Big Data. Everyone is talking about it.
According to an Accenture study, 79% of enterprise executives agree that companies that do not embrace Big Data will lose their competitive position and could face extinction.
So, what is Big Data and how can you use it to maintain and grow your competitive position?
The best definition of Big Data we could find was from SAS:
“Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organisations do with the data that matters.”
The last bit is key. As a large fuel business you have millions of data points running through your portfolio every day, but what can you do with that data?
This is the hardest part.
Without being able to simplify and manipulate your data it is very difficult to understand what the data is telling you. For example, for each transaction it is likely that your PoS alone is capable of telling you the following:
This is just a handful of the data capture. This is then likely sent to your back office where the numbers are rounded and fed into other internal systems and your accounts. This is what we call business hygiene data. In other words, this data is currently only being used to keep the business healthy.
But if you start to take these data points and put them together, you will start to see patterns emerge. With just the data points above you could easily find out:
Once organised and compared to data points from other sources, these questions can be expanded to identify key business opportunities:
A better understanding of your portfolio enables retailers to set better strategies, on a region by region or site by site basis, to hit their business goals - whether they are volume, margin or overall profits.
Organising data is the difficult part. There are tools available to help you do this and a lot of companies go in house. Don’t be afraid to invest in the method that works best for your business as they long-term payback will be huge.
Now you have the ability to query the data, what do you ask it?
The Forbes article “Eleven ways to get more from your data”, emphasises the importance of building hypothesis that you need to prove, rather than making assumptions and introducing bias in the query.
We’ve found the best way to formulate questions that give important answers; start simple and go from there.
A good example is our performance reporting tool, powerful but simple, that allows our users to slice and dice through all their data with only a few clicks. They could see by looking at the fuel card volumes that retail sales were up at the weekend whilst fuel card volumes were down. This prompted a hypothesis:
“Because fuel card sales are lower, our net margin (gross margin - fuel card fees) at weekends is higher, giving us the opportunity to make a change”
There was indeed an extra 50% of margin at the weekends. The hypothesis was proven! So where did they go from here?
We tested this hypothesis with several clients, with portfolios ranging from 1 to 50+ sites, and across sites with various percentages of fuel card volume as part of the total sales. What we noticed that every single forecourt experiences an increase in their net margin in the weekends, when the lorry and man-and-van drivers stay at home.
In order to make the most out of your data, you need to be willing to challenge the status quo. You now have what we call business motivation data, which drives you to take action.
Retail is 50% art and 50% science. What action you take is where art is added to science. You have the information to make a better informed decision, but what will you do?
What did our users do with the discovery in point 2?
The decision was made to reduce the price at weekends by to try to attract the locals. All the exciting results will be published soon on our blog, so keep an eye on it!
Once your experiment is over, being able to assess the results will open up new experiments in the future.
Within 8 weeks our retailer had seen a 35% month on month increase in retail volumes at weekends and an increase in volume during the week. Fuel profit remained constant, but the retailer had won more customers. More customers meant:
And now, being able to see the impact on these areas opened them up to a whole new set of experiments.
In conclusion, a business should never stop looking for opportunities within its data set. Whether you are organising your own capabilities or looking elsewhere for inspiration, don’t leave these opportunities lost in the millions of lines of data. Find them and run with them and never leave a penny on the table.