Defining success or failure is critical to the success of every marketing strategy, and measuring success or failure is not always as straightforward as it may seem.
If sales on your eCommerce site are up 10% over the previous week, is this a success? Not necessarily. Maybe this week of the year is historically your busiest, and your sales should have been up 20% relative to the previous week.
What if sales are up 20% relative to the same week of the previous year? This does not necessarily represent a successful week either, since sales could have already been trending 30% higher year over year.
What if, instead, you could predict with greater certainty that the past week performed better than projected…45% better?
The first steps of defining, visualizing and analyzing your KPIs are challenging exercises by their own right; setting benchmarks for your KPIs is even tougher but equally as critical to ensure that stakeholders – both internal and external – are held accountable for performance.
Imagine this hypothetical: you have the perfect Marketing Measurement Model with well defined KPI’s (not metrics!) and your CMO is singing your praises. The first week goes by and it’s reporting and analysis time. All the data is collected, prepared and visualized, so now it’s time to prepare to answer the critical questions your CMO will likely ask you during tomorrow’s meeting:
If you’re struggling to come up with these answers… predictive modeling can help!
In this post, we will:
Let’s drill down further into our hypothetical scenario and imagine that we are the marketing manager for the Google Merchandise Store. We have an amazing marketing team – that includes YOU, the marketing manager, of course! We also have an enterprise set of website data collection and storage tools that include: Google Tag Manager, Google Analytics 360, and the Google Analytics 360 BigQuery Export. Most importantly – besides our amazing team – is that we have a well defined Marketing Measurement Model. Here is one of the objectives with its related sub-elements:
Objective | Increase awareness of Google Merchandise brand |
Goal | Increase sales |
KPI | Revenue |
Segment | Paid search (cpc) |
Target | ? |
We want to answer the question for our meeting with the CMO next week:
Was the week of Monday, July 24 – Sunday, July 30 a success or failure for our revenue goal by all users from the paid search (cpc) channel?
But how do we accurately define this success or failure?
If it’s midnight before your meeting with the CMO, you can stop reading here and set a target for a 10% improvement (vs last year or last reporting period) as Avinash states:
“…Yes, and I’ve said this frequently, if all else fails just set your target for a 10% improvement. Anything, absolutely anything, can be improved by 10% with just a small amount of effort. But, you likely want to do something more complicated, and more sound, over time.”
But we doubt that’s your situation and we have a more complicated, sound but long-term solution:
We will forecast revenue from all users for the paid search (cpc) channel based on the previous 50 weeks of historical data.
For the purposes of this post, we chose to use the public GA360 BigQuery dataset because it makes our example reproducible. Click here for the source code used in this forecasting analysis.
In our hypothetical example, we chose to use GA 360 BQ dataset because:
We chose R, RStudio and the respective R packages because they are:
We won’t dive into the details or too much code for the purposes of this post. Instead, we’ll offer a high-level overview and focus on the business use case!
Arguably the most critical, difficult phase of any predictive modeling project. Good news for us, we’ve already defined our business question to answer (you know, the one for our meeting with the CMO next week):
Was the week of Monday, July 24 – Sunday, July 30 a success or failure?
This is typically the most time-consuming phase, but in our case, it’s not so bad. In summary, we have only 1 simple query to write to obtain the data we need from the GA 360 BigQuery public dataset:
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#legacySQL SELECT DATE(date) AS date, (trafficSource.medium) AS medium, ROUND(SUM(IFNULL(totals.transactionRevenue/100000,0)),2) AS transactionRevenue FROM (TABLE_DATE_RANGE([bigquery-public-data:google_analytics_sample.ga_sessions_], TIMESTAMP('2016-07-01'), TIMESTAMP('2017-08-31'))) GROUP BY date, medium HAVING medium = 'cpc' ORDER BY date ASC, transactionRevenue DESC
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Next, we prepare the data on our local machine using R, RStudio and the following R packages:
The plot in the bottom right shows:
This is the phase where the magic happens! In a nutshell, we:
Here are the results of our forecast vs. actual for revenue from paid search (cpc) as well as a visual:
The plot shows:
Since our CMO – like most exec’s – loves concise communications, we’ll communicate our results as:
After our presentation of the CMO we would likely take the following actions:
Additionally, the details of integrating these results is beyond the scope of this post but generally speaking, we would:
We hope you now can see at least one example of how forecasting can be used to answer the age-old question of: Was this reporting period a success or failure? and improve accountability within your marketing team.
Forecasting is a powerful methodology and fundamentally speaking, it can answer fundamental questions like:
Specifically, here are some other use cases that we can apply forecasting to:
At E-Nor, we can help with the entire forecasting process – from defining the business objective, collecting and storing clean, quality data and of course, selecting and integrating the right forecasting model for the job.
Contact us to learn more about how we can help with Predictive Modeling and more!
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