△2018 China (Luohe) Food Marketing Innovation Summit and the 3rd Leisure Food Manufacturer-Dealer Precision Matchmaking Conference Free registration is in full swing; scan the QR code for details. Sales data analysis reports are a necessary part of sales work. If sales personnel cannot create sales data analysis reports, they are only good at fighting battles, not accounting! In the end, it's all a messy ledger! A sales data analysis report is composed of statistical data and the salesperson's analysis of that data. In other words, data without scientific analysis is just numbers, not a sales data analysis report! When creating a sales data analysis report, you may hear the following voices:

  1. Isn't it just data statistics? Then organize and summarize it—how simple is that?

  2. At the very least, organize the purchase-sales-inventory data; that should be enough.

  3. What you're saying is too troublesome. Just tell the boss whether the task was completed or not. Giving the result saves so much trouble!... What are the benefits of a sales data analysis report for you?

  1. Speaking with data is the prerequisite for a salesperson's right to speak; it's professional integrity!
  2. Analyzing data reports is a summary of your own sales work, allowing you to view the market more clearly and macroscopically, then identify and fill gaps. It's like giving your territory a routine "physical checkup." It helps sales personnel deeply understand sales situations, analyze market dynamics, enhance business awareness and control, and cultivate market forecasting abilities.
  3. Sales data analysis is one method to "manage customers"—it's in black and white, hard to dispute, highlighting your professionalism and earning trust. Why do sales personnel not value sales data analysis?
  4. The company has no specific requirements or standards for sales report analysis. Even if there are requirements, the boss just says, "Analyze the market for me," and that's it. Then the salesperson is confused, scratching their head, piecing things together. Finally, the boss looks at it, sees the analysis is "incoherent," and says, "Redo it!" You're left dumbfounded.
  5. The company has standards for sales analysis reports, but they are too cumbersome and complex. They try to make it the "most comprehensive database on earth," causing frontline staff to spend days on it, making it impossible to finish. Sales personnel resist internally and start producing "counterfeit" data, and the execution results are predictable. Then bosses take the "counterfeit" data and report it up the chain—no wonder company strategic decisions go astray! Such things are common around us, aren't they?
  6. The company lacks promotion of sales analysis reports. Sales personnel lack awareness of the reports and don't know the benefits. Managers should lead by example. The content of sales analysis reports should keep pace with the times; don't keep the same policy from 1999. Of course, I'm not suggesting you change things frequently or flip-flop! Enterprises need a "guidance system," not just a sentence or a notice, and then everything is fine! Common methods for sales data analysis Generally, analysis is conducted from two dimensions:
  7. Product. Focus on each product, including sales, price, inventory, promotion, market share, etc. Identify problem areas and problem customers.
  8. Customer. Analyze by customer or region to see which customers have abnormal performance, find the real reasons, and identify improvement points for better target management. Sales Volume Analysis The simplest and roughest data is: whether the sales task was completed and the completion rate. Of course, it's more than that. Performance Achievement Daily data: Analyze the problems in that day's sales. Daily data is for reference only. Basic data: Monthly cumulative sales progress compared with time progress. Shipment data: This data is often overlooked by manufacturers. From shipment data, you can analyze logistics, inventory, and turnover rates. So, be sure to clarify the number of customers shipped to and the frequency of shipments, find out the reasons for reduced shipment volumes, and optimize logistics costs. This data can be analyzed monthly or quarterly. Comparative Analysis We must mention "year-on-year and month-on-month." Year-on-year (YoY) compares data at the same time point in similar periods. For example, March 2017 and March 2016 are similar periods, both in March; comparing data from these two periods is YoY. Month-on-month (MoM) compares adjacent time periods. For example, May 2017 and April 2017 are adjacent periods; comparing data from these two periods is MoM. Usage of MoM Honestly, MoM is not commonly used because sales are highly seasonal, with peaks and troughs. Those who say sales have no off-season or peak season are just fooling you! Even if you sell steel or coal, the state has macro-control! Given the large seasonal factors in monthly MoM, why not just do weekly MoM? Weekly MoM has some reference value, provided there are no holidays between the weeks. For example, comparing the National Day Golden Week with the second week of October is also inappropriate. Comparative Analysis with Competitors I don't need to explain this comparison; you all understand it better than me. However, many people don't know who their competitors are! In principle, any brand in the same industry is your competitor! But that's what books say; in reality, you can't do that, or everyone would be your enemy—are you starting a "world war"? A goal that can't be focused on is a dream, not a goal! Remember this sales iron rule forever. Who Are Your Competitors? Let me give you a simple method: find a competing brand that is within reach, one you can grab, and whose sales are 30% greater than yours. Focus on that brand and attack it! Don't challenge an "elephant" that is absolutely N times bigger than you. You can treat the "elephant" as a dream or vision, but first, do well in the "present"! Otherwise, you're aiming too high and being impractical. Once you've found your competitor, what do you compare? In principle, all your sales data should have competitor comparisons alongside. Only then can you know yourself and your enemy and win every battle! This tests your "intelligence capability" in data statistics. Product Sales Structure Analysis For example: Analyze key product sales from price points and inventory adjustments. Also, analyze the company's key products, identify problems, and provide suggestions. Be sure to point out to sales personnel:

