How to Make “Big Data” Work For You
Franchisors who understand “Big Data” and how to leverage it will be able to identify actionable insights.
By Corey O’Donnell
Using data to determine the best way forward is the difference between making a well-informed decision and taking a leap of faith. This applies to a franchise network as much as any other business. Armed with data, we have the information needed to understand the past, interpret the present and predict the future. If data-informed decisions are better decisions, and bigger is better, then logic says that “Big Data” enables us to make the best possible choices. However, the term Big Data is an often used buzzword for which you truly have to understand the definition to comprehend its importance and how to best use it to make decisions.
Like “the cloud” before it, there is a lot of confusion and misinterpretation around what big data really means. There isn’t a terabyte file size that a data set has to exceed to be defined as big data. In fact, the size of the data set is less relevant to the principles of what big data represents. Big data is more about connecting and analyzing disparate sets of facts to discover insights, connections and trends that may otherwise go unnoticed.
For franchisors who by definition have a variety of data silos at each franchisee location, compiling varied data sets presents a unique challenge. However, by learning to analyze and interpret the data, this wealth of information also provides a great opportunity to capture unique insights and generate more revenue for each location.
Imagine a cleaning services franchise — we’ll call it XYZ Cleaning — with 100 locations. Each location hosts a local website, and occasionally uses Google Analytics to determine its web traffic. They also use a variety of different systems to track local advertising leads, paid search leads, social presence, reviews and other marketing activities. In addition, the franchisees leverage a diverse set of tools to manage human resources/payroll, revenue reporting, supply ordering and vendor payments, scheduling and other business logistics. In this scenario there could be six or more different data sets within each location, multiplied by 100 locations.
Now imagine that all this information could be brought together under the eyes of the franchisor. Not only would it reveal obvious correlations between, for example, lead performance and revenue, it could also unlock secrets about supply chain efficiency, personnel management and much more. In addition, a unified data set could be appended with general public data like weather, news or cultural influences to reveal trends that affect the business that would have been impossible to see before.
Unlocking Big Data
The key to unlocking big data for franchise networks like the imaginary XYZ Cleaning boils down to these steps:
1. Collect the Data. Bring together the varied data sets from across your franchise network to produce a broader, extensible data library. You can do this through a unifying platform. For example, distributed marketing automation systems can create a junction point for marketing data from all franchisees and provide the visualization tools that allow for in-depth data insights. Alternatively, a franchisor can collect data by manually conjoining it, leveraging either a revenue report or some other system wide data-set. The franchisor can then purchase a visualization engine to perform the queries.
2. Put the Data to Work. Once you have unified your data sets, you can start analyzing the big data to develop insights into the brand, the ability to convert leads into paying customers, and the external factors that impact the business. With tools to query the data and an inquisitive, exploratory approach, the franchisor will learn some revealing facts about their business.
In the XYZ Cleaning scenario, let’s assume the united data reveals key information about marketing, sales and operations. For example:
- Online lead volumes at the New York location peak every Wednesday morning, with most of those clicks resulting in calls to the business to book appointments.
- The employee who is most effective at converting calls into booked cleaning appointments is Mary, who currently only works Thursday through Monday.
- The cleaner with the highest review rating is Gloria.
- The jobs that offer the best margins are small apartments on the east side of town.
Analyzing these united data points could lead to a transformative yet simple decision to shift schedules so that the best salesperson (Mary) is working on the busiest mornings (Wednesday) and booking the best cleaner (Gloria) into the most profitable jobs (east side homes). Scheduling and planning in this way is more effective than guessing or random selection.
3. Overlay Correlation Data. Pull in data on your industry or other factors that correlate to your business and generate new hypotheses. Sometimes the most meaningful information appears unrelated at first. Maybe XYZ Cleaning’s Wednesday peak is driven by Tuesday night’s hit TV show featuring a maid? Certain businesses have a clear causal relationship to factors such as weather (you sell more umbrellas when it rains), time of year (you sell more air conditioners in August than in December), holiday (you sell more fireworks in late June than in February) or cultural influence (you sell more martinis following James Bond movie releases). Understanding the possible relationships that drive the data can lead to new ideas to increase sales, like offering promotions that capitalize on these outside influences.
It’s important to also consider that applying the principles of big data might reveal a different way to interpret data you are already using to make decisions. For example, many companies determine their digital advertising budget based on the previous month’s performance data. So, if XYZ Cleaning gets more clicks on searches for “same-day cleaning,” the company may decide to spend even more money against those terms. What XYZ Cleaning may not take into consideration is that this information may also show that the brand is known for speed. Clients may be telling XYZ Cleaning, by virtue of their clicks, that they perceive the business to be a good same-day, fast, speedy vendor. However, current industry data could show that the market for environmentally friendly cleaning services is bigger than the market for same-day cleaning. By reviewing all this information together, XYZ Cleaning may find that a bigger driver of revenue could come from spending some money on a brand-building campaign to increase the market perception of their environmental values.
Although it has become the business buzzword du jour, Big Data is not a passing fad. In today’s technology-driven business landscape, analyzing big data can create opportunities to operate more effectively, make increasingly strategic decisions, and improve your bottom line. For franchise networks that have multiple sources of data to analyze, effectively applying big data can be a powerful tool for optimizing success whether it’s related to marketing, operations or the myriad of other things involved in running a business.
As you’ve read, XYZ Cleaning was able to make impactful decisions about scheduling, branding and sales by combining various data sets and using intuition to develop and test hypotheses. Franchisors who understand Big Data and how to leverage it will be able to identify actionable insights that can lead to more effective decisions across the network and opportunities to increase revenue at each location.