Forecasting

Forecasting
Indicators

Macro-economic indicators

To highlight the production/ output of an economy, government or any sector, we make use of Macro-economic indicators. These indicators differ in frequency, impact and definition.

Micro- economic indicators

Based on economic outlook of your local market, we assist entrepreneurs in making strategic decisions. This outlook is based on various factors depending upon the nature of your business.

Technical indicators

Using Technical indicators, we help you to get insights on the supply and demand of securities and market trends.

Social and political indicators

These indicators give a clear definition to your social objectives. We help you to collect data to measure and monitor social results, and assess report progress to improvise decision making and notifying when your social objectives are achieved.

Environmental indicators

Environmental indicators provide insights of environmental conditions over time. By using these indicators, we analyze the activities of an organization and assist you with its impact on cost of production and competitiveness.

Data Collection

This stage involves collecting data or information through multiple sources. These sources can be -

Secondary Sources

Data collected through sources such as journals, books, private or public companies’ reports, research papers and articles, among others.

Conducting Surveys

Conducting surveys is one of the most optimal ways of gathering data. The data gathered is basically through quizzes on single or multiple topics.

Observation of Market Trends

Processing of information is based on analyzing day-to-day market trends. As the nature of market trends is dynamic so as its data is gathered. We thoroughly check the data variables to find the best results.

Experiments

There are two types of bases to do experimental research, dependent and independent variables. The data gathering process in this term is analyzed and is considered that one of the variables can be changed anonymously. The data gathered is based on the changes these both variables have. This way of data collecting is used in the quantitative research approach.

Interviewing Sources

Gaining data through conducting interviews is one of the most reliable sources. The data is gathered through a series of interviews undertaken on a specific topic through experienced personals. These are subjected to formal or informal interviews conducted. This method of gathering data can be conducted through online basis or telephonic conversation.

Information Analysis

Information comes into your association through various sources, it might be your Customer relationship management (CRM), your website or your Enterprise Resource Planning (ERP) programming. We analyze this data and find valuable insights out of it. We discover connections among factors and discover how this information is affecting your business and how it can be improved further.

Business knowledge (Bk) Through Python, VBA, or Tableau/Power BI

To find in-depth knowledge about your business, clients and products in order to gain competitive edge over others, you need software solutions that give you this information readily available. Our data analyst specialists create various applications that are valuable to you by utilizing intensity of Python, VBA, or Tableau/Power BI. We create different dashboards through these tools and provide brisk solutions to business leaders.

Data Analysis

This process involves analysis and allocation of all the data collected in the previous step. This analysis is done by the following methods:

Regression Model

This model involves comprehensive understanding of statistics and the factors that impact your company’s sales performance. Regression analysis involves:

  • Identifying and adding independent variables (factors) that impact the sales to determine growth
  • Quality Function Deployment modeling to understand the customer needs, develop a product plan, and determine and deploy product requirements.
  • Creating base estimates by comparing historical charts
  • Using exponential smoothing for better forecasting

Time Series Analysis

Time series analysis deals with study of trend analysis. This model involves:

  • Study of Time series data, Cross-sectional data, Pooled data
  • Plotting of data and creating graphs (to visualize the data and understand patterns, unusual activities, changes over time, and relationships between variables)
  • Making the series stationary, de-trending, and controlling auto-correlations
  • Testing linear and nonlinear relationships of dependent variables (by using ARIMA or ARCH model)
  • Exponential smoothing time series analysis for better forecasting

Financial and Predictive Modeling

Financial modeling is very helpful in financial planning and analysis to create a forecast differentiating with the budget model. Financial model involves:

  • Structured approach to monthly cash flow modeling in Excel
  • Forecasting monthly quarterly/yearly sales on basis of assumptions and formulas
  • Forecasting financial statements based on the business plan
  • Calculating monthly cash flow and liquidity
  • Analyzing the impact of the forecast on the balance sheet and capitalization
  • Creating relevant graphs to illustrate the corporate cash flow financials profile to management

Predictive modeling utilizes data and statistics to predict outcomes with data models. This model involves:

  • Benchmark analysis
  • Gathering, cleansing and analyzing data
  • Evaluating goals and KPIs
  • Creating action plans based on analysis
  • Plan Execution
  • Streamlining processes

Cluster Model

This is a process of partitioning a set of data or objects into clusters on the basis of homogeneity within clusters and heterogeneity between clusters. This model involves

  • Identifying the parameters of forecasting sales (price of the product, time of product introduction, promotions, inventory, base demand as a function of time of the year and random effects)
  • Clustering classes with similar seasonality to reduce errors
  • Clustering by using K-means, hierarchical, and Ward’s technique
  • Error based clustering and average forecast error calculation

Market Formulation, Validation & Publishing

In this step, we place the data points in the appropriate market spaces to draw valuable conclusions. Following are the steps for market formulation:

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