Would you believe that even something as tiny as pollen can significantly influence a demand forecast?
Let us convince you.
Demand forecasting–as you’re aware, is a complex undertaking for any organization. And an especially difficult one when done for niche or seasonal products. It relies heavily on accurate and rich data and on the selection of the most relevant correlating factors.
To do this, businesses use various data points, from historical sales data to economic indicators–using them to predict how much of a particular product they need to have on hand to meet customer needs.
However, more often than not, even after testing multiple models and hypotheses, decision-makers find themselves unable to act confidently upon them.
This is because some factors that have a significant correlation with retail sales and demand often get overlooked.
As it turns out, pollen data can provide some extremely valuable insights into how different consumers behave. For the sake of this post, we shall focus on an area where pollen’s impact is most potent.
Of course, the effects of pollen on consumers are most visibly seen in certain kinds of products and services. The largest of them being in the space of allergy medication and retail pharmaceuticals. Here, the link is very strong but also very underutilized.
Allergy medication and pharmaceuticals are both huge markets. Based on different consumer surveys, the average American roughly spends somewhere between $140-$300 per year only on allergy medicines–roughly the same amount they spend on buying clothes.
And by the end of this decade, this industry is expected to reach a valuation of roughly $40bn. Given the stratospheric rise of big pharma in the world and the cutthroat competition it faces, a single product can make or break companies.
In the coming years, as the effects of climate change strengthen, allergy seasons will continue to get longer–leading to even more symptoms and a greater need for the right medicines.
So how does pollen impact the industry?
When pollen levels are high, allergy sufferers are more likely to experience symptoms such as sneezing, itching, and congestion–or even end up with aggravated asthma. This relationship between pollen levels and its consequent diseases is very crucial and has been the subject of heavy research.
We, too, sought to understand it better through Dr. Adam Bohr, CTO and co-founder of Sonohaler, whose expert insights you can find here.
While analyzing pollen, It’s important to note that the various sub-types of pollen also have a big hand in determining not only what symptoms emerge but also when they’ll appear and which specific medicines help in aiding them.
For instance, tree pollen allergies, common in spring, can cause symptoms such as sneezing, runny nose, itchy eyes, and congestion. Grass and weed pollen allergies, on the other hand, cause similar symptoms but are more common in summer and fall, respectively.
When these symptoms rise, people naturally turn to relief medication out of instant necessity. Over a certain period of time, this leads to an increase in demand for over-the-counter allergy medications such as antihistamines, decongestants, and nasal sprays.
Similarly, when pollen levels are lower, people prefer to spend their time outside, which triggers a spike in outdoor activities and goods like insect repellents, sunscreens, et cetera.
There’s even a study that investigated the relationship between tree pollen levels and over-the-counter allergy medication sales in the New York City metropolitan area. It discovered a significant association between tree pollen peaks and allergy medication sales– with the strongest correlation found at a 2-day lag.
Imagine the power of this result implemented in day-to-day supply and inventory management. A pollen-based demand forecast can present a great opportunity for over-the-counter sellers and manufacturers to understand the seasonality of pollen in greater detail. Not only for demand forecasting, but such a correlation also establishes the foundation to carry out better demand planning and marketing campaigns.
We conducted a study at Ambee to understand the correlation between pollen levels and the sales of antihistamine medications, which are commonly used to alleviate allergy symptoms. In order to perform this analysis, our team of researchers developed Multiple General Linear Models (GLMs) to predict sales at daily, weekly, and monthly intervals, both for current and future periods.
To gather the necessary data, we obtained a publicly available dataset that contained six years' worth of sales information for various drug categories. This dataset allowed us to analyze the sales patterns over time. For the pollen data, we utilized Ambee's historical Gspatial dataset, which provided information on different types of pollen, such as Tree, Grass, and Weed, along with their corresponding risk levels. This data covered the years 2016 to 2019.
By examining the relationship between pollen levels and antihistamine sales, we aimed to gain some very fascinating insights into the impact of allergens on medication purchasing behavior.
Here’s what we found out.
In general, out of the three classes of daily, weekly, and monthly–normalized monthly comparisons showed us the best noise-free correlations.
However, the best model was achieved when modeling the next month’s total sales using the current month’s total pollen. This model had an R² score of 0.71. This suggests that approximately 71% of the variation in total sales can be attributed to the fluctuations in the total pollen count.
It is clear–there is a very significant impact of allergens on antihistamine sales.
If you’d like to read this study and its implications in greater detail, please keep an eye out on this space for more updates.
The question now is–
What can one do with this correlation?
Glad you asked.
This correlation establishes something which is intrinsically known but not explored.
Pollen data is essential for understanding consumer behavior in allergic medicines.
Once one is able to verify this for their product, this opens the door to conducting rigorous demand planning at a very granular level. In pollen’s case, this can pertain to subspecies, various spatial and temporal resolutions, and, as illustrated above–different geographies.
If a company, thus, discovers a strong correlation between pollen levels and sales of allergy medication, it can use this information to adjust its inventory levels in advance of periods of high pollen levels. By doing so, they can avoid stockouts and lost sales when demand spikes unexpectedly. This can help the company to reduce costs, increase customer satisfaction, and improve overall profitability.
This also opens the door to many effective marketing strategies to reach allergy sufferers at the right time by utilizing real-time pollen data. As pollen counts spike or dip, a smart company can immediately send out promotional offers or awareness campaigns to its affected consumers–in a space as heavily competitive as OTC drugs, this can be the difference between you and your competitors.
In fact, we have explored this strategy with Boots–one of the UK’s biggest retail pharmacy chains, in increasing engagement and sales.
Be it allergy medicines, outdoor gear, or indoor appliances–chances are pollen is affecting your sales significantly. Ambee’s state-of-the-art pollen data is here to help you discover that and more.
Ambee’s range of pollen offerings–including real-time, forecast, and historical pollen data, can help you discover and reinvent your products and services with industry-validated accuracy and credibility. If you wish to learn more about our data and how it can help you, feel free to contact us here or simply leave a comment below.