This week’s guest blog is contributed by David Kelly, Founder & CEO, Analytics IQ. Kelly is an analytics entrepreneur with strong business acumen. After successfully creating and selling Sigma Analytics in the early 2000s, Dave founded AnalyticsIQ in 2007 and was named the “Analytic Marketer of the Year” in 2012.
Let’s admit it—business is all about making money. And marketing is perhaps the most integral aspect of this money-making mission because it must create the perception of value in the minds of consumers to entice a purchase.
That said, it is easy to understand why many analytical marketers look to household income data for insights since their prospects must be able to afford their offering. But is that really the best information to leverage to achieve the greatest results?
Our team found that predicting consumer annual discretionary spending by product category gives data-driven marketers the granularity needed to target the most responsive audiences with the best offers.
Despite the enhanced observations category discretionary spend provides, many marketers still look to annual income alone for targeting. However, there are two undeniable reasons why predicted discretionary category spend should be considered over income: annual income does not necessarily indicate annual spending and discretionary consumption is limited by nature.
Conventional wisdom says the more someone makes, the more he can afford to spend. While that may make sense when initially determining which consumers should and should not be targeted, this logic only takes marketers so far.
Not convinced? Consider this. The wealthiest 14% of U.S. households are in the lowest 10% when it comes to annual spending, and 16.5% of the least affluent U.S. households make up the top 25% in terms of spending. Furthermore, research suggests approximately 40% of consumers go through income and consumption changes that actually move in opposite directions, completely negating the misconception that higher income equates to higher spending.
If the amount a household or individual chooses to spend is not dependent upon income, how can marketers confidently rely on such information to identify the most profitable customers?
Nevertheless, consumers do use money to purchase discretionary products and services based on personal preferences. Unfortunately, consumers do not have the freedom to spend the entirety of their paychecks on non-essentials like luxury items, vacations, and retirement savings.
In fact, more U.S. consumers increased their spending on necessities like groceries, utilities and healthcare than all other categories considered from Q2 of 2014 to Q2 of 2015. Conversely, the three categories where the fewest U.S. consumers increased their spending over that period were discretionary categories like travel, electronics and retirement investments.
What does this mean for marketers?
Well, it seems that more American consumers are increasing their spending on what they need than those consumers who are increasing their spending on what they don’t, further limiting the amount of discretionary income available for marketers to attract. Household income simply does not provide the type of accuracy needed to successfully hit a target that small, but the granularity achieved by examining category discretionary spend greatly increases the chances of a direct hit.
Unfortunately, the discretionary income target marketers will have to hit is projected to grow. Disposable personal income is expected to increase approximately 15% in the
U.S. by 2020. As spending increases, the opportunity to capitalize increases as well but only with the right actionable insights. Predicting discretionary spending by product category will become even more imperative in 2017 and beyond as spending surges because it will allow marketers to strategically position their offerings and target the most rewarding audiences in specific product categories.
This distinct, definitive data provides type of insight can make all the difference for marketers and their businesses in the pursuit of profit.
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