Saturday, July 21, 2018

demand forecasting in retail


To achieve optimal fulfilment performance, retailers must now forecast how consumers will shop and understand the influences behind purchasing decisions.

 Forecasting demand for products and positioning inventory has never been a simple task for retailers, but it has become increasingly difficult in the digital age when shoppers’ purchasing behaviour has become varied and unpredictable.

Demand forecasting gives businesses the ability to use historical data on markets to help plan for future trends. As more data on consumers and products becomes available, the need to use this data to anticipate demand is critical for establishing a long-term model for growth.

In a sense, demand forecasting is attempting to replicate human knowledge of consumers once found in a local store. Long ago, retailers could rely on the instinct and intuition of shopkeepers. They knew their customers by name, but, more importantly, they also knew buying preferences, seasonal trends, product affinities and likely future purchases.
Demand forecasting attempts to replicate that sophistication through analytics-based evaluation of available data. By examining buying behavior and other bits of data left behind by the consumer, a retailer can mimic that knowledge on a broader scale.


Anticipating demand for the modern buyer
Today’s consumer often journeys from digital space to physical space and back again, moving among devices, apps and displays. The buying process might start with researching a product online, continue with comparing prices from a mobile device, and finish with an in-store purchase. Or consumers may see merchandise in a store, then search on their phones to score a last-minute deal.
In a world where you can have practically any item shipped to your door, it’s important for retailers to make a connection with the buyer. A variety of buying options is a delight to consumers – and a rich source of intelligence for retailers, if you know how to capitalize on it.
This omnichannel retail environment intensifies the need for better answers to the perennial questions of retail planning. What merchandise should be stocked, in what sizes/colors, at what quantities, in which locations? How, where and when should products be displayed, priced, promoted, ordered or shipped? How can we maximize profit without eroding the quality of the shopping experience and customer satisfaction?
Demand forecasting gives you the ability to answer these questions. But the sheer number of variables involved in the omnichannel world makes demand forecasting and merchandise planning on a global scale highly complex.

Specifically, the winners were the ones who engaged in seven productive habits:
  1. They put a high value on analytics. Fully 77 percent of winning retailers rated analytics as “very important” to their retail success, compared to 48 percent of average or lagging retailers. They know they can’t get by without integrating more predictive capabilities into their decision-making processes, and they understand why this is so.

  2. They value more rigorous forecasting. Nearly two-thirds of winning retailers (74 percent) rated demand forecasting technologies as “very important” to their success, compared to 58 percent for the others.

  3. They bring analytics into the process earlier. They know that basing future plans on prior year or season sales will create self-fulfilling prophecies. The lost opportunities of the past will be repeated in the future. Instead, they bring insights from customer analytics into the demand forecasting process upfront, not just as a sanity check at the end of the planning process.

  4. They are optimistic about information. Higher-performing retailers are twice as likely to expect big things from demand forecasting. They are also nearly twice as likely to “highly value” attribute-based merchandise planning systems.

  5. They are ahead of the game in implementation. Winners were twice as likely to have technologies already in place for attribute-based merchandise planning and demand forecasting to help with price, promotional or assortment planning. And they are twice as likely to have integrated their planning, allocation and replenishment systems.

  6. They make smarter allocation decisions. Winners know they need to put products where they are most wanted or needed, and they trust demand forecasting to help them make the best localized store assortments and fulfillment decisions for direct-to-consumer orders.

  7. They blend art and science. While information is seen as a critical asset – along with tangible assets such as stores, distribution centers and inventories – 62 percent of the winners also credit their success to “a healthy blend of art and science.” They value decision makers’ years of experience and understand the importance of well-tuned internal operational processes.
The Retail Systems Research report closes with a checklist of do’s and don’ts related to demand forecasting, customer analytics and localized assortments for retailers who want to be (or remain) winners.
“If retailers can follow these simple steps, they’ll go a long way towards optimizing their merchandising life cycle and creating a more compelling buying experience for customers,” the report states. “If they don’t, they risk being consigned to the dustbin of history.”

Drive Profitable Planning and Operations

  • Translate detailed and summarized data into forward-looking actionable insights
  • Automate data management and cleansing, and make manual updates by exception
  • Apply the appropriate forecasting method to observed or predicted selling patterns
  • Adjust forecasts to correct for out-of-stocks, seasonality, recent trends, and other causal factors
  • Improve forecast accuracy for promoted, non-promoted, short- and long-lifecycle items
  • Deploy via Oracle Cloud for Industries to simplify integration, improve agility, and ease the burden of implementation for your IT resources (optional deployment method)

Demand forecasting or demand planning?

