销售预测方法入门指南

What is sales forecasting? \n

Sales forecasting is the process of estimating future revenue by predicting the amount of product or services a sales unit (which can be an individual salesperson, a sales team, or a company) will sell in the next week, month, quarter, or year.

At its simplest, a sales forecast is a projected measure of how a market will respond to a company’s go-to-market efforts.

Why is sales forecasting important?

Forecasts are about the future. It’s hard to overstate how important it is for a company to produce an accurate sales forecast. Privately held companies gain confidence in their business when leaders can trust forecasts. For publicly traded companies, accurate forecasts confer credibility in the market.

Sales forecasting adds value across an organization. Finance relies on forecasts to develop budgets for capacity plans and hiring, and production uses sales forecasts to plan their cycles. Forecasts help sales operations with territory and quota planning, supply chain with material purchases and production capacity, and sales strategy with channel and partner strategies.

These are only a few examples. Unfortunately, at many companies these methodologies stay disconnected, which can produce adverse business outcomes. If information from a sales forecast isn’t shared, for example, product marketing may create demand plans not aligned with sales quotas or sales attainment levels. This leaves a company with too much inventory, too little inventory, or inaccurate sales targets — all mistakes that hurt the bottom line. Committing to regular, quality sales forecasting can help avoid such expensive mistakes.

Benefits of having an accurate sales forecast

An accurate sales forecast process confers many benefits. These include:

Improved decision-making about the future
Reduction of sales pipeline and forecast risks
Alignment of sales quotas and revenue expectations
Reduction of time spent planning territory coverage and setting quota assignments

Benchmarks that can be used to assess trends in the future
Ability to focus a sales team on high-revenue, high-profit sales pipeline opportunities, resulting in improved win rates

Bottom-up sales forecast or a top-down sales forecast?

In general, there are two types of sales forecasting methodologies: bottom-up forecasts and top-down forecasts. Bottom-up forecasts start by projecting the amounts of units a company will sell, then multiplying that number by the average cost per unit. You can also build in the number of locations, number of sales reps, number of on-line interactions, and other metrics.

A top-down sales forecast starts with the total size of the market (the total addressable market or TAM), then estimates what percentage of the market the business can capture. If the size of a market is $500 million, for example, a company may estimate they can win 10% of that market, making their sales forecast $50 million for the year.

The idea behind a bottom-up sales forecast is to begin with the smallest components of the forecast and build up from there. The advantage to a bottom-up forecast is that if any variables change (like cost per item or number of reps), the forecast is easy to modify. It also provides fairly granular information.

When making a sales forecast, it’s important to use both methods. Start with a top-down method, then use the bottom-up approach to see if your first estimate is feasible, or do the two separately and see how well they accord. To produce the most accurate forecast, companies should perform both types of forecasts, then tweak both until they produce the same number.

How to accurately forecast sales

To create an accurate sales forecast, follow these five steps:

Assess historical trends

Examine sales from the previous year. Break the numbers down by price, product, rep, sales period, and other relevant variables. Build those into a “sales run rate,” which is the amount of projected sales per sales period. This forms the basis of your sales forecast

Incorporate changes

This is where the forecast gets interesting. After you have your basic sales run rate, you want to modify it according to several changes you see coming. For example:

Pricing: Are you changing the prices of any products? Are there competitors who may force you to modify your pricing schemes?
Customers: How many new customers do you anticipate landing this year? How many did you land the previous year? Have you hired new reps, gained quantifiable brand exposure, or increased the likelihood of gaining new customers?
Promotions: Will you be running any new promotions this year? What is the ROI on previous promotions, and how do you expect the new ones to compare?
Channels: Are you opening any new channels, locations, or territories?
Product changes: Are you introducing new products or changing your product suite? How long did it take for previous products to gain traction in the market? Do you expect new products to act similarly
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Anticipate market trends

Now is the time to project all the market events you’ve been tracking. Will you or your competitors be going public? Do you anticipate any acquisitions? Will there be legislation that changes how your product is received?

