Financial forecasting

Sales forecasting is a crucial part of the financial planning of a retail business, essentially is the process of estimating future business’s sales. A forecast is based on historical sales data and is done for a particular period of a time in the near future, usually the next calendar year. A sales forecast enables a company to make informed business decisions regarding inventory or cash flow or plan for growth.
It is for this reason, forecasting is a central activity in any retail operations. It is a self-assessment tool that uses past and current sales statistics to intelligently predict future performance.
Forecasting methods anticipate the future purchasing actions of consumers by evaluating past revenue and consumer behavior over the previous months or year to decipher patterns and develop forecasts for the upcoming months. Data is adjusted for seasonal trends, and then a plan for ordering and stocking products may follow the analysis. After fulfillment of current and forthcoming customer purchases and orders, an assessment of the results is compared with previous forecasts, and the entire procedure is repeated.

Function

Optimization of business decisions in a retail organization largely depends on the ability to forecast demand accurately. The optimization of order quantities, stock levels, or store shipment allocation depends on the aptitude of a retailer to forecast accurately demand at store and/or SKU level.

In retail management, forecasting serves to predict and meet the demands of consumers while controlling pricing and inventory. Holding excess inventory adds to overhead costs for a business whereas under stocking may lead to loss of revenue. Forecasting helps the retailer to meet the demands of the customer by understanding consumer purchase patterns better. It is also known to assist in more efficient use of shelf and display space within the retail establishment, in addition to optimal use of inventory space.

Benefits:

Accurate forecasts that meet the forthcoming consumption demands of customers help retail business owners and management to maximize and extend profits over the long term. Forecasting permits price adjustments to correspond with the current level of consumer spending patterns. Maintaining and controlling a sufficient but moderate inventory that meets the need without being excessive also adds to long-term profits in the retail industry.
Major financial benefits of inbuilt forecasting in iVend Retail include:

• Return on capital: Inventory is the largest investment in a retail organization. It is therefore important to forecast inventory by an accurate demand prediction that helps generate return on capital.  High inventory is a drain on capital for any retail organization.
• Cash flow: Accurate forecasts are important to keep capital free that is otherwise tied up in excess inventory.
• Revenues: It has become important to sense customer demands to provide visibility and to market acceptance.  Forecasting helps companies to support better revenue management by adjusting sourcing and distribution strategies and avoiding unproductive accumulation of inventory.
• Earnings: It is seen that good business sense is in adapting to supply chain optimization strategies.  Accurate demand helps in precise supply chain decisions. Costly write offs are cre
  Retail organizations today must strive to satisfy the unique demand for each of their customers. Gone are the good old days of the mass market where a single assortment standard pricing and a single “average location” forecast would satisfy consumer demand in all stores. Often, predicting demand for a single item in a single store can be a difficult proposition. Forecasting demand for all stock-keeping units (SKUs) across all stores and all geographies is a much greater challenge. Forecasts need to adjust to changing demand and quantity patterns, handling seasonality, including difficult-to-predict demand for slow-moving goods. To be able to do that retailers needs to store data for points-of-sale, events, promo­tions, price, weather and other effect parameters which can have an impact on demand. By applying big-data advanced analytics to determine the net effect of promotions and price changes on whole categories retailers can evaluate cross-effects between products, forecast new product sales and account for lost sales to generate a demand forecast. This helps a retailer improve their in-stocks and reduce out-of-stocks.  Complex demand forecasting models can be created using a number of factors like sales figures, basic economic indicators, environmental conditions, etc. If correctly implemented, a data warehouse can significantly help in improving the retailer’s relations with suppliers and can complement the existing supply chain application.
  
