There is no shortage of products to retrieve and organise data, and no shortage of actual data to review. There have been countless articles written, presentations given, online posts and trade show demonstrations on how important data is, yet precious little on how to interpret data and place it in an order that allows for it to be interpreted. What are the insights? How can I action what I see? How can I plan around it? These are the real reasons we analyse Members and revenue parts of the business.

While getting the data is one skill, interpreting it is another skill entirely. When you go to a Doctor, the best results are obtained when you can explain the problem and locate it. This is like data, if you can isolate the issue, you have a better chance of addressing problems and capitalising on advantages.

This article will provide 2 examples of important data sets, how to organise it, how to construct the hypthesis and then how to interpret it.

  1. Interpreting player trends
  2. The importance of time and day

Interpreting player trends

What data you need Monthly data per player – unique visits, turnover, theo net.
How do you order it Member name/badge number down columns. Visits, Turnover and Net per month across rows
Special tips Average the turnover in all the periods, keeping in mind there will be 4 or 5 days. Date isn’t a consequence here, just the days


Step 1 – select a number of months to analyse (anywhere between 4 and 6 captures sufficient trends). Isolate each month by detailing the month, badge number and key inputs (Turnover, Theo Net, Visits) across the page


Step 2 – stack the monthly data on top of each other down the page (example on the left). The months should be in order.



Step 3 – place the cursor on the cell titled ‘Month’ and go to the top ribbon and select ‘Insert’ from the top ribbon and then click on ‘Pivot Table’ option (example below)


Step 4 – once in a pivot table format, you want to arrange the data to represent one line for each player by chronological month, with the sub set data of key data points.

Drag the ‘Player’ filter to ‘Rows’, ‘Months’ to ‘Columns’ and key data points to ‘Values’.


Step 5 – depending on your metric of choice, add the totals up across the months and sort by that column by selecting ‘data’ then ‘sort’ and  the ‘Largest to smallest’ option

pg2step5             pg2step5a

Step 6 – compare the last month to the average of the previous months. If you divide the last month versus the average of the others, you will get an integer on each key data point. Greater than 1 means that has improved for that player, and less than one has declined.



The Insight – once that you understand the key metrics, what is the relationship between each? There is a degree of inconsistency with this reflecting the fact that people and changing circumstances are hard to capture in a number, but over time the relationship between each will come into sharper focus.

  • Visits to turnover – an increase in visitation and turnover may indicate increased income or improved financial situation or a change in total visitation (your Club may have got more visits at the expense of another Club in that Member’s ‘rotation’). A decrease in visitation and stable or increasing turnover may indicate less time available to that Member, while a stable visitation and decreased turnover number may indicate a deteriorating financial position or other commitments.
  • Theo Net to visits – this can often reflect if the Member is having a good or month compared to the theoretical. If a player’s visitation is down and the Theo Net value is down even more, it may be an indication that player has had a bad run and is gun shy or has changed venue. The opposite will hold true if the situation were reversed – a player getting a good run will increase visitation.


Next level stuff – the real skill in understanding your Members is knowing the why. Sending out offers for $X and the like doesn’t improve your knowledge base, only your base line costs and has a finite effectiveness. Understanding your Members and being empathic rather than throwing money at it has positive long-term consequences and refines the personal approach everyone strives to give. Key ingredients to get insight include;

  • Duty Managers – more often than not will have some insight into Members absence, whether due to illness, vacation, moved out of area, work or other reasons. Engage them and include them in the discussion.
  • F&B likes and favourite machines – if you are looking for some other hints, see if their favourite machine or F&B offering has been removed.

The importance of time and day

What data you need Hourly turnover by day and hour for the last month, then the corresponding month of last year
How do you order it Trading hours down columns, days across rows
Special tips Average the turnover in all the periods, keeping in mind there will be 4 or 5 days. Date isn’t a consequence here, just the days


As days of the week have patterns and trends, so do the hours within those days. Beside the general trend of Members, this can also be affected by the offering of the Club during that time, your competition and the Macro environment (election, economy, weather etc).

Step 1 – line up the 2 comparable months of data, for the example below, November 2017 and November 2016. These will be the aggregated data from the entire month and divided by the number of those days in that month, be that 4 or 5.



Step 2 – create a third table and create a formula that subtracts the Nov-16 result from Nov-17 result for the corresponding cell. For example, subtract the 10:00am Monday morning in Nov-16 from 10:00am Monday morning in Nov-17. You will start to see some negative results, unless of course every hour of every day was better than the previous year.


Step 3 – using the excel ‘Conditional Formatting’ function in the top ribbon, highlight all the cells and select a colour coding format that will heat map the results.


In the example below, RED shows an increase while BLUE is a decrease. The more intense the colour, the better/worse the result depending on the colour.



The Insight – the areas of BLUE are a concern. The greater the decline, the greater the need to understand the reason. To do this you need to look both internally (own venue) and externally.

  • Internal – has there been a promotion cancelled? Have F&B services been disrupted for any reason? Has there been a chance in car parking? Have a few large players been absent?
  • External – what have the competition been doing? Have they added promotions or new F&B offering, or even improved car parking?

Once you can pinpoint the cause, you must then find the effect. Isolate the main declining players, then formulate a plan to get them back. If it’s a more endemic issue, then a larger plan is needed, understanding the root cause of the decline.

In the areas where RED is dominant, understand the success of this, and in some instances the cost of the success. As this data is turnover based, you have to understand if the success is cost effective or just ‘bought business’.

Next level stuff – once you have a format, you can then apply this to sub groups of Members. Whether that’s the tiered loyalty groups, Bingo players, Poker players, Raffle enthusiasts or sub Clubs, you can refine the review process. This also is applicable for F&B.

Always a work in progress

They key to insight and analysis is that it is a hypothesis you constantly adjust. The world of members and players have far more inputs and variables to assess than Gaming machine analysis will ever have – which makes it that much harder to get a constant handle on. Working on a theorem means you start at the end – what is the answer you want, and how does it represent itself. Once you have that, work out what the inputs are; time of day, day of week, gaming turnover/net/theo net, POS spend, visitation, tier level…..the list is long. Map it out, think through the process and then go for it. Discuss it with someone else if you can. Odds on you will be wrong more than you are right at the start, but that will improve.

There is only so much time in the day, and the idea of trawling through data sounds time expensive and possibly unproductive. The old saying is ‘how do you eat an elephant? One bite at a time’. Use one of the 2 examples above as a starting point. Once you developed your own approach and process, you have solved half of the puzzle. Best of luck.

Next Edition: the connection between Food and Gaming and activities like Raffles and Bingo.


Terry O’Halloran is a self-confessed Gaming and Player data devotee. He is currently researching the world of eSports and how it can be applied to the world of Registered Clubs.

He can be contacted on or 0449 968 394 for ideas and theories.