---
title: "Data Analysis: Uncovering Sales Potential"
description: "As offline foot traffic continues to decline, it's crucial to maintain the offline channel's core business through meticulous management and professional data analysis. This article, part of the Key Account (KA) Management series by New Distribution and former Coca-Cola China executive Cao Yang, explores five practical data analysis methods to uncover sales potential."
author: "曹扬Geoffrey Cao"
publisher: "New Distribution"
email: "zhaobo258@gmail.com"
telephone: "+8615854817671"
published: "2022-06-06"
language: "en"
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# Data Analysis: Uncovering Sales Potential

> As offline foot traffic continues to decline, it's crucial to maintain the offline channel's core business through meticulous management and professional data analysis. This article, part of the Key Account (KA) Management series by New Distribution and former Coca-Cola China executive Cao Yang, explores five practical data analysis methods to uncover sales potential.

**Editor's Note:** The continuous decline in offline foot traffic is an indisputable fact. However, this does not mean that offline channels are no longer important or that attention and investment should be reduced. Compared to embracing the endless stream of new retail formats, holding the line on the offline channel's core business is more critical in today's volatile market environment. How to hold the line? Only through meticulous cultivation and professional management to increase volume and efficiency.
To this end, **New Distribution, in collaboration with Mr. Cao Yang, former General Manager of Channel Management for Key Account Groups at Coca-Cola China, has launched the "Key Account (KA) Management Practice" series, aiming to provide frontline channel managers with a complete methodology for managing offline key accounts in a "chaotic market."** This series consists of about 20 issues in total; this is the ninth issue, as follows.
Data has always played a crucial role in business, and at the company level, the higher the position, the more data is used.
In the current stage of digital transformation, data usage has spread to frontline sales teams, making it urgent for regional managers and account managers to master data usage.
This article will give you an in-depth understanding of two aspects:
_**1\. In the digital age, learn to analyze data**_ _**2\. Five common applications for analyzing sales potential**_
Since Shi Jun took over as the manager of the Key Account Department, after a period of team integration, he gradually understood the team's characteristics. The team's biggest strength is its youth and eagerness to learn, which Shi Jun likes, but he also discovered some problems, one of which is that the team does not do data analysis and does not know how to do it.
He found the team had the following problems:
1\. They have developed a habit of "emphasizing execution over thinking," with more execution and less thinking. Here, thinking refers to insufficient depth and breadth of thought. This situation is common among regional sales representatives, who mostly handle small shops with relatively simple operations, where execution is more important.
However, in key account management, thinking ability must be improved. Not only must they think, but they must also use data—this is efficient thinking. Efficient thinking is necessary to meet the challenges of professional clients.
2\. They adopt an inertial way of thinking to achieve sales targets: Specifically, for monthly tasks, they start by following the plan, but by mid-month, when they find progress is lagging and targets cannot be met, they resort to forcefully pushing inventory. Throughout this process, you will notice a lack of planning ability or adaptability.
3\. In communication, they talk more about concepts and less about data, use an insufficient variety of data types (we will discuss data types later), and lack methods for analyzing data. One of the foundations of these abilities is data analysis capability!
4\. They don't know the real reasons for both failure and success: When summarizing lessons from failure, they often attribute causes to objective factors such as the pandemic or declining offline foot traffic. Competitors face the same objective factors, yet outcome indicators differ. In short, analyzing causes cannot identify leverage points.
When a task is successful, they also don't know the exact reasons, vaguely attributing it to the boss's leadership and team effort. This fully reflects insufficient analytical ability.
After a routine meeting, Shi Jun happened to have some time with General Manager Liu, so he wanted to seek his advice.
Shi Jun: Mr. Liu, these four situations are quite common. How can we solve them?
Mr. Liu smiled slightly: Very observant! Do you know the disadvantages of not doing data analysis?
Shi Jun: I can think of two points:
1\. It forms a comfort zone, making task completion methods increasingly monotonous and more difficult; 2\. It increasingly relies on experience to achieve goals, making sales work more tiring over time.
Mr. Liu: You see it quite accurately. Do you know why the team doesn't use data?
Shi Jun: I haven't figured it out.
### Mr. Liu: There are three reasons for this:
### 1\. Some people have their own experience in business and worry that data may be inaccurate, feeling that experience is more reliable;
### 2\. Quite a few people don't know what data to use, have no data, and don't know analysis methods;
### 3\. Most importantly, the boss hasn't made requirements. Without requirements, naturally they don't use it; without KPIs, they naturally won't use data.
