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Mobile Order Sales Predictions
Primary Use case
- Assess order volume and suggest procurement and growth plans
- Recommend delivery and activation expectations for improved customer experience
Secondary Use case
- Optimize process efficiency
Client Expectations
Build mobile device inventory based on sales trend.
A large mobile service provider in US found it challenging to build mobile device inventory based on sales trend. Mobile device sales are influenced by a myriad of features such as models, service plans, promotions and demographics. This requires forecasting of demand-supply ratio in order to alleviate impact of missed sales opportunities and excessive warehousing costs. As a result, the company was looking for a real-time sales forecast in order to improve their inventory management and facilitate a better customer experience.
What did we do
Predict sales trend for each model of mobile phones.
GBee framework was leveraged to predict sales trend for each model of mobile phones. This was done by analyzing and learning over the historical order volume data. A combination of machine learning algorithms was incorporated in the model to improve the prediction accuracy to around 90%.
key features
Learn how we stand out from the crowd
benefits
Feeds downstream functions
Behaviour by region / division / cmts
- Deep feature synthesis
- Auto Feature Engineering
- Auto Feature Engineering
Behaviour by region / division / cmts
- Deep feature synthesis
- Auto Feature Engineering
- Auto Feature Engineering
Behaviour by region / division / cmts
- Deep feature synthesis
- Auto Feature Engineering
- Auto Feature Engineering
Behaviour by region / division / cmts
- Deep feature synthesis
- Auto Feature Engineering
- Auto Feature Engineering
Behaviour by region / division / cmts
- Deep feature synthesis
- Auto Feature Engineering
- Auto Feature Engineering
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
“Lörem ipsum kroligt onat mimanas, ogon. Aktigt fimpomat. Megans pos.”
Inventory Management
- Recommendations across Regions and Channels by Make, Model etc.
- Advice Buy Trends
Previsioning Activation
- Warn Activation Issues by Model
- Warn Issues due to device upgrades.
Shipping & Delivery
- Warn Delays by Region, Channel, Make and Model
Plans/Promotions
- Performance of Plans/Promotions
- Warm potential Revenue Loss
- Advice Fraud Behavior
Inventory/Process/System Issue
- Warn System Flow Upgrade
- Warn Network Maintenance
External Event Influence on Order Pipeline
- Industry buy trends
- Group/Regional Events
results
The numbers say it all
- 4 recommendation
- 72 Predictors
- 100’s of Makes, Models
- 5 recommendation
- 10's pf Plans and Policies
- 10's of Regions/Divisions
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92%
Prediction Accuracy
4
Recom
1 Millions
Readings / day
90 Days
History
our techstack
We build and run efficient application
Make data-driven decisions
