How can you predict demand for a new product in E-Commerce during the holiday season?

To predict demand for a new E-Commerce product during the holiday season, combine market research, historical sales data of similar products, and competitive analysis. Utilize social media trends and SEO keyword research to gauge consumer interest. Implement predictive analytics for accurate forecasting, and adjust strategies based on real-time data and feedback once the product launches. Flexibility and continuous monitoring are key to meeting consumer demand effectively.

Here is a tried-and-tested way to predict demand for a new e-commerce product during the holiday season: - Monitor trends and past traffic data (keyword enquiries, demand analysis) on digital platforms like Google and all the relevant marketplaces - Cross-reference with similar information from historical sales data with Avg. Order Value and conversion with traffic accruing from search. - Run micro-experiments through regression analysis and time-series factoring to figure out how the demand is influenced by other factors and will be shaping up respectively. Try this and don't forget to bring in agility to the process. Tx

Al ser un producto nuevo, no podemos creer muchos en datos muy antiguos. Algunas ideas serían: -Google Trends. Es gratis, pero nos marca las estacionalidades del producto y su crecimiento en búsquedas en los últimos 5 a?os, incluso por meses en el último a?o. -Google Ads. El generador de palabras clave nos puede ayudar a ver qué crecimiento están teniendo las búsquedas de ciertas palabras clave. -Comscore. Saber cómo está creciendo el tráfico de un competidor que sólo vende ese producto. -Herramientas de análisis de perfiles en redes sociales. Podemos ver cómo han aumentado los seguidores y la interacción de un marca en su perfiles de Instagram, Facebook, Tik Tok o Linkedin y su engagement, siempre que se refiera sólo a ese producto

The Number 1 approach if you haven't subscribed to any tools/softwareavailable and if you already have sales and a keyword set: Check the demand increase of your 10 most important keywords in the last season. This can be done even with free tools for a marketplace. If that is not available, go for Google Trends. Not very specific but free and surprisingly more often than not: good enough.

1.) Pay attention to past holiday season trends and sales data. Analyze which products performed well and identify any seasonal patterns in consumer behaviors. 2.) Utilize analytics and forecasting tools to predict demand based on market trends, customer preferences, and competitor analysis. Consider leveraging historical data to make informed projections. 3.) Target your marketing efforts towards the holiday shopper demographic. Focus on creating compelling content and offers to entice customers during this lucrative time. Create a buzz to increase demand for your product. 4.) Optimise your inventory management and supply chain to meet the projected demand. Anticipate high sales volume and plan accordingly to avoid stockouts.

This is a step by step set of instructions on how we build a demand curve model for a SKU, category or business. 1) First, get as much data as you can and divide it into monthly chunks. Use your typical KPIs such as conversions, AOV, CPCs, etc. 2) Build two formulas alongside two curves: MoM and YoY. So what you will do is calculate the % change in these KPIs across each month, then you will do the same for the YoY metrics. 3) Average out the MoM and YoY metrics across months. Your output will wind up with something like: Dec tends to be +5.4% stronger than Nov, and we tend to grow around +12% YoY in Dec. 4) Append assumption notes on why you think certain months drop or rise sharply. 5) Use this to forecast for the future

Adding to it, a great way to analyze the trend for a category of product is to look into the google & marketplace ( amazon similar ) search volumes for the general keyword of the product during the same period last year. If you want to launch a product in moisturiser, look at the broad set of keyword search volume for “moisturiser” and you will be able to understand the demand.

Take as much historical data as possible on either lines that are the same or similar. It’s always difficult with new lines as they could either bomb or fly but make sure if it’s the latter that you have swift replen plans in place to keep us with demand.

By examining the performance metrics of past products, sales figures, inventory turnover, outcomes of marketing campaigns, fluctuations in website traffic, and customer feedback, you can discern patterns and trends that are likely to repeat.

To predict demand for a new product means you must have the "know how" of similar previous products about how they performed out during the holiday season. Analyze data (sales, marketing insights) to see their performance in the same season taking in consideration the external and internal factors that had influenced their demand. Those factors may have important changes that could influence an important change in your present demand, so you need to take them under serious consideration to be successful.

I have not yet found a forecast tool that can move nimbly enough to keep up with rapidly growing e-commerce businesses. Instead, what we do is frequent post mortem reports. We always drill down to attribution and "why" - why did we think conversion rates would go up 15% initially, and where did we falter when it only went up 5%? We notate all these learnings down and use this to sharpen our next set of forecasts. Now if we don't feel confident about that particular KPI, we have historical data to help justify future guesses. We also like to calculate slopes of particular KPIs in order to see how certain variables (like Ad Frequency and ROAS) relate to each other, and we use these as leading indicators to reverse poor trends.

En mi opinión, para estimar la demanda, es conveniente fijarse también en los productos sustitutivos. La mayoría de los profesionales dedican, muchos recursos para analizar productos idénticos de la competencia o a los competidores más cercanos. En definitiva, mientras que en numerosas ocasiones, nuestro producto puede venir a satisfacer a un usuario que esté utilizando un producto completamente sustitutivo. En este sentido, pienso que podríamos fijarnos en los consumidores que emplean una cantidad de tiempo y dinero en adquirir productos, aunque no sean exactamente iguales que nuestra propuesta, para estimar posible demanda del nuestro

In my experience, create future demand and use those data points to manage stock earlier and more precisely. Less reliance on historical data

Utilizing forecasting tools like regression analysis, time series analysis, and machine learning can significantly improve demand prediction accuracy by analyzing historical data and identifying patterns and trends. However, businesses should remain adaptable to unforeseen factors that can influence demand, ensuring flexibility in response to evolving market conditions.