Does your company do ‘demand forecasting’ or ‘demand planning’? I have seen numerous consultants address this semantic difference and argue for one over the other – most often for ‘planning’ over ‘forecasting’ as it gives a more active echo.
However both are needed in supply chain management and are indeed different things. Planning involves a collection of actions you are going to perform to obtain the results that you are seeking. In sales that might include, for example, pricing, campaigning and other marketing activity. Forecasting is always a numeric estimate of future outcome, and is based on historical performance, a selected plan, and the potential changes in other factors in the environment. A plan can be controlled, but the forecast always has an element of uncertainty; and the more you understand that uncertainty the better.

What’s the difference?

The difference between a plan and a forecast is best seen in stock management in a retail DC. For example for a future sales promotion you need to calculate a demand forecast for each product for each store – just to be able to order the right amount to fulfill the demand. Then you come to the delivery plan side – you can plan to ship e.g. 70% of the forecast volume 2 days before the promotion to give the stores enough time and goods to build up promotional displays. The pre-delivery is the biggest demand fluctuation of the promotion from the DC point of view.
However it doesn’t even need to be forecast – it is basically a delivery plan, and you can get a precise picture of the volumes to be sent to each store and thus the required goods and resources to make those deliveries beforehand. This example also shows that even though retail demand forecasting and replenishment are often seen as separate processes, they cannot be totally separated.


Most future predictions hardly turn out to be true due to some unexpected circumstances or changes in the external environment. The same can be said for demand forecasting in the retail industry as well. But, retailers still perform demand forecasting as it is important for inventory management, production planning, and measuring future capacity requirements. Precise demand forecasting provides businesses with valuable information about their potential in the current market to make smart decisions on market potential, pricing, and business growth strategies. In this blog, Quantzig has listed the top demand forecasting trends in the retail industry.
According to the demand forecasting experts at Quantzig, “The retail industry simply can’t survive without demand forecasting as they risk making poor decisions about their inventory and products, which might result in lost opportunities.”
Speak to an expert to know more about the scope of our research.
Top demand forecasting trends in the retail industry
  • Rising popularity of bottom-up forecasting: Today, the retail industry functions over many channels, which demands inventory positioning in several locations. As a result, retailers have to emphasize on bottom-up forecasting to meet the demand through various channels. Using such an approach helps them fulfill orders from both e-commerce and traditional retail channels for a wide array of varieties. It allows the retailers to meet customer demands more rapidly and distribute goods through the customers’ choice of channel. When the need arises, such methods can also help retailers balance inventory between distribution centers and stores through high-frequency inter-depot transfers.
  • Focus on forecast quality: When carrying out demand forecasting, retailers usually look at demand signals. But, retailers with less sophisticated planning abilities often seek constancy in demand signals, which is frequently fragmented. As a result, they look for a combined model that lets all stakeholders cooperate via “what-if” simulations. Consequently, retailers have started looking for ways to quantify forecast quality by looking at external associations, including end users and suppliers to get better forecasts, which can then be shared with the sales team and suppliers.
  • Fresh view towards long-tail items: A majority of the slow-moving or long-tailed items sell because they are in the inventory. Ensuring service levels is the key to mastering demand forecasting for slow-moving items. Such items cannot be formed consistently, so the retailers turn towards supply chain planning software to robotically model stock-to-service level, which precisely lists how much stock they need.
  • Visit our page, to view a comprehensive list of top demand forecasting trends in the retail industry
Quantzig is a pure-play analytics advisory firm concentrated on leveraging analytics for prudent decision making and offering solutions to clients across several industrial sectors.
Request a free demo to see how Quantzig’s solutions can help you.
View the complete list of the top demand forecasting trends in the retail industry:
https://www.quantzig.com/blog/trends-demand-forecasting-retail-industry
About Quantzig
Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India. For more than 15 years, we have assisted our clients across the globe with end-to-end data modeling capabilities to leverage analytics for prudent decision making. Today, our firm consists of 120+ clients, including 45 Fortune 500 companies. For more information on all of Quantzig’s services and the solutions they have provided to Fortune 500 clients across all industries, please contact us.

