1. Time Based Analysis Without Time Series
David Dipple & Ross Swain

2. Who We Are Dip & Ross Fellow of Royal Statistical Society
Work with Not For Profit and Charity Clients for over 25 years
Recognised as an expert data modeller
Trained numerous analysts and fundraisers in the use of analysis
Analyst at Adroit Data & Insight
His youthful good looks and charm belie his vast experience in charity analysis
Expert in Excel and FastStats Reporting
Understands analysis from a client’s perspective
Dip & Ross

3. The Question
“If time is money are ATM's time machines?”

4. The Issue
Whenever time based analysis is envisaged the analyst’s Cri de Coeur is to use time series analysis…….
……..But, in many cases this this approach fails to find anything interesting as effects can be short lived and based around one or more singularities rather than a trend

5. Case Study – World Vision Media Optimisation
World Vision wanted to see how spend on advertising effected brand awareness and Child Sponsorship/ Regular Giving recruitment.
Other analysts had looked at the data and tried to use time series analysis to find patterns in the data, but could not find anything interesting……..
…… and so we looked at the issue in a different way to see if we could answer the question another way
Project Objectives
Q1. Understand media interplay and effects on sponsor recruitment
Q2. True return generated by spend in each channel in terms of sponsorships recruited
Q3 True impact of spend on TV advertising
Q4. Experiential activity
Halo effect of spend in another acquisition channel (e.g. TV to F2F)
Q5. Impact of Competitor Spend

6. Issues and Relationships
There can be upper and lower spending thresholds
Spend profile may not be continuous.

7. Additional Challenges
Time
Seasonality and time-based effects
Media Lag is different by media type
Activity & weight varies over time e.g bursts of TV
Data
The information came from many systems, each with its own format
The data had widely different means and ranges
Cost data had to be married back to the actual media schedules (NB. often TV slots change/get pulled so intended schedules are not the same as actual).
Factors
External Factors such as competitor ad spend
Any factor can be secondary in nature and not a key driver (the halo effect)
The butterfly effect is always possible

8. The Answer
“Time and tide waits for no man

9. So How Did We Do it?
Looking at the data and also our statistical toolkit, we decided to use some a good olde statistical techniques….

10. Understanding Two or More Distributions
The data had widely different ranges and distributions and so had to be standardised before they could be used – all data was normalised with a mean of zero and a standard deviation of 1.
The data could then be displayed on charts and the patterns examined both visually and statistically. Whilst this sort of analysis can be carried out using tools like SPSS it quickly became evident that this was not going to be possible.
To be changed

11. Curve Fitting
Curve fitting and estimation have been used in mathematics and science for a long time
This is how we approached our question…
… but without over complicating the answer
In addition to using visual inspection to understand the similarities between the distributions we used correlation as our statistical measure.

12. Linear Correlation
When most people think of correlation that use the idea of a linear relationship between two variables as shown in this diagram.
The type of relationship can then be determined as either positive or negative and also the strength of the relationship.

13. Non Linear Correlation - example
Correlation (r) = 0.91
Strength (r2) = 83%
Correlation can also be used to compare two non-linear relationships and this allows us to see how good the fit is between two distribution as correlation is calculating the differences between them.

14. The Results
“Time is a great teacher, but unfortunately it kills all its pupils.”

15. Overlaying the Distributions
Overlaying the distributions show that the lines are very spikey and there does not look like there are any direct patterns, especially as there was a long period where there was no TV spend.
It does look like there might be some relationship later in the time period.
Taking the data from the start of 2013 and running for two years looks a lot more promising.

16. An Example Relationship from the Analysis
Looking more into the relationship it was realised that the spend preceded the adverts by at least a month.
By adding a lag of 2 months to the distributions the patterns had a much better overlay and the correlation was also much higher.

17. The Art of the Possible
No piece of software would allow us to examine all of the combinations of factors and relationships that we needed to do the data justice so we built our own in Excel.
Figures Deleted
A time slice could be focused in on rather than using the whole period.
Up to 3 different distributions could be compared at the same time. Each with different weights and lags.
The relationships could be examined both visually and statistically.

18. Our Solution
Using different statistical methods we created a media planning app for both the client and ourselves
The data can be updated any time so that the latest patterns can be uncovered.
Additional econometric data was included so that the underlying relationships could be referenced to external factors.
Competitive (Nielsen) relative ad spend statistics for other charities was included to see how that impacted the overall position.
The client could understand the impact of different media combinations & forward plan to change their media spend, combinations and weight

19. A Cautionary Tale Underwater Cage?
Source:

20. Thank You For Listening
“Time to listen to other’s words of wisdom”