What are the most effective strategies for seasonal demand planning?

I believe that effective sales strategies are the cornerstone of any successful business. It's crucial to have a well-defined Ideal Customer Profile (ICP) and a clear understanding of our unique value proposition. This not only guides our prospecting efforts but also ensures that we're targeting the right audience with the right message.

Analyzing historical data, especially like-for-like (LFL) data, is the crucial initial step in making accurate sales projections. However, it’s equally vital to complement LFL data with market intelligence and external factors analysis. Without this broader perspective, you may miss critical insights needed for robust demand planning. LFL data alone might not capture shifts in customer behavior, market trends, or competitive forces, which can significantly impact future sales. Therefore, a holistic approach that integrates both historical and market-driven insights is essential for effective demand planning.

Successful seasonal demand planning hinges on meticulous consideration of three critical factors: Past Performance Analysis: Scrutinizing shifts from pre-season (BAU) to in-season in the previous year is pivotal for informed planning. Year-over-Year Comparison: A comprehensive evaluation of this year's pre-season demand, supply, and sales metrics compared to the preceding year provides invaluable insights. Contingency Planning for Uncertainties: Preparing for unforeseen variables by allocating buffer investments, managing stock, and devising promotional strategies is essential. This includes accounting for competitor tactics, logistical challenges, and unpredictable consumer behaviour.

Historical data, though rich in past patterns, is essentially a rearview mirror. It provides insights into where we've been, not necessarily where we're headed. The online marketplace is so dynamic, that you have to leverage historical data for demand planning, but it should be complemented with a forward-thinking approach. Embrace real-time feedback, moving averages, intentions for your specific SKUs.

Current Market conditions should also include macroeconomic conditions to understand disposable income patterns. This will help in prioritization of products based on ticket sizes

Key steps: - have the right person in charge of logistics - make sure they coordinate with marketing - invest on the right to inventory management software and solutions These actions properly implemented will keep you on track to provide correct amounts of product for whatever demand occurs (except for black swan events like Covid probably)

Creating a demand forecast is both an art and a science. One thing I've found useful in this process is to start by leveraging historical data to establish a baseline. An example I have seen is that incorporating qualitative insights from sales teams, customer feedback, and market trends can greatly enhance forecast accuracy. It's about striking the right balance between data-driven analysis and human intuition. When creating a demand forecast, remember to consider factors like seasonality, market dynamics, and external events that can impact demand. This holistic approach can lead to more agile and responsive forecasts, crucial in today's fast-paced business environment.

One innovative tactic in seasonal demand planning is adopting a "dynamic demand response" approach. Instead of solely depending on historical data, embrace real-time data analytics to adjust strategies on-the-fly during peak seasons. This agility allows #brands to respond to unexpected demand surges or drops, maximizing profitability. Cadbury, under #Mondelez, during a certain Easter saw a sudden surge in demand for its Creme Eggs due to an unanticipated viral marketing campaign. By tapping into real-time sales data and ramping up production swiftly, Cadbury was able to meet the increased demand, ensuring customer satisfaction and capitalizing on the buzz. It's not about predicting the wave, but also surfing it efficiently.

Picking right season on right time with a great plan can meet your end goal. Understand the season and need: First check what is the size and type of season. Create a matrix and check is it a big holiday season (like, Christmas, Thanks Giving, etc.) or Winter, Summer or targeting some other types of event (like, we have in India, Dussehra, Diwali, Eid, Holi, Raksha Bandhan, etc) or it is back to school. Your targeted product and customer segment are tightly coupled for each season. As per your focused target create a plan, use your inputs (historical data, market survey and logistic logistics capacity). Finalize your marketing tools, if possible a refreshed UI to your customer as per the season. Plan some gifts and attractive PROMOs.

If your company has an analytics department or there's an analyst on your team, delegate this task to them. Describe in as much detail as possible what you need, and it's advisable to explain how you plan to use the results of such research. If there are no analysts, then using the Holt-Winters method (this method is suitable for building forecasts based on data that has seasonality) and Excel (this method has its own formula in Excel), you can quickly build a forecast taking seasonality into account

After reaching a certain level, you can’t wing inventory management anymore. This applies to almost any business regardless of being the manufacturer of the goods or just reseller. Make sure you vet a capable head of logistics and operations to regulate your stock and maximise efficiency.

Correct! Efficient inventory management is the difference between profit and loss, especially during peak seasons. It's also worth integrating with suppliers' systems wherever possible to ensure quicker replenishment cycles. Leveraging technology, such as IoT, can aid in real-time inventory tracking, ensuring there's neither overstocking nor stockouts.

Consider adopting an "Agile Inventory Replenishment" strategy in today’s #eCommerce landscape. By integrating real-time sales data with advanced AI-driven forecasting tools, #brands can swiftly adjust their inventory in response to real-time demand, not just historical trends. A good example is Dove from Unilever, which, during a surprise viral campaign, experienced a sharp spike in demand for a specific skincare product. By leveraging real-time #analytics and agile supply chains, Dove quickly adjusted its inventory levels, capitalizing on the unexpected buzz without facing stockouts. This shows the power of agility in inventory management in an era dominated by rapid #digital trends.

