Three businesses where data analytics delivers better decisions

Three businesses where data analytics delivers better decisions

Every day more companies and more industries are tapping into the power of data analytics to make better decisions on sales, operations, and investments.

Here are recent examples of our work across three different industries.

Demand forecasting for multinational manufacturer

We worked on a project for a large integrated manufacturer who wanted to improve their demand forecasting.

The manufacturer was often taken by surprise by customer orders and had to scramble to fulfil them. Sometimes they had to use more expensive materials and pay overtime to produce the required products.

They were also shipping part orders and sending the remainder later, which added to their costs.

We drew on their historical sales data to produce daily demand forecasts for the coming week, which the company can use for day-to-day production and logistics planning.

We also produced weekly forecasts for the quarter ahead, to improve the S&OP process, and monthly forecasts for the next 18 to 24 months to help guide longer-term supply chain decisions.

The forecasts allow our client to improve the efficiency of their supply chain and to reduce transportation costs.

They can also reduce inventory levels due to higher confidence that they can meet customer orders when they arrive.

And most important of all, it will lead to improved shipping of orders to clients on time and in full.

Pricing review for beauty chain

We took a two-pronged approach to a pricing review for a chain of upmarket beauty salons.

Firstly, we carried out a financial analysis of the business to look at its profit margins and to set a floor price for each of its services based on the margins.

Secondly, we did a value assessment of the services. We drew on five years historical pricing and sales data for each outlet and took into account alternative services providers in each outlet’s catchment area. We also looked at how frequently customers come in, how long since their last visit, and how much they spend when they visit. This helped create a profile of customer and the likely impact of a change in prices.

We then built a data-driven pricing simulation model for each outlet that told them what would likely happen to their volumes and margin for each service in each location when they changed the prices The simulation was used to build their new pricing menus to improve margins and profits.

Market assessment tool for a property developer
We recently build a tool for a property developer to test the likely demand for a residential development in any given location around Australia.

To use the tool, the developer identifies a potential site and the tool uses a range of data sources to predict the demand within that catchment area, say 20 minutes’ drive from the proposed development.

It looks at the area’s demographics, not just now, but in five and 10 years, to assess the local cohort of the population. The tool also takes into account other demographic factors such as wealth, household composition, and profession, all important factors in determining the market attractiveness of that location.

The tool is used in two different ways. First as a location attractiveness index, it tells the developer how much demand there would be for a residential development in a particular location.

Second, it’s an investment attractiveness index, which is used to build a case with banks and investors the fund the development.