AI Sales Forecasting: How to Predict Sales with Accuracy and Confidence

Predicting future sales using previous data is one of AI and machine learning’s most obvious uses. Historical data provides you with an accurate and realistic view of the amount of money your team should be producing over a specific time period. According to a study by Salesforce, 62% of high-performing sales teams are currently using AI to improve their sales forecasting accuracy.

Every organisation relies on sales forecasting as a critical business operation. For sales managers who utilise sales projections to plan inventory, operations, and strategy, it's also one of the most crucial duties.

Advanced algorithms, machine learning methods, and historical data are used in AI-powered sales forecasting models to find patterns, trends, and correlations that can help predict future sales. The advantages of AI sales forecasting, the essential elements of a powerful AI sales forecasting model, and best practices for integrating AI into your sales forecasting process are covered in this article.

What is Sales Forecasting?

Sales forecasting is the process of estimating the volume of sales and ensuing revenue over the course of a future time period using knowledge, historical data, sales activity data, and predictive analytics technology. Every successful sales team, and almost all sales teams, has a system for predicting sales. 

An excellent sales forecast does not rely on guesses but rather analyses real-world facts to determine what outcomes are likely. Sales forecasting is crucial because it may inform resource allocation and employment decisions, financial estimates, risk management, and other processes.

Great Forecasts Need Good Data

The accuracy of forecasting has grown critically dependent on the quality of the data as firms depend more and more on data-driven decision-making. Great predictions are built on solid data, which offers insights into market trends, consumer behavior, and other elements that affect sales success.

Making accurate projections that allow firms to deploy resources efficiently, plan for the future, and optimize revenue requires good data. Companies may make confident data-driven decisions by identifying patterns, trends, and correlations in their data and using this information to influence sales estimates.

Despite the benefits of good data, businesses often face challenges in obtaining and managing high-quality data for forecasting. According to a report by Experian, 83% of companies faced data quality issues in 2019, among these challenges: 

– Data quality issues: When there are mistakes, exclusions, or inconsistencies in the data, it can be challenging to make reliable inferences and predictions.

– Data accessibility: Data may be kept in several places or in various forms, making it challenging to utilise for forecasting.

– Data integration is challenging because different data sources may utilise various data formats or different types of data, making it challenging to combine and evaluate the data.

– Data privacy and security: Organizations must abide by data privacy laws and protect the security of their data, which can make data collecting and administration more difficult.

Benefits of AI sales forecasting

1-Improved Reliability and Accuracy

In order to provide extremely precise forecasts of future sales, AI sales forecasting models may examine huge volumes of data, including historical sales data, market trends, and consumer behavior. With conventional forecasting techniques, which frequently rely on small data sets and subjective assessments, this degree of precision is just not achievable. With AI, businesses can depend on data-driven insights to lower guessing and boost prediction accuracy and reliability. According to a report by Aberdeen Group, companies that use AI for sales forecasting have an average forecast accuracy of 79%, compared to 51% for those that don’t.

2-Real-time Insights and Adaptability

Conventional sales forecasting techniques sometimes depend on outmoded historical data and presumptions about the state of the industry. On the other hand, AI sales forecasting algorithms can instantly adjust to shifting market conditions, giving businesses the most recent information and advice. This increases firms’ chances of success by enabling rapid and efficient strategy adjustments in response to fresh information.

3-Improved Efficiency and Time Savings

Sales teams will have more time to devote to more important duties because to AI sales forecasting models’ ability to evaluate enormous volumes of data considerably more quickly than humans. As a consequence of more effective resource allocation and decreased waste, companies may also see cost savings as a result of this enhanced efficiency.

4-Increased Revenue and Profitability

AI sales forecasting may help companies in defining goals and allocating resources by giving precise and timely insights into upcoming sales. Due to the capacity to concentrate efforts on high-potential areas and prevent resource wastage, this can ultimately boost revenue and profitability for businesses.

According to a survey by Accenture, companies that use AI in sales forecasting reported an average revenue increase of 6.1% and a profit increase of 5.6%. Additionally, the same survey found that 81% of companies that used AI in sales forecasting reported an improvement in their sales and revenue forecasting accuracy

Types of AI models

Time series forecasting is a data analysis method for making future value predictions based on past data. In order to create predictions, univariate time series forecasting focuses on tracking a single variable through time. Comparatively, multivariate time series forecasting analyses several factors at once to produce predictions.

With the help of several tools and frameworks like Statsmodels, Prophet, and TensorFlow, Python programmers may forecast time series utilizing both univariate and multivariate variables. Simple models like ARIMA (AutoRegressive Integrated Moving Average) or sophisticated models like LSTM (Long Short-Term Memory) can both be used for univariate time series forecasting. Multivariate time series forecasting calls for more sophisticated models, such as VAR (Vector Autoregression) and VARMAX (Vector Autoregression Moving-Average with eXogenous inputs), that can handle numerous variables and their interactions.

Depending on the data at hand, the issue being handled, and the level of precision sought, one may choose between univariate and multivariate time series forecasting. Although univariate forecasting is quicker and easier, it could not account for all the variables that affect the forecasted variable. By taking into account several factors and their interactions, multivariate forecasting is more complicated but can produce forecasts that are more accurate.

Predict Car Sales using TSForecasting models on papAI Solution

Context

In this use case, we will see how papAI solution can be used to predict the number of cars each car dealer needs and therefore predict the number of cars sold.

