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As a marketer, you are always on the lookout for ways to gain insights into consumer behavior to tailor your marketing strategies. Understanding consumer spending patterns is one such way that can give you a competitive edge in targeting your audience more effectively. In this blog, we will dive deep into predictive consumer spend data and how it can be leveraged to drive business growth. We will discuss the importance of discretionary income, nuances of consumer behavior, and how marketers can use predictive datasets to better understand their customers. 

Additionally, we will look at real-world examples of how industries like luxury apparel and home furnishings have benefited from leveraging consumer spend data in their marketing campaigns. Lastly, we will outline data solutions available from AnalyticsIQ that make use cases grounded in spend behavior a reality. So, let’s get started and unlock the treasure trove of insights that consumer spend data offers!

Understanding Consumer Spending: A Marketer’s Perspective

Analyzing consumer spending patterns is crucial for tailoring effective marketing strategies. Understanding behaviors, preferences, and decision-making processes helps target marketing efforts and gain insights into habits. Leveraging predictive consumer spend data is essential to optimize strategies and unlock potential for successful campaigns. By utilizing data ranging from high-level household income to discretionary income to specific categories where consumers are likely to spend more or less than the general population, marketers gain valuable insights into what products a consumer may consider purchasing. This approach ensures that marketing strategies are targeted efficiently, leading to more impactful and successful campaigns.

The Role of Discretionary Income

Understanding the portion of consumer spending behavior which is discretionary is crucial for marketers. Economic conditions and discretionary income directly impact consumer spending choices, making it essential to understand which categories are important for consumers. As an example, two households could have a similar income, but one chooses to splurge on apparel while the other spends their discretionary income on needed home furnishings. Furniture brands may see the two consumers as similar unless they have that insight into their discretionary spend choices. By gaining insights into these preferences, marketers can tailor their strategies effectively to optimize marketing campaigns and target specific consumer groups.

The Role of Economic Data

High quality consumer spend data assets must also be underpinned by economic data. This is because shifting economic trends, even at a hyper local level impact how consumers approach their discretionary spend choices. As an example, while a consumer may typically spend a great deal on personal care purchases, if their local economy is going through challenges they may choose to buy discount brands or jump at a value-based offer. 


The Impact of Predictive Consumer Spend Data in Marketing Strategies

Understanding the buying habits of customers allows businesses to personalize marketing messages, promotions, and offers for specific customer segments. Predictive consumer spend data provides insights into popular product categories among different demographics and helps predict future purchases and trends. Additionally, it identifies areas for product or service improvement, leading to better customer satisfaction By analyzing spending patterns, businesses can make informed decisions and continuously optimize their marketing strategies.

Leveraging Consumer Spend Data for Targeted Marketing

Incorporating consumer spend data allows marketers to create highly targeted audiences that fuel targeted campaigns. This is beneficial for the marketers who will see improved metrics for their campaigns. Media waste is reduced, and formerly missed opportunities with hot prospects that were outside of the original targets now turn into conversions. Essentially, this new set of data unlocks the key to include everyone that is truly a hot prospect and exclude those who are unlikely to convert. This is true not only for true prospect acquisition, but customer retention, and upsell as well. 

Leveraging Consumer Spend Data for Personalized Marketing

Applying consumer spend data to define a high performing target is just the first step. Understanding consumer spending habits allows marketers to personalize their messaging and offers. By tailoring promotions and recommendations based on past purchases, businesses can create a more personalized and engaging experience for customers.  This tactic has been primarily applied to retention campaigns, and limited to prospects that exist within an advertiser’s first-party data. But supplementing a dataset with forward-looking discretionary spend purchase categories, marketers can personalize high funnel and more scalable strategies. 

Leveraging Consumer Spend Data for Predictive Analytics

Consumer spend data serves as a goldmine for predictive analytics. Marketers can leverage historical purchase data while also examining likely future spend using predictive data to forecast future trends, allowing them to stay ahead of the curve. The end result could be actionable insights that help guide the business forward from an executive level.  This foresight enables businesses to adjust their marketing strategies, product offerings, and pricing to align with anticipated consumer behavior. The output could also be powerful personas used by marketing to make planning decisions. And further still, sophisticated predictive models could be fueled by consumer spend data to score leads and prospects for triggered marketing. Essentially, predictive analytics using consumer spend data are not locked in a tower, only accessible by data scientists. They are applicable to every part of a growth focused organization.

