Domino’s Pizza Streamlines Franchise Planning Using Location Intelligence
Accurate address data is the key for Domino’s Pizza
As the Domino’s Pizza brand grows, managing franchise territories has become more complex. When a franchisee invests in a new business, the contract will set out the territory and number of homes within that territory. Franchise areas and associated purchase prices are determined by the number of reachable households, overlaid with socio-economic data. One of the major issues that franchise businesses encounter is territory disputes. To minimize this risk Domino’s must ensure that the territory data is accurate and up to date.
Historically, Domino’s has used Australian census data to determine territories. As the census data is only updated every five years, the data quickly becomes out of date and does not keep up with rapid changes in the urban landscape. As well as causing territory disputes, a lack of accurate data can also negatively impact franchisees’ businesses through lost customers and poor customer service. A lack of up-to-date data can also make a business less attractive to potential new franchisees.
“ With some areas rapidly expanding and developing, it is vital that Domino’s has an up-to-date view of its franchise territories so we do not miss out on new opportunities.”
– Wayne McMahon, Chief Information Officer
Previously, to determine serviceable addresses within a territory for a new franchise, Domino’s would take the UBD® street directory data, plot the territory and manually add all the streets to a spreadsheet that was then sent to the store to use as a reference. This process would take around two weeks to complete and was rarely ever updated.
Only listed addresses are permitted to be serviced by the franchised store. This could lead to potential revenue loss and a poor customer service experience as well as a negative impact on the Domino’s brand. Staff would often enter new address details into the system themselves, which were then shared with the main Domino’s database. This resulted in a lot of incorrect or incomplete data.