How To Incorporate Financial Data For Better Ad Targeting
The rise of data-driven marketing has brands increasingly looking for insights that allow them to better target their most likely customers and eliminate any potential media waste. But with so many resources at their fingertips, marketers are still learning how best to combine different kinds of data assets. In fact, they’re often either not using enough data, or basing too much of their plan on sources that only tell part of the target audience’s story.
Marketers looking for a data source that enhances the power of their in-house data and helps better predict consumer behavior should look at estimated consumer financial capacities, such as income, spending, and discretionary spending. Beyond the use of survey-based income data, this area has been relatively untapped by marketers. However, these valuable insights can help marketers to build an audience and then focus on the portion likely to be most interested in your offers.
Entry point behavioral data
Past purchases and survey data are often used as primary marketing data sources, but neither source is typically strong enough on its own to predict a consumer’s behavior and their qualifications.
When it comes to transactional data, it is often the case that a consumer who previously purchased a product or service may buy that product or service (or a similar, adjacent one) again. Of course, this data is confined to existing customers, so while it works well for predicting repeat purchases, it doesn’t help predict prospects’ likelihood to spend.
This is where marketers supplement with third-party data sets, which are often survey-based, presenting their own issues. Surveys cover a small set of the population, and it’s hard to accurately project that information onto the entire U.S. population. It’s harder still to determine how consumers’ wants and desires align with their actual future purchase behavior.
For example, many respondents can list Audi as the next type of car that they’d like to buy, but that certainly doesn’t mean that they will buy an Audi, or that they’re economically able to make such a purchase. Many survey takers commonly say they enjoy international travel, but that sentiment is widely shared. The fact is not everyone can afford plane tickets and hotel rooms.
Survey and purchase data are valuable as starting points – they show emotional desires and past behaviors. But taken alone, they often fail to define a qualified audience. This is where financial capacity data helps.
Factoring in financials
As we outlined above with respect to auto and travel, financial capacity data can play a major role in predicting whether consumers are likely to buy and what they are likely to buy. This helps marketers focus on a narrow, qualified audience. When supplemented with the other data sources we’ve discussed, marketers now know about past purchases, behavior (such as favorite TV shows or activities, derived from surveys) as well as the crucial component of what they can likely afford.
This identifies a very specific audience segment that may be a better match, and it even helps marketers craft the message. Let’s look at the restaurant industry as an example. Consumers are still recovering from the recession, and as a result are going to fast-casual restaurants when they previously would have spent a little more money at fine-casual eateries. Armed with past purchase behavior plus discretionary spending data, a fine-casual chain could tailor its message to win back this budget-conscious audience via coupons, bigger portions, or promotions.
The high and low ends – both matter
Lots of marketers use income, but many don’t take advantage of estimated discretionary spending data, or other measures that take account of the fuller economic behaviors of a household. One reason is that many don’t know this is even an option. Others feel their products aren’t expensive enough to justify using economics measures. The truth is that every business segment has determining economic factors. Auto brands are obvious beneficiaries of financial data, but the value extends right down to smaller purchases – lawn products, home electronics, even groceries.
Marketers can also use this data to identify customers on the lower end. For instance, a utility company could use financial data to identify an audience whose estimated discretionary spending is less than $20,000 a year, then build a campaign around this audience promoting features that save the consumer $10 or $20 per month. In this instance, a wealthier consumer may feel the extra work isn’t worth their time, but the marketer can better serve a savings-conscious segment with these features.
When it comes to assessing consumer behavior, it’s clear that economic data is an important qualifier. Leveraging this insight helps marketers identify the right people for campaigns, match customers with appropriate offers, and helps them message appropriately. A past history of buying a product or service is a great signal, but if the consumer does not currently have the financial capacity to purchase that same product or service today, then they will not make that purchase. Insights derived from single data sources will often miss the mark, but by factoring financial data with other signals, marketers are more likely to find the best audience.
Previously published in MediaPost’s Engage: Affluent Blog.
Since 1996, Mediapost.com has been the largest and most influential media, marketing and advertising site on the net, providing news, blogs & directories to help a community of more than 100,000 members better plan and buy both traditional and online advertising.
Recommended For You
I grew up watching various science fiction drama TV shows and recall one particular character that could shape-shift. He would […]
Mobile Usage is Changing Member Experience Did you know 88% of U.S. online adults now use a smartphone?[i] Living in an […]
The U.S. Department of Agriculture (USDA) recently updated its policy on how states can leverage modern technologies when administering the […]
In today’s business environment, auto lenders need data to get ahead. But not just any old, raw data – it […]