Improving an Inherited PPC account

Improving an Inherited PPC account which is already successful can be tricky. A data-driven and scientific approach can often make it possible to increase the PPC account’s ROI even more.

Inheriting a Pay-Per-Click (PPC) account can be daunting at first, especially if the account is already performing well and/or spending large sums of money. In fact I’m sure any search marketer would agree that it’s often easier to take on an under-performing PPC account – At least that way you can approach the low hanging fruit first. Never the less, with a some in-depth analysis coupled with scientific and logical account management it shouldn’t be too long before you start to identify some experiments worth exploring in order to squeeze that little bit of extra Return on Investment out of the account.


The first thing to remember is that your exposure to the PPC account (in general terms) means that there is room for improvement. There’s a couple of reasons why you might be exposed to the account, and doing some early analysis can help identify exactly why that is.

Reasons why you may be inheriting a PPC account

  • Previous account manager didn’t get the desired results – What results were expected? How were they trying to reach those targets? Why didn’t they?
  • Previous account manager moved on – What were they aiming for? How could you improve their approach? What parts of the strategy worked / didn’t work?
  • Account was set up but never managed – How is the account structure currently? Why is the account structured like that? How were the keywords identified?

Ask around

First things first, ask around. In all three scenarios above the first action should be to find out a couple of simple answers. Asking stakeholders of the PPC account will often reveal some early insights and give you a good idea where to focus some more in-depth analysis within the account.

Knowing the desired objectives of a PPC account is really important in order to understand how well it’s performing within the businesses context. Of cause having high click-through rates and conversion figures is great, but these need to translate into real business objectives (often ROI and sales). Again, asking account stakeholders will help you quickly identify these objectives and gain a valuable insight into the best approach to improve the PPC campaigns.

Account history

In Google AdWords (under Tools > Change history) there is a comprehensive list of changes which have been made to the PPC account. This allows you to view any changes to ads, budgets, bids, Keywords, networks, statuses and targeting options.

Your AdWords account contains a history of changes that lets you see what you’ve done in the past so that you can understand the recipe that led to success – and aim for even better results in the future.

These changes may have been made directly by a previous account manager or by an automated rule that they set up. Change history allows you to see the changes to the PPC account over time, which can be easily cross-referenced against performance statistics (in the graph on the same page) to give a good idea of what works and what doesn’t for the account. It’ll also demonstrate the general strategy of the previous account owner, which if working well, could point you in the right direction.

Numerical analysis

In addition to reviewing previous account activity and getting subjective information from stakeholders it’s equally important to gather as much objective data as possible. Luckily PPC is full of metrics which will help you identify performance issues such as Impression Share, Click-Through Rate (CTR), Cost-Per-Acquisition (CPA) and Daily Spend.

Reviewing the Opportunities Tab will give additional information on how the account could be enhanced, and the Search Terms Report will help identify what traffic the account is currently receiving. Seeing the relationship between search terms and keywords can be useful when analysing how to expand or granulate campaigns.

External factors

Consider factors which may affect account performance and quality score (landing pages, site speed, conversion processes). Tools such as the WayBackMachine will help identify any changes to landing pages, but asking someone who’s been involved in website management may identify changes across a more specific time-frame.

Depending on the metrics being reviewed, If there’s a sudden decline in historic data (and there’s nothing obvious in the account history) then it’s possible that an external factor has affected quality score (Clicks, Impression share, position) or simply made it less desirable for visitors to convert (CPA, Conversion Rate). If this is the case then it’s worth revising the changes and identifying why they were made, and what can be done to ensure the PPC account doesn’t suffer as a result.


Experiments are great ways of accurately testing an idea or princible in a measurable and scientific way. PPC experiments can be ran on keywords, bids, ad groups, ads and other areas of the PPC account. With AdWords Campaign Experiments (ACE) you’re able to apply an experiment to a percentage of auctions, meaning you can more accurately measure experiments against the control. In other words, ACE mitigates the affects of external factors by comparing two sets of results. So if there’s a seasonal dip, this would show in both the control and experiment groups.

AdWords experiment showing worse impressions compared to control

A sample experiment running on a specific keyword, with worse impressions compared to the control

Experiments and inherited PPC accounts

Using experiments for inherited PPC accounts is a fantastic way to test out hypothesis in a scientific environment whilst ensuring the continued success of the PPC performance. You can also run multiple experiments at the same time (although it’s one per campaign) and can use the data to make more data-driven decisions.

The experimental attitude may seem like taking baby-steps, but it’s definitely better than the ‘bull in a china shop’ approach. There’s a great reason for taking your time and setting up experiments to test theories, and that’s because what’s considered ‘best practice’ may not always work across every vertical.

Example inherited PPC experiments

When you consider the scope of ACE it’s easy to see how a scientific and logical approach is so desirable. A couple of example experiments include:

Experiment Explanation Metric/s to measure
Dynamic ad copy Populate ad copy with triggered keywords Click-through rate & conversion rate
Ad destinations Same ads, different landing pages Conversion rate
Granularity Trial splitting topics into more niche ad groups Impression share & Click-through rate
Positional changes The affects of ad position on performance Click-through rate & cost-per-click (CPC)
Nuclear tests Shutting down parts of the account – More below! Impressions, Impression share, organic traffic

Nuclear PPC experiments

Going nuclear is an interesting topic, contrasting with the scientific affects of ACE. Nuclear tests basically require part of an account (for example a brand campaign) to be shut down, with the expectation being that other campaigns (product based) or channels (organic brand) will pick up the traffic and conversions at a lower cost.

It’s certainly not without risk, and it does highlight the advantages of ACE, but it can be a useful test when used moderately in particular situations. There’s more on nuclear PPC tests over on Search Engine Land.


Once your experiments have proved successful you can apply them to the wider account. Of cause, if you’re applying multiple experiments at once, keep a particularly close eye on performance as various changes could have untested results. Once your account has undergone those experimental changes, it’s really about two things: Reporting the findings and iterating between analysis and experimenting.

Understanding ACE results

Statistical significance is what you’re looking for from your experiments – Either positively or negatively. Within ACE there are three levels of statistical significance:

  • 95% probability that the experiment was related to the metric’s movement (Single arrow)
  • 99% probability that the experiment was related to the metric’s movement (Double arrow)
  • 99.9% probability that the experiment was related to the metric’s movement (Triple arrow)

It’s important to remember that an upwards pointing arrow is not always a good sign, for example if cost-per-click has increased but conversions has decreased. In this scenario the experiment would have shown a significant statistical change, but not one that you’d want to apply to your account.

ACE results showing insignificant, positive and negative statistical change

Example ACE results showing insignificant change, significant negative and significant positive change, respectively.

Also remember that the absolute figures don’t always compare directly, unless you’re using a 50% experimental group. There’s more on understanding ACE results in this video published by Google.

Results and objectives

Once you apply experimental changes to your account the changes will be recorded in the change history. However it’s important to document these changes and the affects they will have on the wider paid advertising channel, specifically related to business objectives.

For example, increasing the click-through rate is great for the account, but if this isn’t increasing the number of conversions, and ultimately sales, then it’s business value could be questioned. Keep in mind the objectives that you identified during analysis, and always prioritise experiments which aim to reach and exceed those targets.

John Alexander Rowley

An enthusiastic digital marketing professional passionately dedicated to increasing the online presence of businesses and individuals in order to improve engagement and ROI.

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