Demand Forecasting

With customers expecting immediate responses irrespective of location or time, you need to meet these needs rapidly, and cost-effectively. Our experts can build predictive algorithms to better forecast demand based on internal/external factors and ensure that your Supply Chain is well-oiled to cater to these dynamic needs. We can create custom solutions? - reflecting your unique sales history while utilizing correct statistical methods and the most effective process to refine statistical results and capture collective knowledge.
  • Point of Sale: POS-based forecasting at retailer, distribution center, and store level
  • Deviation Analysis: Forecasted and actual sales are compared at both the store and SKU level
  • Dashboards and Customized Solutions: Key performance enterprise dashboards with trend analysis and predictive analytics for a multi-dimensional drilldown to increase visibility and insight on a global level. Customized solutions adjust to the needs of each individual organization, taking instead of retrofitting a pre-defined product or application

Most retailers are facing a shrinking operating “margin for error”. So it’s not surprising that many are looking for more accurate demand forecasting and intelligent stock replenishment. In a report entitled Market Guide for Retail Forecasting and Replenishment Solutions, Gartner analyst Mike Griswold spotlights seven recent trends in this area.
1/ Multichannel retailing is requiring inventory positioning in more locations than ever before and causing retailers to focus on “bottom up” forecasting, says Griswold. Until now, he says, many retailers have planned less than half of their assortment at the item/location level, but now they’re looking for platforms that can scan disaggregated demand streams down to the channel and stock-keeping unit (SKU) level. These retailers want their supply chains to be able to fulfill both e-commerce and brick-and-mortar purchases for a wide item assortment of items in a way that is “inventory agnostic”. They look to match the dynamic evolution of demand and give customers multiple order collection and delivery options. If needed, they look to balance inventory between stores and DCs via high-frequency inter-depot transfers.
2/ Less mature retailers are also focused on the demand signal. Griswold reports that retailers with less mature planning capabilities are seeking more consistent ownership of the demand signal, which is often fragmented and often owned by merchandising (especially in apparel). They want a single, unified model that allows stakeholder collaboration via “what-if” simulations of trade-offs. More retailers are now measuring forecast quality, forecast value-add (FVA) and bias, Griswold adds. They’re also looking for more external collaboration, to get better forecasts and share them with sales channels and suppliers.
3/ More sophisticated retailers understand that servicing “lumpy,” long-tail demand is driven by inventory and not forecast accuracy. For the long tail—slow-moving items with unpredictable demand—the key to meeting demand is to ensure service levels. For these items that can’t be reliably planned, retailers want supply chain planning (SCP) software that can accurately and automatically model stock-to-service levels to offer a clear picture of demand variability: how much stock they need, the mix, and where and when they need it.
4/ Retailers of all maturities are looking to automate forecasting and replenishment to improve planner productivity. With increasing pressure on margins, retailers don’t want to add more planners, and automation is no longer a dirty word, he says. They want the automated application to do the “heavy lifting”, allowing their planners to add value and business acumen.
5/ Product returns are increasingly costly. More e-commerce means more returns and some retailers see that more diligence on the forecasting and replenishment side—as well as analytics—can help them better predict and minimize returns at the outset, and then better manage and reposition the returned goods across their inventory.
6/ Cloud-based applications are becoming more acceptable as deployment platforms. Smaller retailers have been quicker to adapt to the cloud than larger ones, Griswold says, but retailers in general are becoming more comfortable with cloud platform viability and scalability. They see that cloud offers the flexibility to scale to demand, help companies get up and running quickly without extensive IT resources, and hedge against IT infrastructure obsolescence.
7/ Retailers are beginning to understand how machine learning differs from statistical techniques. Griswold says that retailers are beginning to comprehend the benefits of machine learning in the retail supply chain. Machine learning can analyze demand variables and their complex interactions and patterns in automated fashion, and “self-learn” demand profiles. Machine learning’s ability to collate and analyze clusters of big data can also help predict demand beforehand for launches, promotions, and markdowns involving products that share similar characteristics.
  • Forecasts that Improve Over Time: An explicit process creates a benchmark against which future forecasts can be compared and improved
  • Forecast Integrated with Marketing and Promotions: Procurement and production managers can respond to demand planning projections from sales and marketing activities

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Why Are Operating Margins Important?

