Forecasting demand and revenues for new variants of existing products is difficult enough. But forecasting for radically innovative products in emerging new categories is an entirely different ball game. There are no past trends to reassuringly extrapolate into the future, just a ton of uncertainty about whether the latent demand that the marketing folk suggested to secure the R&D funding is real or not. And after so much investment, the board is sure that this is the product that is going to become the next cash cow. Sure, you could manage their expectations by reminding them that something like 80% of new products fail and name drop a few of the spectacular flops of Fortune 500 companies. But that would be career limiting. A better alternative is to take control of the situation and adopt some of the forecasting best practices approaches that others have found to work.
Step 1: Make it a collaborative effort
Identify a handful of key people from marketing, sales, operations, and relevant technical departments and form a working group. This core team will be responsible for developing and managing the reforecasting process through the launch period until demand planning becomes more predictable.
Step 2: Identify and agree upon the assumptions
Collectively review all the available qualitative and quantitative data from market research, market testing, and buyer surveys. Use the data to identify a set of assumptions that can form the basis of a forecasting model. Ideally this will include assumptions about:
- Number of consumers in the target market
- Proportion expected to buy the product
- Anticipated timing of their purchase
- Patterns of repeat purchasing and replacement purchasing
Be prepared to commission additional research or consult external industry experts to fill any important data gaps. And always let the working group use their collective judgement to identify a realistic range of values for each assumption.
Step 3: Build granular models
Not all consumers will purchase a new product at the same rate. Some may be prepared to queue all night around the block to get their hands on it, but others will want to wait for subsequent versions when any unforeseen bugs are fixed and prices are typically lower. So it is important to build a forecasting model that is sufficiently granular to reflect how and when different market segments in different geographies might purchase the product and at what price.
Step 4: Use flexible time periods
Sales over the first few days and weeks in the life of any new product need to be carefully monitored as they will quickly show how demand is likely to grow in the future. So although the sales and finance function may only be interested in monthly data, it pays to develop detailed daily forecasts for the first quarter against which to track actual sales.
Step 5: Generate a range of forecasts
Run through a number of iterations, changing various assumptions and probabilities in the model to generate a range of forecasts. This is easily done if a modelling solution that can be recalculated in real-time is deployed as internal experts and business leaders can generate and test alternative scenarios on the fly.
Step 6: Deliver the outputs that users need quickly
In new product launch planning, agreements may have been reached with a number of suppliers to deliver rapid replenishment designed to prevent stock outs in the most uncertain period immediately after the launch. However if reforecasting the exact replenishment needs of every distribution point in the supply chain involves multiple steps, much of that precious time will evaporate.
Building a fully integrated forecasting model that compares existing stock level and automatically generates a detailed replenishment report for every location as soon as any high level assumptions change precludes such delays and shortens the replenishment cycle.
Step 7: Combine different techniques
Bottom up modelling based on purchasing intentions is not the only method available for forecasting demand for new products. In some markets, such as technology and consumer electronics, products can go through an entire life cycle in a matter of months. Such narrow windows of opportunity make it vitally important to assess demand as accurately as possible. The most damaging situation is having a stock shortage while the product is still hot, leading disappointed consumers to purchase a competitor’s product.
These sectors make use of sophisticated modelling techniques developed by academics that use substitution and diffusion rates to forecast how rapidly new technologies replace older ones. Such methodologies might not be appropriate to many businesses, but the message is the same; combining different forecasting techniques gives more accurate results.
Step 8: Reality check the forecast
Whenever reliable data exists, always check the forecast against the sales evolution of comparable products to see if it is realistic. Similarly you should also estimate how your market share might evolve as new competitors came into this emerging category and how the total market might grow. Unless this macro overview is credible, be prepared to rework the assumptions behind the model.
Step 9: Reforecast, reforecast and reforecast some more
Diligently monitor sales and qualitative feedback such as product reviews, media mentions, and customer feedback, and agree with the members of the working group how the assumptions in the model might need to change. If it’s appropriate, reforecast daily.
Step 10: Be prepared to cut your losses
Finally, always have a contingency plan. A high proportion of new products fail and it is better to pull the plug on an ailing new product that is unlikely to achieve a viable level of profitability at the earliest opportunity. So quantify and agree what level of sales penetration constitutes failure well before the product launch. That way, the decision will be swift and the existing stock can be quickly and cost-efficiently depleted.