Monitor competitors

You’re likely doing this already but take into account the products and campaigns of competitors, especially the major players in the space. Also check around to see if new competitors may be entering your market.

Include business plans

Add in all your business’s strategic plans. Are you in growth mode? What are hiring projections for the year? Are there any new markets you’re targeting or any new marketing campaigns? How might all this impact the forecast?

Once you’ve quantified these things, build them into your forecast. You want everything to be itemized, so you can understand the forecast in as granular a level as possible. Different stakeholders in the company will likely want to understand different aspects of the forecast, so it behooves you to be able to zoom in or out as far as needed.

Keys to success in sales forecasting

Improving the accuracy of your sales forecasts and the efficiency of the forecast methodology depends on multiple factors, including strong organizational coordination, automation, reliable data, and an analytics-based process. Ideally, sales forecasts should be:

Collaborative. Leaders should synthesize input from a variety of sales roles, business units, and regions. Frontline sales teams can be of great value here, providing a perspective on the market you hadn’t considered before.
Data-driven. Predictive analytics can reduce the impact of subjectivity, which is often more backward-looking than forward-looking. Using common data definitions and baselines will foster alignment and save time.
Produced in real time. Investing in the real-time capability to course-correct or reforecast allows sales leaders to quickly gain insight so they can make more informed decisions. This enables them to quickly and accurately update the forecast based on demand or market changes.

Single-sourced with multiple views. Generating the forecast as a single source of data gives you great visibility into rep, region, and company performance, and helps align different business functions across the organization.
Improved over time. Use the insights provided by an improved sales forecasting process to create more refined future forecasts where accuracy improves over time against a set of accuracy goals.

Companies with more advanced forecasting processes and tools perform better than their peers because they more deeply understand their business drivers and can shape the outcome of a sales period before the period closes.

Key sales forecasting challenges

It can be difficult to produce a consistently accurate sales forecast. Some of the keys to success in sales forecasting include:

Accuracy and mistrust

When companies use spreadsheets for sales forecasting, they can run into issues with accuracy, which in turn creates a less trustworthy forecast. These issues with accuracy can be exacerbated by:

Poor adoption of CRM across the company and employees not entering data in a timely manner
Inconsistent data across teams, or salespeople not inputting complete data
Company stakeholders using different methodologies to produce their forecasts
Insufficient collaboration across product, sales, and finance teams. This lack of collaboration can be heightened when companies produce sales forecasts manually or using spreadsheets.

Subjectivity

Although producing a quality sales forecast does rely to a small degree on the forecaster making good decisions about how to use the data, in general, companies rely more on judgement and less on credible predictive analytics than they should. For example, companies forecasting with simple arithmetic pipeline weightings may miss the nuances of the real drivers of accuracy, which may be headcount, pricing decisions, or route-to-market points of emphasis.

Usability

When a sales forecast isn’t generated in a way useful for stakeholders across the company, it becomes far less effective than it should be. A good forecast should produce relevant and understandable data for multiple teams.

Inefficiency

Sales forecasts can be especially difficult to produce when inefficiencies are built into the forecasting process. For example, when a forecast has multiple owners, or the forecast process is not clearly spelled out with a standard set of rules, there can be disputes about how the forecast will be produced. Similarly, if inputs into the forecast are not reconciled before the forecast is produced, the forecast itself may be subject to many revisions, which can reduce trust if versions are rolled out and then revised.

Company forecasts across the enterprise

To forecast across the enterprise, a company needs different elements from each business function. Here’s what different functions can contribute to the sales forecast:

Sales: Provides the bottom-up view, using data from the CRM and PRM, building in judgment from sales leaders. Sales can manage this process through the sales operations function, using the right tools, and reporting.
Finance: Provides macro-economic guidance and works with the product teams. Finance can help integrate the forecast with their financial planning software.
Marketing: Provides macro-market guidance, especially in industries like telecom, retail, and CPG. Marketing can also provide finance teams with market data.
Supply Chain: Provides input on supplies and production.
IT: Assists sales forecasting by providing platforms, data, integration, and technical support.