******In the digital age, learn to analyze data**
**1\. Modern channels are highly digitized**
Modern channels include four types: hypermarkets (e.g., Walmart), supermarkets (e.g., China Resources Vanguard), convenience stores (e.g., 7-Eleven), and cash-and-carry (Sam's Club and Metro). Compared to the above channels, they have the following distinct characteristics.
1) National chain customers are professionally managed companies with corporate culture, management concepts, standardized operating procedures, and modern inventory management systems. In short, they use data extensively in their business.
2) The KPI systems for customer purchasing departments and stores are systematic and professional. To achieve revenue and profit targets, they set indicators including front-end gross margin, back-end gross margin, order fill rate, turnover, etc. Purchasing departments always negotiate with companies around key indicators. These indicators all require data analysis.
3) In store layout, they fully consider the consumer's route from entry to exit, designing and arranging shelves, end caps, floor displays, etc., according to customer traffic flow, intercepting consumers multiple times along the path. This interception is the action of consumers grabbing products and putting them into shopping baskets. Increasing the grab rate means increasing sales! Some of these operations use consumer data, and some use experiential data.
4) Professional customers' POS data, membership data, and supply chain data contain a wealth of business information, characterized by completeness, systematization, and diversity. Through data analysis, execution, planning, and evaluation effects can be obtained.
**2\. What data is available in modern channels?**
It is mainly divided into three major categories:
**1) By company:** Divided into suppliers and retailers. Supplier frontline teams commonly use their own sales data, execution data, and supply chain data, corresponding to the customer's sales data, consumer data, and supply chain data.
**2) By nature:** Divided into system data and experiential data. System data is obtained from systems such as ERP and CRM, characterized by completeness, systematization, and accuracy. Experiential data is accumulated by teams over the long term in different positions and can supplement system data.
For example, a merchandiser in a hypermarket may need to replenish stock 2 to 8 times a day, with varying quantities each time, and a floor display may be rotated 0.5 or 0.9 times per week.
**3) By function:**
1Sales data: by category, brand, package, SKU, unit price, quantity, amount; 2Consumer data: including membership data, basket data, average transaction value, etc.; 3Execution data: shelf, end cap, floor display, equipment location, quantity, execution status, etc.; 4Supply chain data: (supplier and customer) inventory, availability rate, order fill rate, shelf life, etc.; 5Financial data: divided into two types—one is receivables and payment terms, the other is efficiency, such as gross margin and profit.
Shi Jun: We have come across most of the data in daily work. Now the team mainly focuses on sales data in data analysis, rarely analyzing customer data, and even sales data is limited to our own data.
Mr. Liu: That's the problem. The team doesn't understand the purpose and benefits of using various types of data.
**3\. The purpose and role of data analysis**
Mr. Liu: Data runs through every aspect of modern channel sales work.
There are roughly three purposes for analyzing data: judging the current situation and trends, identifying problems and analyzing causes, and formulating plans.
**1Judging the current situation and trends:** Determine whether the current situation is good or bad, or whether we won or lost. This mainly involves comparing with targets and judging share wins or losses. Additionally, look at whether the trend over the next few months is increasing or decreasing.**
****2Identify problems and analyze causes:** Compare gaps, whether against targets, historical data, or competitors, to identify problems. The most typical situation is a sales decline—is the decline in stores, brands, or packages? What are the reasons for the decline?
**3Formulate plans:** Whether it's a new plan or an improvement plan, data analysis helps in formulating a practical, achievable plan.
Mr. Liu: In a word, customer professional requirements are constantly increasing, and our company's requirements for the sales team are also rising. One way to respond is that the team must learn to use data!
**4\. The five stages of data analysis capability**
Shi Jun: Data analysis seems quite profound. Will it be too complex for the team to master?
Mr. Liu: Overall, data analysis has a certain technical content. It is challenging to enable account manager-level personnel to master it. But I have a way: data analysis level follows work content, and different work content corresponds to different data applications.
Earlier, we divided operations from zero foundation to joint business plan into five stages, from simple to complex, from low to high. We can match the work content of different stages with corresponding data applications.
  * Stage 1: Data applications for ordering and inventory
  * Stage 2: Data analysis for ordering, inventory, and store execution
  * Stage 3: Data analysis and business review for ordering, inventory, store execution, and rolling three-month plan
  * Stage 4: Data analysis for ordering, inventory, store execution, rolling three-month plan, annual plan, business review, and order fill rate
  * Stage 5: Joint business plan, covering all content data analysis
Mr. Liu: These five stages give you a more intuitive understanding of data analysis, which is closely related to business content.