  
  Forecasting
   Within an organization, generating forecasts is an important first step in many planning and decision-making processes. Typically, forecasting is performed on a regularly repeating basis for a wide variety of planning purposes. For example, future demand for products and services may be forecast in order to support production planning, marketing activities, resource scheduling and financial planning. Forecasters often follow the same iterative process with each forecasting and planning cycle.
  The process usually involves generating updated forecasts based on the most recent data, reconciling forecasts in a hierarchical manner, identifying and remedying problematic forecasts, adding judgmental overrides to the forecasts based on business knowledge, and publishing the forecasts to other systems or as reports.
  Upon completion of the forecasting process, the planning process determines what actions the organization will take in light of the forecasts. Planning processes not only take into account the forecasts but also the constraints upon the business and overall corporate goals. Much of an organization’s time and effort is spent in the planning process. Improving the reliability of statistical forecasts that feed these processes can result in huge rewards. Improved forecasting often leads to greater operational efficiency, reduced expenses and increased profits.
  Although forecasting is an important function, many organizations rely on a relatively small number of forecasters to generate large numbers of statistical forecasts. Given the relatively small number of forecasters in most organizations, a large degree of automation is often required in order to complete the forecasting process in the time available each planning period. There are multiple planning functions within an organization that the forecasts can serve. Examples include forecasting future demand for production planning purposes, supporting marketing plans (promotion planning), resource planning, financial planning and reporting. Forecasting managers typically follow an iterative process for each forecasting and planning cycle. The steps usually include obtaining the latest data, generating updated forecasts, fixing problem forecasts, conferring with other internal or external parties about the forecasts, adding ad hoc adjustments based on business knowledge, obtaining executive review and change approval, and publishing the forecasts.
   Forecasting managers may reside in a centralized department that serves other departments, or they may be embedded in the individual business units or departments (e.g., supply chain, finance, marketing or sales). As the forecasting function tries to address the more tactical/operational decision level (e.g., support daily and weekly planning processes), forecasters will need to rely on more automated forecasting functionality because of a lack of bandwidth (both time and analytic modeling expertise).  
  Although many people use the word “forecast” to imply only prediction, a forecast is not one number for each future time period. Forecasting looks at historic behavior. The basic assumption is that the future is a repetition of the past. Controllable historic events (like promotions) and uncontrollable historic events (like the SARS outbreak) can be taken into account during modeling.
  However, when creating forecasts the uncontrollable future events are extremely challenging due to their nature (they are completely unknown to us). Measuring the impact of uncontrollable future events is the task of risk management. Techniques like Value at Risk are used to come up with an idea of how much risk we are willing to take in our decision process.
  
  FORECASTING TECHNIQUES
  Forecasting situations vary widely in their time horizons, factors determining actual outcomes, types of data patterns, and many other aspects. To deal with such diverse applications, several techniques have been developed. According to Makridakis (1997) these fall into two major categories: quantitative and qualitative methods.
  Quantitative methods:
• Sufficient quantitative information is available
  • Time series: predicting the continuation of historical patterns such as the growth in sales or gross national product (time series data)
  • Explanatory: understanding how explanatory variables such as prices and advertising affect sales (cross-sectional data)
  • Combination of both approaches. Models which involve both time series and explanatory features are for example dynamic regression models and state space models
• Can be applied when:
  • Information about the past is available
  • This information can be quantified in the form of numerical data
  • It can be assumed that some aspects of the past pattern will continue into the future (assumption of continuity)

Qualitative methods:

• Little or no quantitative information is available, but sufficient qualitative knowledge exists
  • Predicting the speed of telecommunication around the year 2020.
  • Predicting how a large increase in oil prices will affect the consumption of oil
• Examples are judgmental and consensus forecasts using techniques like the so-called Delphi approach

Unpredictable:

• No information is available:
  • Predicting the effects of interplanetary travel
  • Predicting the discovery of a new, very cheap form of energy that produces no pollution
• Underlying process is entirely random
  • Predicting numbers in a lottery

There are special areas like new products forecasting (i.e. no historic data is available), which does not fit into this classification. There are special techniques available like diffusion models and clustering approaches for this kind of task. In general the classification holds, though.

Categories of forecasting methods

Qualitative vs. quantitative methods

Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers, experts; they are appropriate when past data are not available. They are usually applied to intermediate- or long-range decisions. Quantitative forecasting models are used to forecast future data as a function of past data; they are appropriate when past data are available.

