Sales history

Sales history includes all achieved sales measured during defined period of time. It is usually in form of a database where all records are accurate numbers of number of sold items and other measurements. It is done systematically. The sales history measurements may refer to:

• Value of sales - value that can be shown in specific amount of money in period of time (daily, weekly, monthly, quarterly, periodically, yearly etc.),
• Sold units - can be shown in specific quantity in period of time (daily, weekly, monthly, quarterly, periodically, yearly etc.), might refer also to guests served in food industry etc.,
• Sales to date - value or units from start date point in the past to defined day, for example weekly sales by today if today is Thursday would mean sum of values from Monday, Tuesday, Wednesday and Thursday,
• Sales average - average of value or units in period of time (daily, weekly, monthly, quarterly, periodically, yearly etc.):
  • Fixed sales average - average of value or units in defined period of time for example first 14 days of a month,
  • Rolling sales average - average of value or units over changing time for example average from last 5 days,
• Variance of sales - difference between value or units, both from closed period of time for example sold units in 2017 minus sold units in 2016.

Sometimes sales history is named demand history and in the opposite which is wrong understanding. The demand data are sales plus lost sales. Sales value is smaller than demand value. Lost sales is value of potential sales that would happen if inventory would be enough to meet demand.

Advantages of accurate sales history

Keeping sales history accurate and using databases for calculations, estimations, evaluations and predictions might have many benefits for the whole company by:

• Accurate estimations of future sales,
• Ability to predict expenses,
• Efficiency is preparing schedules for both workers and production,
• Higher accuracy of purchases,
• Ability to keep right level of stock that can meet needs coming from the market,
• Better budgeting for future periods,
• More attractive selling prices as operational costs might decrease,
• More money saved for investments as operational costs might decrease,
• Increased profit level and stakeholder value.

Examples of Sales history

• Financial: This is a record of all the financial transactions in a company. It includes sales reports of different products and services, payments, invoices, and other financial records.
• Consumer: This is a record of all the consumer purchases made in a particular period of time. It includes detailed information about each purchase, such as the item purchased, the quantity purchased, the price paid, and the consumer information.
• Product: This is a record of all the products sold in a particular period of time. It includes detailed information about each product, such as the description, the quantity sold, the price paid, and the customer information.
• Market: This is a record of all the market activities in a particular period of time. It includes information about the market trends, the competition, the sales figures and the customer feedback.

Limitations of Sales history

Sales history can be an important tool for understanding the performance of a company, however, it has certain limitations. These include:

• Data Accuracy: Sales history data can be unreliable if the underlying data is inaccurate. This can be caused by errors in data entry or if the data is not collected in a consistent manner.
• Timeframe: Sales history is limited by the time frame that it covers. It is not possible to analyse long-term trends or changes in customer behaviour over a longer period of time.
• Geographic Limitations: Sales history data is limited to a specific geographic area and does not accurately reflect the performance of the company in other regions.
• Data Granularity: Sales history does not provide detailed insights into customer behaviour or product performance. It is not possible to analyse the individual behaviour of customers or the performance of specific products.
• Data Volatility: Sales history data can be volatile as it is based on a snapshot of sales at a particular point in time. It is not possible to accurately predict future trends or customer behaviour.

Other approaches related to Sales history

A sales history includes all achieved sales measured during a defined period of time. Other approaches related to sales history include:

• Sales Forecasting: It is the process of analyzing an organization's past sales performance and extrapolating the data to predict future sales. It helps organizations plan sales strategies and allocate resources to increase sales.
• Sales Analysis: It is the process of evaluating an organization’s sales performance. It is used to identify areas of improvement, develop sales strategies, and measure the effectiveness of a sales team.
• Customer Relationship Management (CRM): It is a system used to manage customer interactions and to track customer data. It helps organizations build relationships with customers and better understand their needs.
• Pricing Strategies: It is the process of determining the optimum price for a product or service. It involves evaluating customer demand, competitor pricing, and market conditions.
• Sales Promotion: It is the process of offering incentives and discounts to attract customers and build brand loyalty. It includes activities such as coupons, contests, and giveaways.

In summary, sales history is an important tool for businesses to measure past performance and plan for the future. Other approaches related to sales history include sales forecasting, sales analysis, customer relationship management, pricing strategies, and sales promotion.

Footnotes

Sales history — recommended articles

Sales trend — Incurred cost — Production capacity curve — Sales target — Market metrics — Segment margin — Capture rate — Direct expense — Market mapping

References

• Dopson L. K., Hayes D. K. (2010), Food and Beverage Cost Control, John Wiley & Sons
• Fleder D., Hosanagar K.(2007), Recommender Systems and their Impact on Sales Diversity, in "EC’07, June", University of Pennsylvania, USA
• Kahn K. B., Adams M. E. (2001), Sales forecasting as a knowledge management process in "The journal of business forecasting. Winter"
• Wilson R., Hill A. V., Glazer H. (2013), Tools and Tactics for Operations Managers (Collection), FT Press
• Yucesoy B., Wang X., Huang J., Barabási A. L. (2018), Success in books: a big data approach to bestsellers, in "EPJ Data Science", Springer Open Journal

Author: Aleksandra Puchała