Forecasting demand for new products is not an exact science and relies on judgement rather than statistical techniques. Key to success are collaboration, using all the quantitative and qualitative data that is available and having a modelling solution that can quickly and easily be updated to generate detailed forecasts for all users across the business. The benefits can be impressive both in terms of reduced inventory costs and improved customer satisfaction, something that is vital for a new product to flourish.
Demand forecasting is the process of making estimations about future customer demand over a defined period, using historical data and other information.
Proper demand forecasting gives businesses valuable information about their potential in their current market and other markets, so that managers can make informed decisions about pricing, business growth strategies, and market potential.
Without demand forecasting, businesses risk making poor decisions about their products and target markets – and ill-informed decisions can have far-reaching negative effects on inventory holding costs, customer satisfaction, supply chain management, and profitability.
Why is demand forecasting important?
There are a number of reasons why demand forecasting is an important process for businesses:
- Sales forecasting helps with business planning, budgeting, and goal setting. Once you have a good understanding of what your future sales could look like, you can begin to develop an informed procurement strategy to make sure your supply matches customer demand.
- It allows businesses to more effectively optimize inventory, increase inventory turnover rates and reduce holding costs.
- It provides an insight into upcoming cash flow, meaning businesses can more accurately budget to pay suppliers and other operational costs, and invest in the growth of the business.
- Through sales forecasting, you can also identify and rectify any kinks in the sales pipeline ahead of time to ensure your business performance remains robust throughout the entire period. When it comes to inventory management, most eCommerce business owners know all too well that too little or too much inventory can be detrimental to operations.
- Anticipating demand means knowing when to increase staff and other resources to keep operations running smoothly during peak periods.
Types of demand forecasting
Most traditional demand forecasting techniques fall into one of three basic categories:
Qualitative forecasting techniques are used when there isn’t a lot of data available to work with, such as for a relatively new business or when a product is introduced to the market. In this instance, other information such as expert opinions, market research, and comparative analyses are used to form quantitative estimates about demand.
This approach is often used in areas like technology, where new products may be unprecedented, and customer interest is difficult to gauge ahead of time.
Time series analysis
When historical data is available for a product or product line and trends are clear, businesses tend to use the time series analysis approach to demand forecasting. A time series analysis is useful for identifying seasonal fluctuations in demand, cyclical patterns, and key sales trends.
The time series analysis approach is most effectively used by well-established businesses who have several years’ worth of data to work from and relatively stable trend patterns.
The causal model is the most sophisticated and complex forecasting tool for businesses because it uses specific information about relationships between variables affecting demand in the market, such as competitors, economic forces, and other socioeconomic factors. As with time series analyses, historical data is key to creating a causal model forecast.
For example, an ice cream business could create a causal model forecast by looking at factors such as their historical sales data, marketing budget, promotional activities, any new ice cream stores in their area, their competitors’ prices, the weather, overall demand for ice cream in their area, and even their local unemployment rate.
Key sales forecast metrics
Once you have the basis for your sales forecast in place, you should define and track the following metrics over the entire forecast period.
1. Product lead time
The number of months it takes from placing a purchase order to being ready to sell each product.
2. Sales period
How many months of sales are expected from each product.
3. Costs paid per purchase
What percentage of the costs of products are paid when a purchase order is placed.
4. Days payable
How many days you have to pay the remainder of the unpaid inventory costs.
5. Stock levels
The amount of each product you need to keep in stock, based on sales forecasts*
6. Purchase costs
The cash needed to make purchases*
*QuickBooks Commerce’s inventory and sales forecast tool automatically populates appropriate inventory purchases and the cash required to make those purchases based on your data from the first four metrics.
Sales forecast calculator
We have built a sales forecast calculator to help you anticipate future demand.
Forecasting sales is always a challenging task because of the many variables and unknown factors involved. The job becomes all the more difficult when you’re forecasting sales of a new product because you have no past performance on which to base your estimates.
Despite the difficulties, sales forecasts are necessary for planning the resources you will need to meet actual demand, including inventory, staff and cash flow. A sales forecast is also an important tool in measuring the performance of your sales, marketing and operations.
