The timing and intensity of seasonal illness is highly volatile from season to season and place to place, making it difficult for pharmacy retailers to predict when and where consumers will need cold, cough and flu medications and products the most. This results in out-of-stock products, lost sales opportunities, and consumers still in need of relief.

In this case study, learn how a leading pharmacy retailer addressed this problem with Kinsa Insights’ Inventory Optimization solution. With Kinsa, the retailer was able to predict 14 days in advance when and where illness product demand would increase, increasing forecast accuracy from 75% to 95% and helping to avoid out of stocks.

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While the timing and intensity of seasonal illness is always unpredictable, illnesses like cold, cough, and flu have been more volatile than ever since the emergence of COVID-19. This has made it difficult for cold, cough and flu relief brands to predict where and when consumers will need their products the most, resulting in out-of-stock products and potentially permanently-lost sales.

A leading cold, cough & flu relief brand addressed this problem with Kinsa Insights’ Retail Sell-In solution, which identifies geographies where illness-based sales demand is likely to surge 4 weeks ahead of time, and identified $69M in under-forecasted sales opportunities.  Learn more in this case study.

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At the height of the COVID-19 pandemic, it was a safe bet that almost everyone almost everywhere needed products like hand sanitizer and disinfectants. But now, as we move into what looks like a more regular illness season, how will brands decide where products with illness-based demand, like decongestants and tissues, will be needed and when? With illness data, that’s how!

1. Map Demand by Location

One of the most potent tools illness data can unlock is forecasting where exactly people will become sick. If you’re stuck using historical information, you’ll miss out on the changing and dynamic landscape of illness. But with illness data, you can understand where customers will need your products up to 20 weeks before their illness episode.

2. Reduce Forecasting Error

Forecasting anything, let alone illness, is both a science and an art. No matter how complex the modeling or data is, there is always a chance of something unpredictable. But using illness data, you can reduce forecasting errors by up to 50%. That gives you extra time to adapt your strategies and make sure your product is in front of the people who need it most.

3. Account for Pantry Loading  

An essential aspect of demand planning is understanding what customers already have. You won’t sell much toilet paper if everyone has enough stock for a small army in their closet. Prior to the pandemic, illness products were generally purchased close to when a consumer needed them – but since then, purchase patterns have changed. Consumers are now more likely to “pantry load” and keep multiple products in their homes in advance of when they actually want to consume them, creating inventory in consumer homes. Kinsa Insight’s Illness Forecast can help assess how much product is actually used based on underlying illness levels. 

4. Avoid Out-Of-Stocks:

Illness seasons are volatile. Unexpected surges in demand can outstrip inventory in stores and distribution centers. Out-of-stocks quickly translate into “lost sales” for retailers and impact shopper loyalty. For example, stopping by your go-to store for cold and flu remedies, only to find they’re out, will cause loyal shoppers to shop elsewhere, potentially losing them as a customer altogether. 

5. Plan Ahead

With high accuracy and long lead time, illness data can help your business get and stay ahead of your competitors. You can make sure your products are on the shelf long after other brands have sold out. 

Kinsa Insights is a leading provider of illness data, allowing your company to be the first mover in the illness market. Our illness forecast is the earliest and most accurate, giving you up to 20 weeks lead time. With Kinsa Insights as part of your demand planning strategy, you can stay a step ahead of the competition and keep your products in stock. Interested in learning more? Schedule some time with our team to learn how Kinsa Insights can help you optimize your media targeting strategy and get ahead of flu season before it starts.

As the illness season looms and the delta variant pushes COVID cases higher each day, retailers are hard pressed to make informed plans for the future of products with illness-based demand. During these always-changing times, it’s important to know WHEN and WHERE illness will strike. 

Illness Insights can help companies plan for an uncertain illness season. With early knowledge of when and where illness will occur, retailers can plan media buys and stock shelves ahead of demand. The alternative – stock outs, lost basket sales and a loss in customer loyalty.

Beginning in March each year, Kinsa’s team of data scientists, epidemiologists and doctors create an illness season outlook. They normally look at factors like flu vaccine coverage and rates of global travel. This year, they also take into account things like remote school and work arrangements, mask mandates and levels of international travel. This is all combined with proprietary, localized illness data to create an outlook that predicts when and where illness will rise up to 20-weeks in advance. 

Retailers and marketers with illness insights know what to expect during the upcoming illness season, with enough lead time to take action. They can target media buys to exact geographies weeks before an outbreak begins to build awareness and purchase intent. They can direct product inventory to the right stores ahead of demand to avoid stock outs, drive incremental sales and grow market share. Knowing what you need, when you need it, is the key to success this upcoming illness season.

Kinsa Insights is a leading provider of illness data, allowing your company to be the first mover in the illness market. I’d love to share our latest illness season outlook with you! Drop a comment below or shoot me an email at [email protected] to learn more! 

