We combine artificial intelligence, the experience of industry experts and a flexible planning platform into a single forecasting process.
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OUR FORECAST METHODS:
TRADITIONAL STATISTICAL METHODS
Traditional methods operate with the simplest statistical methods, small data sets, samples and assumptions.
WITH MACHINE LEARNING
Complex single- and multivariate models that process large amounts of data,
with the selection of the optimal forecasting method
for the available data
and determining the degree of influence of external factors
on the forecast.
Machine learning, in conjunction with traditional methods of statistics. It helps to analyze the totality of events in the past,
future events with high accuracy, both positive
and negative (it is necessary to avoid).
WHAT TASKS DO WE SOLVE
CURRENT SALES FORECAST
To predict sales of a wide range of products with a wide geography of presence, the automatic selection of single-variant forecasting models
with a minimum root-mean-square error
of forecasting is ideal.
FORECAST OF SALES OF NEW PRODUCTS AND PRODUCTS WITH A SHORT LIFE CYCLE
Traditional statistics will not help to predict the sales of new products on the market. There is no historical data and fine-tuning of forecast models will be required
using data on related products (similar parameters) or data from a new market in comparison with analogues.
SALES FORECAST FOR PRODUCTS DEPENDENT ON WEATHER AND OTHER EXTERNAL FACTORS
Products whose sales are sensitive
to weather, temperature, humidity, light level
and other external factors are more suitable for forecasting using multivariate models than using traditional statistics, especially
for a short planned horizon.
PLANNING PROMO ACTIVITIES
Qualitative forecasting of the effects of promotions helps to save billions of marketing budgets and improve the quality of working capital. To do this, the methods of correlation search, clustering and machine learning based on multifactorial predictive models are combined.
HOW DO WE WORK
HISTORICAL DATA FROM
point-of-sale data, marketing research, social media data.
2. DATA CLEANING
Identification and correction of errors, data inconsistencies in order to improve data quality.
3. SELECTION OF THE ANALYSIS METHOD TIME SERIES WITH THE SMALLEST FORECAST ERROR
Automatic ranking and recommendations.
AND ADJUSTMENT OF THE MODEL TO NEW CONDITIONS AND FACTORS
Identification of differences and conditions for successful application of the model.
5. CREATING A PLATFORM FOR COLLABORATION AND DATA EXCHANGE
IN THE PROCESS OF FORECASTING
Accounting for the expertise of industry professionals.