Deal Analytics

We were provided with several large data sets for each year to clean and analyse in a short period of time. Therefore, one of the challenges of the project involved efficient data transformation and integration techniques in order to create a unique data source for our analysis.

After cleaning and merging the information, we carried out detailed analysis and provided our client with a clear and accurate understanding of the company’s figures and trends. Finally, our client had access to our selection of tableau dashboards with the possibility of playing with the data as they preferred.

The accounting and profitability data of 150+ pharmacies with inconsistent reporting processes and dictionaries needed to be harmonized and analyzed. The financial information were gathered by the single pharmacies’ accountants. Business data were available in similar formats but not used due to their volume. The client wanted to create a unique monthly financial report and use the profitability data to make informed decisions about the business.

In the first phase a clear understanding of the client different processes was fundamental to recognize where improvements could be made and which BI tool to use. We then focused on structuring the process itself, designing it in the most efficient and automated way. Financial and business dashboards were available in 10 weeks. The tool is usable by client analysts who can customize it or simply download what needed.

The client wanted to understand its brand and products perception in different geographic area compared to its competitors.

Starting from public data we gathered .ca 20k reviews which have then been processed and analyzed using state of the art statistical models (e.g. Neural Networks, BERT algorithms and k-Means).

We delivered a collection of dashboards where we combine geospatial analysis and text mining results. Thanks to machine learning algorithms we are able to plot the topics’ discussions comparing the sentiment between competitors, delivering strategic insights to the client starting from complex and unstructured data.

The company's supply chain was disrupted due to unclear responsibilities and non-standardized processes; data size was large and on different IT systems. The company was facing high levels of inventory and service level issues both internally and to customers.

By focusing on three product macro clusters: inactive, overstocked and continuously sold, we reduced the stock value by 18%, DIO by 15% and increased the service level, all the while increasing sales. The definition of key clients-items-suppliers further optimized the service level, leading to increased customer satisfaction and operational improvements.

The client firstly wanted to establish their current local pricing position and determine price consistency for similar operations in hospitals across different locations. Secondly, they wanted to benchmark this against their competitors, to get an understanding of the price variation overall and assess potential margin gain. They also wanted to identify and quantify other opportunities to improve their price setting strategies to facilitate more sustainable and consistent margin delivery in the future.

We conducted a detailed review on the individual transaction data of the pricing, volume and revenue data across a selection of key operations to assess pricing elasticity.

It was found that prices were broadly in line with competitors, but there was significant variability within hospitals across different locations and in how the organisation was positioned against competitors. This suggested potential opportunities to increase prices and improve margin.

Through localised price increases, an immediate margin uplift of €750k was identified without even changing market position. Further upsides of over €1m were also identified that required more radical changes to the client's proposition and marketing.