Beegol logo, blue type, TRANS

 

 

Most telemetry programs run TR-181 data collection commands inside the CPE at regular intervals. They have a data path. However, Beegol has both a data path and a control path.

The control path allows for changes to the data collection scope and granularity, including real-time adjustments. For example, Beegol can change the latency measurement from a time interval of one minute to a one-second interval whenever potential transport congestion is detected. It can also collect data from all clients in a building to detect MDU failures. The control path provides the flexibility to gather data at any time and anywhere in the network.

 

 

 

 

      Challenge   Our Solutions
           
Suggested order   High complexity to determine the product mix and quantity to offer to each client at each interaction
  • Thousands of clients
  • Hundreds of products
  • Several contact points (sales force, tele-sales, app, WhatsApp, etc)
  Machine Learning model to calculate Suggested order including
  • Demand forecast for recurring products
  • Recommendation of new products
  • Inclusion of strategic products

Other business elements considered
  • OOS
  • Restricted items by channel/region
       
Route-to-market   High complexity to define mode and frequency and to serve each client:
  • Sales person visit
  • Tele-sales call
  • WhatsApp message  
  Analytical model to define optimal service level according to desired objective, e.g:
  • Maximize sales while maintaining costs
  • Minimize cost while maintaining revenue

Simulator to assess different scenarios.

Multiple variables used, for example:
  • Total sales
  • Frequency of purchase
  • Recent growth
  • Usage of digital channels
  • Complexity of POS Execution
       
Promotional optimization  
  • Lack of clear criteria to allocate promotional budget

  • Lack of understanding of Return on Investment of promotional efforts

  • Uncertainty of optimal pricing to maximize revenues
  Analytical model to measure Return on Investment by product/channel/region;

Optimization model to allocate promotional budget considering
  • Market potential
  • ROI
  • Business constraints

Calculation of price elasticity and price point to maximize sales
       
Insight generation machine  
  • High availability of data but lack of integrated view

  • Siloed approach to data management causing difficulty to generate actionable insights
 
  • Integrated database consolidating data from different sources and enriched with external information

  • Analytical suite to identify commercial opportunities with high granularity and at large scale

  • Machine learning models to predict trends (e.g. growth) ate granular level and levers to change them