No matter how well we research and present our numerical data, there is always one question that we will probably always face: "Is that a lot?" Does it show a lack of understanding on the part of our audience, or did we just not make it perfectly clear that our recommendation is earth-shattering, game-changing and generally just awesome? There are various reasons why our data isn't being received with the awe that it deserves; here are some ways of addressing the gaps.
External Comparison
If you want to give an effective image of how many people visited your site (either normally, or in response to a marketing campaign) then it may be useful to compare them to an external figure. For example, if you saw 90,000 people respond to your marketing campaign, you might get asked if that's a lot. One answer: it's equal to the capacity of Wembley Stadium in London.
As another example, 8 million people fly in an airliner each day. Is that a lot? On the one hand, it's about 0.11% of the total population of the world. On the other hand, it's almost equal to the population of London (8.67 million). Is that a lot?
Naturally, it helps to have a list of populations for various cities, towns and countries if you want to keep using external comparisons.
Internal Comparison
Probably more effective than external comparison, this uses your current data on your website, sales, revenue, whichever, and calls out how current performance compares to other parts of your site. For example, you might compare sales or traffic for shoes with shirts, trousers and socks; or perhaps you'd compare SUVs with sports, hatchbacks and estate cars.
This is most effective if you find that traffic to the different internal sections of your site changes (e.g. seasonally) but isn't going to work well if there's little change in the relative traffic to each part (e.g. if shirts always has more traffic than shoes, and shoes are more popular than trousers, etc.). You could also express this as a share of total traffic: "Menswear traffic rose from 30% to 40% of total site traffic this week" (which also eliminates the overall variation in site traffic - whether you want to make use of that effect or not).
Trending
- This was the highest for six months
- ...the lowest for eight months
- ... the second highest this year
- ...making it the third lowest in the last five years
If you're going to pursue this strategy, then it also helps to have a reason why things were high six months ago, or low eight months ago e.g. "This month was the lowest for 15 months, when one of our competitors had a massive sale and undercut us for three consecutive weeks." or "This month was the highest for six months, when we had the pre-Christmas sale." This helps connect the data to real-life events and brings the data to life. "Do you remember that time when our site was really busy? Well, it's even busier than that."
- The UK Meteorological Office do this with their "since records began" expression, and according to NASA, July 2016 was the world's hottest month since records began.
- The UK census showed a population boom that was also the largest since records began.
- TV data shows that the Rio Olympics in 2016 got the smallest TV audience in Brazil since the 2004 games. The reason is that more people streamed the games online: it's always good to have a reason why a metric jumps or falls sharply (read more in my article about moving from reporting to insight).
As you can see, these kinds of 'highest since/lowest since' statements really make great headlines, so don't be afraid of using them if you want to instil a sense of urgency into your reporting or analysis.
If it's been a fairly average month, and hasn't been the biggest/best/worst/lowest month since Christmas/Thanksgiving/Easter/ever, then you could always do a comparison with the previous period. Year on year, or month over month comparisons are widely used - especially year-on-year (YoY) which conveniently removes any seasonal effects (if it was Back to School this year, it will have been Back to School last year too).
Trends, of course, are vey easily represented as graphs - line charts or bar charts, depending on your personal preference. Here's an example I've used in the past, showing the current year trend, and last year's trend. I thought it was fairly intuitive, and with a bit of stakeholder education (I showed them what it was and what it meant), it became the standard way of showing YoY trends, and the current trend. The bars are last year; the line is this year. The colour of the line matched the colour of the particular part of the site being discussed (e.g. blue could be men's wear, pink could be ladies' wear - beware of using red and green, as these are shortcuts for 'bad' and 'good' respectively).
Financial Metrics
If you really want to make your stakeholders take action, connect your recommendations and analysis to the money. Nobody's sure if 19,354 visitors is a lot, but everybody knows how much £19,354 is, or how much $19,354 will buy you. Whether you go for a trended view, or an external or internal comparison, you can still say, "We made $54,218 this week. Is that a lot? It's 15% more than the week before, but 4% less than the same week last year." Suddenly everybody's paying attention; and if you're lucky, they'll ask you what you recommend doing about it. Have your answers ready!
I've written before about actionable analysis - connecting any metric to a KPI or to a financial figure immediately makes analysis more actionable.
Conclusion
So, when you tell your manager that the figure is $150, and your manager decides it's time to emulate Admiral Kirk by asking "Is that a lot?" you can be ready with a comparative or trended view of the data to say, "Well, it may not buy you a gold watch, but it'll get you two bus tickets to the whales on the other side of San Francisco".