Leveraging Consumer Spend Data for Competitive Intelligence

Understanding how a consumer is likely to spend their discretionary income is typically achieved by analyzing the buying executed directly with a brand. Essentially, if the data exists within the first-party dataset, it’s analyzed, but otherwise the consumer spend choices are a mystery. By taking a broader view and analyzing likely future discretionary consumer spending data by category, you can tell if your brand is over or under indexing. Essentially, does the consumer match your target parameters, AND have high spend in the category, but has shown low or no spend with you? By identifying large volumes of consumers and grouping them you can uncover product or offer gaps that may keep them from purchasing with your brand.

Leveraging Consumer Spend Data for Optimized Media Spend

Once models and targets have been created, marketers can use the output to influence their media buying strategy. This could look like evaluating different content sources to best align with the target. But for more advanced optimization, marketers could adjust their bidding strategy using new insights. 

Here’s an example – an impression is available to a programmatic campaign for a luxury apparel brand. Based on the existing data (age, gender, auto brand, homeowner, household income), this user is a high value target, so a high bid is submitted, and the ad impression is won. However, based on predictive consumer spend data, a marketer could have learned that while the user has the ability to buy, they under index on discretionary spend in apparel. Essentially, they overpaid for an ad impression that they are unlikely to convert.

On the other side – an impression may become available for a user that doesn’t appear to be a high value target based on current data. As a result, no bid is made. If the buyer had access to predictive consumer spend data, they would’ve seen that this user over indexes for discretionary spend on apparel annually and would’ve been an ideal target. 

Which industries can benefit from predictive consumer spend data?

Marketers and product leaders across industries can benefit from using predictive consumer spend data in their existing processes. This is especially true for any discretionary purchases, and also regular and repeated purchases such as consumables or apparel. Here are a few companies that apply consumer spend data consistently:

  • Consumer packaged goods (CPG): Categories including everything from alcohol to personal care brands. 
  • Apparel: Can be categorized by gender or multiple members of the family, such as children apparel.
  • Technology: Mobile devices as well as online services like streaming services, ride share programs, or smart home devices.
  • Home focused categories: Everything from housewares to home furnishings. 
  • Retail: Retailers can apply purchase behaviors across categories but can also understand which competitive types see consumer spend (EX: organic grocers vs. discount stores).
  • Entertainment: This includes concerts, sports events, etc.
  • Dining out: This includes both Quick serve restaurants (QSR) or high-end dining.
  • Supplemental Insurance: Beyond traditional insurance offered by employers, many consumers opt to add life insurance, pet insurance and more.
  • Self-improvement: These categories would include online education, reading purchases and more.
  • Travel: Everything from domestic to international, cruises, etc.

InMarketIQ: A Revolutionary Solution for Marketers

Unlock revolutionary marketing potential with InMarketIQ’s innovative capabilities for in-depth consumer spending analysis. Gain comprehensive insights into spending patterns, optimize strategies, and achieve precise targeting for personalized marketing. Leverage the power of this data to understand consumer behavior and make informed marketing decisions. 

What is InMarketIQ?

InMarketIQ is AnalyticsIQ’s proprietary method of identifying key buying attributes about the consumer population. This dataset covers a wide variety of industries and buy choices. It is underpinned by our unique approach to data creation which is grounded in cognitive psychology and data science. Although consumer survey research is one aspect of our innovative data creation methodology, our data asset is also fed by hundreds of validated and quality data sources.


Making Adjustments based on Census and Bureau of Labor Statistics Data

Two key sources used to validate our InMarketIQ product suite are Census data and Bureau of Labor Statistics (BLS) data. However, the 2022 Census data included periods of dramatic change, driven by behaviors during the COVID-19 global pandemic. For example: consumers spent less on dining out and more on streaming. As a result, the data products needed to be optimized in a way to account for the short-term changes. 

Our new and improved Spendex variables, which are part of the InMarketIQ product, are refreshed and highly predictive, despite tumultuous consumer spend behavior in recent history. That is standard operating procedure for all of our data products: continuous examination, improvement, and validation to ensure the data coming from one source doesn’t skew the data in a way to reduce reliability. 

Reach out to craft your consumer spend strategy

Predictive consumer spending data is a goldmine for marketers. It provides valuable insights into future consumer behavior and preferences, helping businesses tailor their marketing strategies for maximum impact. By leveraging discretionary spending behaviors, predicting trends, and understanding customer choices, businesses can optimize their marketing budgets and achieve improved metrics.

Real-world examples have shown the power of predictive consumer spend data in industries like luxury apparel and home furnishings, where targeted marketing based on future spending patterns has yielded significant results.

For marketers looking to harness the potential of consumer spend data, Spendex offers a revolutionary solution. With reliable, comprehensive, and up-to-date data now available, marketers can make informed decisions and refine their strategies. Reach out now to hone your consumer spend strategy and take your marketing efforts to new heights.