Data Science Powered Sales Forecasting

Generate faster, accurate sales forecasts with sales prediction models

Data Science Powered Sales Forecasting

The problem with the current method of sales forecasting

For large enterprises, sales forecasts are as critical as they are tedious and slow. However, current tools and processes, more often than not, fail to deliver the confidence in the forecast that is required to support critical decisions on everything from budgets to profit. In most of the organizations:

  • Sales forecasting has been supported by primitive techniques, systems and tools like spreadsheets and CRM.
  • The sales team continues to carry out this process using a series of individual spreadsheets that are biased by personal instincts and emotion.
  • Even the most valued companies in the Fortune 100 sometimes get their forecasts dead wrong.

Softweb Solutions’ sales forecasting project for a nationwide retailer

Data Acquisition

Softweb Solutions carried out a data science-based project to accurately predict sales for the year 2016 in the US. In order to get appropriate results, we collated sales data to create data visualizations in Power BI.

Sales data was available by segment and was further bifurcated on the basis of categories, sub-categories and specific products. We also had the sales figures of various states and cities of the US along with the sales and profit details of the country for every month between 2011 and 2014.

Data Structuring and Smoothening

In order to get the results for historical and predicted sales by date, we made use of the R programming language as well as the Time Series algorithm. Further, we identified seasonality effects, modeling and trends, and then used Holt-Winters to smoothen the data using the exponential smoothing constant. The next step was to identify the features to be considered to forecast sales using the time series linear model (TSLM). These features are trends, seasonality effects and the day of the week. We used a built-in feature of the R script to convert sales data into the time series format.

Visual Graph

As depicted in the graph above, we had the data from the year 2011 to 2014 to use as a reference for making predictions. The analysis of this historical data helped us accurately forecast the collective sales in the country for the coming months.

Outcomes

  • This data visualization shows collective sales of the past month and even lets you select a particular year for a detailed analysis.
  • You can hover over any state in the map to get the sales figure of that particular area and click on the dots for details.
  • It is possible to get sales details of any of the major cities by simply clicking on it in the ‘sales by city’ section.
  • Similarly, you can click on any of the segments, categories, sub-categories or product names to get specific sales details.

How data science can help make sales forecasting more accurate

  • Identify principal factors that influence an opportunity’s probability of success.
  • Have a rich visualization of how each of the factors affects sales and profit.
  • Monitor the health of deals throughout the sales pipeline and keep it in the best possible state by reporting actionable intelligence.
  • Data science will transform the traditional approach into automated, reliable, and scalable one.
  • Companies and product manufacturers can use predictive analysis to focus on products that are more likely to be popular in the coming months.
  • It can help them plan their sales in advance and concentrate more on areas where their products are more likely to be in demand.
  • This analysis can also help companies and manufacturers stay ahead of their competitors.
  • It is perfect for ensuring that companies make a profit by investing in markets that are likely to witness a boom and avoid losses.
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