Key features of effective forecasting software

Best-in-class sales forecasting software should be able to immediately improve the accuracy of your forecasts and make the forecasting process more efficient. It should therefore offer the ability to:

Execute sales forecast simulations and outcomes. Make changes to drivers and execute sales forecast simulations to project future impact on sales performance.
Analyze trends, changes, and seasonality of the sales forecast over time. Develop time-based dashboards and key performance indicators (KPIs), such as velocity calculations, trending analytics, and seasonality fluctuations.
Model and analyze “What if” scenarios. Create “what-if” scenarios and modeling to analyze the impact to the sales forecast if a specific business, economic, or competitive situation were to occur. Prepare for challenges you might encounter in upcoming deal cycles.

Build sales forecasting calculations with familiar formulas. Apply an easy-to-use formula builder to configure sales forecast benchmarks using familiar formulas and syntax.
Snapshot Salesforce CRM accounts and opportunities to compare period-over-period. Compare week-over-week, month-over-month, and year-over-year changes to current periods.
Compare forecasts based on multiple modeling techniques. Create sales forecasts based on qualitative, time series analysis and projection, and casual modeling techniques while determining the degree of uncertainty with the sales forecast accuracy and predictability.
Forecast across geographies, products, and accounts. Develop sales forecasts by geographic locations, product lines, and accounts, or change any of these dimensions to analyze the sales forecast at any granularity of these hierarchies (by state/city, a specific set of product SKUs, or a group of accounts in a selected vertical).
Analyze performance with data visualization. Built-in dashboards, reporting, and analytics with data visualization (charts, graphs, maps, and more). Dashboards and reports are updated immediately. Analyze sales forecast and sales performance metrics to make better decisions with actionable insights.

The future of sales forecasting: predictive analytics

Predictive analytics is already transforming many areas of business and sales forecasting is no exception. Even so, terms like “predictive analytics” and “machine learning” can still be intimidating. Abe Awasthi, Senior Manager at Deloitte, shared a short example explaining how predictive analytics can improve forecasting:

A tech company asked Deloitte to produce a predictive model to improve sales forecast accuracy. To create their model, Deloitte leveraged the company’s pipeline data from the previous few years with customer and employee names removed. Deloitte then used machine learning to extrapolate from historical trends and fill in the gaps in the data.

Deloitte then used this data to build two predictive forecasting models: one calculated the probability that any given deal would close, and the other predicted the time frame in which that close would happen. When combined, these models provided highly actionable, very specific recommendations to the company’s sales team: “push opportunity number five to qualified within the next 10 days or you’re going to lose it!”

Importantly, Deloitte was able to build these predictive forecasts in 8-12 weeks — a timeline feasible for many companies.

Why use Anaplan for sales forecasting?

The Anaplan platform is uniquely configured to improve sales forecasting. By putting all relevant employees—salespeople, sales leaders, ops teams, finance, supply chain, marketing, and executives—on the same platform, companies can do the following:

Increase accountability and ensure the sales team reports sales pipeline activity more accurately. Identify sales deals at risk, eliminate “sandbaggers,” and reduce overcommits.
Standardize sales forecasting and pipeline management. Provide a single line of sight across the entire organization so everyone has a view into revenue projections, sales projections, and operational insight.
Create accurate and trusted sales forecasts. Allow functional leaders to make better and more informed decisions by providing accurate and trusted sales forecasting to all business units, including sales, finance, operations, HR, and marketing.

Access data-driven sales benchmarking and trend analysis. Enable sales leaders to use historical and current sales performance as a benchmark to predict future sales results. Make changes to functional plans and implement these changes across all other business models.

By adopting a Connected Planning approach, bringing together people, data, and processes from across the enterprise, companies can produce an accurate sales forecast that connects teams throughout the company, keeping everyone better prepared for the future.

Watch an on-demand webinar with Anaplan and Deloitte, Feeling the Heat? Five ways to improve sales forecasting, to learn the five ways to improve your sales forecasting in turbulent times and focus on ready-to-use models and customer examples.

