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This is perhaps the most common question asked at the beginning of every BI project, and the answer is never trivial. When finance people say “sales,” they typically mean revenue. Their definition encompasses delivered and invoiced products or services that are ultimately paid for. Operations executives, on the other hand, may be more interested in “shipments.” Sales people like to count the number of customer orders written, whether or not the product or service was delivered or charged for.

Depending on the audience, sales numbers may represent any of the above, including or excluding various discounts, returns, liquidations, and more. It is very important to know what you’re including in your sales analyses. If you don’t have an accurate understanding of what’s driving your sales, you run the risk of making incorrect pricing or volume decisions.

We have all heard the saying, “they are so good at sales they could sell ice to an Eskimo.” Making a sale is first and foremost a relationship event. After the relationship has been consummated, what is the actual result of the transaction? There are many things to think about when trying to answer this question. The answer to the question will come by analyzing the data associated with the analytical questions posed throughout this chapter.

Sales commonly represent the amount that a company bills or invoices its customers for goods and/or services. It is known by a number of business labels, including revenue, billings, sales, and so on.

Many people believe that “sales” analysis is the most understood and straightforward business metric out there. To most, there are only two components to understanding sales – the unit price and how many units shipped. Because of these simplifying assumptions, sales data is too often analyzed only at the aggregate level. This approach can leave many organizations in the dark with respect to significant drivers of their business.

What Is the Real Value of the Sale?

Consider going to a store to buy a retail item of almost any kind. First, you may have received a coupon. With the coupon in hand, you are feeling good about this form of incentive, which has added to your rationalization to purchase! Then when you get to the store you are bombarded with all kinds of point-of-purchase information about further markdowns, customer loyalty programs, and even “point” systems that can be used against future purchases. The item you set out to purchase may normally have a price tag of $100, but with all the incentives, it costs you only $75. You are now convinced that you just received the deal of the century!

This example raises some basic questions, such as what is the real price of the item you bought and what should it be? A related question is what is the revenue, or sale, to the company that sold you the item?

NOTE

Do you know the real price of your company’s products? Is it the “list” price or list price reduced by terms, discounts, and other programs that you don’t have much visibility into?

Another example involves a manufacturer of consumer products that sells to major retail chains in North America. In order to set the stage for this example, it is important to know that retail chains, like most businesses, are constantly challenged with achieving results in terms of sales and margin. To help meet this challenge, retailers have ways to “ask” suppliers for money to increase their margins. These many ways include co-op advertising paid for by the manufacturer, free goods, in some cases as a promotion, and slotting allowances. In addition to the demands placed on suppliers to pay for these programs, suppliers typically have to pay commissions to those who represent their products.

NOTE

Slotting allowances, for those of you who are not painfully aware of them, are the costs of buying out the competitors’ merchandise to make room for your products on store shelves.

The company in question was attempting to understand their sales information and why their margins continued to erode. The company controller was attempting to help so he went to see the sales folks. After a number of well placed questions and intentionally vague answers, the controller believed he finally had an understanding of some of the issues. After verifying with the sales folks that he had heard the facts correctly, he said, “So, we paid sales commissions and we owe the customer advertising money for the privilege of shipping them free product? Is that right?” To which the stunned sales folks replied, “free goods that qualify for sales credit and co-op dollars…yep that’s it!” Each individual decision was made for a good cause and may have made sense by itself; however, the sales personnel didn’t recognize the cumulative impact of these decisions.

You can see from these examples a number of variables that affect the revenue you receive when you sell a product to someone. These are common sense variables such as:

● Customer allowances with the intent to grow sales over a period of time

● Product promotions to add velocity to new product introductions

● Advertising to grow sales and brand or product awareness

● The payments required by customers to gain shelf space in their outlets

● Payments to reduce price in an attempt to move slow-moving product

The variables in a business to business (B2B) sale may be called something different than the variables in a business to consumer (B2C) sale; however, those variables present the same challenges to understanding the real revenue of the product you are selling.

The revenue you receive should represent the “price” component of your sales. The other component is volume. Regardless of what channel you sell in, it is important to be able to identify the elements that answer the question, “What does the result of a sale tell you about the current state of your business and its future potential?” The analysis starts with some basic questions, which are discussed in the following sections.

What Happened?

Typical sales analysis includes reporting the outcomes of “what happened?” These outcomes are reported in a set of so-called lagging indicators, which are measures of results.

Although only part of the story, absolute sales amounts are important to know. You are probably familiar with looking at an absolute amount of sales and comparing it to a history or to some expectation. You might compare the absolute amount of sales to a budget or a sales quota (the two are usually not the same), to the latest estimate, or to the same period from a previous year. Some of the most common time comparisons include:

● Actual year-to-date sales compared to actual prior year-to-date sales

● Actual quarter-to-date sales compared to actual prior year quarter-to-date sales

● Actual month-to-date sales compared to prior year month-to-date sales

● Recent rolling 12-month sales to the previous rolling 12-month sales

Trend analysis is rather important in the effort to understand your business. Trend comparisons can be done at a variety of data levels. In addition to comparisons at an aggregate level, we recommend that an organization have the functionality in their systems to analyze the data at the following levels of detail for both units and dollars:

● Total for the organization

● Customer/channels/geographies

● Product/styles/sizes/color/brand

● New and existing customers

● New and promotional products

Again, this type of analysis is common. However, analyzing lagging indicators won’t always help you answers the questions of why it happened and what it means.

