Lifetime value is the monetary contribution each patient makes to your eyecare business. Understanding this data is crucial for maximizing the overall financial dynamics of your practice. It helps in effectively managing acquisition and retention budgets—funds dedicated to reaching new patients and bringing back existing ones. These budgets are vital for your practice’s growth. Comprehending the economic value of a patient helps business owners allocate these resources wisely.

Lifetime Value models play a key role in understanding this interplay. They capture the entirety of a patient’s engagement with your practice, from their first visit to the most recent one, and sum up the total revenue they generate during this period. This is known as the “Monetary” value in the lifetime value model. You can use this comparison between the monetary value and acquisition/retention costs to guide decisions on investments in advertising, SEO, email, mail, and recall campaigns.

Two essential metrics—“Frequency” and “Recency”—greatly influence lifetime customer value. They shed light on consumer spending behaviors over their entire journey.

Frequency refers to the number of visits, often associated with higher monetary contributions. Patients who engage more frequently tend to significantly impact the overall revenue. This trend is observed across many businesses. A core group of frequent customers tends to drive a substantial portion of the revenue.

Recency signifies the freshness of the last visit. Recent transactions suggest a higher probability of the patient choosing your practice for future needs. For certain procedures, returning recent patients are more cost-effective than acquiring new patients, recognized even by CMS Medicare.

This report delves into the historical purchase patterns of recency and frequency and their influence on the cumulative revenue from all visits.

METHODS

Data from 2,250 ECP locations across the US in the GPN dataset tracked 1 million unique patients over nine years, from the beginning of 2014 to the end of 2022. Recency values, based on days elapsed since the most recent transaction during the 9-year period, were grouped into three-year bins (“<=3 YRS”, “4-6 YRS”, and “7+ YRS”) for simpler analysis. Frequencies were calculated by counting transactions during that period minus one, categorized into three groups (“<=3”, “4-6”, and “7+”). The median of total revenue for all transactions by the same patient constituted the patient’s lifetime monetary value, collapsed into three bins (“<$1K”, “$1K-$2.49K”, and “$2.5K+”).

RESULTS

DISCUSSION

Understanding the relationship between frequency, recency, and monetary value provides valuable insights for business enhancement:

STRATEGY

The data indicates that recent and frequent purchasers tend to spend more. This aligns with the RFM model’s notion of recency, frequency, and monetary value as significant indicators of patient value. These insights assist businesses in targeting marketing efforts effectively, retaining valuable patients, and boosting revenue. This analysis echoes typical RFM narratives; patients making recent, frequent, and higher-value purchases often stand out as the most valuable.

• Patient Segmentation: Segment patients based on frequency and recency to tailor offerings and marketing strategies.
• Loyalty Programs: Implement programs encouraging repeat purchases to enhance frequency and recency.
• Re-engagement Campaigns: Reach out to patients with low recency through special offers or reminders to boost purchases.
• Upselling and Cross-Selling: Employ strategies like upselling or cross-selling for frequent purchasers to increase transaction value.
• Pricing Strategies: Regularly review and adjust prices to align with increasing monetary value trends.
• Patient Feedback: Gather patient feedback to understand purchasing behavior and improve strategies.

Tailor these strategies to fit your unique business and market context. Regularly reviewing data and adjusting strategies based on patient behavior and market changes is key to staying responsive.

This report focuses on historical patterns. In the future, we’ll include RFM predictive models to project patient visit duration and lifetime spending in your practice.