“There’s a lot of expense involved in launching products,” says BDC Business Consultant Jennifer Rikely. “It’s going to cost you x dollars to launch it, and you need to estimate how soon you can recoup that money.”
Forecast based on sales of existing products
The most common forecasting method is to use sales volumes of existing products to forecast demand for a new one. This method is particularly useful if the new product is a variation on an existing one involving, for example, a different colour, size or flavour.
In this case, your new product will likely sell very much like your (or someone else’s) existing ones. This is especially true if you’re making no major changes in marketing or distribution.
A sales forecast becomes more complex if you’re launching a completely new product. Here, you will need to do more market research to learn about the potential market for your product.
Use affordable market research techniques
Large companies use sophisticated market research techniques, but there are affordable methods you can use to help you project sales of your new product.
Rikely, who advises companies in Vancouver on improving their sales performance, offered the following tips for putting together your sales forecast.
Ask your sales team
Sales representatives know your market intimately, including what your competitors are doing. Your customer service team will also have insights into the potential of a new product. Discuss your project with them and get their help in estimating how many units you can move in the initial months as well as what the ramp up rate might be. As an added bonus, their participation may make them feel more engaged in the successful launch of the product.
Seek other sources of intelligence
Talk to trusted customers, suppliers and sales partners such as dealers or distributors to get their take on how the product will do in the first year. “You can say: We’re thinking of doing something along these lines, what do you guys think?” Rikely says. “It’s amazing what people will tell you if you ask.”
Consider primary research
Primary market research involves such techniques as conducting surveys, organizing focus groups and observing customers. It can produce valuable insights into potential customer demand for your new product, but it involves a commitment of time and money. To make your investment worthwhile, it’s a good idea to hire a market research professional to guide you.
Start with a pilot project
It often makes sense to test your product on a small scale before rolling it out to all potential customers. You could try it out with a small number of sales reps to gauge market reaction, Rikely says. “Use the information to rejig the product and your marketing. And then do a full launch.”
Monitor your results and adjust
Your initial sales forecast for a new product will involve a lot of guesswork, which is why you should adjust your forecast as soon as you get actual sales results.
That means you have to be disciplined about monitoring sales on a monthly basis. The first few months will give you crucial information about product pricing, production and overall customer reaction, Rikely says.
“Every month you need to be looking to see if you’re on track with your forecast. If you’re not, you want to figure it out sooner so you can take action, rather than at the end of the year when it’s too late.”
Disciplined research pays dividends
Rikely says some disciplined research before the launch and during the first year can pay huge dividends in producing a sales forecast that will support a new product’s success.
“Sometimes our gut instinct is spot-on, but sometimes it’s not,” she says. “Anytime you can put some science and numbers, some discipline, into your decision-making, it’s a good thing.”
Factoring events into your demand forecasting improves accuracy and profitability. PredictHQ's APIs enables your models or teams to be prepared upcoming demand fluctuations, so you can make the most of them.
Demand forecasting errors are frequent, frustrating and expensive
Demand forecasting has been stuck looking backwards for too long. Building strategies on last year or 2019’s transactions is meaningless, but knowing the evolving context and what is impacting each key location is hard. Yet profitability depends on being able to accurately build out accurate forecasting models that make use of forecast-grade external data.
Make your demand forecasting models real-world aware
Events drive demand. More than $1.1 trillion per year worth of demand each year in fact. Discover which event categories impact your demand, so you can turn demand anomalies into competitive advantage. Combine your historical data with PredictHQ’s seven years of forecast-grade event data and then use our API in forecasting to reduce costly errors.
Gain visibility into the external factors impacting demand fluctuations.
Use our demand intelligence API to train your models with relevant, accurate data.
Reduce MAPE and improve forecasting accuracy.
What is demand intelligence?
Unlock the external factors driving demand
Millions of events, one global source of truth.
Demand forecasting requires external data, machine learning, and feature prioritization to generate accurate predictions. PredictHQ’s systems capture and verify millions of events that impact businesses globally, from sports games to severe weather. Our forecast-grade data is also enriched, with predicted attendance, impact rankings and more so your models and teams can make focus on the most relevant events.