Another key point: strong business capability cannot rely solely on data; otherwise, you could just hire a few PhDs to analyze data.
The high level of business is achieved through the flexible use of data + experience.
Shi Jun: Indeed, we cannot completely rely on data extracted from systems for analysis; the limitations of data need to be compensated by experience.
**5 common data analysis applications for uncovering sales potential**
Mr. Liu: When using data analysis to uncover potential, how do you interpret the meaning of potential?
Shi Jun: Potential is a broad concept. Does data analysis reveal potential that could double sales?
Mr. Liu: For account managers or regional managers, the concept of potential is not that large. As long as data analysis reveals opportunities that help them achieve their goals—sales opportunities they couldn't discover through experience—that is potential.
Uncovering sales potential through data analysis is a hard skill that requires long-term continuous improvement. A one-time explanation won't bring significant change.
Mr. Liu: The five methods I teach you are selected from key business points, combining work content with data analysis, making it easy for frontline teams to master.
**1\. Judging win or loss**
Shi Jun: How can judging win or loss uncover potential?
Mr. Liu: For individuals like account managers or regional managers, judging win or loss is very important for achieving their goals! They value growth but ignore win or loss!
Let me ask you a question: Your sales growth at a customer is 86%. Is this growth good?
Looking at the number alone, it's good—86% is a high growth rate.
But remember, growth is relative. An 86% alone doesn't fully explain the situation!
Because if your product grows 86% but the competitor grows 120%, you are losing!
Shi Jun: I understand! You can't just look at your own growth rate.
Many colleagues always compare their growth rate with others in the team and subtly imply to their boss that their performance is good, e.g., 32% growth is higher than the team's 21%. But in reality, their customer's growth is 36%, and they are behind the customer's growth rate.
Mr. Liu: This kind of growth is not winning but losing! You and your team must constantly emphasize the concept of win or loss.
Shi Jun: Okay, when account managers realize that 32% growth is a loss, they will start to uncover sales opportunities!
So win or loss is very important for uncovering opportunities and maintaining growth!
**2\. Comparable store growth**
Mr. Liu: Still on the growth issue: your growth is 86%, and the customer's overall growth is 80%. What do you think?
Shi Jun: It means my growth exceeds the customer's growth, which is good growth.
Mr. Liu: Not necessarily!
Shi Jun: Didn't you say that exceeding the customer's growth is winning?
Mr. Liu: That judgment is correct, but there is still growth potential that you may have overlooked.
Suppose I tell you that 80% of the customer's growth comes from new store contributions, and comparable store growth is only 3%, indicating there is still significant sales potential. Simply put, comparable stores, also called same stores, refer to the growth rate of existing stores' current sales compared to last year. Comparable store growth is the foundation of the business.
Therefore, customers often tell suppliers, "You need to work hard to improve comparable store growth," because the growth from new stores is a dividend, and the pace of store openings will eventually slow down. Comparable store growth is the source of sustained business growth.
**3\. Increasing visible inventory turnover**
Visible inventory turnover is a very important operation.
When a company delivers products to the warehouse of a hypermarket, supermarket, or convenience store, the sales of the products begin. Sales are achieved through the turnover of products on display positions such as shelves, floor displays, end caps, and cold/hot equipment.
We call the products placed on these displays visible inventory, which is also front-store inventory—a vivid metaphor for inventory that consumers can see and grab.
The speed of visible inventory turnover determines the size of sales. High turnover means high sales; conversely, low turnover means low sales.
If visible inventory turnover speed is the same, increasing the display quantity can also increase sales!
Most of the data on visible inventory turnover comes from accumulated experience, obtained through long-term and repeated records by sales representatives and merchandisers in stores.
Let's use a hypermarket example to illustrate the impact of visible inventory on sales.
The displays in a hypermarket roughly include:
1) Shelf display: Products are placed on each shelf layer, a very important display form in hypermarkets, where all manufacturers' products are displayed on shelves.
2) Floor display: Grouped by pallet, with the top layer displaying single items, brand logos, and promotional information.
3) End cap display: Adjacent to the end of a shelf, an extension of the shelf display, usually with full cases at the bottom and single items on top.
4) Equipment display: Including air-cooled cabinets, refrigerators, warm cabinets, etc., also a form of display.
5) Checkout display: Small shelves or equipment at the checkout counter, displaying small items such as batteries, gum, etc.