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什么是销售预测?

销售预测是通过预测销售单位(可以是个人销售人员、销售团队或公司)在下一周、一个月、一个季度或一年销售的产品或服务的数量来估计未来收入的过程。

简单地说，销售预测是对市场对公司上市努力的反应的预测。

为什么销售预测很重要?

预测是关于未来的。对一家公司来说，做出准确的销售预测是多么重要，怎么强调都不为过。当领导者能够相信预测时，私营公司就会对自己的业务充满信心。对于上市公司来说，准确的预测赋予其在市场上的可信度。

销售预测增加了整个组织的价值。财务部门依靠预测来制定产能计划和招聘预算，生产部门利用销售预测来规划周期。预测帮助销售部门制定区域和配额计划，帮助供应链制定物料采购和生产能力，帮助制定销售战略，帮助制定渠道和合作伙伴战略。

这只是几个例子。不幸的是，在许多公司，这些方法是脱节的，这可能会产生不利的业务结果。例如，如果来自销售预测的信息没有共享，产品营销可能会创建与销售配额或销售完成水平不一致的需求计划。这使得公司的库存过多，库存过少，或者销售目标不准确——所有这些错误都会损害公司的利润。定期进行高质量的销售预测有助于避免这种代价高昂的错误。

有一个准确的销售预测的好处

一个准确的销售预测过程会带来很多好处。这些包括:

提高对未来的决策能力
减少销售渠道和预测风险
协调销售配额和收入预期
减少花费在规划领土覆盖范围和设定配额分配上的时间

可用于评估未来趋势的基准
能够将销售团队集中在高收入，高利润的销售渠道机会上，从而提高胜率

自下而上的销售预测还是自上而下的销售预测?

一般来说，有两种类型的销售预测方法:自下而上的预测和自上而下的预测。自下而上的预测首先是预测公司将销售的产品数量，然后将该数字乘以每件产品的平均成本。您还可以构建位置数量、销售代表数量、在线交互数量和其他指标。

自上而下的销售预测从市场的总规模(总可寻址市场或TAM)开始，然后估计业务可以捕获的市场百分比。例如，如果一个市场的规模是5亿美元，公司可能会估计他们可以赢得该市场10%的份额，从而使他们的年销售额预测为5,000万美元。

自下而上的销售预测背后的思想是从预测中最小的组成部分开始，然后从那里开始建立。自底向上预测的优点是，如果任何变量发生变化(如每个项目的成本或代表的数量)，预测很容易修改。它还提供了相当精细的信息。

在做销售预测时，使用这两种方法很重要。从自顶向下的方法开始，然后使用自底向上的方法来查看您的第一次评估是否可行，或者分别执行这两种方法并查看它们是否一致。为了做出最准确的预测，公司应该同时进行两种预测，然后调整两种预测，直到得出相同的数字。

如何准确预测销售

来创建准确的销售预测，请遵循以下五个步骤:

评估历史趋势

检查上一年的销售情况。将数字按价格、产品、销售代表、销售周期和其他相关变量进行分解。将其构建为“销售运行率”，即每个销售期间的预计销售额。这是你销售预测的基础

将改变

这就是预测变得有趣的地方。在你有了基本的销售运行率之后，你想要根据你看到的几个变化来修改它。例如:

定价:你们有没有改变产品的价格?是否有竞争对手会强迫你修改你的定价方案?
客户:今年你们预计会有多少新客户?前一年你们找到了多少?你是否雇佣了新的销售代表，获得了可量化的品牌曝光，或者增加了获得新客户的可能性?
促销活动:你们今年有什么新的促销活动吗?以前的促销活动的投资回报率是多少?你对新促销活动的预期如何?
渠道:你是否打开了新的渠道、地点或地区?
产品的变化:你是在推出新产品还是在改变你的产品组合?以前的产品在市场上获得吸引力需要多长时间?你认为新产品也会有类似的表现吗

预测市场趋势

现在是时候预测你一直在追踪的所有市场事件了。你或你的竞争对手会上市吗?你预计会有收购吗?是否会有法律改变你的产品被接受的方式?