We cover 19 categories of impactful events, we’re always adding new categories that drive volatility for demand forecasting. For example Live TV events allows businesses to tap into the stay-at-home economy and understand how predicted TV viewership impacts demand.
Train your forecasting model with our API
PredictHQ’s API can be plugged into any forecasting platform or model to provide context and intelligence about your demand. It is impactful for enabling event visibility, and especially powerful used in quantitative forecasting methods such as:
С помощью прогноза спроса можно узнать будущие тенденции в вашей сфере деятельности. Для создания такой статистики используются данные, на основе которых можно спрогнозировать рост уровня интереса в отношении определенных товаров и услуг в течение следующих 180 дней.
Используйте прогноз спроса, чтобы определить важные для вашей компании события, и узнать, когда, насколько и как долго будет расти спрос. Чтобы лучше настроить кампании Google Рекламы, изучите информацию об отдельных тенденциях и определите, какая из них наиболее вероятно приведет к повышению спроса.
Когда спрос на прогнозируемую тенденцию начнет расти, нужная информация будет показана как статистика поисковых тенденций. Отслеживая эти данные, вы поймете, что именно является причиной повышения спроса и какими дополнительными возможностями можно воспользоваться.
У вас будет доступ к двум следующим типам статистики:
- Аккаунт. Поисковые тенденции, связанные с существующими объявлениями.
- Рекомендовано. Возможность расширить охват и показывать рекламу по новым запросам, релевантным для вашей компании. Сейчас вы не показываете объявления по этим запросам.
В этой статье рассказывается о том, как работает прогноз спроса:
- зачем нужен прогноз спроса;
- как использовать статистику прогноза спроса.
Зачем нужен прогноз спроса
- Узнавайте, когда спрос, скорее всего, начнет расти. Вероятно, вы уже знаете, какие события являются важными для вашей компании, например Черная пятница. С помощью прогнозируемых поисковых тенденций вы узнаете, когда и насколько начнет расти спрос на товары и услуги, связанные с такими событиями. Используйте эту информацию, чтобы запланировать показ объявлений.
- Определяйте новые события, связанные с деятельностью вашей компании. Существуют менее известные события, которые также могут увеличивать спрос на товары и услуги. Ознакомьтесь с ними и настройте кампании так, чтобы удовлетворить этот спрос.
- Узнавайте о возможностях для роста компании. Ознакомьтесь с предложенными прогнозируемыми тенденциями, чтобы определить товары и услуги, спрос на которые вскоре возрастет в соответствии с данными прогноза. На основе этой информации можно решить, являются ли эти тенденции возможностью для развития вашего бизнеса и маркетинговых кампаний.
- Анализируйте данные о спросе, собранные с начала года. Ознакомьтесь с поисковыми запросами, которые набирают популярность в этом году. Так вы будете понимать, есть ли новые тенденции, к которым нужно подготовиться.
- Сравнивайте показатели эффективности. Вы можете сравнивать эффективность объявлений, размещенных вами и рекламодателями, которые участвовали в тех же аукционах, что и вы. Используйте эту информацию, чтобы создавать стратегии конкуренции для будущих событий.
Как использовать статистику прогноза спроса
1. Просматривайте прогнозируемые будущие тенденции
Перейдите на страницу статистики и найдите раздел с будущими тенденциями. В нем показаны связанные с вашей компанией товары и услуги, спрос на которые начнет значительно расти в течение следующих 180 дней (согласно данным прогноза).
Каждая тенденция содержит следующую информацию, с помощью которой можно подготовиться к изменениям:
- Дата начала прогнозирования. На основе статистики система прогнозирует увеличение количества поисковых запросов в течение этой недели. Учитывайте эту информацию, чтобы подготовиться к росту спроса на отдельные товары и услуги.
- Прогнозируемый уровень интереса. На основе статистики мы прогнозируем увеличение поисковых запросов на указанный процент во время определенного события. С помощью этой информации вы поймете, сколько дополнительных возможностей можно использовать во время события.
- Самый высокий уровень интереса. Система прогнозирует, когда уровень запросов достигнет своего максимального ежедневного объема во время определенного события. С помощью этой информации вы поймете, сколько дополнительных возможностей можно использовать в самые активные дни.