Além dos métodos tradicionais de previs?o, como modelos estatísticos e séries temporais, a IA está se tornando um recurso quase que essencial na formula??o de previs?es, por vezes oferece uma abordagem mais avan?ada, podendo se criar contextos maiores.

Listening to customers feedbacks, validating demands by offering products on a preorder basis and analysing available market data and keeping an eye on social media sites for new trends and products. It's about being agile, learning from operators in other markets, industry experts and aligning the approach based on real-time insights.

The magic word is: attribution. We always seek to find the root cause for why certain KPIs move the way they do. The first step in doing this is overlaying the actual performance vs. forecasted performance. If the model is built correctly, you will know exactly which KPIs missed and which ones hit. We take a look at the misses and figure out why they didn't pan out the way we thought: was it a lack of creative? Did CPMs spike up 20% unexpectedly due to competitors? We then use this information to tweak our next month's forecasts and we adjust our tactics and strategy accordingly.

In my perspective, validating assumptions and testing demand forecasts are crucial steps in ensuring the accuracy and reliability of projections. Continuously comparing forecasted figures with real-time data helps refine models and adapt strategies dynamically. Employing analytical techniques like scenario and sensitivity analysis adds depth to forecasting methodologies, enabling businesses to anticipate and navigate various market dynamics effectively. This iterative process fosters adaptability and precision, enhancing decision-making in dynamic business landscapes.

In my opinion, post forecasting demand for our new e-commerce product, it's crucial to validate assumptions & test results. Compare forecasted data with actual sales figures, adjusting models accordingly for accuracy. Employ scenario analysis and sensitivity analysis to understand how forecasts change under varying conditions and uncertainties. Utilize A/B testing and pilot launches to gauge consumer response and iterate on product features or marketing approaches. By validating assumptions and testing results iteratively, we can improve the accuracy of our forecasts, mitigate risks, and optimize strategies for success in the dynamic e-commerce landscape.

Validate *before* launching. This may be counterintuitive, but a great way to test your forecasting model is to run a product from a prior year through it. If the result you obtain—standing in the same point in time as you are now, but a year earlier—has an acceptable deviation from what actually happened, then you're on the right path. Otherwise, it may be wise to review your assumptions.

A watched metric is an improved metric. Hence, we watch a lot of metrics very closely with dashboards. Generally the KPIs that we track fall into the following buckets: 1) Customer Acquisition: the cost, quality and efficiency of our customer acquisition channels. 2) Site Engagement: once they arrive on the site, how do they navigate and do they get to the desired spot in the funnel? 3) Conversion Rates rule everything around me: CR % is the make or break KPI for many businesses. 4) Customer satisfaction: NPS, CS ticket resolution and shipping speed are the biggest contributors to maximizing LTV 5) Surveys: this is key not only for insights, but also for attribution in a post cookie world

Some other points that should be exercised before launching a new product during the holiday season are as follows: 1. Conducting market research. 2. Conducting consumer surveys. 3. Monitoring Social Media trends and ad activity for similar products and their demand during your target time frame. 4. Introducing the product in limited numbers to test how well the product is received during the target time frame before spearheading with the launch.

Monitor and optimize your strategy: Continuously monitor your sales performance and adjust your strategy accordingly. Keep track of changes in consumer behavior, competitor actions, and market dynamics. Be prepared to adapt your marketing campaigns, inventory levels, and pricing strategies as needed.

Monitor conversion rates of your product. If it's not selling, you need to act fast to research, validate and launch a new product before the opportunity goes.

Relatórios e dashboards s?o os seus maiores aliados, o acompanhamento de KPIs lhe servirá tanto para analisar sua performance, quanto gerar os seus próprios dados de sazonalidade. Possibilitam também análise cruzada junto ao mercado, com isso, você se torna mais efetivo na sua mudan?a de estratégia.

A useful way to identify products that might be hits in holiday season is to use first-party consumer behaviour data platforms provided by Marketplaces like Amazon. On Amazon vendor central, you can quickly check the search volume rank of consumer queries in real-time. For instance, the highest searched keyword on Amazon UK last week was ‘Mother’s Day gifts’. You can then look through your assortment to see if any products might make for good gifts at this occasion and promote them or change the content to be more Mother’s Day relevant in this example. This will help capture demand more accurately for these products .

Start by identifying competitors who are selling similar products or targeting similar customer demographics. Analyze traffic trends of your competitors during past holiday seasons. Look for patterns and spikes in traffic, indicating increased demand for certain products or categories during specific times. Also analyze keyword search volumes, and clicks, related to your product category during the holiday season.

Use prior year and the months leading up to holiday to monitor, track, and build a strategy around your customer behavior: purchase intent, search queries, time spent, channel purchased, return rates. Then couple the customer behaviors with the behaviors of the product itself; which category, skus, collections drove high conversion, which of those were based on marketing, and which were driven by discovery, "hidden gem". This insight can help your ecomm, merch, and planning teams to determine how best to invest for the holiday season and which products to feature in campaigns.

During my stint at Walmart, I made supplier site visits to Shenzhen and Hong Kong. One observation that struck me was how much knowledge, thinking and prediction of customer tastes goes into the new products that are showcased by these suppliers. The game is not about demand prediction but about predicting emotions during the holiday season. However, as a business, you need a measurable target to go with and using demand prediction models helps there. Emotions are hard to measure, but easy to observe. Suppliers get a range of perspectives from others retailers that is like a secret sauce. Take inputs from your suppliers, share each others numbers and see what your top sellers would be during the holiday season.

Olhar para a concorrência é sempre um bom ponto de análise. Nem sempre é necessário inventar a roda, mas pode ser necessário ter a roda.