Which are the company's "fist products" (to directly attack competitors, like Popeye eating spinach).

Which are the company's "steamed bread products" (to fill your stomach).

Which are the company's "cornbread products" (just to have a bite). Cost Analysis This cost analysis is generally not conducted for frontline sales personnel; it requires more attention from regional managers! Let me add: Regional managers should treat "basic finance" as an important course to learn. Return on Investment (ROI) = Total output revenue / Total cost × 100%. As a manager, when managing costs, you should:

  1. Focus on the big and let go of the small; don't dampen everyone's enthusiasm.

  2. Decompose authority; one person shouldn't have the final say. A one-man show in costs definitely has problems—if you don't believe me, go check!

  3. Settle accounts after the fact; every cost must have this step, or it becomes a "messy ledger"! Sales personnel cannot forgive themselves for not meeting sales targets just because sales costs are low. Channel Analysis Analyze the supply price, gross profit, and profit for each channel, and be sure to compare with competitors. Classify and grade each channel, then allocate different sales resources (distribution, inventory, logistics). Problems encountered in channels need digital support and should be written down. Pay special attention to inventory in each channel, especially inventory turnover days. Turnover days = Current inventory quantity / Current daily average sales quantity. Judging whether a product is selling well based solely on turnover days is biased. For example, if sales volume remains unchanged, larger inventory leads to longer turnover days; similarly, smaller inventory leads to shorter turnover days. So, don't just look at turnover days; only by combining with inventory can you make a correct judgment. Analyze inventory composition, and ensure computer data matches actual inventory. Don't rely solely on computer data for judgments; you need to regularly count inventory and identify problems in time. Only then can you grasp the sales situation of each SKU. Focus on which regions and channels perform well, and analyze development trends and sales characteristics. Pay special attention to regions with significant growth or decline to avoid potential threats, be wary of competitors taking advantage, and conduct multi-dimensional analysis. Personnel Analysis Number of new employees (cumulative this year), number of departing employees (cumulative this year), performance undertaken by each employee, personnel salary costs, and daily work analysis (distribution, visits, etc.) In sales analysis reports, avoid the following problems:

  1. Most people report work by avoiding the real issues, beating around the bush, and reporting only good news. The dumbest employees are those who think "your boss and boss's boss are fools."

  2. Misreporting the situation: making unimportant things sound serious and avoiding important topics. This trick is used skillfully by sales veterans! They extract company resources for personal gain. I strongly suggest that when reports lack specific analysis and only present facts to superiors, leaders should not make decisions hastily, because it's easy to make wrong decisions in such cases.

  3. Focusing only on frontline battles while neglecting internal team improvement, causing mismatched internal and external resources. Such salespeople are prone to flash-in-the-pan success. They are suited to be snipers, not managers.

  4. Making plans without analysis, always going by gut feeling. This is a common detour for many managers; the sales analysis report becomes a mere formality. A good sales analysis report should have:

  1. First, data must be presented truthfully, completely, and in detail. Where does the data come from? What is the meaning behind each data point?

  2. Data analysis must be objective, without subjective factors, to avoid bias in the report. Source: 土拨鼠跟我走, a top author in the business management column on Jianshu https://www.jianshu.com/u/9c5672a94a6d -END-