As it was rightfully stated here, overusing discounting and promotions is a guaranteed way to erode all the potential margins awaiting a retailer within a peak period. The most common mistake is applying blanket discounting (30% off on all items, etc.) and not really treating the assortment analytically by not considering the varying competitiveness, availability, or seasonality of items. The right way to tackle that would be running a data-driven deep dive into the full assortment and coming up with a segmented discounting and promotions approach that would both help the retailers with the top-line and bottom-line growth.

Indeed in seasonal demand planning, the power of optimizing pricing and promotions is incredible. by tailoring your pricing and promotional strategies to align with specific seasons or holidays, you can maximize revenue and customer engagement. For instance, during peak holiday seasons, offering limited-time discounts or bundling products can stimulate demand. Conversely, during slow seasons, focusing on value-added services or loyalty programs can help maintain customer loyalty. By continuously monitoring and adjusting pricing and promotion strategies based on seasonal trends and market conditions, you can strike a balance between profitability and customer satisfaction. It's about being agile and responsive to shifting demands.

To maximize the impact of pricing and promotions, embrace "Smart Promotion Personalization." Using AI-driven insights, customize promotional offerings based on individual customer preferences and past purchase behaviors. For instance, Ben & Jerry's (a Unilever brand) once executed a targeted #promotion for its seasonal flavors, offering personalized discount codes to customers who had shown a preference for similar tastes in the past. By doing this, they not only increased sales but also enhanced customer #loyalty by making them feel seen and valued. It's a testament to how #personalization, when used judiciously, can amplify the effectiveness of seasonal #promotions.

Plan ahead. Plan ahead. Plan ahead. Before launching any campaigns, make sure your online store is as fit as possible - optimised for conversions and increased order value. Don’t do any major changes before running a major campaign. Instead, try to do any releases to your website at least in the month before you kick off the marketing activities. When selecting a promotional approach, make sure to consider: your margins, the market mood/status, your brand value, your target audience, your goal for the campaign. This can lead you to specific ideas that will work for you. From a simple percentage discount applied across the store to a complex multi level discount gains based on previous orders and cart content… anything is possible.

Dynamic pricing is a potent strategy, especially when combined with real-time analytics. Understanding consumer elasticity and segmentation is key. For instance, newer customers might need more aggressive promotions compared to returning ones. A/B testing promotional strategies can help understand what resonates best with different customer cohorts.

We go back to step 1 - full circle. If you’re not creating, implementing or running a campaign, then you must be collecting data. The most informed you are the better your next marketing efforts will turn out, generating the highest results your team is able to achieve.

Iterative refinement is essential. In the fast-paced e-commerce world, agility is key. Regular reviews allow businesses to pivot quickly based on actual outcomes versus forecasts. Using AI-driven tools can even provide predictive insights on potential discrepancies, enabling proactive adjustments.

My point of view: Running out of inventory can be a great thing. Covid upended supply chains - the forced need to create product waitlists + back-in-stock emails delivered REALLY well for the brand I was working with. Also ever seen FB / IG ads about how product x has sold out x times (I see you American Giant)? That always strikes me as part popularity, part inventory issues. PS. Be kind to your buyers + demand / inventory planners. Their jobs are hard. With a lot of magic 8 ball around buying behavior + sweating out sales.

Consider externalities like socio-economic shifts or even geopolitical situations, as they can impact purchasing behaviors. Additionally, always keep an ear to the ground. Encourage customer feedback; often, they'll provide insights that raw data might not. Lastly, foster a culture of adaptability. In e-commerce, change is the only constant, and flexibility can be a significant competitive advantage.

When talking about peak season on Amazon for instance, we're really talking about 4 major events: - Prime Day (Fall edition) - Black Friday - Cyber Monday - Xmas & NY While all these aspects (inventory management, price management, promotion management and monitoring) are critical to be successful, one thing that is very oftentimes disregarded is content alignment. All these things will only matter if your product detail pages & brand store are ready to convert effectively. Here are a few things you can also do to make sure you're ready from A to Z: - Create a seasonal version of your brand store to showcase ongoing deals/promotions and a more curated SB - Fix all that needs to be fixed on your PDP Good luck !

What I would add to all those very pertinent points mentioned above, based on my experience gained working with many multinational brands during the COVID period, is to look for signals and anticipatory factors in more "advanced" markets and sectors. This is especially important for daily and FMCG products, and when the market ceases to behave rationally and predictably to us for some reason. At that point, it was only the use of proper external data combined with AI prediction capabilities that could help answer the question of what products, when and in what quantities, should be placed at a given location, to satisfy the changing needs of the market and maximize profits.