Why Did it Happen? What Does It Mean for My Business?

These questions are near and dear to every business leader wanting to know what is happening in the marketplace and what the opportunities are for the future. To answer these analytical questions, you need to go deeper than just looking at measures of results (lagging indicators). You need to understand the drivers (leading indicators) of your sales.

The Importance of Understanding the Real Drivers of Sales

One company found itself in a situation where raw material costs were skyrocketing (over 25 % in one year!). As a result they calculated that they needed to raise their prices by an average of 8 % to recover the raw material increases. They even built this expected result into their financial plan. Their customer base was made up primarily of mass retailers who did not want to accept price increases at all. Six months into the year, the company noticed that sales had increased in total by 6 %. They believed that price had increased by 8 % because they announced the increase at the beginning of the year. They then concluded that volume had decreased 2 % since the net total increase was 6 %. Based on this level of analysis, the company embarked on a course of action of what to do about losing volume. This in turn led to looking at spending reductions that could ultimately harm their long-term goals.

The real issue was not volume. If the company had been able to look at sales at the detailed level of volume and average selling price (ASP) by customer and product, they would have found some completely different drivers. Volume had actually grown 10 % and average price had actually decreased 4 %!

There were in fact a number of important drivers that the company could not see:

● Volume was up significantly due to new customers and some new products.

● Some of the invisible price decrease was due to the product mix. The new customers were buying products in categories that were lower priced on average.

● The price increase to existing customers, announced at the beginning of the year, was delayed due to customer timetables to accept the increase.

There was additional critical information that the company could not see. While some new products were contributing to the volume increase, some other new products were not meeting expectations. In the independent channel of distribution of their products, they were losing customers faster than they could add them (as measured by the number of store doors).

Defining Measures of Drivers for Sales Analysis

We recognize that companies may understand their unique drivers of sales. Here are some examples of drivers (leading indicators) of sales that we will use in the QlikView example.

Pricing drivers:

● Average sales price (ASP)– Calculated by dividing the sales dollars by units in total. ASP should also be calculated at relevant levels for the business, such as product categories and customer channels. Each channel and category may have its own pricing dynamics that affect ASP.

● Price mix– With the calculation of ASP comes the ability to understand the price mix of your products. The mix impact is revealed by calculating the percentage of volume that each category of average price is to the total of all sales. This “weight” is then compared against the same categories of another period to analyze the effect of the change in mix.

Customer drivers:

● New customer sales– Calculating the percent of new customers based on ship dates gives you a picture of whether you’re meeting organic growth goals or not.

● New and lost customers– Measuring the number of new store doors added based on ship to data (your own or that of a distributor) will give you market penetration achievement. Conversely, measuring the number of store doors lost will give you your attrition rate for a given channel. The net doors added/lost is a key measurement of the future of a given channel of distribution.

● Channel mix dynamics– Like price mix, this measurement will allow you to understand what is happening to your business as the channels that customers use to buy your product change. For example, in many categories the channels where customers buy products have changed from independent local merchants to big box retailers, which are more centrally located. We also see a shift from “brick and mortar” (physical) stores to “virtual” (online) stores. You can calculate channel mix by taking the sales in each channel and dividing it by the total sales across all channels. You can then compare this calculation to other periods to determine trends.

Product drivers:

● Unit volume– It is important to be able to analyze unit volume by product categories. These categories are groupings of like products (for example, same sizes, same use, and so on).

● New products– Percent of new product sales to total sales will help reveal any issues with acceptance or penetration.

● The rate of sales– You can calculate the rate of sales for any product or group of products based on unit movement information. This calculation frequently takes the form of units per week per selling location. The selling location is usually the store where your products are sold. If that information is not available, shipment information out of your warehouse or your distributor’s warehouse can be used.

Service-level drivers:

● Service level– Defined as percent of orders on time and complete. The calculation here is based on actual units shipped versus the original order and the actual ship date versus the original order requirement. If you have three orders and two of them are complete in units and on time while the third order is on time but not complete in units, your service level is 66 %. You get zero credit for any orders that are not on time and complete.

● Service level by reason– Calculating service level is one thing, but being able to understand service level failures requires being able to describe those failures by some relevant reason code. Losing sales because the product was not available is an example that is important to understand at a more detailed level. More detailed levels of understanding for lost sales could reveal that a supplier did not deliver to your business on time, or that your own production issues delayed delivery to your warehouse, or that your inventory system is inaccurate, and so on.

Sales person drivers:

● Sales per person– This is a calculation of sales productivity. It is calculated just as it sounds; you divide sales by the number of sales people. The number can be calculated in total for the business as well as for each salesperson for comparison purposes.

● New accounts closed– The purpose of this driver is the same as when looking at sales in total. At the individual level, it can be a leading indicator of expected sales growth.

● Placement of new products– Like new accounts closed, this metric can be a leading indicator of expected sales growth.

● Attainment of quota– This is a measure of actual sales achieved by a salesperson compared to the quota, or goal, set in advance for that person. It should be noted that quota is not always the same as the goal in the published financial plan. This particular driver will not be illustrated in our example analysis in the following chapters.

In order to make this analysis of these and other drivers a reality, you need business intelligence tools.