I N T E R R O D A T A

Loading...

A picture of a user of statistical modelling

Interrodata uses intelligent modelling to help business leaders and executives make better, more informed choices. This is part 1 of a series of articles we will be writing on modelling – and how it is used in the FMCG industry.

Broad overview of modelling in the world

Modelling can mean a variety of things to different people, and to one person in different contexts. To one person it could mean showing off the latest fashion, to another it could be building miniature train sets and someone else might think of weather forecasting! To someone working in data and insights, it often means the ability to use data to make better forecasts for what might happen in the future. 

This opens many opportunities for individuals and companies to find ways to sell more and beat the competition. It leads to much more forward-thinking and predicting what might happen, rather than constantly chasing your tail whenever new data becomes available.

Problems currently faced

There is so much data and so many different data sources thrust onto people these days. Obtaining useful insights from data and, subsequently, knowing what actions to take is increasingly complicated and time consuming. 

Furthermore, due to this wealth of data, customers often expect you to be able to give them answers to any, and all, questions that they ask. Increasingly, customers are asking these questions during meetings and being able to collaborate in real-time allows for faster, better decision making. Doing this is hard enough for historic data. Doing it in a relevant and accurate manner for that which might happen in the future is even harder.

How can data modelling help?

Data modelling allows you to manipulate data in a way that is impossible for standard ‘Excel-based’ analysis. Simply, it finds commonalities and differences in many cuts of the data. Therefore, allowing the generation of a significantly greater volume of insights.

How does this help FMCG businesses?

Why should FMCG businesses care about modelling? It’s not particularly relevant, right? Wrong. FMCG businesses are constantly trying to predict the future. What product might be their next top seller? What might the competition come up with next? How are consumer choices are evolving? All of this while treading water with the tsunami of data coming through monthly, weekly, and sometimes daily!

Category teams are in a constant tug of war with their competitors for the customer’s shelf space. By leveraging data modelling they can prove that their products deserve more space on the shelf. Models do this by showing, with increasing accuracy, the higher rate of sale that would help grow the category.

There are many things to consider when recommending that customers should change their range. Ultimately, the retailer is looking for increased sales and net margin from their space – and doing this in a way that improves the shopping experience for their customers. Modelling can predict the impact of ‘switching out’ one product for another or boosting one’s distribution at the expense of another. As this is done in a quantitative way it makes your recommendation to the customer even more compelling.

Modelling can help suppliers to work with retailers on the most effective range choices on their shelves.
Successfully predicting the consequences of removing and adding products to the shelf is an effective way of building confidence with the retailers

Incrementality & Substitutability

Additionally, data-model based Ranging tools can also assist with assessment of incrementality and substitutability ‘flows’.

Incrementality

The incrementality of a product is the additional sales that a retailer makes compared to if the product wasn’t available. Products with high incrementality are considered to be more unique than those with low incrementality. It is important for customers to know the incrementality of products to assess the potential impact that removal from the shelf might have.

Substitutability

Understanding where sales would flow upon removal of a product from the shelf is, possibly, more important than knowing their incrementality. However, due to the vast permutations of different (and multiple) products being removed at once it is arguably impossible without data modelling. Being able to do so allows for the aforementioned successful customer relationships that are key to category growth.

Interrodata and Modelling

Interrodata is utilising modelling and Machine Learning to build automated guidance to action. We have a range of tools to help Category teams optimise their relationship with the customer and boost sales for their trading teams. Get in touch below to learn more about these solutions or just discuss the mind-blowing world of modelling!

References

https://towardsdatascience.com/clearly-explained-how-machine-learning-differs-from-statistical-modeling-967f2c5a9cfd

https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/will-innovation-finally-add-up-for-consumer-goods-companies

Leave a Comment