The pandemic changed everything. How we work, attend school, meet people, and, of course, how we buy things. In 2020, consumer behavior shifted as well. Suddenly, every consumer wanted curb-side pick-up or online purchases delivered right to their homes. As if that disruption wasn’t enough, supply chains buckled and out-of-stocks abounded due to panic-buying and pantry-stocking This led retailers and suppliers to adapt to the new conditions and this shuffle came at a price.

In a normal year, out-of-stocks for all categories of CPG goods cost retailers about $48 billion, according to a report from IHL Group. In 2020, just the top 10 CPG categories totaled more than $2.93 billion in missed sales due to out of stocks, according to NielsenIQ data. More than $800 million was from bath tissue out-of-stocks alone. In the face of these harsh numbers, companies began to look for solutions.

 One of the most prominent solutions seen across many different sectors is a renewed appreciation of how technology can help strengthen supply chains, target ads and predict demand. CPG brands in particular turned to tech solutions, revving up their digital media buys while channeling resources to “buy online and pickup in store” (BOPIS) options for consumers. And, of course, they also turned an eye towards how technology can help create more robust supply chains to prevent out-of-stocks.

In March, The Consumer Brands Association, released a study after analyzing the effect of the pandemic on CPG companies. The CBA called for distributed “supply networks” to become the new normal, askewing the traditionally hyper-efficient supply chains from the before times. These networks, they say, will be built with an underpinning of technology and data. According to the report, “[d]ata is the lifeblood of shaping the future of knowing demand.”

The easiest way to prevent out-of-stocks is to know exactly when and where consumers will want your product. But, according to experts, this hasn’t been the focus for CPG companies. Dr. Kurt Jetta, who has more than 30 years in the CPG and grocery analytics industry, made the observation that a major failure of CPG companies during the pandemic has been a lack of good demand planning. “Over the years, CPG companies have become a bit lackadaisical when it comes to demand planning,” Jetta said. And other industry stalwarts agree. A study from Bain & Company unpacked lessons learned about supply chains during the pandemic. The key takeaway: build resilient supply chains with a focus on improving forecasting accuracy. To do this, the study recommends seeking advanced analytics, which they say can improve supply forecast accuracy by 20-60%.

Kinsa Insights captures unique data the healthcare system misses entirely — data from mildly symptomatic individuals before they go to the doctor, data on how fast an illness spreads in the home or in school, and data from underserved communities that are underinsured, seek care late or not at all. This allows Kinsa to understand emerging illness trends weeks ahead of other systems. 

The ability to accurately forecast demand depends on hyper-local and timely data. Kinsa Insights’ proprietary demand forecasting engine pinpoints where outbreaks are occurring in real time and accurately projects where future outbreaks will occur.

Brands use Kinsa Insights to plan their supply chain down to individual store locations.. Our solution enables brands to drive in-store sales by avoiding out-of-stocks and deploying products where they are needed the most.

Learn how brands saw not only a 50% reduction in forecasting error, by optimizing for illness level and geography,  they also experienced $2 million in additional product demand, a 55% increase in ad engagement, and a 4-to-1 return on incremental ad spend (iROAS).

Contact [email protected] or send me a message to see for yourself how Kinsa Insights helps make unpredictable demand predictable.

Did you know Kinsa Insights real-time illness signals are available in the Trade Desk Data Marketplace?

Illness has never been more unpredictable – making it impossible to predict timing, severity and location of seasonal illness. This has made it challenging for digital marketers and agencies looking to plan and optimize media spend for illness-based brands/retailers.

Kinsa Insights is the earliest and most accurate predictor of the “when” and “where” for illness-based purchase intent, providing localized, predictive, and real-time illness signals allowing brands and retailers to plan media campaigns and budget up to 20-weeks in advance or adjust and optimize in the moment.

Brands and agencies are leveraging Kinsa’s geo-targeted illness insights to:

• Optimize campaign spend to increase ads in areas where illness levels are severe, and decrease spend in areas where it isn’t.
• Enable dynamic creative optimization (DCO) based on illness levels & signals to make sure the right customer is seeing the right message at the right time.
• Execute campaigns that increase brand awareness and grow purchase intent ahead of illness-based demand.

Our proprietary network of over 5-million users provides insight at the first sign of illness – weeks earlier than the Centers of Disease Control/ILI network and other claims based data sources. Our five-year historical data base provides predictability into illness and symptom trends at the DMA, county, and store-level up to 20 weeks in advance.

Interested in learning more? I’d love to show you how Kinsa Insights integrates with the Trade Desk and how it can help brands and agencies with a 4:1 improvement on ROAS! Email me at [email protected] or message me to learn more!

There’s an art and science to planning a media spend during the illness season. You need to know which types of users want to see your ads, when they’ll need your product and where they are. Because of these complexities, many media planners and agencies take the “spray and pray” approach— spending more money on a broader audience to hopefully catch the right person at the right time to drive a sales conversion. But it doesn’t have to be like that. Use illness insights.

