Top 10 Types of Analytics Projects – Examples

In this post, you will learn about some of the most common types of data analytics projects which can be executed by the organization to realise associated business value from analytics projects and, also, gain competitive advantage with respect to the related business functions. Note that analytics projects are different from AI / ML projects. AI / ML or predictive analytics is one part of analytics. Other types of analytics projects include those related with descriptive and prescriptive analytics. You may want to check out one of my related posts on difference between predictive and prescriptive analytics.

Here are the key areas of focus for data analytics projects:

• Cost reduction: Cost reduction is one of the most important type of analytics projects which are taken as one of the first projects by organizations starting on analytics. You may refer to my other post, Analytics maturity model for assessing analytics practice, where it is shown as to how analytically challenged organization takes the cost reduction as one of the key goals of their analytics practice. Several different types of projects such as some of the following could result in cost savings / reduction:
  • Analyse spending (spend analytics) and identify opportunities for cost reduction / savings by optimizing spending / buying / purchasing processes.
• Pricing models: Pricing models can be used for deciding prices of the products / services based on the pricing strategies. Questions such as what is the best price on a particular day, or, what is best price to sell everything within a given
• Budget allocation (annual, quarterly)
• Forecasting such as financial / cash forecasting, demand forecasting, inventory forecasting: Forecasting problems are time-series problems and can be solved using different machine learning algorithms. The following are some of the common forecasting problems:
  • Cash forecasting: The problem is related to forecasting / estimating cash that the business will have on a certain day, week, month etc. It is a time-series problem and machine learning algorithms such as ARIMA, neural networks, regression can be used to train model which can predict / forecast cash flow.
  • Demand forecasting: Demand Forecasting is related to estimating / forecasting the future demand for a product. Different methods such as survey and statistical methods can be used for demand forecasting. In relation to demand forecasting for products, statistical methods make use of the time-series (historical) and cross-sectional data to estimate the long-term demand for a product. Statistical methods such as some of the following can be used for demand forecasting. The details for these methods will be dealt with in future posts.
    • Trend projection methods (Least square method, Box jenkins method etc)
    • Barometric methods (leading series, lagging series etc)
    • Econometric methods (regression methods)
  • Inventory forecasting: Inventory forecasting is a method used to predict or estimate demand of a product for a future time period. Estimating near-accurate demand of what will be required to fulfil customers’ request in future will Knowledge of how demand will help the company keep the right amount of stock on hand without over stocking or under stocking specified items.
• Operational efficiency: According to report by IBM ESG group, 60% of respondents indicate that improving operational efficiency is one of the most important objectives their organization expects to achieve from their AI/ML investments.
• Target market / customers identification: Machine learning can be used to recommend target customers. This is very useful especially in the case of upsell / crosssell.
• Identifying right marketing campaigns: Predictive analytics methods such as segmentation can be used to group similar customers and use targeted marketing campaigns for the customers in the same group / segment.
• New products / services identification
• Customer experience enhancement: The following are some of the areas where AI/ML can make great impact in terms of enhancing customer experience:
  • Intelligent chatbots to respond to customer queries by invoking different systems
  • Identification / segmentation of topmost concerns / issues raised by the customers at regular intervals such as daily, weekly etc.
  • Prioritisation of worklist for customer service executive based on customer grievances such that customers’ issues could be addressed based on the priority.
• Recommendation (customers, products, services)