Thursday, January 2, 2020

Supervised Machine Learning Model Forecasts Impact of Co-Sell Deals on Sales revenue using Azure Databricks


Supervised Machine Learning Model forecast impact of Strategic deal on Sales:
Business executives require visibility into the impact of decisions at all levels of their companies. Executives appreciate having this visibility at each stage of a project,  from initiation to  closing .  Reports showcasing  the results of business decisions across the entire organization help with such visibility. This case study reviews one level of a management decision chain, where revenues are based on sales managers’ offers to customers.

Business Scenario:
Our client, a team of sales managers at a multinational software organization, sells software product solutions to retail customers via dealers (co-sellers).  Our client needed to quantify the value of successful deals on the revenue driven by end retail customers. Also, our client wanted to know how much  their collaborative business model with dealers drove revenue. With this information, the client could focus on the factors with a higher impact on sales.

To provide our client with insights on co-sell deals, we created a Power BI dashboard powered by a machine learning model.

How We Did It?

Architecture:

Data Gathering: We realized that we needed additional data points to generate a successful and accurate model. We integrated different data sources to ensure access to important key features, such as customer geography and the status of deals between clients and dealers.

Data Consolidation: We performed a deep dive into the customer data set to find basic insights and possible key features that might impact revenue. We collected data from various systems, such as CRM and sales systems, which we used in subsequent steps for data engineering.

Feature Engineering: After gathering the required inputs, we feature engineered our input dataset and used Databricks to create a forecasting model using linear regression. We used existing and custom regression techniques to improve the forecasting model’s accuracy.

OptimizationWe cut down the model run time from 3 hours  to 15 minutes by parallelizing  the forecasting algorithm for each customer. This optimization provided the customer with near real-time analytics information. 

Key Challenges:
The major challenge we faced was forecasting revenue using existing data sources.

Another key challenge involved the model’s run time. The model was based on existing forecasting libraries of ‘R’ environment, which were not as agile as we wanted. We modified the ‘R’ libraries to ‘Spark R’,  enabling the model to run and provide forecasts quickly. We also had to improve scalability , considering future scenarios when our model would run not just on thousands, but millions of records. We achieved scalability using Azure Databricks, thereby providing a complete cloud-based, near real-time analytics solution.

Business Outcomes:
The end result was a Power BI report which helped our client understand the lifetime value and growth trend of co-sell wins. The report also provided the functionality to slice and dice the dataset with built-in filters for further insights.

Our dashboard allowed our customer to visualize impact by fiscal year, customer details (such as area, region, subregion, subsidiary, segment, and subsegment), and performance.

Our supervised machine learning model resulted in five benefits for our client:
1.    Optimal cost through end to end model on cloud with smart infrastructure
a.    Automated SKU scalability with accelerated ML model run.
2.    Quicker insights from accelerated model run
a.    Model run time reduced from 3 hours to 15 min with parallel data processing.
b.    Option for scalability of input data, leading to future support for growing data volumes.
3.    Near real-time impact analysis for input data
a.    We were able to achieve near real time modelling and forecasting.
4.    Ability to develop marketing strategy based on:
a.    Geography of end customer.
b.    Strategic relevance of partner for business.
c.     Lifetime and time-based impact of deal on sales revenue.
5.    Enhanced security through role-based access control, so that current and forecasted revenue data is safe from unauthorized access.