1. Predict When Illness Will Spread

What if you could optimize your media buys before the illness season even begins? With predictive illness insights data, marketers can know exactly when to target their campaigns up to 20 weeks before an illness event. Not only will this information let you stay ahead of competitors, but you’ll also save money by only serving impressions to consumers with illness-based needs.

2. Know Where to Buy 

Buying media in every DMA in the country is a great way to  spend all of your media money, but is it the best way? With illness insights data, marketers can target the  audiences where illness is rising and they need your product the most. Effective illness data can even tell you which counties will see outbreaksin the future.. With illness insights data, you’ll only spend your media money on the areas you know people need your products. 

3. Sell Only What is Useful

The final piece of a successful media buy using illness insights data is to know which products to put in front of people. Illness insights data makes that process easy by telling marketers which illnesses and symptoms are rising or falling in a particular area. Should you advertise your cold medicine in Metuchen? Not if the state is already recovering from the flu. How about thermometers in Nashville? If illness insights data points to an increase in body aches and fevers, that’s a good idea! 

Kinsa Insights is a leading provider of illness insights data, allowing your company to be the first mover in the illness market. By telling you what symptoms and illnesses are spreading, where they are spreading and when, Kinsa Insights helps marketers spend media money where it counts! Contact  [email protected] to learn more or visit kinsainsights.com 

Have you ever looked at the weather forecast and planned for sunshine, only to be met with an unexpected rain storm? For illness-related brands, forecasting demand and creating a media plan ahead of cold and flu season can feel a lot like packing for a sunny destination and getting caught in the rain, having to pivot at the last minute and spend more than you planned on a rain jacket and umbrella.  

So what does this have to do with your media targeting and sales? 

In order for illness and disinfectant brands to create an effective media plan and forecast demand, they need insight into the illness trends for the upcoming weeks and months ahead. Traditionally, it’s been impossible to predict when and where cold and flu season is going to strike – varying both in timing and intensity each year. Illness season peaks anytime between October and February with no early indicator available – and strikes in varied geographies each year, leaving brands with little data or strategy to effectively plan ahead. 

As a result of this unpredictability, sales of illness-related products are affected dramatically year-to-year. For example, in 2020 with COVID-19 illness rates plummeted by 90% and OTC category brands saw sales decline over 50%. However, in a severe season like 2017-2018, decongestants saw a significant growth in sales. 

Media planners and brand managers need the ability to predict illness to more accurately target and optimize illness-based product campaigns in the areas where illness season is striking and pull-back spend in areas where it’s low.  

So, why Kinsa Insights?

Like a reliable weather forecast, Kinsa’s proprietary illness data and predictive insights gives you visibility to when and where illness will strike, allowing brands to build awareness and purchase-intent leading into an illness outbreak. Getting ahead of your competitors will drive significant in-store sales lift improvement. 

Our hyper-local data also provides brands and brand planners the ability to speak to target consumers at their exact moment of need – just as they become ill. Kinsa Insights’ brands and retail customers have seen a 55% increase in ad engagement with our insights and a 4-to-1 return on incremental ad spend (iROAS) by optimizing for illness-specific messaging and calls-to-action by illness level and geography. 

But, how does it work? 

Everyone’s illness episode begins the same way – we’re exposed to illness and then the first signs and symptoms appear. In this event, one of two things happens: symptoms improve or they don’t, causing us to seek medical attention to recover. 

Alternatively, Kinsa captures illness insight directly from consumers at the first sign of illness, ahead of symptom relief seeking. Traditional illness data capture their claims-based data after someone visits the doctor, creating a 7+ day lag in illness intelligence from the onset. More importantly, according to the CDC, 55% of those who get the flu never even visit the doctor. 

Kinsa data reaches over 5 million users, capturing illness at the home level, allowing us to monitor and predict illness spread with unprecedented accuracy at various levels of geography. Our insights are built from a sensor network of smart thermometers in households and schools, capturing illness across a broad range of populations. 

What Do I Do with this Data? 

Kinsa Insights delivers real-time visibility into illness onset, spread out by geography and symptom. Our solution allows you to target geos based on DMA, store trading area and region to give you the “when” and “where” illness is striking and at what severity level. 

This enables you to: 

• Allocate your spend levels accordingly
• Deliver the right message and call-to-action based on illness levels and location
• Complement your DCO efforts with highly targeted insights 

So, just like you pack for vacation and check the weather to make sure you’re not caught in a storm – Kinsa Insights will keep you dry, figuratively speaking, as you plan ahead for your media campaigns as we prepare for and approach illness season. 

Interested in learning more? Schedule some time with our team to learn how Kinsa Insights can help you optimize your media targeting strategy and get ahead of flu season before it starts.