Future of Control | Control Automation: D.SEE (D. Sales Effectiveness Engine)

Published date: July 18, 2022

In enterprise sales management, there are often two major pain points. Firstly, the formulation of strategic objectives relies on subjective experience and is often decided arbitrarily, given the complexity of factors that drive market potential and consumer stickiness; second the established mathematical models are often too basic, considering the difficulty of collecting and analyzing the big data required for accurate modeling. For the above data collection, modeling and interpretation, an effective intelligent tool is needed to enhance the management of decision-making risk. This is done through allocating resource optimally in support of business managers and maximizing the benefit to the enterprise through controlled modeling.

In order to help enterprises analyze, monitor and forecast sales more efficiently, comprehensively and accurately, and formulate market strategies objectively and reasonably, Deloitte China has designed D. Sales Effectiveness Engine (D.SEE) to ensure this process is effectively controlled through a systematic approach.


D.SEE is based on modeling of historical data to provide baseline sales forecasts. It reveals sales drivers through visual reports, digs deeper to identify markets and channels that are more responsive to investment increases or decreases, and identifies potential market opportunities and spaces. Using simulation tools with interactive scenarios, we can project and rehearse sales trends and reasonably adjust asset allocation in each market and channel to maximize investment returns. This article will briefly introduce D.SEE to help you quickly understand our services and the value they can bring to your business.

i. Service Introduction

D.SEE aims to apply Deloitte's industry knowledge and experience in Risk Advisory to provide an integrated, digital intelligent sales platform for pharmaceuticals, retail, manufacturing, media and other enterprises with a large volume of complex data, as well as potential demand for an objective data driven strategy. This enables intelligent monitoring, analysis and forecasting of the whole process of internal and external data of enterprise operation, and helps enterprise executives objectively. This supports business executives to have confidence in the optimization of modelling, through a model that takes account of resources constraints and risks, as well as a range of automated controls to formulate market strategies, improve sales efficiency and optimize sales resource allocation to maximize Return on Investment (ROI).

D.SEE is mainly based on machine learning algorithms to automatically analyze and deduce sales effectiveness; through automatic collection, modeling, interpretation and visualization of sales, expense, marketing, personnel, customer base and other data. This provides managers with in-depth value mining and helps them gain objective insight and decision support based on models and data in the process of strategic target setting, budget allocation and budget adjustment. The sales forecasting, driver identification, market potential, and scenario simulation tools provide an optimized solution to support resource allocation decisions and the potential for sales growth.


  • D.SEE automatically crawls internal and external enterprise data and provides reports, saving labor and improving accuracy.

Deloitte adopts the technical framework of RAP (RPA+AI+Platform) to automatically crawl internal data such as the company's financial system and sales system in real time, and also crawls external data such as market research reports, search engine results and related news. The data is stored in a structured manner and reports are produced for clarity. Efficiency can be increased by more than 90% compared to manual work. Together with a model to estimate uncertainty that needs to be taken account of as part of the forecasting simulation.


  • Use of machine learning and other models for classification and forecasting to improve the objectivity and accuracy of sales drivers and market potential.

Integrate crawled internal and external data, combine machine learning models to identify the drivers affecting sales, uncover the potential of each market channel, and set strategic goals at the data level to drive growth with data rather than subjective judgement. Meaning enhanced effectiveness with multiple data sources over your modelling, optimization, and forecasting process to mitigate the risk of uncertainty.


  • Visualization technology is used to provide intuitive insights and tools for scenario simulation.

The D.SEE platform also provides a visual interface that users can customize to dynamically adjust sales drivers, simulate and predict sales trends, and provide decision makers with the most intuitive insights and the most reliable business decisions.


  • Sales Forecast

Machine learning models predict future sales while the internal and external environment is kept constant, revealing baseline scenarios to identify the margin for error within forecasts, which allows mitigation strategies to be developed.


  • Driving Factors Identification

Visual representation of the sales drivers derived from the machine learning model, including:

  • Types of expenses that could drive sales growth
  • Potential constraints on profit growth



  • Market Potential

Visual representation of the market potential derived from machine learning models, including:

  • Segments with higher ROI or earnings growth when inputs are increased
  • Segments that are not responsive to decreasing inputs


  • Scenario Simulation

Based on predictive models, we provide interactive scenario simulation tools to project sales trends under different resource allocation scenarios to support effective and well-controlled decision-making process.

ii. Value

  • Mitigates decision making risk and cost due to uncertainty in the future
  • Provides baseline sales forecasts based on modeling of historical data
  • Self-learning based on models to select the most applicable models and data
  • Data-based modeling results to identify sales drivers and market potential
  • Visualization-based technology to provide intuitive resource allocation simulation tools

The advantage of D.SEE is that based on a large amount of history combined with internal and external data, professional data scientist build machine learning models, based on the model's self-learning ability, constantly optimize the results of the deduction calculation, and finally select the most suitable model and data. Based on the visualized model results, decision makers can quickly discover the driving forces affecting sales and explore potential markets. Through simulation and prediction tools, the resource allocation of each channel is dynamically adjusted and future sales trends are predicted, helping companies achieve sales targets faster and more efficiently. This also improves the overall business effectiveness and control within the organization.

Sales growth is a top priority for enterprise development, and the market and channels should be able to be accurately and timely analyzed and forecasted with consideration of multiple factors including market risks.

We hope that through our long-term accumulated experience in the area of enterprise digital transformation, we can use digital technology to help enterprises on their transformation journey.

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