Naïve approach

Naïve forecasts are the most cost-effective forecasting model, and provide a benchmark against which more sophisticated models can be compared. For stationary time series data, this approach says that the forecast for any period equals the historical average. For time series data that are stationary in terms of first differences, the naïve forecast equals the previous period's actual value.

Time series methods

Time series methods use historical data as the basis of estimating future outcomes.

• Moving average
• Weighted moving average
• Kalman filtering
• Exponential smoothing
• Autoregressive moving average (ARMA)
• Autoregressive integrated moving average (ARIMA)

Causal / econometric forecasting methods

Some forecasting methods try to identify the underlying factors that might influence the variable that is being forecast. For example, including information about climate patterns might improve the ability of a model to predict umbrella sales. Forecasting models often take account of regular seasonal variations. In addition to climate, such variations can also be due to holidays and customs: for example, one might predict that sales of college football apparel will be higher during the football season than during the off season.  Some forecasts take account of past relationships between variables: if one variable has, for example, been approximately linearly related to another for a long period of time, it may be appropriate to extrapolate such a relationship into the future, without necessarily understanding the reasons for the relationship. Causal methods include:

• Regression analysis includes a large group of methods for predicting future values of a variable using information about other variables. These methods include both parametric (linear or non-linear) and non-parametric techniques.

• Autoregressive moving average with exogenous inputs (ARMAX)

Quantitative forecasting models are often judged against each other by comparing their in-sample or out-of-sample mean square error, although some researchers have advised against this.

Judgmental methods

Judgmental forecasting methods incorporate intuitive judgments, opinions and subjective probability estimates. Methods used for these types of forecasts are:

• Composite forecasts
• Cooke's method
• Delphi method
• Forecast by analogy
• Scenario building
• Statistical surveys

Planning
While the area of decision making is very broad, the term is used for the specific process of exploring how different decision options will influence the plan. During the decision making process the aim is to select the most suitable single number from the forecasted values – taking risk measurements into account. For example: deciding to go for the values of the lower confidence limit instead of the prediction value (mean or median), is a part of the decision making process.
There are more advanced ways to come up with sensible decisions. In particular there are techniques available which involve specific algorithms:
Goal seeking:

• This technique is in particular valuable for defining fact based plans. It requires advanced forecasting models (like a dynamic regression model) which consist of both “inputs” (like business drivers and calendar events) and an “output” (which is the variable to be forecasted)
• Goal seeking algorithmically varies the future values of the “inputs” in order to determine the values that achieve a certain goal (profit, revenue, or cost goal) based on the forecasts
• This technique is potentially useful for understanding how inputs need to be modified in order to achieve a certain goal
• Example: “What are the combinations of sales price and advertising expenditures that achieve a specified sales target?”

Scenario analysis (What-If analysis):

• Again this technique requires advanced forecasting models (like a dynamic regression model) which consist of both “inputs” (like business drivers and calendar events) and an “output” (which is the variable to be forecasted)
• This time the aim is to figure out the impact of changes in the inputs on the output. In scenario analysis the user modifies the future values of the inputs to specific values and then evaluates the effect on the forecasts
• Example: “What happens to the predicted sales numbers if the organization increases the sales price and decreases the advertising expenditures?”

Optimization:

• This technique requires advanced forecasting models (like a dynamic regression model) which consist of both “inputs” (like business drivers and calendar events) and an “output” (which is the variable to be forecasted), also
• The aim is to come up with the best possible combination of inputs. Optimization algorithmically varies the future values of the inputs to find the optimum of an objective function (profit, revenue, or cost function) based on the forecast model
• Example: “What is the optimal sales price and advertising expenditure combination that maximizes profit?”

Other areas which need to be considered in decision making are techniques for market research, management science, and quality management. While all of these provide valuable insights to the decision making process they are not considered here. The decision making process requires human intervention, but it can be supported by software. Upon completion of the forecasting process, the planning process determines what actions the organization will take in light of the forecasts. Planning processes not only take into account the forecasts but also the constraints upon the business and overall corporate goals. Much of an organization’s time and effort is spent in the planning process. Improving the reliability of statistical forecasts that feed these processes can result in huge rewards. Improved forecasting often leads to greater operational efficiency, reduced expenses and increased profits.  

