Technical Consultancy

Data management for trade marketing strategies

The Fast Moving Consumer Good (FMCG) industry is a high-speed market. It requires businesses to be agile and nimble in its everyday operations. Unfortunately, there remains a large divide between getting the data and presenting it. Today, most FMCG brands still revolve their data collection via pen, paper, and spreadsheets. The estimated time to deliver the consolidated report to their stakeholders takes an average of 5 weeks of lead time. Modern trades do offer weekly data solution, but it still requires preparation for management. General trades like petrol stations, convenience stores, pharmacies, mini-marts, and other modern trades can account for more than 860 outlets all over Singapore but lacks proper data facilities. In all, a brand can run more than 12,000 rows of data every month, where account managers, operations, human resource, finance, and more departments have to generate their own reports from it.

In this project, we took a further complex step helping FMCG agencies accomplish the management of vast data collections, further segregated across different brands, each represented by their own entities, to generate an instantaneous report.

Understanding technical requirements, domain, host, platforms, frameworks, and et cetera.

For a company lacking technical know-how, it can be difficult to overcome the hurdles required to deploy digital solutions. We provide recommendations and understandings of different platforms and service providers on the advantages and disadvantages. Our expertise won us long-time partnerships; but, it is not just about technical knowledge.

Plans and design technology to meet the circumstances

If your companies do not know the limitations and strength of technology, you might be planning to fail. In this project, it started around the same time where Artificial Intelligent (A.I) was gaining democratization. The stakeholders were asking if the system could implement some form of A.I. We explained the current A.I model is based on Machine Learning (ML) algorithm which relies heavily on having massive training datasets.

We drew up the possibility of having unsupervised training methodology; but, the investments required and the expected returns are not immediately clear. The correct solution is to focus on data collection and kick the can down the road.

We completed the consultancy with development work, and proceeded to deploy with a couple of brandowners and ultimately as a complete solution for agencies.