Promotional blackouts, defined as strategically timed periods during which marketing promotions are intentionally withheld, have emerged as a noteworthy component of contemporary marketing strategies. This blog delves into the role of promotional blackouts, examining their impact on consumer behavior, brand perception, and overall marketing effectiveness. The blog touches upon the diverse motivations behind adopting promotional blackouts, from creating a sense of urgency and exclusivity to mitigating consumer fatigue. However, at the core, this blog will explore the nuanced relationship between promotional blackouts and baseline sales. It will investigate the extent to which these intentional breaks impact the long-term and non-promotional performance of drugs.
A promotional blackout is a deliberate and complete cessation of promotional activities, such as field force promotions and speaker programs. A promotional blackout usually happens around a significant event, such as when the drug approaches its loss of exclusivity (LOE) date or when sales remain flat for a sustained period. While it might seem paradoxical to halt efforts designed to drive sales and revenue, the blackout can yield various benefits when employed strategically. There are two main objectives that companies try to achieve through promotional blackouts:
Despite their intended objectives, some of the potential impacts and considerations of promotional blackouts include:
Baseline erosion refers to the gradual decline in a brand’s loyalty over time. There may be multiple explanations for the fall. One prominent reason is when customers, healthcare physicians (HCPs), or patients are not exposed to any promotional activity due to blackout, which increases hesitancy to prescribe or adopt the drug as usual.
While there are multiple approaches to measuring the rate of baseline sales erosion, in this blog, we will limit the discussion to the following two methods:
1. Marketing mix model parameters-based calculation: This approach leverages different marketing mix (MMx) model parameters to estimate the decay in baseline sales. Generally, three components in the model contribute to total sales:
Sales generated through brand equity and sales carryover are considered baseline sales. Once promotions have stopped, those are the only components expected to generate sales; the impactable sales component ceases to exist. The estimated sales generated in the post-blackout period can be derived as a function of sales carryover coefficients (calculated through MMx analyses):
Current Month Sales = [One-month-prior sales * One-month-prior coefficient] +
[Two-month-prior sales * Two-month-prior coefficient]
The figure below illustrates the broader steps that this approach takes to estimate the projected baseline sales once all promotions stop.
Figure 1: Estimating decay of baseline sales
At a broad level, this approach is more of a back-of-the-envelope calculation, as it assumes that sales generated at any point once the promotions go dark is just a function of carryover sales coefficients and hence is an inward-looking approach to estimate decay in future sales.
There are limitations to this approach:
2. HCP-level patient retention rate-based calculation: Aside from marketing efforts, several factors can influence the sales decay rate once promotional activity goes completely dark. For example, marketplace conditions or competitor behavior can affect HCP-patient retention rate and physician loyalty to the brand. Therefore, a suitable approach should be based on more than just prior months’ sales carryover coefficients. Assessing the patient churn at an HCP level will give us all the parameters to estimate the decay rate for each HCP or HCP segment.
The crux of this approach is to compare the change in behavior of a small group of HCPs (the test group) between pre- versus post-blackout periods. We do this by measuring the number of patients they initiate or maintain on the therapy during those periods. We then overlay the retention rate for new and continuing patients onto a broader pool of HCPs (the control group).
There are five steps when outlining an HCP-level patient retention rate-based calculation:
Step 1: HCP Test and Control Group Identification: Since the promotional blackout has not taken effect, the obvious question is how we identify HCPs for the test group. To do that, we must find HCPs who had been on the marketing target list for the drug and subsequently removed. This situation is similar to a promotional blackout for the broader HCP pool.
Step 2: Retention Rate Calculation: Among the HCPs in the test group, we look for patient-related metrics during both the pre-event and post-event periods, namely:
The retention rate in the post-event period serves as the monthly or quarterly decay rate estimates.
Step 3: HCP Archetype Creation (for Both Test and Control Groups): We then use a combination of sales, patient-based metrics (new or continuing patients), and promotional volume to create various HCP archetypes for the test and control groups.
Step 4: Overlay the Test Group’s Decay Rates Onto the Control Group’s HCPs: This step aims to find HCPs in the control group that mimic HCPs in the test group. We then overlay the retention rates calculated in step two onto similar control group HCPs.
By completing steps one through four, we should have the retention rate or decay rate for the HCP pool for at least 12 months (assuming we have the retention rate estimates for the test group HCPs for 12 months). We can then move to step five.
Step 5: Projected Baseline Decay: Suppose the need is to have a long-term projection of how the baseline is expected to decay, not just limited to what is likely to happen in the next 12 months. In that case, we must base it on mathematical models that account for the decay rate (the retention rate calculated above) and estimate forward-looking sales curves. There could be various approaches to fit the retention rates into a mathematical equation, such as the Bass diffusion curve, exponential smoothing, etc. As an example, a simplified version of the baseline decay equation looks something like this:
Sales(t) = Sales (0) * exp (-r * t), where
Sales(t) is the sales at time t
Sales (0) is the initial sales at t=0
r is the decay rate (a positive constant). The higher the value of r, the faster the sales decay.
t is the time
Axtria’s advanced analytics team applied the above two approaches for a life sciences company to measure the baseline erosion of a drug in its mature lifecycle stage. Upon comparing the results, the HCP-level patient retention rate-based approach yielded a more organic erosion curve, which was more reasonable than the MMx model parameters-based method. The chart below shows that while the shape of the MMx model parameters curve is similar to the HCP-level patient retention rate approach, the decline in baseline erosion is much faster.
Figure 2: The Baseline Erosion Curve
Some caveats to consider:
In the dynamic business world, baseline sales erosion is both a challenge and an opportunity. Recognizing and addressing the baseline sales erosion is vital as it challenges businesses to remain vigilant, innovative, and customer-centric. In this blog, we have explored the various facets of this phenomenon, from its causes to its impacts and how to measure its effects. This can help pharmaceutical companies make better-informed decisions on their marketing strategy.