There are multiple planning functions within an organization that the forecasts can serve. Examples include forecasting future demand for production planning purposes, supporting marketing plans (promotion planning), resource planning, financial planning and reporting. Forecasting managers typically follow an iterative process for each forecasting and planning cycle. The steps usually include obtaining the latest data, generating updated forecasts, fixing problem forecasts, conferring with other internal or external parties about the forecasts, adding ad hoc adjustments based on business knowledge, obtaining executive review and change approval, and publishing the forecasts.

Planning is a fairly diversified task, which happens on different levels: strategic planning, tactical planning and operational planning. In general it is concerned with sketching out a pathway for specific actions which a company or an individual are going to make. While forecasting is more concerned with what will happen, planning deals with what should happen, given the alignment of the resources to make it happen. 

The relationship aligns different activities and how they interact with each other:

Forecasting	An unbiased "best guess" of what will happen – provided that “history repeats itself”, together with some estimate of the uncertainty.
Risk management	Measuring the impact of uncontrollable future events
Decision Making	Exploration of how different decisions will impact the plan.The process of coming up with a decision.
Planning	What should happen, given the alignment of resources to make it happen

Sometimes management disagree when the forecasts start to differ from their plans (especially in an unfavorable direction) but that is precisely the point of the forecast - to tell you when you are off plan. When you know a gap exists, you can at least take action (either adjust the plan to more accurately reflect the forecast, or else re-align your resources (advertise, change pricing, etc.) to cause the forecast to become closer to the plan). This leads to decision making activities in turn.

New product forecasting 

Forecasting new products is a very difficult, but essential, task for any retailer. There are three issues with forecasting new products – response to initial price, predicting sales volume with no history and modeling cross-effects. Based on similar products within the share group, one can determine how the consumer responds to price and promotions for products in the share group, and it also knows how cross-effects with other products in the group affect it, so it will be able to generate a predicted sales curve for the new item.

Identify intermittent/slow-moving items 

This is a feature of most retailers and is an important consideration when forecasting demand. Some Forecasting software has functionality to pool data across stores or up the product hierarchy in order to find sufficient and relevant data with which to generate a forecast.

Consumer response to price and promotion 

In order to understand how consumers will respond to price changes, promotional and marketing activity, analytically-mature retailers can analyzes share groups defined by product and consumer attributes in order to calculate elasticity at a store level. When the offering has quantified how the consumer will respond, it can calculate the impact on future demand.

When you have in-depth analysis of past performance combined with plans and forecasts of future customer demand, you can more accurately allocate and restock merchandise across channels and stores. Truly understanding customer demand patterns, not just what was purchased, but what those patterns reveal about future potential, enables you to send the correct assortments, size and case-pack distributions to the correct stores. Daily price, promotion and markdown optimization ensures that items are priced for optimal profitability, both preseason and in-season. Space automation and optimization ensure that departmental sales and profit per square foot are maximized, and that products are given the correct inventory and space on the shelf. Optimized fulfillment ensures that products are allocated or replenished according to demand. Accurate analysis also results in a more efficient use of manpower in picking, packing and shipping the first wave of product while minimizing additional expenses. In-store and customer-facing activities rely on a multitude of support functions behind the scenes, all of which must also be optimized. For example, now that analytics have given you an accurate forecast of demand, by hour, by day, by location, by promotion and by price change. This knowledge must guide decisions for inventory replenishment, as well as for staffing on all store floors, catalog call centers and fleet crews delivering orders from distribution center to stores.

Your sales forecast is the backbone of your business plan. People measure a business and its growth by sales, and your sales forecast sets the standard for expenses, profits and growth.
When it comes to forecasting sales, don't fall for the trap that says forecasting takes training, mathematics or advanced degrees. Forecasting is mainly educated guessing. So don't expect to get it perfect; just make it reasonable. There's no business owner who isn't qualified to forecast sales--you don't need a business degree or accountant's certification. What you need is common sense, research of the factors, and motivation to make an educated guess.
Your sales forecast in a business plan should show sales by month for the next 12 months--at least--and then by year for the following two to five years. Three years, total, is generally enough for most business plans.