What is considered a healthy operating profit margin?
A:
Typically, an operating profit margin of a company should be compared to its industry or a benchmark index like the S&P 500. For example, the average operating profit margin for the S&P was roughly 11% for 2017. A company that has an operating profit margin higher than 11% would have outperformed the overall market. However, it's important to take into consideration that average profit margins vary significantly between industries.
Operating profit margin is one of the key profitability ratios that investors and analysts consider when evaluating a company. Operating margin is considered to be a good indicator of how efficiently a company manages expenses because it reveals the amount of revenue returned to a company once it has covered virtually all of both its fixed and variable expenses except for taxes and interest.

What Does Operating Profit Margin Tell Investors and Business Owners

The operating profit margin informs both business owners and investors about a company's ability to turn a dollar of revenue into a dollar of profit after accounting for all the expenses required to run the business. This profitability metric is calculated by dividing the company's operating income by its total revenue. There are two components that go into calculating operating profit margin: revenue and operating profit.
Revenue is the top line on a company's income statement. Revenue, which is sometimes referred to as net sales, reflects the total amount of income generated by the sale of goods or services. Revenue refers only to the positive cash flow directly attributable to primary operations. 
Operating profit sits further down the income statement and is derived from its predecessor, gross profit. Gross profit is revenue minus all the expenses associated with the production of items for sale, called cost of goods sold (COGS). Since gross profit is a rather simplistic view of a company's profitability, operating profit takes it one step further by subtracting all overhead, administrative and operational expenses from gross profit. Any expense necessary to keep a business running is included, such as rent, utilities, payroll, employee benefits, and insurance premiums. 

How Operating Profit Margin Is Calculated

By dividing operating profit by total revenue, the operating profit margin becomes a more refined metric. Operating profit is reported in dollars, whereas its corresponding profit margin is reported as a percentage of each revenue dollar. The formula is as follows:
One of the best ways to evaluate a company's operational efficiency is to view the company's operating margin as it changes over time. Rising operating margins show a company that is managing its costs and increasing its profits. Margins above the industry average or the overall market indicate financial efficiency and stability. However, margins below the industry average might indicate financial vulnerability to an economic downturn or financial distress if a trend develops. 
Operating profit margins vary greatly across different industries and sectors. For example, average operating margins in the retail clothing industry run lower than the average operating profit margins in the telecommunications sector. Large, national-chain retailers can function with lower margins due to the massive volume of their sales. Conversely, small, independent businesses need higher margins in order to cover costs and still make a profit.

Example of Operating Profit Margin

Apple Inc. (AAPL)
Apple reported an operating income number of roughly $61 billion (highlighted in blue) for the fiscal year ended September 30, 2017, as shown in its consolidated 10K statement below. Apple's total sales or revenue was $229 billion for the same period.
As a result, Apple's operating profit margin for 2017 was 26.6% ($61/$229). However, the number by itself doesn't tell us much until we compare it to prior years. 
  • 2017 Operating margin = 26.6% ($61/$229).
  • 2016 Operating margin = 27.9% ($60/$215).
  • 2015 Operating margin = 30.0% ($71/$234).
By analyzing multiple years, we can see that a trend has developed over the past three years where Apple's operating margins have fallen by 3.4% since 2015. Analysis of a company's operating margin should focus on how it compares to its industry average and its closest competitors, along with whether the trend of the company's margin is generally increasing or decreasing year by year.

The Bottom Line

A consistently healthy bottom line depends on rising operating profits over time. Companies use operating profit margin not only to spot trends in growth, but also to pinpoint unnecessary expenses to determine where cost-cutting measures can boost their bottom line. To gauge a company's performance relative to its peers, investors can compare its finances to other companies within the same industry. However, this metric is also useful in the development of an effective business strategy as well as serving as a comparative metric for investors.
For more financial analysis, please read "How the Income Statement and Balance Sheet Differ?"

We find accounting profitability exclusively on the income statement, which teases out four levels of profit or profit margins: gross profit, operating profit, pre-tax profit and net profit.
Conceptually, the income statement assumes the following sequence: A company takes in sales revenue, then pays direct costs of the product of service. What’s left is gross margin. Then it pays indirect costs like company headquarters, advertising, and R&D. What’s left is operating margin. Then it pays interest on debt and adds or subtracts any unusual charges or inflows unrelated to the company’s main business with pre-tax margin left over. Then it pays taxes, leaving net margin, also known as net income, which is the very bottom line.
Three logistical points before getting to the math:
  1. Semantically, “profit,” “income,” and “margin,” are all used interchangeably, although margin usually refers to a percentage, whereas profit and income exclusively denote monetary amounts.
  2. When talking about profitability analysis, percentages are more frequently used than raw numbers because they enable comparison among companies and across a company’s own time horizon.
  3. Finding margin numbers is easy these days. You can never go wrong pulling the actual numbers from a company’s filings, but many of financial websites have them pre-calculated. Be careful though! Auto-calculating tools have been known to goof on rare occasion.
The major profit margins all compare some level of residual (leftover) profit to sales. For instance, a 42% gross margin means that for every $100 in revenue, the company pays $58 in costs directly connected to producing the product or service, leaving $42 as gross profit.

