Are Generational Differences Affecting Frame Upgrades?

by | Aug 4, 2023 | Pending

Background:

Understanding the purchasing behavior of different generations when it comes to eyewear frames is crucial for eyewear providers to tailor their offerings and marketing strategies effectively. Various factors, including income levels, spending power, price sensitivity, and managed care coverage, can significantly impact the willingness of individuals to upgrade to higher-priced frames. These factors tend to vary across age groups, leading to generational differences in consumer preferences and behavior.

By delving into these generational differences, eyewear providers can gain valuable insights into the preferences and needs of different age groups. This knowledge empowers them to offer a diverse range of products that suit varying budgets and style preferences, ensuring that they cater to a broad customer base.

Importance of Understanding Generational Differences:

  1. Optimized Frame Pricing: Analyzing generational differences in frame purchasing behavior allows providers to determine the price points that resonate best with each age group. By aligning their pricing strategies accordingly, eye care practices can attract more customers and boost sales.
  2. Adjusted Inventory Assortment: Armed with knowledge about the frames preferred by different generations, eyewear providers can curate their inventory more effectively. Offering a well-balanced selection of frames that appeal to various age groups can lead to higher customer satisfaction and increased sales.
  3. Tailored Marketing and Promotions: Targeted marketing campaigns are far more effective than a one-size-fits-all approach. Understanding the inclination to upgrade frames by generation enables providers to segment and target their market with precision. By crafting promotions, discounts, and rewards that resonate with each generation’s preferences, providers can drive customer loyalty and brand engagement.1
  4. Enhanced Frame Selection Experience: When assisting customers in selecting frames, understanding generational preferences can significantly improve the frame-pitching process. By offering frames that align with each age group’s style preferences and price expectations, eyewear providers can create a more personalized and satisfactory shopping experience.

Methods:

To gain insights into the likelihood of upgrading frame purchases across generations, we conducted a comprehensive analysis of eyewear sales data. The dataset consisted of aggregated GPN data on over 4 million frames sold during 2022 across more than 2,200 eye care professional offices throughout the United States.

Generational Frame Purchasing Behavior:

To understand how different generations approach frame purchases, we analyzed the median frame price, patient out-of-pocket amount, and 3rd party payment for frames within each generational category.

Results:

In this sample, the median frame price was $207, with patients paying a median out-of-pocket amount of $100 and a median 3rd party contribution of $70. As median is equivalent to the 50th percentile, it is the benchmark that divides the distribution into two equal groups, half the results above the median benchmark and half below that amount. The results are shown in 3 pie charts, each asking if the median benchmark was exceeded, “yes or no”. Without any generation selected in the top slicer panel, the results are split evenly or nearly so. Choose a generation and see the pie chart shares update to reflect the share of the selected generation that buy above or below the median value for each metric.

The results can be summarized in the following table illustrating the share of frame sales by metric and generation that are above the median benchmark.

Discussion:

1. Gen Alpha (Born 2013 – 2023; Age 10 and Under):

  • Least Likely to Upgrade: Only 10% of Gen Alpha individuals buy frames above the median list price.
  • Moderate Upgrade in Out-of-Pocket Spend: Approximately 34% of Gen Alpha paid more than the median out-of-pocket amount for frames.
  • Moderate 3rd Party Contributions: Around 27% of Gen Alpha had 3rd party payments exceeding the median amount.

2. Gen Z (Born 1995 – 2012; Age 11 – 28):

  • More Likely to Stay Below Median: Gen Z is also more inclined to spend less than the median for all three frame price metrics.
  • Only 40% of Gen Z members spent more than the median value across all three metrics.

3. Millennials (Born 1980 – 1994; Age 29 – 43):

  • List and 3rd Party Upgrade: Millennials, being the youngest generation to spend above the median value, have 57% buying frames above the median list price and 54% contributing above the median amount via 3rd party payments.
  • Slightly Below Median Out-of-Pocket Spend: However, millennials under-index the median for out-of-pocket spend, with 48% spending more than the median amount.

4. Gen X (Born 1965 – 1979; Age 44 – 58):

  • Similar to Millennials: Gen X individuals are also more likely to buy frames with a list price exceeding the median (59%).
  • Comparable Out-of-Pocket Contributions: They are on par with 50% exceeding the median out-of-pocket amount.
  • Higher Share of 3rd Party Contributions: Gen X has a higher share of 3rd party payments above the median amount (56%).

