Internet Marketing Services

When analyzing PPC campaign data, have you come across difficulties in analyzing the data to

1. determine if the data has seasonality and external factors
2. how to adjust the data for seasonality and external factors

Often when we analyze Adwords PPC campaign data, we have to ensure that the data is free from seasonality and other external factors before we determine how we can improve the performance of PPC campaigns. I will try to share the simple methods i use which you use to analyze the data.

Always start by understanding your client business, the products/services, their target market, any new products they have released, their offline marketing initiatives etc. before proceeding to analyze data. Depending upon the type of business and what you are intending to achieve, you can choose the time period (months,1 year 2 year) and the specific metrics to analyze. If you are looking at determining seasonality, then it’s good to compare data across more than one year as it helps identify trends, ensures you are not looking at one time spikes caused due to offline promotions, product releases and other external factors.

Method 1

The easiest and simplest is to obtain the weekly or monthly average number of conversions from the client and compare it with the actual data set for specific time period. For example if the average number of monthly conversions is 250, then compare it with the actual number of conversions for the specific time period. The best is to always use graphs as it makes it easy to spot trends, seasonality and other external factors in the data. Once you have spotted trends/seasonality/external factors, then select the data for that specific time period and analyze further to determine the cause of it.

PPC Data Analysis for Seasonality - Method 1

Method 2

Calculate the averages of data set for specific time period you have selected and divide each data point by average and multiply by 100. The data column “(Conv./Avg.) X 100” shows by what percentage is each month’s conversions big or small corresponding to the average. You can use it to determine which months are higher and which months are lower. Once you have the data, analyze and determine why some months are bigger and some months are smaller and some months are same as the average.

PPC Data Analysis for Seasonality - Method 2

Method 3

Calculate the average for the whole year and subtract it from each month’s conversion data. The data column “Conv.-Avg.” shows the difference between the monthly conversions and the average and whether it’s higher than the average or lesser than the average. If it’s negative, the monthly conversions are fewer than the average by whatever is the number. If it’s positive, the monthly conversions are larger than the average by whatever is the number. Once you have the data, analyze and determine why some months are bigger and some months are smaller and some months are same as the average.

PPC Data Analysis for Seasonality - Method 3

Conclusion

The only difference between method 2 & method 3 is the data unit format. You can choose to use either one of the methods depending upon your preference. Now by looking at the data from the table in either Method2 or 3, it’s clear that months 11 & 12 have significantly large number of conversions. Now you need to determine why there is such a big difference and if it’s due to seasonality, other offline marketing promotions, external factors etc. These are simple ways to start analyzing the data. I will write a follow-up article on how to adjust or normalize data to remove seasonality and external factors.

Pitstop Media offers ROI based internet marketing services. If you need help with PPC management or PPC optimization please contact us for a free, no obligation quote. We’ve helped companies reduce their paid advertising cost by as much as 48% and increase AdWords conversion rates by as much as 410%. See our internet marketing case studies.