1- Data set

We will use a database that exists in open data. This database contains 79 weeks of daily data and includes the number of car orders placed by individuals.

As we can see in the first few rows of the dataset above, the dataset has 8 columns which are as follows:

  • Date :  Datetime (DD/MM/YYYY)
  • Sessions : Number of website visits on a specific day
  • Online_leads_actuals : Number of visits to online shop (order online)
  • Test_drive_request : Number of test drive requested
  • Offer_request : Number of offers requested
  • Total_configs_finished : Number of car configuration completed
  • Keep_me_updated : Number of users asked to be kept updated
  • Dealer_locator : Number of users who looked for a dealer location
  • Private_orders : Number of times a private order has been made. This represents the total number of offline sales received. We should try to predict this field.

2- Data preparation

 papAI solution allows to perform general processing operations, which allows the processing and preparation of data, without having to manipulate lines of code. If you want to do processing on a time series database, then a separate module is dedicated to this kind of data.

As you can see in the database (previous picture), the date column is of type ‘string’. This column contains dates, so the first thing to do in papAI is to use the general processing cleaning to convert the type of this column to the correct format ‘date’.

The second processing we will apply is the datetime feature generation which is in the TS Cleaning module of papAI Solution. This operation extracts additional information from the date such as: Day of year, Week of year, Day of week, Day of month ……etc .  This allows us to have more features and it will allow us to have a better accuracy in the forecasting.

3-Machine Learning

Model training

papAI is a platform that allows to run of several machine learning models at the same time, without coding. These models are first displayed with the default settings of scikit learn, and these settings can be modified by the user if he wants. When creating a machine learning use case, papAI Solution offers us to choose between Single series ‘ Univariate ‘ or Multiple series ‘ Multivariate.

3.a) Univariate: 

The TsForecasting univariate is to train a machine learning model by taking into account the target variable on the time series, so in this method, we will use only two columns of our dataset (Date and Private Order). 

3.a.1) Data analysis: 

After choosing Private Order as a target, and univariate as a series type, papAI automatically displays a visualization of the evolution of ‘Private order’ on the Temp.

In the tab next to it, we can see an analysis of our database/

The statistics generated give us very useful information that could help us to become more familiar with our database.

The observations contain : 

  1. The periodicity of the database is 1 day
  2. The data were collected between 01/01/2017 and 07/07/2018
  3. The minimum value was 0 on 01/01/2017
  4. The maximum value was 453 on 07/07/2018
  5. The Median was 18, the Mean was 33.46.
  6. No missing values. 
  7. The size of the dataset is 553

3.a.2) Create Prototype :

When creating the univariate prototype we must first choose the ‘Data history to consider’, and the number of ‘Future values to forecast.

After defining the Forecasting windows, we have to choose the models we would like to train, in multivariate, there are 12 models which are the following 

  1. ARIMA forecaster
  2. Block RNN
  3. LSTM
  4. GRU
  5. FFT
  6. LightGBM
  7. NBEATS
  8. Prophet
  9. Regular ML Models (Linear regression)
  10. Regular ML Models (Random Forest)
  11. SES
  12. TCN

papAI solution guarantees an evaluation of each machine learning model. In order to set up the evaluation of our models.

After starting the training, we get the evaluation of each model.

The best-evaluated model is the NBEATS.

We can see in the evaluation which metrics have been taken into account:

  1. Metric table with the (sMAPE, MAE, MSE, MAPE, R2 score)
  2. Plot of the Target over time 
  3. Plot of predicted value vs true value on a backtest
  4. Risidual distribution and risidual hilight

3.b) Multivariate:

When creating the Multivariate prototype we must first choose the features that can contribute to a better result.

The second step would be to define the forecasting window with the number of points to be considered in the past and in the future.

The third step is to choose the models on which we would like to train the forecast.

In multivariate, papAI Solution has 16 models, which are the following 

 

  1. Block RNN(RNN)
  2. Block RNN (LSTM)
  3. Block RNN (GRU)
  4. LightGBM
  5. NBEATS
  6. Regular ML Models ( Linear Regression) 
  7. Regular ML Models ( Random Forest)
  8. TCN
  9. DeepAR (LSTM)
  10. DeepAR(GRU)
  11. Temporal Fusion Transformer
  12. MQ-CNN
  13. DeepState (LSTM)
  14. DeepState (GRU)
  15. DeepFactor (LSTM)
  16. DeepFactor(GRU)

4- Prediction

After evaluating and comparing the models we ran, the NBEATS model gave the best results, we will predict the number of sales per day for the next 4 months with the NBEATS model (univariate).

Viewing the results:

The result of the prediction is a data frame, with the values of the private order over the next 4 months following the last date of our dataset.

This visualization shows us the true values in blue, and the values that were predicted by our model between July 7, 2018 and November 09 of the same year.

Create your own AI-based tool with papAI solution to improve the accuracy of your sales forecast

In conclusion, organizations of all sizes are finding that using AI-based technologies for sales forecasting is more necessary. Businesses may build their own AI-based tools that are uniquely suited to their needs with papAI solution. This might lead to more accurate and trustworthy sales estimates, which would enhance decision-making and boost profitability.

papAI solution helps to explain and make transparent the results through a process of data mining, cleaning, and visualization that can accelerate the deployment of AI projects and drug discovery and reduce the time and cost of bringing new therapies to market.

Book your demo now. Our team of experts can help you create a custom AI-based tool that meets the unique needs of your organization.

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AI Sales Forecasting: How to Predict Sales with Accuracy and Confidence
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