If you have more than one line of sales, show each line of sales separately and add them up. If you have more than 10 or so lines of sales, summarize them and consolidate. Remember, this is business planning, not accounting, so it has to be reasonable, but it doesn't need too much detail. Here are some tips to get you started:

• Develop a unit sales projection. Where you can, start by forecasting unit sales per month. Not all businesses sell by units, but most do, and it's easier to forecast by breaking things down into their component parts. Product-oriented businesses obviously sell in units, but so do a lot of service businesses. For example, accountants and attorneys sell hours, taxis sell rides, and restaurants sell meals.
• Use past data if you have it. Whenever you have past sales data, your best forecasting aid is the most recent past. There are some statistical analysis techniques that take past data and project it forward into the future. You can get just about the same results by projecting your two most recent years of sales by month on a line chart and then visually tracking it forward along the same line. Statistical tools are a nice addition, but they're rarely as valuable in a business plan as human common sense, particularly if it's guided by analysis.
• Use factors for a new product. Having a new product is no excuse for not having a sales forecast. Of course you don't know what's going to happen, but that's no excuse for not drafting a sales projection. Nobody who plans a new product knows the future--you simply make educated guesses. So break it down by finding important decision factors or components of sales. If you have a completely new product with no history, find an existing product to use as a guide. For example, if you have the next great computer game, base your forecast on sales of a similar computer game. If you have a new auto accessory, look at sales of other auto accessories. Analysts projected sales of fax machines before they were released to the market by looking at typewriters and copiers.
• Break the purchase down into factors. For example, you can forecast sales in a restaurant by looking at a reasonable number of tables occupied at different hours of the day and then multiplying the percent of tables occupied by the average estimated revenue per table. Some people project sales in certain kinds of retail businesses by investigating the average sales per square foot in similar businesses.
• Be sure to project prices. The next step is prices. You've projected unit sales monthly for 12 months and then annually, so you must also project your prices. Think of this as a simple spreadsheet that adds the units of different sales items in one section, then sets the estimated prices in a second section. A third section then multiplies units times price to calculate sales. The math is simple--the hard part is making that estimated guess of unit sales.

A fourth section of your projected prices will set the average costs per unit. You want to set costs because a lot of financial analysis focuses on gross margin, which is sales less cost of sales. For financial reasons, cost of sales, also known as costs of goods sold and direct costs, are different from the other expenses that come out of profits.
The cost of sales isn't what you pay salespeople or for advertising. It's the amount you pay to buy what you sell. This is usually easy to understand. In any retail store, for example, the cost of goods sold is what the store pays for the products it sells. In service businesses, the costs of sales can be less obvious, but it can still be figured out.
Finally, in a fifth and final section, you multiply unit sales times average cost per unit to calculate your cost of sales. This gives you a sales forecast that you can use for the rest of your financial projections. The first place you'll use it is at the beginning of your profit and loss statement, which normally starts with sales and cost of sales.
Of course, not all businesses fit easily into the units sales model. Some business plans will have sales forecasts that project dollar sales only, by line of sales, and then direct costs, by other factors. For example, a taxi business might simply estimate total fares as its sales forecast and gasoline, maintenance and other items as its cost of sales. A graphic artist might stick with the simple dollar-value sales forecast and project cost of sales as photocopies, color proofs, etc. In the end, it's always your plan, so you have to make the decisions that are best for you.


Online retail businesses have to a lot to worry about and fight against but perhaps nothing quite screams danger as the financial aspect. Shallow pockets and thin resources, overwhelming competition from both big brand-name businesses and and smaller online retailers, declining sales and so on – these are just some of the challenges faced on a regular basis. Thus, it’s only natural to try and squeeze an extra dollar here and there out of the existing financial operations. Naturally, there are some quick fixes that turn things around but these are all short-sighted solutions. Any online retail store built on a sound financial basis knows better than this but not every business has the benefit of that insight.