Данные обновляются ежедневно и добавляются в прогнозы по мере их поступления. Прогнозы можно корректировать каждый день, чтобы получать самые точные сведения.
2. Ознакомьтесь с данными по спросу за прошлые периоды
Проанализируйте, какие тенденции были популярны в прошлом месяце. Так вы узнаете, какие товары и услуги могут стать востребованными, когда спрос начнет расти.
Посмотрите, какие поисковые запросы использовались чаще всего в связи с определенной тенденцией в течение последних 7 или 28 дней. Затем сравните свои показатели эффективности, чтобы узнать о дополнительном спросе, который можно удовлетворить.
Изучите информацию о том, какие кампании соответствуют нынешним тенденциям, и проанализируйте настройки этих кампаний. Так вы узнаете, сможете ли удовлетворить прогнозируемый рост спроса. Чтобы подготовиться к изменениям, примените рекомендации, указанные на странице.
Изучите показатели эффективности своей компании и рекламодателей, участвующих в тех же аукционах, что и вы, и создайте стратегию конкуренции для прогнозируемых будущих тенденций. Решите, когда запускать кампании и как максимально удовлетворить спрос.
3. Отслеживайте эффективность в течение периода длительности тенденции
Как только спрос начнет расти, отслеживайте поисковые тенденции и их изменения, а также сравнивайте показатели эффективности и спроса.
Demand forecasting is a combination of two words; the first one is Demand and another forecasting. Demand means outside requirements of a product or service. In general, forecasting means making an estimation in the present for a future occurring event. Here we are going to discuss demand forecasting and its usefulness.
Browse more Topics under Theory Of Demand
It is a technique for estimation of probable demand for a product or services in the future. It is based on the analysis of past demand for that product or service in the present market condition. Demand forecasting should be done on a scientific basis and facts and events related to forecasting should be considered.
Therefore, in simple words, we can say that after gathering information about various aspect of the market and demand based on the past, an attempt may be made to estimate future demand. This concept is called forecasting of demand.
For example, suppose we sold 200, 250, 300 units of product X in the month of January, February, and March respectively. Now we can say that there will be a demand for 250 units approx. of product X in the month of April, if the market condition remains the same.
Usefulness of Demand Forecasting
Demand plays a vital role in the decision making of a business. In competitive market conditions, there is a need to take correct decision and make planning for future events related to business like a sale, production, etc. The effectiveness of a decision taken by business managers depends upon the accuracy of the decision taken by them.
Demand is the most important aspect for business for achieving its objectives. Many decisions of business depend on demand like production, sales, staff requirement, etc. Forecasting is the necessity of business at an international level as well as domestic level.
Demand forecasting reduces risk related to business activities and helps it to take efficient decisions. For firms having production at the mass level, the importance of forecasting had increased more. A good forecasting helps a firm in better planning related to business goals.
There is a huge role of forecasting in functional areas of accounting. Good forecast helps in appropriate production planning, process selection, capacity planning, facility layout planning, and inventory management, etc.
Demand forecasting provides reasonable data for the organization’s capital investment and expansion decision. It also provides a way for the formulation of suitable pricing and advertisement strategies.
Following is the significance of Demand Forecasting:
- Fulfilling objectives of the business
- Preparing the budget
- Taking management decision
- Evaluating performance etc.
Moreover, forecasting is not completely full of proof and correct. It thus helps in evaluating various factors which affect demand and enables management staff to know about various forces relevant to the study of demand behavior.
The Scope of Demand Forecasting
The scope of demand forecasting depends upon the operated area of the firm, present as well as what is proposed in the future. Forecasting can be at an international level if the area of operation is international. If the firm supplies its products and services in the local market then forecasting will be at local level.
The scope should be decided considering the time and cost involved in relation to the benefit of the information acquired through the study of demand. Cost of forecasting and benefit flows from such forecasting should be in a balanced manner.
Types of Forecasting
There are two types of forecasting:
- Based on Economy
- Based on the time period
1. Based on Economy
There are three types of forecasting based on the economy:
- Macro-level forecasting: It deals with the general economic environment relating to the economy as measured by the Index of Industrial Production(IIP), national income and general level of employment, etc.
- Industry level forecasting: Industry level forecasting deals with the demand for the industry’s products as a whole. For example demand for cement in India, demand for clothes in India, etc.
