Published in Franchise Leaders Publication, by Mellissah Smith.
- In The News
- Published: 11 February 2019
The roll out of a successful franchise business in both new and old markets has been tried and tested since the grandfather of franchising, Howard Deering Johnson,
established his first restaurant franchise Quincy in Massachusetts in 1932.
What followed over the years with great success is the likes of McDonald’s, Hungry Jacks, Anytime Fitness and the myriad of other franchise systems that catapulted from one to thousands. Then, of course, there are those that failed, but we won’t dwell on them because it could have been another outcome, if only…
The landscape has moved, changed, turned upside down and been completely disrupted. How we now determine the right location, people and products has also shifted significantly, and that is largely due to the folks down in technology. The ‘nerds’ as they are affectionaly labelled, have been given a superpower, so important to management decision making, that we have almost hit a peak where, let’s face it, you and I are no longer required.
Simply, the data speaks for itself. Our sense of ‘knowing’ is… irrelevant. Sad but true, at least to a degree.
The problem is what data do we use, how do we use it and why are we putting our trust, and not our gut, into making all the big decisions.
Tech-savvy franchise outfits are now using predictive analytics by analysing big data, statistical algorithms and information based on machine learning techniques to identify the likelihood of future outcomes. A franchisee’s success and the livelihood of the brand, depend on it.
When you merge that with psyche profiling of franchisees and employees, store profiling, financial results, location, customer personas and technology can inform decision makers as to whether or not a person is right to be a franchisee, if the location is such that customers are most likely to choose your brand over another, and other valuable insights.
Basically, it runs something like this… a customer goes up to the counter at a fast-food store and requests a hamburger with chicken, minus the bacon, no cheese, lettuce and tomato with a dash of mustard. This tells the machine learning application a number of things: The customer may not eat red meat because they are a pescatarian or simply they don’t care for it. They choose not to eat bacon based on religious reasons, or simply they don’t like it. Cheese may be forfeited due to the perception that it makes you put on weight, or perhaps their bodies may not agree with it, and they choose lettuce and tomato because it is a healthy option.
The next time they visit, the customer says to the assistant, “Is the chicken grilled?” Immediately, if this data is recorded through voice activated technology applications, the machine would learn that the person is most likely to be health conscious or on a diet. Therefore, it makes perfect sense to send this person a marketing message offering them low fat options and explaining the difference between one meal choice and another. All of a sudden, the fast food outlet no longer is branded as an ‘unhealthy choice’ but is in fact a place to go to get fast, affordable meals that are low fat.
If a majority of customers in an area request no bacon, then the machine might ascertain due to other community based data that the population in that area may have a religious reason for not eating bacon. If this is the case, data may indicate that more sales will be achieved if the menu is adapted to suit the majority profile of customers in the area. Content and marketing messages will then be sent to these customers based only on what they are likely to order or be enticed by, not generic based marketing material.
There’s a conundrum between marketers and data scientistsIf you understand your customer and you have a good product or service in the right location, you will be successful. The more successful franchisees are, the more successful the franchisor becomes. It makes more sense to use valuable insights to nurture more profitable customers. Running customer data through predictive models can help you better anticipate behaviour and better inform your marketing strategy. However, marketers are still reluctant to take data in its enormity onboard. They might receive data, but not enough marketers do anything with it.
Multi-channel marketing experiencesThe most popular brands use data-driven marketing to enhance their customer’s experiences. Pre-empting their needs and wants and orchestrating data driven marketing to promote a brand based on what customers want to buy is game-changing. It also helps develop better products diminishing product failure rates. Data driven marketing is helping franchisors solve complex problems. At their finger tips they can capture big data from all of their franchisees. The bigger the franchise system, the more data that is collected and the more accurate the prediction becomes. It saves time, money resources and brand failure.
5 Steps to knowing what to do with big data
- Use technology to capture not only the transaction, but the frequency of transaction and the verbal communication that takes place at point of purchase.
- Hire a data scientist to analyse the data and provide valuable insights to your marketing team.
- Adapt fast and keep your marketing agile. There is no time to waste in a highly competitive marketplace.
- Listen to your customers. It’s more than just the data of the transaction that has to be taken into account. It’s what they say.
- Let the data make the decisions, not your gut instinct. When using predictive analytics based on machine learning, your data will be more accurate and reliable to make better decisions, faster.