Upgini • Accurate Sales Predictions with Data Enrichment

Predict Sales

9% match rate for Postal code worldwide

Proven uplift for your models

Use case description

Sales forecasting plays a vital role in the retail industry for several reasons:

Business Growth: Accurate sales forecasting gives retailers the power to project future revenue and grow their business. It enables teams to take swift action on their deals, invest in the right sales tactics, and capitalize on new opportunities for expansion.
Inventory Management: Sales forecasting assists retailers in determining the number of products they need to stock. By predicting the number of sales they are going to make in the future, retailers can plan their inventory accordingly and thus increase their profits.
Problem Identification: Sales forecasting reveals potential issues that might impact sales, allowing businesses to address them before they arise. This information is crucial in formulating the right strategies to prevent a drastic reduction in sales.
Company Growth and Investment: Sales forecasting is crucial to the growth and investment of a company. When a team consistently meets their sales targets, the company can invest and grow with confidence, which can result in more marketing campaigns, increased headcount, and new technology to sustain and boost growth.
Resource Planning: Sales forecasts help businesses plan resources to ship products, pay for marketing, hire employees, and more. Accurate sales forecasting results in a well-organized system that meets customer demand, both presently and in the future.
Risk Management and Budgeting: Sales forecasting aids in overall business planning, budgeting, and risk management. It allows companies to efficiently allocate resources for future growth and manage their cash flow. Additionally, sales forecasts help sales teams achieve their goals by identifying early warning signals in their sales pipeline and making corrections before it's too late.

How Upgini could help with Sales prediction?

Upgini could improve the quality of your sales prediction system by utilizing new, relevant data. This data can aid you in leveraging knowledge about your customers to create the most accurate predictive solutions.

How to test Upgini for Promotions?

Search for new relevant features to predict sales with new promo price on any item:

Obtain a training dataset with store ID, date, postal code of the store, historical prices and sales of each item.
Search for new relevant features for your task: launch FIT method of Upgini and get a list of relevant features from connected data sources.
Make a test enrichment: launch TRANSFORM method of Upgini and enrich your dataset with only new relevant features.
Use Upgini in production: Upgini moves relevant features for the all Phone numbers, Emails and IP-addresses to your DWH. You may use this data directly in your DWH.

Try Upgini to predict Sales more accurate

Just copy the code and send it to your analyst to repeat!

Upgini will quickly generate a quality report.Ask your analyst to send it to you to check the result.

Copied

%pip install -Uq  upgini

df=pd.read_csv("sales_historical_dataframe.csv")

from upgini import FeaturesEnricher, SearchKey
from upgini.metadata import CVType

enricher = FeaturesEnricher(
    search_keys={
        "day": SearchKey.DATE,
        "postal_code": SearchKey.POSTAL_CODE,
        "country": SearchKey.COUNTRY
        },
  	cv=CVType.time_series
    )

enricher.fit(df.drop(["num_sales"],axis=1), df.num_sales)

#enrich dataset with new features
df_enriched=enricher.transform(df)

Use Upgini to predict Sales better

Predict Sales

Make ML&AI models happier
with more relevant data

Use Upgini to predict Sales better

Predict Sales

Make ML&AI models happier with more relevant data

Make ML&AI models happier
with more relevant data