Mr. Liu, let me explain using the three most typical types of displays:
  * Shelf product display value is 5,000 yuan, with a weekly turnover of 0.5 times, generating 10,000 yuan in sales per month;
  * Floor display with 4 pallets has a display value of 8,000 yuan, with a weekly turnover of 0.9 times, generating 28,800 yuan in sales per month;
  * An end cap display value is 1,200 yuan, with a weekly turnover of 0.5 times, generating 2,400 yuan in sales per month;
Mr. Liu: If this customer's sales target is to grow 17%, from the perspective of display turnover, how can we increase sales?
Shi Jun: Can sales be calculated using turnover?
Mr. Liu: Yes, this type of data mainly uses experiential data.
Shi Jun: Let me try.
The total monthly sales from the three displays is 41,200 yuan. A 17% increase on 41,200 yuan is 7,004 yuan. Increasing display points can increase sales. For example, if one pallet of floor display generates 7,200 yuan, I can increase the floor display from 4 pallets to 5 pallets, adding one pallet to gain an additional 7,200 yuan.
Mr. Liu: Correct. This increases sales by increasing the number of floor displays. You can also increase sales by accelerating turnover or increasing shelf space or end caps. There are many possible variations.
**4\. Finding opportunities from trends**
Trends refer to the increase or decrease of product brands, packages, SKUs, etc., or store turnover over different time periods. That is a trend.
When a trend increases or decreases in our favor, we need to invest resources to support the trend and maximize sales.
When a trend is unfavorable to us, we need to reduce investment to avoid increasing losses.
Take sugar-free food as an example. In 2022, sugar-free beverages are very popular and widely welcomed, which is no longer surprising. Many companies have already begun to vigorously promote sugar-free products.
In fact, as early as 2017, various data in modern channels already showed that the sales share of sugar-free beverages was slowly growing in modern channels. Only a few companies noticed this trend.
In convenience stores, the share of sugar-free beverages is particularly prominent at 7-Eleven, because one of 7-Eleven's business strategies is to highlight health-conscious products, and the customer is very proactive in promoting sugar-free beverages and foods. After comparing various data from hypermarkets and supermarkets, it was confirmed that sugar-free beverages were becoming a trend.
Mr. Liu: If account managers seize the opportunity earlier, they can enjoy the sales dividend months earlier than others. This is the benefit of analyzing data to find trends.
Shi Jun: Analyzing trends requires a sufficient amount of data!
Mr. Liu: From the overall market perspective, yes. But account managers handle a limited number of customers, so they need to find data from multiple sources. Finding data is also within the scope of data capability.
**5\. Supply chain opportunities**
Mr. Liu: Analyzing supply chain data can bring sales, but most of our team doesn't understand this!
This is a method to gain sales without spending an extra cent on market expenses!
Shi Jun: Sales without spending more money—I really hadn't thought of that.
Mr. Liu: Through research and practice, it has been proven that for every 4% increase in order fill rate, there is a 1% increase in retail sales (POS).
For example, if the customer's order fill rate is 60%, it means that when the customer orders 100 cases, they only receive 60 cases, and 40 cases are not delivered. If the customer orders 100 cases, it means they believe they can sell 100 cases, but you only delivered 60, losing 40 cases of sales.
Shi Jun: Let me calculate: if the order fill rate increases from 60% to 88%, the fill rate increases by 28%, so POS sales would increase by 7%, meaning a 7% growth rate. Indeed, this is sales that can be obtained without spending market expenses.
Mr. Liu: By analyzing supply chain data, we can also bring sales growth. I am now talking about sales potential; how to improve order fill rate is a topic for another discussion.
# **In conclusion:**
Shi Jun: Thank you very much for your patient guidance. I have summarized the following key points:
**1\. Data analysis is very important in modern channels. It can obtain multiple types of data, and through analysis, better achieve performance goals;****
****2\. Data acquisition and processing are also important. Some data is relatively easy to obtain, while some requires finding the right person to get it;****
****3\. The five opportunity points analyze data from different business perspectives to uncover opportunities. Combining business key points, data analysis, and experience accumulation, it won't cause fear of data, making it easier for account managers to get started.**
Mr. Liu: Data analysis is a very important professional skill for account managers. These five opportunities are just breakthroughs; deeper exploration can yield more sales opportunities! If you persist, your data analysis ability will definitely improve.
 _PS: If you are interested in KA management topics, please long-press to add the enterprise WeChat account, and be sure to note "KA" to apply to join the New Distribution "KA Exchange Group."_ __
_**-END-**_


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