- Firm-level forecasting: It means forecasting the demand for a particular firm’s product. For example, demand for Birla cement, demand for Raymond clothes, etc.
2. Based on the Time Period
Forecasting based on time may be short-term forecasting and long-term forecasting
- Short-term forecasting: It covers a short period of time, depending upon the nature of the industry. It is done generally for six months or less than one year. Short-term forecasting is generally useful in tactical decisions.
- Long-term forecasting casting: Long-term forecasts are for a longer period of time say, two to five years or more. It gives information for major strategic decisions of the firm. For example, expansion of plant capacity, opening a new unit of business, etc.
Solved Example on Demand Forecasting
Q. Which of the following is not correct about demand forecasting?
- Predicts future demand for a product or service.
- Based on the past demand for the product or service.
- It is not based on scientific methods.
- Helps in the managerial decision making.
Ans: The correct option is C. Demand Forecasting is based on scientific methods and proper judgment in order to correctly predict the future demand for a product or service. It gathers information about various aspects of the market like future changes in the selling price, product designs, changes in competition, advertisement campaigns, the purchasing power of the consumers, employment opportunities, population, etc. All the information gathered is scientifically analyzed so as to forecast the future demand for the product.
Pre-season planning is one of the most complicated problems in forecasting. Every year, fashion retailers face the challenge of accurately predicting future demand for the next season.
What will be the baseline demand for a new item that will be introduced to the market six months from now on? This is a billion-dollar question.
Fashion retailers need to recognize and accept that uncertainty is a fact of life in demand forecasting. The first step for retailers to handle this is by segmenting products using advanced prescriptive and predictive analytics such as clustering algorithms to segment products and defining a supply chain strategy for each segment.
When planning for items with high forecast error, there is very little information available on what will be prevailing fashion in the future.
Forecasting for basic items such as a white t-shirt is relatively easier than fashion items, as forecasts can be based on the sales history of similar items.
But consider forecasting for a new fashion item such as a floral printed neon dress. That’s when things get more complicated.
Fashion items have short life cycles, long lead times, and no historical data to draw upon. Rapidly changing customer preferences, new competition, macro influences, and ‘see now buy now’ trends make it incredibly hard to predict demand accurately in the long run. That’s why judging how many units a fashion retailer will need to order from the supplier becomes more like guesswork.
Guess wrong, and you will either run out of inventory -which is a deal-breaker for many consumers, or stock too much inventory that will need to be marked down later.
To our knowledge, there isn’t ‘one right way’ to accurately forecast demand for new items in fashion. But these days, data is plentiful and there are different approaches that retailers apply.
Here are 6 commonly used methods.
1. Relying on designers, buyers, and merchandisers’ opinion
Despite all the developments in AI-based demand forecasting, many fashion retailers still use a gut-based approach and trust their buyers, merchandisers, and designers to make pre-season forecasts.
Merchandisers read the market, buyers pay visits to production and design houses, and designers use their personal observations of what people will buy. In this method, long-term forecasts are limited by intuitions. This is more of an art and a creativity-based method rather than anything scientific.
Besides, every designer or buyer can work on a narrow segment of the merchandise. For example, one can be working on the scarfs, whereas the other can be working on the crop tops. Therefore, using this method alone, fashion retailers can’t foresee the effects such as cannibalization or product substitution accurately.
2. Finding similar items in the past and projecting from there
Fashion retailers might have similar products that are close enough to make comparisons. Think of a retailer who wants to forecast demand for a ‘never-out-of-stock product’ like a black dress for the next season.
Typically, the retailer has access to the historical data of existing or previously sold black dresses for the past few years. Looking at previous years’ data can help in forecasting demand at sufficient levels for existing black dresses. But they can’t be 100% efficient in predicting demand for a new item. Because of the fast-changing nature of the fashion industry, it’s quite impossible to fulfill the demand of tomorrow’s consumers if forecasts are based solely on yesterday’s data of similar products.
3. Working with a trend forecasting agency
Unlike other retail industries, fashion is heavily trend-driven. Fashion retailers can work together with data-driven trend forecasting companies that offer predictive analytics on upcoming trends and products.
