A detailed observation on the potential benefits of using modern Machine Learning technologies in the FMCG vertical
This paper investigates these challenges and opportunities in detail and focuses on the use of Machine Learning technologies to optimise processes to increase profits for FMCG companies.
Machine Learning Introduction
Modern Big Data technologies and advanced machine learning algorithms can analyse data in any format, such as images, videos, text, emails, social media messages, server, logs, etc. Whereas traditional analytics can only analyse structured data in databases.
2. Combine Data
Modern technologies allows us to quickly merge datasets together and form rich data collections that can merge internal company data with external public data sets. This allows the data scientist to enrich sales and marketing data for instance with government social demographic statistics. Traditional analytics is usually performed on data silos and when data sets are combined this is usually done at a huge expense by building data warehouses which still only usually have internal company data.
3. Future vs Past
Machine Learning is often called predictive analytics as one of its major use cases is to predict the future. Advanced machine learning algorithms will ingest all your data and find patterns that can then be used to make accurate inferences about the future. These predictions are qualified with an accuracy metric so management can make intelligent decisions based on these predictions. Traditional analytics rarely tries to infer future events and only deal with explaining and visualising past events.
4. Answers vs Reports
Using the predictive power of machine learning, management can start asking smart questions from their data. Questions such as:
- What is the optimal marketing campaign to increase market awareness for product X
- How many of product Y should we product to reduce oversupply next winter season
- What sales rep should I use to manage our new customer to maximise potential profit
This is very different from existing business intelligence suites which usually deliver dry reports or charts which are very often misinterpreted.
5. Speed of delivery
Traditional analytics / business intelligence implementations can take years to complete. They are intrusively integrated into an organisations IT and as such move very slowly. Modern machine learning technologies allow for management to get answers from their data very quickly and efficiently. A simple question can be answered in weeks not years.
6. Machine analysis vs human interpretation
Machine Learning uses advanced computer algorithms to analyse unlimited quantities of data. This analysis is done totally impartially and free from any biases that are common in many manual analysis. The outputs from these algorithms are also very easy to interpret and leave very little room for misrepresentation making them very objective and quantifiable tools for decision making.
Machine Learning in FMCG
- The massive volumes involved
- Access to good quality sales data
- Short shelf life
- Current forecasting techniques are relatively inaccurate
- Current marketing strategies are less than optimal
- Current manufacturing practices are less than ideal
- Current supply chain strategies are less than optimal
- Consumer numbers are very large
We now explore each of these attributes in detail.
1. Large volumes / access to good quality sales data
Given the enormous volumes of transactions generated by FMCG this data is usually very hard to analyse manually as it overwhelms most brave analysts. Currently many organisations have not gone beyond basic analysis at a very high aggregated level, for instance: sales for the week, sales for a store, etc. And where they do drill down deeper into the data, this is usually done by senior analysts with years of experience (and biases) at a huge cost.
2. Short shelf life
3. Sales and marketing
- Which product should we promote this month
- What type of campaign will be most profitable for this product
- What consumer segment should we target
- How can we get value from our social media data and use current consumer sentiment to create timely marketing campaigns
4. Manufacturing and supply chain
- How can we guarantee on time delivery
- How can we shorten the time to manufacture a product
- How can we increase the yield for a product
- How can we minimise product returns / complaints