监控竞争对手

你可能已经在这么做了，但要考虑竞争对手的产品和活动，尤其是该领域的主要参与者。同时也要看看是否有新的竞争者进入你的市场。

包括商业计划

加入你所有的商业战略计划。你是否处于增长模式?今年的招聘预期是什么?你们是否瞄准了新的市场或开展了新的营销活动?这一切将如何影响预测?

一旦你量化了这些东西，把它们纳入你的预测。您希望将所有内容逐项列出，这样您就可以在尽可能细的级别上理解预测。公司中不同的利益相关者可能会想要了解预测的不同方面，所以你应该能够根据需要放大或缩小。

成功预测销售的关键

提高销售预测的准确性和预测方法的效率取决于多种因素，包括强大的组织协调、自动化、可靠的数据和基于分析的过程。理想情况下，销售预测应该是:

协作。领导者应该综合各种销售角色、业务部门和地区的意见。一线销售团队在这里可以发挥很大的价值，为你提供一个以前没有考虑过的市场视角。
数据驱动的。预测分析可以减少主观性的影响，主观性往往更倾向于向后看而不是向前看。使用通用的数据定义和基线将促进一致性并节省时间。
实时生产。投资于实时修正或重新预测的能力，使销售主管能够快速获得洞察力，从而做出更明智的决策。这使他们能够根据需求或市场变化快速准确地更新预测。

单源多视图。将预测作为单一数据源生成，可以让您很好地了解代表处、地区和公司的绩效，并有助于在组织中调整不同的业务功能。
随着时间的推移而改善。使用改进的销售预测流程提供的洞察力来创建更精细的未来预测，其中准确性随着时间的推移而提高，相对于一组准确性目标。

拥有更先进的预测流程和工具的公司比他们的同行表现得更好，因为他们更深入地了解他们的业务驱动因素，并且可以在销售期结束之前塑造销售期的结果。

主要销售预测挑战

做出一贯准确的销售预测是很困难的。成功进行销售预测的一些关键包括:

准确性和不信任

当公司使用电子表格进行销售预测时，他们可能会遇到准确性问题，这反过来又会导致预测的可信度降低。以下情况会加剧这些准确性问题:

全公司客户关系管理采用不佳，员工没有及时输入数据
跨团队的数据不一致，或者销售人员没有输入完整的数据
公司利益相关者使用不同的方法进行预测
产品、销售和财务团队之间的协作不足。当公司手工制作销售预测或使用电子表格时，这种缺乏协作的情况会更加严重。

主体性

尽管做出高质量的销售预测在一定程度上依赖于预测者对如何使用数据做出正确的决策，但总体而言，公司更多地依赖于判断，而较少依赖于可信的预测分析。例如，使用简单算术管道权重进行预测的公司可能会忽略准确性的真正驱动因素的细微差别，这些驱动因素可能是员工数量、定价决策或重点市场路线。

可用性

当销售预测不能以一种对整个公司的利益相关者有用的方式产生时，它的有效性就会大大降低。一个好的预测应该为多个团队提供相关和可理解的数据。

效率低下

当预测过程效率低下时，销售预测尤其难以实现。例如，当一个预测有多个所有者，或者预测过程没有用一套标准规则清楚地阐明时，就可能存在关于如何产生预测的争议。类似地，如果预测的输入在预测产生之前没有协调，那么预测本身可能会受到许多修订，如果版本被推出然后被修订，这可能会降低信任度。

公司对整个企业的预测

为了预测整个企业，公司需要来自每个业务功能的不同元素。以下是不同的功能对销售预测的贡献:

销售:提供自底向上的视图，使用来自CRM和PRM的数据，建立来自销售领导的判断。销售人员可以通过销售操作功能、使用正确的工具和报告来管理这个过程。
财务:提供宏观经济指导并与产品团队合作。财务可以帮助将预测与他们的财务规划软件相结合。
市场营销:提供宏观市场指导，特别是在电信、零售和消费品等行业。市场营销还可以为财务团队提供市场数据。
供应链:提供供应和生产方面的投入。
它:通过提供平台、数据、集成和技术支持协助销售预测。