Traditionally to forecast sales, you only use the past to project the future. In today’s world, this approach is no more relevant. Although forecasting is still a true craftsmanship, Tech helps to make it a science.
Our Beauty Tech program ‘Demand Sensing’, developed in our Tech Accelerator, re-invents demand forecasting process in a digital world, leveraging data, consumer insights and machine learning. It is a key enabler of the digital transformation of L’Oréal’s Supply Chain.
How does it work?
Accessing multiple high frequency sources of data through connected data platforms optimizes the sales comprehension and anticipation, allowing machine driven planning across the entire distribution network and ensuring the right stock is in the right place at the right time, automatically.
Currently being rolled out at Group level using Agile methodology, the project mixes people from Business, Supply Chain, IT and new skills of Data Engineers and Data Scientists in one team with a common ambition.
What is the magic behind?
L’Oréal has built the Beauty Tech Data Platform with Google that compiles all the relevant data and then uses algorithms and artificial intelligence to automatically create more detailed and more reliable sales forecasts. The data and underlying drivers are exposed to marketing, sales, digital, finance and supply chain departments, driving closer collaboration, breaking down the traditional silos and focusing exchanges on true business drivers rather than on absolute forecasts.
All inputs are integrated in real time enabling algorithms to propose the best decisions to demand planners at business pace. And that’s a real revolution!
The expectations are huge: improve sales forecasts accuracy, product availability, optimize inventory and reduce obsoletes, improve how to steer the business, identify more quickly any changes in trends and low signs of sales acceleration. Magic, right?
For any business that deals with the public, it’s essential to have a way to predict the demand for products or services. Knowing how many customers you’ll have over the course of a given day will help you as you order inventory and schedule staff to accommodate the customers that arrive. Even online businesses need to be able to predict customer demand in order to prepare.
But as many businesses have discovered, there are methods for demand forecasting that can make it more accurate and less of a headache. I’ve had to deal with this on a daily basis for our products and services. What if we order to much, or even worse, too little to keep up with demand.
Here are a few ways to improve your business’s demand forecasting efforts.
Use the right numbers.
Big data may be the hottest trend in business today but as Duetto Research points out in its blog, “8 Steps to Improving Data Forecasts,” those numbers are only as good as the information feeding it. It’s important that you hone-in on the numbers that give you the information you need to make decisions.
In this case, you’re looking for information on pricing trends and demand for certain products. If, for instance, you want to see how many people shopped in your store on a specific day in order to meet that demand, you should narrow down your data to those days. It’s important that your system feed that information accurately throughout the year so that when you’re ready to review the numbers, you have the right information.
Adjust for variables.
There are many factors that go into a business’s daily interactions with customers. When a business is estimating customer traffic, that business may go straight to last year’s numbers to see what those numbers were on that day a year ago. However, there could have been other variables not reflected in those numbers, such as weather or different economic conditions in the area.
Being aware that these variables can exist is the first step. If you aren’t capturing information about those variables, you begin to question sudden spikes or drops in customer activity and question what might have been the cause. You can likely research historical data like weather and realize that you may not have the same customer interactions this year as you have in previous years.
Know your business.
As great as statistical data is, sometimes your intuition tells you it’s going to be a busy day. Once you’ve been in business for a while, you begin to learn more about your own customers. Over time, this allows you to sometimes make an educated guess when it’s going to be a busy day. You may also have a greater understanding of your local area than any statistical software ever could. You know your neighborhood and surrounding areas and this knowledge can sometimes lead you to make decisions that go against what the numbers say.
For best results, employ a combination of data, analytics and customer awareness to forecast demand for your business. You’ll have better results and your customers will notice that you’re interested in the products they want you to keep in stock.
Demand forecasting is an always-evolving practice, with businesses learning as they go. Data science has only recently become readily available to smaller businesses on a larger scale, so many SMBs are still learning to put it to use.
Each year, you’ll reevaluate your demand forecasting efforts and refine them to be more accurate. You’ll also learn to capture additional data as you go that will help you better understand your customers. As a result, you’ll have a customer-focused business that strives to constantly provide better service to consumers.
The best way to make sure you can handle customer demand is to study business activity and identify trends based on historical information. While demand forecasting is a great way to accomplish this, it’s not a perfect science, so it’s important to listen to your own instincts, as well.
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