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CUSTOMER STORY

Pepsico Delivers Smarter Sales Forecasting with Designer Cloud

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Core Challenges

In order to supply the right product quantities to its retailers, PepsiCo must constantly calibrate sales forecasting. Any given sales forecast may incorporate warehouse data, store stock data or promotional forecast data, all of which is provided by retailers in a variety of different file formats and delivered to PepsiCo using a variety of different methods. The name of the game for PepsiCo is speed—prepare a sales forecast fast enough, and the team could anticipate a spike in demand and increase product shipments. But under Microsoft Access and Excel-based processes, the huge amount of time required to prepare this data meant that analysts only leveraged it once a month (or not at all), which risked under or oversupplying retailers.

How Designer Cloud Solved this Problem

PepsiCo built a Microsoft Azure data lake that allows for centralized data storage and access. Analysts trained in Excel and SQL easily adapted to Designer Cloud and can now prepare and standardize diverse retailer data significantly faster for visualization in Tableau. Due to Excel’s size limitations, PepsiCo analysts often had to stitch together multiple spreadsheets; with Designer Cloud, PepsiCo can see the full picture of its data and quickly spot any errors or inconsistencies. Since spending less time spent on data preparation, PepsiCo analysts are now able to dedicate the majority of their time analyzing data and predicting trends, driving more value for the company.

BENEFITS OF USING ALTERYX
Accelerated Analytics

Analysts were able to reduce the total reporting time by 70% and build dashboards for new customers 90% faster than with Excel and Access

Expanded Reporting

Less time spent on collecting, cleaning and preparing data means that analysts now have time to create dashboards for more customers including online retailers

Reduced Errors

Designer Cloud’s visual profiling capabilities allow the team to easily identify gaps and outliers in the data to get ahead of larger issues

 

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