External Comparison
If you want to give an effective image of how many people visited your site (either normally, or in response to a marketing campaign) then it may be useful to compare them to an external figure. For example, if you saw 90,000 people respond to your marketing campaign, you might get asked if that's a lot. One answer: it's equal to the capacity of Wembley Stadium in London.
As another example, 8 million people fly in an airliner each day. Is that a lot? On the one hand, it's about 0.11% of the total population of the world. On the other hand, it's almost equal to the population of London (8.67 million). Is that a lot?
Naturally, it helps to have a list of populations for various cities, towns and countries if you want to keep using external comparisons.
Internal Comparison
Probably more effective than external comparison, this uses your current data on your website, sales, revenue, whichever, and calls out how current performance compares to other parts of your site. For example, you might compare sales or traffic for shoes with shirts, trousers and socks; or perhaps you'd compare SUVs with sports, hatchbacks and estate cars.
This is most effective if you find that traffic to the different internal sections of your site changes (e.g. seasonally) but isn't going to work well if there's little change in the relative traffic to each part (e.g. if shirts always has more traffic than shoes, and shoes are more popular than trousers, etc.). You could also express this as a share of total traffic: "Menswear traffic rose from 30% to 40% of total site traffic this week" (which also eliminates the overall variation in site traffic - whether you want to make use of that effect or not).
Trending
- This was the highest for six months
- ...the lowest for eight months
- ... the second highest this year
- ...making it the third lowest in the last five years
If you're going to pursue this strategy, then it also helps to have a reason why things were high six months ago, or low eight months ago e.g. "This month was the lowest for 15 months, when one of our competitors had a massive sale and undercut us for three consecutive weeks." or "This month was the highest for six months, when we had the pre-Christmas sale." This helps connect the data to real-life events and brings the data to life. "Do you remember that time when our site was really busy? Well, it's even busier than that."
- The UK Meteorological Office do this with their "since records began" expression, and according to NASA, July 2016 was the world's hottest month since records began.
- The UK census showed a population boom that was also the largest since records began.
- TV data shows that the Rio Olympics in 2016 got the smallest TV audience in Brazil since the 2004 games. The reason is that more people streamed the games online: it's always good to have a reason why a metric jumps or falls sharply (read more in my article about moving from reporting to insight).
As you can see, these kinds of 'highest since/lowest since' statements really make great headlines, so don't be afraid of using them if you want to instil a sense of urgency into your reporting or analysis.
If it's been a fairly average month, and hasn't been the biggest/best/worst/lowest month since Christmas/Thanksgiving/Easter/ever, then you could always do a comparison with the previous period. Year on year, or month over month comparisons are widely used - especially year-on-year (YoY) which conveniently removes any seasonal effects (if it was Back to School this year, it will have been Back to School last year too).
Trends, of course, are vey easily represented as graphs - line charts or bar charts, depending on your personal preference. Here's an example I've used in the past, showing the current year trend, and last year's trend. I thought it was fairly intuitive, and with a bit of stakeholder education (I showed them what it was and what it meant), it became the standard way of showing YoY trends, and the current trend. The bars are last year; the line is this year. The colour of the line matched the colour of the particular part of the site being discussed (e.g. blue could be men's wear, pink could be ladies' wear - beware of using red and green, as these are shortcuts for 'bad' and 'good' respectively).
Financial Metrics
If you really want to make your stakeholders take action, connect your recommendations and analysis to the money. Nobody's sure if 19,354 visitors is a lot, but everybody knows how much £19,354 is, or how much $19,354 will buy you. Whether you go for a trended view, or an external or internal comparison, you can still say, "We made $54,218 this week. Is that a lot? It's 15% more than the week before, but 4% less than the same week last year." Suddenly everybody's paying attention; and if you're lucky, they'll ask you what you recommend doing about it. Have your answers ready!
I've written before about actionable analysis - connecting any metric to a KPI or to a financial figure immediately makes analysis more actionable.
Conclusion
So, when you tell your manager that the figure is $150, and your manager decides it's time to emulate Admiral Kirk by asking "Is that a lot?" you can be ready with a comparative or trended view of the data to say, "Well, it may not buy you a gold watch, but it'll get you two bus tickets to the whales on the other side of San Francisco".
 |
| Admiral Kirk asks, "Is that a lot?" |
Nice article, its very good info.
ReplyDeleteThanks David
Delete