5. Boomers (Born 1946 – 1964; Age 59 – 77):

  • Moderate List Price Upgrade: Boomers are slightly less likely to buy frames with above-median list prices (54%).
  • Above-Median Out-of-Pocket Spend: However, they are more likely to pay an above-median out-of-pocket amount (56%).
  • Moderate 3rd Party Contributions: Boomers have a similar share (54%) contributing above the median 3rd party payment amount as Gen X.

6. Silent Generation (Born 1925 – 1945; Age 78 – 98) & Greatest Generation (Born before 1925; Age 98+):

  • Highest Out-of-Pocket Upgrade: These two generations are the most likely to spend more than the median value for the out-of-pocket amount, with both having 64% of buyers who do so.
  • List Price Spending Above the Median: Silent Generation – 43%, Greatest Generation – 35%.
  • 3rd Party Contributions: Silent Generation – 48%, Greatest Generation – 41%.

Correlation with Median Wage Earnings:

We observed a correlation between frame purchase behavior and median wage earnings by generation:

  • Gen Z: Median wage earnings – $63,000.
  • Millennials: Median wage earnings – $69,000.
  • Gen X: Median wage earnings – $102,512.
  • Boomers: Median wage earnings – $77,600.
  • Silent: Median wage earnings – $47,800.2, 3

Correlation with Household Expenditures:

Similar to wage earnings, we found a pattern in household expenditures by generation, aligning with their frame purchase behavior.4

Impact of Managed Care Plans:

The share of 3rd party payments exceeding the median correlated with wage earnings, likely due to higher rates of managed care plans during peak wage-earning years.

Conclusion:

Analyzing the generational differences in frame purchasing behavior has unveiled valuable insights for eyewear providers. Gen Alpha shows the least inclination to upgrade, while Gen Z is more likely to stay below median spending levels. Millennials and Gen X demonstrate higher purchasing power, being more willing to pay above the median for frames. Boomers exhibit a moderate upgrade in out-of-pocket spending, while the Silent and Greatest Generations are most likely to spend more than the median in this category.

It is evident that different generations have varying needs and financial capacities, which greatly influence their eyewear preferences. Older generations like baby boomers and the Silent Generation, with higher incomes and more savings, may be more willing to invest in eyewear. On the other hand, younger generations such as millennials and Gen Z may face challenges like student debt, low wages, and high living costs, leading them to be more price-conscious.

To cater to each generation’s unique desires, here are some AI-generated tips:

1. Gen Z: Emphasize customization and personalization, offering a diverse range of styles, colors, and features. Leverage social media and influencers to build a sense of community and engagement around your eyewear brand.

2. Millennials: Highlight the quality, durability, and sustainability of your frames, using premium materials and ethical manufacturing processes. Showcase how your brand contributes to social causes that resonate with your customers.

3. Gen X: Focus on the value, convenience, and functionality of your frames, offering flexible payment options and delivery services to make your eyewear more accessible.

4. Baby Boomers: Appeal to their lifestyle and wellness needs by offering frames that enhance vision and comfort during various activities, such as reading, driving, or sports.

6. Silent Generation: Emphasize safety, reliability, and ease of use for your frames, ensuring they are straightforward to maintain and repair.

By tailoring your eyewear offerings to suit the preferences and priorities of each generation, your business can effectively connect with a broader audience and thrive in the competitive market.


References

1 How Generational Differences Affect Purchase Behavior | Square. https://squareup.com/us/en/the-bottom-line/reaching-customers/generational-influences-in-buying.

2 https://www.statista.com/statistics/825883/us-mean-disposable-household-income-by-generation/

3 https://www.pewresearch.org/social-trends/2019/02/14/millennial-life-how-young-adulthood-today-compares-with-prior-generations-2/

4 https://www.visualcapitalist.com/cp/how-americans-spend-their-money-2022/

About Industry Trends

Through robust analysis of anonymized data, we are able to develop insights, profiles, and a deeper understanding of market results and benchmarks.

GPN aggregates millions of transactions from thousands of eyecare providers, and focalCenter performs rigorous analysis for delivering timely and precise micro and macro dashboards with interactive business intelligence to the eyecare industry. Please feel free to contact us for more information on growing your eyecare business with data-driven strategies.

By Ron Krefman, OD

Finding solutions in data science.

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