有效预测软件的主要特点

一流销售预测软件应该能够立即提高预测的准确性，使预测过程更有效。因此，它应提供以下能力:

执行销售预测模拟和结果。对驱动因素进行变更，并执行销售预测模拟，以预测未来对销售业绩的影响。
分析销售预测的趋势、变化和季节性。开发基于时间的仪表板和关键绩效指标(kpi)，例如速度计算、趋势分析和季节性波动。
模拟和分析“如果”的场景。创建“假设”场景和建模，以分析如果发生特定的业务、经济或竞争情况对销售预测的影响。为即将到来的交易周期中可能遇到的挑战做好准备。

用熟悉的公式建立销售预测计算。应用易于使用的公式构建器，使用熟悉的公式和语法配置销售预测基准。
快照Salesforce CRM帐户和机会，以比较各个时期。将周与周、月与月、年与年的变化与当前期间进行比较。
比较基于多种建模技术的预测。根据定性、时间序列分析和预测以及随机建模技术创建销售预测，同时确定销售预测准确性和可预测性的不确定性程度。
跨地域、产品和客户的预测。按地理位置、产品线和客户开发销售预测，或者更改这些维度中的任何一个，以在这些层次结构的任意粒度(按州/城市、特定的产品sku集或选定垂直中的一组客户)上分析销售预测。
使用数据可视化分析性能。内置仪表板、报告和数据可视化分析(图表、图形、地图等)。仪表板和报告立即更新。分析销售预测和销售业绩指标，根据可操作的见解做出更好的决策。

销售预测的未来:预测分析

预测分析已经改变了许多商业领域，销售预测也不例外。即便如此，像“预测分析”和“机器学习”这样的术语仍然令人生畏。德勤高级经理Abe Awasthi分享了一个简短的例子，解释了预测分析如何改善预测:

一家科技公司要求德勤制作一个预测模型，以提高销售预测的准确性。为了创建他们的模型，德勤利用了该公司前几年的管道数据，删除了客户和员工的姓名。然后德勤利用机器学习从历史趋势中进行推断，并填补数据中的空白。

德勤然后利用这些数据建立了两个预测模型:一个计算任何给定交易完成的概率，另一个预测交易完成的时间框架。结合起来，这些模型为公司的销售团队提供了高度可操作的、非常具体的建议:“在接下来的10天内，把第5个机会推给合格的人，否则你就会失去它!”

重要的是，德勤能够在8-12周内建立这些预测性预测，这对许多公司来说都是可行的。

为什么使用anplan进行销售预测?

anplan平台是唯一的配置，以提高销售预测。通过将所有相关员工——销售人员、销售主管、运营团队、财务、供应链、营销和高管——放在同一个平台上，公司可以做到以下几点:

增加责任并确保销售团队更准确地报告销售渠道活动。识别有风险的销售交易，消除“沙袋”，减少超额承诺。
规范销售预测和管道管理。在整个组织中提供一个单一的视线，这样每个人都可以看到收入预测、销售预测和运营洞察力。
创建准确可靠的销售预测。通过向所有业务部门(包括销售、财务、运营、人力资源和营销)提供准确可靠的销售预测，让职能部门的领导者做出更好、更明智的决策。

访问数据驱动的销售基准和趋势分析。使销售领导能够使用历史和当前的销售业绩作为基准来预测未来的销售结果。对功能计划进行更改，并跨所有其他业务模型实现这些更改。

通过采用连接计划方法，将整个企业的人员、数据和流程汇集在一起，公司可以生成一个准确的销售预测，连接整个公司的团队，让每个人都为未来做好更好的准备。

观看anplan和德勤的点播网络研讨会，感受热度?提高销售预测的五种方法，学习在动荡时期改善销售预测的五种方法，并专注于现成的模型和客户示例。

学习更多关于销售预测和商业收入计划与anplan。

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