• Text
  • Text
  • Text

Artificial Intelligence in Trade Promotions

To promote marketing and sales to your business, get trade promotion using artificial intelligence solution. Here we Used Artificial Neural Network with Feed Back Propagation algorithm to enhance trade promotion.

Product Overview

Trade promotion is a marketing technique which provides several benefits to businesses. Many companies use trade promotions to increase distribution of their product at retailers and build strong relationship with retailers. The main objective of implementing artificial intelligence in trade promotion is to increase demand for products in retail stores. Artificial Intelligence has been used in modeling consumer choices. Here we proposed artificial intelligence for marketing products by predicting consumer choices. Consumer choices are decided based on the consumer purchasing behavior toward goods and services involves a five stage which includes problem recognition, search, evaluation of alternatives, choice and outcome. Trade promotion is a process that requires identifying group of consumers described by a set of similar characteristics, in order to improve marketing activities through a better allocation of resources and formulation of customizable strategies. Consumer behavior can be identified by using artificial intelligence techniques such as artificial neural networks(ANNs). Artificial intelligence techniques are statistical data modeling techniques in which interconnected elements (called nodes) process simultaneously the information, adapting and learning. The main purpose of this study is to analyze result obtained when building Artificial Intelligence model that identifies individual with great chance of purchasing products, using artificial neural networks. Artificial neural networks are non-parametric methods used for pattern recognition of consumer purchase behavior and optimization. Artificial Intelligence in trade promotion provides new strategies, tactics and technology choices to maximize trade promotion spend by anticipating demand and predicting revenue, volume and profitability. Modeling data with artificial neural networks allows a flexible approach towards independent variables. For trade promotion analysis a multilayer perceptron was used to model consumer data. The ability to enable trade promotion is a fundamental aspect to success for any business. Trading promotions using Artificial Intelligence provide best approaches, strengths and best application to businesses.


  • To increase sales of new products and services
  • To increase sales of traditional products and services
  • To increase inbound customer leads
  • To enhance customer satisfaction
  • To generate new insights and better analysis
  • To increase operational analysis

  • Features of Trade Promotion

    1) Baseline Prediction     2) Uplift Prediction      3) Promotion Optimization      4) Promotion Plan Optimization      5) Flexible Modelling

    Baseline Prediction
    It is necessary to know all data related customer purchase. We need high quality historical data for trade promotion. By using these data, we can analyze the customer’s choice based on the factors such as seasonality, trend and base price of the product. By analyzing these data in order to make system reliable and automated.
    Uplift Prediction
    Since system need historical data to predict promotion uplifts that can be calculated based on total sales and revenue. System can work on new data. If there is any modification in data, promotion uplift can be recalculated easily.
    Promotion Optimization
    System can identify best product based on the factors such as time, price and available budget. System enables to identify best product at the end of planning cycle.
    Promotion Plan Optimization
    By using positive and negative historical data application can build a new promotional plan. Promotional plan can be generated based on underlying models and data to decide on the frequency, duration.
    Flexible Modelling
    Uses the best modelling to provide accurate results. Tool uses multiple models each model for different tasks.
  • Unit sales (input): Unit sales of product is competitive factor affecting the customer behaviors, especially for independent retailers. It is processed as quantitative information.
  • Product quality (input): This factor includes the evaluation about product quality according to customers via a 1-9 scale. It is processed as qualitative information.
  • Customer satisfaction level (input): This factor shows the sales and post-sales behaviors of retailers to the customers. It is processed as qualitative information.
  • Effect of promotions, holidays and special days (input): This factor means the percent increase of sales related to promotions or special days (such as feasts, new year's day, etc.). It is processed as qualitative information.
  • Product quantity (output): Product quantity is the quantity from customers to retailers. It is processed as quantitative information.
  • Algorithm Used
  • We will use Multi-Layer Perceptron (MLP) and Backpropagation algorithm which will be used to train the data
  • However, we will concentrate on nets with units arranged in layers
  • Multi-Layer Perceptron Methodology works on non-linear separable data

  • Following are steps of algorithm
  • Initialize weights at random, choose a learning rate n
  • Until network is trained:

  • For each training example (input pattern and target outputs):
  • Present inputs for the first pattern to the input layer
  • Sum the weighted inputs to the next layer and calculate their activations using activation function formula
  • Present activations to the next layer, repeating (2) until the activations of the output layer are known
  • Compare output activations to the target values for the pattern and calculate deltas for the output
  • Propagate error backwards by using the output layer deltas to calculate the deltas for the previous layer
  • Use these deltas to calculate those of the previous layer, repeating until the first layer is reached
  • Calculate the weight changes for all weights and biases (treat biases as weights from a unit having an activation of 1)
  • If training by pattern, update all the weights and biases, else repeat the cycle for all patterns, summing the changes and applying at the end of the epoch
  • Conclusion

    Trade promotion using Artificial Intelligence enable customers to make sensible choices. Trade promotion using Artificial Intelligence allows retailers to gain sharper predicting tools that ensure to make sharper business decisions. This tool combines trade promotion with Artificial Intelligence to offer innovative business solution. Innovative software solution will help organizations to address business goals and achieve high performance.