The Major Margins

Now we’ll see how WD-40 Co.’s (WDFC) 2016 margins look (data from page F-3 on the 2016 10-K):
  • Gross profit margin: $214.4m / $380.7m = 56%
  • Operating profit margin: $71.3m / $380.7m = 19%
  • Pre-tax profit margin: $72.8m / $380.7m = 19%
  • Net profit margin: $52.6m / $380.7m = 14%
How should we feel about these numbers? The quick answer is that these numbers show a strong business, at least by most standards.
For example, data from New York University finance professor Aswath Damodaran’s website indicates that as of January 2017, the average U.S. public company industry net profit margin was just 6%, with operating margin around 10%, well below WD-40’s. WD-40 (named for chemist Norm Larsen’s 40th attempt at a water displacement formula to coat intercontinental ballistic missiles) has a dominant market position with strong pricing power. Its products are cheap enough in the first place that consumers aren’t particularly motivated to save a few dimes on a generic substitute.
Peer comparisons are a bit tricky because WD-40 is really the only pure-play publicly traded lubricant company, which, in fairness, is a testament to the company’s dominance. Auto-populated investing websites will list industrial chemical companies like Dow and E.I. duPont as comparables or competitors; they’re much closer kin than, say, biotechs or railroads, but they’re not perfect yardsticks. And because WD-40 has held its competitive ground since it was founded in 1953 (long enough ago that if it were a person, WD-40 would already be collecting Social Security), we don’t have compelling evidence to believe that its margins will mean-revert to industrial chemical averages.

Margin of Error: Caveats About Using Income Statement Profit Margins

  1. Quoting profits in raw dollar (or other currency) terms and using percentage terms both come with problems. The raw currency number accurately depicts aggregate profit, but it is a clunky tool for comparison. Percentages accurately show per-unit profitability, but say nothing about units sold. Returning to professor Damodaran’s data for an example, U.S. grocery stores have a 1.89% average net margin, meaning they net just $1.89 for every $100 of merchandise sold. That sounds wimpy, but the end game is cash in the bank, and supermarkets can make up for their low margin with high volume.
  2. The income statement tells us very little about capital structure. A company could issue a slug of debt or sell a bunch of shares to get cash to boost sales and profits, but profits alone don’t reveal whether this was a value-adding move for shareholders.
  3. Income statement numbers are based on accrual accounting, and are thereby more subject to manipulation than cash flows.
  4. The income statement (at least under many countries’ accounting rules) doesn’t fairly capture the economics of all industries. For Real Estate Investment Trusts (REITs), for example, most analysts massage net income into a measure called funds from operations (FFO) that undoes an accrual called depreciation. Plug-and-chug types may miss these nuances if not careful.
The key limitation in divining profitability solely from the income statement is caveat (2) above: The income statement tells us only part of the profit picture – the inflows and immediate expenses used to generate those inflows – but not about the capital resources, like asset or equity base, required. For that, we’ll have to look at the balance sheet (and another lesson).

EBITDA: Earnings Before Bad Stuff?

