MENU
The client is a leading manufacturer and supplier of components and related products for multiple industries, including HVACR, electrical, consumer hardware retail, electrical utility, automotive retail, and the pool and spa channel. They have data coming in from all over the world, with branches in the UK, Canada, South America, and more.
The company’s sales forecasting and reporting processes were both manual, taking several steps and days to complete. The sales cycle correlated to changes in seasons, but they didn’t fully understand the correlation or how to use it to their advantage. The relevant data was available, but the processes, technology, and expertise needed to understand the best actions to take were not available.
The team’s data mart was also not performing well thanks to a lack of full systems integration. There was also no standard process for new product launch information or analysis, making it difficult to gauge performance and find opportunities to improve sales.
As often happens with manual reporting and forecasting processes, mistakes proliferated and the insights found weren’t trustworthy, making it difficult to make confident and informed decisions. Without the ability to plan for changes in seasonal demand or reference historical data in sales forecasting, they couldn’t optimize the sales cycle or ensure success.
Inventory overproduction and misallocated sales resources also challenged the team. This often led them to spend unnecessary money on related costs. They had no immediate insight into how the business or new products were performing and couldn’t efficiently distribute reports--especially among the traveling sales teams who needed them most.
While the client was collecting a significant amount of data, manual processes made it impossible to properly govern the data or trust the insights gained from it. As a result, their teams were unable to get the immediate insights they needed to inform critical business decisions.
Partnering with Onebridge, the team planned to implement more efficient platforms and processes to allow teams to make informed decisions based on immediate insights from trustworthy data. With all business users operating from a single source of truth, teams would be able to operate in lockstep with one another as they work toward key business goals.
After spending time gathering Information to understand reporting requirements, needs, and current pain points, Onebridge constructed a framework and delivered a centralized agile business intelligence reporting system. This was a complete makeover of their business intelligence strategy to enable more mature use of data. Now the teams have a centralized and consolidated dashboard for multiple reports, with interactive visual dashboards connected directly to a fully integrated data mart.
Onebridge also constructed a model in Tableau to improve their pricing strategy and showed the client how to use weather data with predictive analytics to predict the seasonality of demand.
With the new Tableau model, they can now review historical data to see how much they sell at certain temperatures and what kind of correlation exists.
Their budget forecasting is now done based on modeling in R that completely automated the process into a monthly cadence. Every sales team now has an iPad with Tableau visualizations, with all teams accessing one central dashboard instead of each person maintaining their own spreadsheet. When demand is low in a certain region at a certain time, they are now able to predict that demand and allocate those sales resources as needed.
Now their teams no longer have to waste time manually creating financial reports. Data visualizations provide trustworthy and valuable insights to business users across departments, eliminating department silos, and uniting the team around a single source of truth to drive impactful business decisions.
Sales demand forecasting has also improved, maximizing inventory levels and buying power. Budget forecasting has been completely automated and takes much less time. Sales resources can also now be allocated where they are needed – all because the business is now using their data as an asset thanks to a more mature business intelligence and data strategy.
