As the old saying goes, there’s no such thing as a stupid question. I’m pretty sure I drove my manager mad with questions when I started out as a grad, but I know it certainly helped me understand and learn things at a faster pace.

Often, we receive questions from clients that prompt us to look into, or think about, things we hadn’t previously considered. Either way, it’s always a win-win when everyone is aligned on what’s being actioned and why.

After working across a range of clients in paid media, it’s clear that some questions crop up time and time again, so I thought I’d address some of these in a little more detail. If you have any questions, please reach out to us and you might be featured in FAQs part 2!

Why are channel results so different from Google Analytics?

The key to answering this question is understanding attribution. Each platform has its own default attribution model, counting conversions that occur within a post-click or post-view window. For example, Facebook counts conversions that occurred within 28 days of clicking on an ad or within 1 day of viewing (but not clicking) an ad.


In contrast, Google Analytics uses a last-click model by default i.e. it only counts the last direct click that resulted in a conversion. The issue with this model is that it attributes 100% of a conversion to one channel when we know from extensive studies that there are often several touch-points in a consumer journey that influence the end decision. Data Driven Attribution (DDA) is leading the way to understanding the complexity of consumer journeys. It does so by using large data sets to assign a weighting to each touch-point in a user-journey, giving a more accurate picture of performance.

Should we bid on our brand terms for paid search?

Generally yes... but not always. There are a few factors to consider:

  1. SEO
    Are you already appearing top in the organic search results? If so, you may be best utilising budget elsewhere.
  2. Competition
    Are competitors bidding on your brand terms? If so, you should definitely make sure you are beating them to the top. This shouldn’t be too expensive as your quality scores should be much higher.
  3. Tactical Messaging
    If you need full control of updated messaging beyond what SEO changes can provide, or bespoke messaging for specific audiences, then paid search can be the solution.

If you are top of the organic results and there are no competitor ads, save your money. But make sure that you monitor this regularly, as it might change. It just so happens that we have custom tools at Ayima to monitor this - ask us more if you’re interested.

What's a good CTR/CPC/CR% etc?

This can vary massively from campaign to campaign, sector to sector or audience to audience, but there are rough benchmarks out there, broken down by platform and vertical. If you have previous results, collate your data and see what your average metrics are. If you don’t have any data, we usually recommend going with an industry benchmark as a starting point and then reassessing your targets once you have some data to base projections on.


What customer data would be useful to share?

Generally, as much data as you’re able and allowed to provide (in a GDPR compliant way). The more identifiers (email, phone, postcode etc.) you use when uploading CRM lists on social or search platforms, the higher your match rate is likely to be. Think about the objective of your campaign – if you are driving revenue and want to make a lookalike of previous customers – including their lifetime value in the data will be extremely useful.

What’s considered a good frequency for ads?

Again, this is entirely dependent on the channel and campaign objective, but as a general rule, if your frequency is high and your results are poor, you are probably wasting spend by serving to the same users who are not going to convert. For some campaigns, you may want to reach as many people as possible, and so will want to set a frequency cap to ensure you don’t serve the same people too often.

For conversion-driving campaigns on social, we generally don’t set frequency caps as the algorithms are usually sophisticated enough to determine if a given interaction is likely to result in a conversion (even if it is the person's 3rd, 4th or 5th touch-point).


The bottom line is, monitor your frequency to make sure you’re not appearing as ‘spammy’ but don’t cap it unless you need to or have a limited budget, otherwise, you could be missing valuable visibility moments. When it comes to programmatic, however, we would recommend always setting frequency caps here as people don’t want to feel like they’re being followed around the internet by the same advertiser and this can lead to distrust of the brand.

Why was performance worse yesterday compared to the day before?

The first thing we do when asked this kind of question is to look out for any trends: is there a particular day of the week that performs poorly? Are users experiencing ad fatigue? CTR over time can be a good indicator of this. These things are relatively simple to correct: optimise your budgets towards better-performing times and days, introduce some new, fresh creative or copy. However, there is not always an obvious answer staring us in the face.

There are many things we can control when running a campaign:

  • Budgets
  • Optimisation goals
  • Ad scheduling
  • Frequency
  • Audience targeting
  • Ads that are served

We cannot control user behaviour, however, we can influence it. We can’t make people open their Facebook app or predict exactly how many people are eligible to see our ads on a given day, but we can test which ads drive the most action when they are served.

For PPC we do know what people are searching, but to take maximum advantage of this we must ensure that our ads appear for valuable search terms at the right times with the right message. The art of the deal is in maximising the likelihood of conversion for every single interaction that does occur.

There will always be fluctuations and a plethora of factors affecting performance, but the key in minimising volatility and guaranteeing long term success is to make data-driven decisions based on a sufficient data set. It’s an ongoing process of refinement, with a combination of machine learning and human intelligence at its heart.

If you have more questions you'd like answered, or would like to hear about how we can help you grow through paid media, get in touch.

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