One profitability number you won’t see on the income statement, but will see in a lot of other places (especially the haunts of investment bankers and Wall Street analysts) is earnings before interest, taxes, depreciation, and amortization, or EBITDA.
EBITDA, which would fit in between gross profit and operating profit were it to be on the income statement, is simply operating profit (or earnings before interest and taxes; i.e., EBIT) with the previously subtracted accrual charges of depreciation (the “D”) and amortization (the “A”) added back in.
What’s the point?
EBITDA came into vogue among 1980s investment bankers looking for a quick and dirty cash flow proxy (the Statement of Cash Flows wasn’t the norm until 1988). EBITDA caught a second wave in the tech-crazed 1990s, when earlier and earlier-stage companies saw IPO and acquisition interest.
Acquisition types like to express prices in terms of multiples of profit or cash flow: 20 times earnings, 12 times EBIT, etc.. But many hot companies of this generation didn’t have positive net income (so P/E multiples were out), and often had losses at the pre-tax and operating levels, too. Undeterred, bankers and sell-side Wall Street analysts went up and then off the income statement to produce a number more likely to be positive and easily comparable for early stage companies: EBITDA.
The value of EBITDA depends on its use. The metric leaves out a number of relevant expenses (“earnings before bad stuff” is one nickname), and the metric arguably sees too much use as a proxy for cash flow. Such cases suggest either poor understanding or even a shade of manipulativeness on behalf of the user.
Apparently, WD-40 didn’t get the memo about EBITDA’s warts: Page 21 of its 2016 proxy proclaims that EBITDA is both the #1 and #2 metric (per-segment, and consolidated, respectively) in determining management’s bonus. Fortunately, management appears to be well-behaved and effective, but an EBITDA target is considered risky because it leaves the door open for management to borrow vast sums of money to juice sales and EBITDA: The metric is blind to interest expense, debt outstanding, and capital expenditures.

Framing the Margins

Gross Profit Margin: Start with sales and take out costs directly related to creating or providing the product or service like raw materials, labor, and so on – typically bundled as "cost of goods sold,” “cost of products sold,” or “cost of sales” on the income statement – and you get gross margin. Done on a per-product basis, gross margin is most useful for a company analyzing its product suite (though this data isn’t shared with the public), but aggregate gross margin does show a company’s rawest profitability picture.
Companies have some discretion about whether to include certain expenses in cost of goods sold (COGS) or “selling, general and administrative” (SG&A) expenses, one expense line down the income statement. WD-40, for example, sticks some costs in SG&A that others stick in COGS. For analyzing or comparing WD-40’s operating margins, this is a non-event because the same costs are included; order doesn’t matter. But an investor making an investment decision solely based on gross margin analysis could mistakenly conclude that WD-40 is better than it really is. Fortunately, the situation is well-disclosed, at least to people who take the time to read the notes following financial statements (disclosures like these typically follow the actual financial statements on forms 10-K or 10-Q):
Note that our gross profit and gross margin may not be comparable to those of other consumer product companies, since some of these companies include all costs related to distribution of their products in cost of products sold, whereas we exclude the portion associated with amounts paid to third parties for shipment to our customers from our distribution centers and contract manufacturers and include these costs in selling, general and administrative expenses. These costs totaled $15.8 million and $16.2 million for the fiscal years ended August 31, 2015 and 2014, respectively.
Operating Profit Margin: By subtracting selling, general and administrative, or operating expenses, from a company's gross profit number, we get operating income, also known as earnings before interest and taxes, or EBIT.
Operating profit is a big deal, sometimes more so than net income. All the costs of actually providing the product or service have been taken out, resulting in an income figure that’s available to pay both types of capital providers to the business (debt and equity holders), as well as the tax department. Operating profit is profit from a company’s main, ongoing operations; oddball accounting adjustments like income from discontinued operations and extraordinary items are accounted for below this line (they do get bundled into pre-tax income, discussed below). Accordingly, operating income feels purer and less prone to weird accounting-related fluctuations to analysts than net income. Finally, because operating income is conceptually “owned” by both debt and equity holders (whereas net income is just for equity holders, interest expense having been paid), it’s frequently used by bankers and analysts to value an entire company for potential buyouts.
Pretax Profit Margin: Take operating income and subtract interest expense while adding any interest income, adjust for non-recurring items like gains or losses from discontinued operations, and you’ve got pre-tax profit, or earnings before taxes, or EBT. Note that for whatever reason, pre-tax profit is relatively more popular among UK analysts, whereas operating income reigns supreme across the Atlantic.
Net Profit Margin: If someone asks you, “What’s your company’s profit margin?” they’re most likely asking about net profit margin, or a company’s bottom line after all other expenses, including taxes and one-off oddities, have been taken out of revenue. If you’re a stockholder net income is what you “own,” at least conceptually. If it feels like everybody and his brother gets paid before you do, it’s true. Unlike everybody else, who gets paid a set amount, you as a shareholder get whatever is left, be it much or little. Technically, you seldom actually get it: the company may choose to reinvest that profit, stockpile it, squander it, buy back shares, or pay shareholders a dividend (in which case you actually would get it, or at least some of it). But wherever it goes, net profit, as long as it’s not squandered, adds value to shareholders like us, which is why we bought the stock in the first place.



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