Working With Valid Data

\”Garbage in, garbage out\” or \’GIGO\’ is the phrase and acronym originating in Computer Science to explain the only too-common phenomena of outputs or arguments being flawed if their original premise or data was wrong.

Best practice franchisors will ensure data collected for use is valid.

Franchise systems and other chain organisations can benefit hugely from data, analytics and business intelligence. The replication of a business model in multiple locations with, in turn, hundreds or thousands of customers and associated business, financial and customer data, lends itself to datamining – the process of turning data into useful information.

However, while many chains may undertake benchmarking and other analyses backed by huge amounts of data, questions often remain about its underlying validity and, the resultant impact dodgy research results can have on crucial franchisor and franchisee business decisions.

It is therefore crucial that underlying data is valid, before franchising companies collect it, warehouse it, access and organise it, and then use it. The benefits of statistics, and traditional analysis, and the possibilities of Machine Learning and Artificial Intelligence, depend on good valid inputs. Not garbage.

That means key franchising questions like the following need good data:

• Which customers should we target?
• Which territories should we focus on first?
• What marketing strategies should we adopt?
• Can we segment customers more effectively?
• What are the determinants for a successful location?
• How are franchisees performing and who really are our top performers?
• What are the biggest challenges / limitations faced by our franchisees?
• Where should we be investing our resources first?
• What piloted changes should we implement / discontinue?
• What drives customer satisfaction in our business?
• What changes should we drive for better sales conversions and referrals?

Best practice franchisors will be clear about data to be collected and their reporting structure including, from a financial standpoint, a Standardised Chart of Accounts all franchisees report to. That way not uncommon challenges like the following can be avoided:

• Field managers struggling to understand franchisee performance (and profit), let alone identify performance improvement areas, if all franchisees operate to a different Chart of Accounts.
• Franchisors struggling to convince franchisees of the benefits of a piloted change, when key relevant performance metrics weren\’t identified (and collected) before and after.
• The wrong franchisee winning an award, because some franchisees surveyed customers differently (i.e., selectively).
• Controversy on Group Marketing Fund Management decisions, due to differences in opinion.

Working with valid data provides confidence, and helps remove doubt from insights, recommendations, decisions, and proposed changes. And like many aspects in franchising, it helps (and is much easier down the track) if you think about valuable data and data structure when the franchise system is established – as opposed to when a franchise system already comprises multiple franchisees.

Relevant questions:

• Do your field managers have access to valid (and comparable) franchisee performance information increasingly needed in their roles as Business Advisors?
• Do you, as a franchisor, have sound information on which to build a business case for any proposed franchisee change initiatives?

About the Franchising Best Practice 500 Series

This is part of a series of franchising best practices. Franchize Consultants is sharing and publishing these best practices weekly for the betterment of franchising. We know that better knowledge and execution of franchising best practices leads to bigger and more valuable franchisor and franchisee businesses.

We have assembled the first 40 best practices into The Best Practice Handbook, which is available for purchase.