms to standardize and normalize data, in coordination with artificial intelligence and accessible machine learning infrastructure, allows you to derive insight that can impact your business and marketing strategy. By utilizing these control data, our client’s predictive models have shown over a 50% reduction in model error, greatly improving the overall predictive power.
How Can Demographic and Census Data Benefit Predictive Models?
Testing for and understanding control factors—drivers outside the control of the business itself, whether it be economic, weather or any demographic component—will always improve predictability of models. Further segmenting and normalizing these control factors across population distribution will continue to enhance your ability to predict future behaviors based on past observations. Utilizing the census data can provide you the opportunity to evaluate these impacts by state, county, and even zip code depending on your objective.
Demographic Data and Marketing Benefits
Demographic and census data can be utilized in a variety of different ways. These data can be leveraged on the front-end of strategic marketing and business planning, and on the back-end as an input into predictive analytics and machine learning models to evaluate performance.
Marketing teams and organizations can take advantage of understanding population, age, gender, race, and even things like median household income down to zip code granularity to inform geographic regions to target their budget.
Similarly, micro-targeting and personalization have been the core of programmatic media. However, individual and cookie-based targeting is experiencing a sea change of regulations, greatly impacting the ability for personalization and precision. There are several solutions being proposed as a substitute, but this also means traditional targeting methods have regained their importance. The 2020 census data provides updated granular measures to give media planners additional understanding of their audience demographics distribution as well as creates an opportunity to define TRPs for digital media in a way that has not been done before and will be needed in a more digitally anonymous world in order to maintain and improve upon long term marketing ROI. Being able to identify areas of the country or state which have a rich population of your consumer’s age and disposable income gives you a great place to start to build your targeting strategy.
Working with the Census Data
As with anything in life, it is all relative. Census data are counts, whether it be people, households, families, housing, etc. Censuses, like the Census 2020, serve as intricate snapshots in time, freezing a momentary cross-section of society’s demographics, socioeconomic dynamics, and cultural composition, illuminating the intricate tapestry of that specific year. The absolute number is less important—and can often be misleading—without a basis of comparison. It is imperative to normalize these data to draw correct and consistent insight.
ReadySignal has built this process within the platform, allowing you to define custom populations quickly and easily (gender, age, race, etc.) at a state or zip code level, making it the easiest platform to work with Census data.
For example, if you are a service provider for the senior market in Texas, you can easily query these data to understand which zip codes have the highest distribution of your audience. You can look at both counts to maximize your target reach, as well as identify and prioritize those zip codes with the highest concentration of your audience, which will help eliminate waste when utilizing mass media like television or radio.
These census data can also be used to transform other data sets in your econometric modeling. For example, you may have both national and regional campaigns, and are analyzing or building a model to understand the impact of your marketing for the Pennsylvania DMAs. Utilizing the census data will allow you to transform your national campaign impressions to the regional exposure equivalents to input into your analysis and models, at whatever granularity you need.
The ReadySignal platform has all these data, as well as custom feature applications available to use with a click of a button, saving you time and effort.
What Can be Learned by Using These Demographic Data
There are countless applications of these data in different predictive analytics exercises. One client we work with is a regional retailer who was looking to expand retail locations. Utilizing the census data in coordination with other demographic and economic factors, propensity models were built to analyze thousands of different zip codes to identify areas with the largest potential for growth to facilitate their business and expansion planning. This analysis narrowed down the opportunity to a handful of zip codes to focus the strategic planning efforts, creating data driven rationale for the proposed locations and greatly improving the efficiency and timing of the decision-making process.
Another example is understanding the effectiveness of a promotion. The return and response on a promotion in any given region may differ greatly. It is important to include and understand the context of the target audience within any region, to answer the question of whether or not the promotion was truly effective, or if it was a function of the demographics of that region (age, income, etc.). Including these factors into the analysis will help you avoid arriving at conclusions which are a result of a false positive or negative.
Including demographic data and taking advantage of the most up-to-date census information will both improve your predictive analytics and machine learning applications as well as provide additional context and targeting input to inform and improve your media plans.