So, what can you do?
With growing pressure to generate better sales and minimize costs to increase profits, you must center on creating more revenue from your existing operations while continuing to add new ones to the fold. Easier said than done, right? Not necessarily as there are some things to consider in order to improve your retail store’s financial operations. Don’t worry, we’ll let you in on what you need to do so you can do right by yourself. Ready? Let’s go.

1. Forecast your future

To get a good sense of how your money is flowing around and what the future holds in that regard, what you need is a financial forecast. Being prepared for the future endeavors, especially in the case of growing businesses, allows the power of foresight to know what to expect and how to deal with unfavorable movements, which are all but bound to happen in the tense retail market. A financial forecast is one of the best practices for small and medium retail online businesses to gain meaningful insights on both a monthly or weekly basis. It will allow them to identify peaks in expenses in time of their most selling periods and show where income will be the highest. There is a good, accurate and fast way to do it, which brings us to our next point…

2. Leverage technology by using retail analytics

If there is one thing that makes life on this planet much easier, it’s technology. Running your financial operations can also be much easier if you utilize it the right way. In the case of the aforementioned forecast, the process itself could be as simple as paper and pencil or a digitized version of it in the form of an Excel sheet. Or, you can opt for retail analytics software to get a more formal and accurate result. Naturally, two of the three methods are time-consuming and largely inefficient but are also deemed as more financially acceptable solutions. Looking long-term, they are also not sustainable, something that modern tech puts maximum effort in.
On its own, technology is an investment with significant proportions. However, what you get in return far surpasses the investment-to-benefit ratio. For instance, transferring your modus operandi on cloud simplifies and streamlines the whole business operation and puts in on a higher level. By “upgrading” to cloud technology in the form of retail analytics, you make your data open for multiple users from different locations to access the data and work on it. The software helps minimize the risks of human assumptions that can trigger margin loss, overstock situations, and missing the boat on market trends. That directly impacts the efficiency while reducing labor costs. And that’s just one example out of the many features retail analytics software possesses..
In retail, it’s imperative to know as much about the market as possible. In that regard, you can leverage technology to use data analytics to improve operational performance across all channels. Having insights into store-level demands in real time makes sure your bestselling items remain in stock. Optimize your pricing by keeping a close eye on competitive pricing and discount strategies. From the marketing standpoint, gaining visibility into promotional performance can help you adapt your strategy, while also benefiting your forecasting and recognizing various market movements like seasonal trends, hot items and different opportunities to maximize revenue.
Ultimately, this translates to a win-win scenario – more satisfied customers and reduced cost for you. The bottom line here is that using technology can help your retail store in a variety of ways, from improving productivity by automating a bunch of manual tasks to helping you better oversee your inventories and everything in between. Speaking of inventory…

3. Perform inventory segmentation

Because of the large scope and competitiveness of the retail market, a business is better off segmenting its inventory. Why? Because an inventory is not just a statistical overview. Looking at it, you want to gain insights into your sales. Does your cash flow revolve around products that don’t sell on a regular basis? Can you use that money to create better profits through other better-selling items? There could be lots of money tied up in inventory without actual good reason. It’s all about meeting customers’ demand and needs.
Because the way online stores function, much of their data insights are broken into pieces, spread across the organization. Collecting that data into a single, comprehensive view allows better financial results because you get a scoop on things like growth, consumer trends and else. By uniting and integrating all of its data into a single source, an online retailer is set to reap the benefits of knowing its most important competitive performance metrics, as well as make better overall business decisions. And faster, too, as nothing beats on-the-spot insights.


Retailing is as much about local mindset as it is about thinking globally, with the whole world as a selling stage. As such, it provides ample opportunity and company, which is where handling of your financial operations comes into play. A retail business simply must understand its financial numbers and what they are saying about the state of the business. Along with financial statements, business analysis and intelligence are key to making that happen. Going the modern tech route allows you the dissect all the financial angles of your operations, while also providing you with the benefit of time to have other bright ideas that can improve your business’ standing. That way, you will be able to devise an accurate business plan and hit all the right spots.