Optimize efficiency, productivity and customer service with highly accurate in-store traffic forecasts
How a large home furnishings retailer used ML to drive high efficiency across their unique customer service model, while simultaneously driving ROI and streamlining operations

INDUSTRY TECHNOLOGY

The client

The client is a fast growing home furnishings retailer chain with 20+ stores across the US. In an increasingly competitive retail industry, they are committed to making shopping experience easy and convenient, delivering an unmatched selection, guaranteed low prices, and same day furniture delivery.

Overview

This client selected Google Cloud and Pluto7 to help optimize their store operations by dramatically improving their in-store traffic forecast accuracy. With the right blend of Google Cloud solutions and Pluto7’s expert services, this enterprise successfully leveraged ML, resulting in significant operational efficiencies and increasing profitability.

With over 20 stores spread across multiple states in US, this furniture retailer relies on a unique customer service model to achieve key growth targets and exception customer loyalty. This model demands flexible staffing across stores, which relies on accurate traffic forecasts.

Challenges

In-store traffic forecasts are currently developed manually by one person, managed on spreadsheet, and take up almost a month to prepare

The process is time-consuming, challenging to scale, cumbersome to update, and unresponsive to real-time business changes

As a result, stores can be over or under staffed, resulting in serious impact on customer service increasing unnecessary overhead costs.

Strategy

Identify, collect, analyze and validate relevant internal and external data sets.

Leverage ML and AI on the selected datasets and produce a highly accurate 4 week (28 day) traffic forecast for each individual store location

Utilize the results to optimize staff and stock planning, to maximize each store’s operations budget and determine effective marketing

Results

Forecast accuracy increased to 84+% from previous max of ~65%

Improved operational efficiency and customer service by scheduling staff more aligned with realistic demand (store traffic)

Reduced costs, increased productivity and customer satisfaction

“Forecasting our store traffic closer to real count serves as a critical element in extending our store operations productivity and minimizing our costs.”

Key Stakeholder,
Large Furniture Retailer

This enterprise has grown by designing and delivering high quality furniture at affordable prices, targeting a diverse base of quality conscious customers. Their business has thrived due to its well-knit, family-like operating culture and ability to deliver exceptional customer experience. Employees take pride in the extensive product offerings and the brand image the company has maintained. The client’s core value is about showcasing high end product design, offering a wide range of selection and providing best in class customer service. Essential to this, is being able to detect customer buying patterns.

The key to their growth is to maintain their differentiated customer service model, at scale. To do this, they need accurate in-store traffic predictions so they can allocate their resources effectively, stock their individual stores appropriately, and ensure high levels of operational efficiency. However, their current manual processes has been reliant on one person and one spreadsheet, limiting accuracy, growth, and responsiveness.

What does it take to have a highly accurate store forecast?

Accurately forecasting store traffic is the key first step to driving success in many business areas. Highly efficient store planning, optimizing customer experience, and maximizing profitability all rely on understanding who will be coming into the store, when.

There is a range of data that feed into predictions. Historical data provides insight into customer behaviors - such as past orders and traffic patterns across the year. Marketing campaigns and how they correlate to store traffic is another helpful comparison. In a multi-store environment, regional differences bring complexities but also result in a wider data set. In order to leverage the potential of this internal data, it must be accessible and consumable.

However, internal data is only a portion of the whole picture - a more comprehensive forecast will take into account external factors. For example - does the weather have an impact on store traffic, if so - it should be included in the analysis. What about customer clicks to Google Maps and their correlation to store proximity? A truly effective ML model will take into account both internal and external data, simultaneously processing and analyzing data from diverse sources and process it at business real- time speed..

The perfect problem for Google Cloud to solve

This involves leveraging advanced predictive analytics capabilities like those provided by Google Cloud Artificial Intelligence and Machine Learning offerings. This is a perfect problem for the Google Cloud Platform to resolve - to handle complexities with data storage, scale of analytics and associated machine learning and artificial intelligence.

Currently, it takes one person about 15-20 days to come up with a traffic forecast for one store, which is not scalable. We need to eliminate this dependency and develop the ability to accurately forecast store traffic more granularly at the individual store level. This enables our store operations team to perform at high levels and succeed.

Key Stakeholder,

Large Furniture Retailer

Improved predictions driving transformation

With these strategic objectives in mind, this large furniture retailer partnered with Pluto7 to leverage Google Cloud Platform to help create a dynamic store traffic forecast model with high accuracy.

With Pluto7’s expertise in Advanced Analytics, Machine Learning and Artificial Intelligence, we were able to identify the appropriate Google Cloud solutions and quickly develop a Proof of Concept that demonstrated how intelligently blending both internal and external data produces a high accuracy, multi-store traffic forecast that met the client’s need.

This retailer now accesses highly accurate 28-day forecasts, enabling them to plan more effectively. They have since experienced significant operational efficiency and productivity gains, while continuing to delight customers with their unique and differentiated model for customer service.

Stronger through innovation

The customer choose to work with Pluto7 and experiment use of Google Machine Learning technology to leverage more data, analytics and machine learning. Ultimately, the objective is to achieve breakthrough innovation and business transformation by expanding the success of leveraging Google Cloud, AI and ML to related use cases and beyond.

Why Google

This client chose Google Cloud Platform because it:

  • Enabled an easy way to collect multiple data sources and co-relate them all together in the most cost effective and productive manner
  • Is able to analyze the combined data elements to generate a high accuracy forecast by leveraging machine learning capabilities of the Google Cloud Platform
  • Allowed the client to focus on solving their business problem without getting bogged down with infrastructure issues

Products used

Google Cloud Platform

Google Cloud Dataprep

Google Cloud Dataflow

Google BigQuery

Google Cloud Storage

Google Machine Learning

Google Tensorflow