Mi9’s Jon Devereux answers your top programmatic questions

You asked, he answered. Here, Mi9’s Jon Devereux give us his advice on forecasted trends, suggestions for ad spend, and the do’s and don’t all things programmatic.

  1. Any thoughts on trends to look out for coming out of Lockdown?

In terms of overall market trends, we are still seeing significantly more supply relative to demand, which I believe presents a buying opportunity for some industries.  This is driven largely by a few industries remaining offline such as Airlines, Travel and Tourism and an increase in site traffic looking for News updates.

On the other side retail, mostly Retail (eCommerce), and Telecoms have weathered the storm well, with spend either near or exceeding pre-COVID-19 levels.

Finance spend across the market remains subdued, which looks like an opportunity for a bank to fill the void and take a larger than normal share of voice in the market.

In terms of ad tech trends, I think a key focus remains data solutions in a cookie-less world. As the crackdown on third-party cookies continues, we are seeing ad exchanges such as Index Exchange partner with data platforms such as LiveRamp to provide solutions which pass user ID markers in the bid request rather than relying on traditional client-side stored cookies.  With data being such an integral part of programmatic buying and other initiatives such as header bidding and brand safety tools in a more mature and stable place, it feels like a more robust solution for data is going to come to the forefront of the industry, which I believe is a good thing as cookies were never really a great solution to begin with!

  • Hi Jon, what is the best way to collect customer feedback on programmatic ads, how do you suggest analysing the collected?

There are a few ways this can be interpreted; from a publisher perspective the feedback would pertain to the quality of the ads and the impact to user experience on the site. I think realistically in this scenario feedback will be reactive, rather than proactive, and likely negative due to a bad user experience.

A proactive approach on the publisher’s part makes sense here, no obnoxious pop-up ads, and no low quality or scam advertising. Despite what it may seem to the layman site browser, here in New Zealand all publishers I’ve worked with take ad quality and user experience very seriously, there is a constant battle on the publisher side to block scam, unpleasant and illicit ads, naturally the people that are making ads to scam are using tricks to circumvent category blocks imposed by publishers, so publishers need to use a number of manual checks to try and catch the ads as quickly as possible.

From a brand perspective regarding the efficacy of an ad, the data after an ad is seen or clicked is your best bet, having a site well tagged up to record conversions and inform what creative drove the most ROI. Attribution gets a bad rap, because it is impossible to get an absolute result, for example; if you try and attribute ROI based on post-impression, sites that serve more impressions are more likely to be attributed the last-view and will look better in the results. 

Likewise, when attributing based on post-click you usually end up with unfavourable looking data, for example the data might say it takes $200 of ad spend to generate one lead for a product worth $20. This understandably makes agencies and marketing departments reluctant to use it as a source of measurement, but personally I still see value in using this to drive decisions on where to upweight spend based, rather than default to CPC or CTR which might look great at a $1.50 CPC, but means a lot less in reality. In fact, there’s often an inverse relationship between high clickthrough rates and conversions, i.e. most people who buy via ads online do so because the ad is relevant to them and rarely click ads otherwise, whilst there is a subset of people who click a lot of ads with no intention of converting.

  • What are your thoughts on public knowledge of programmatic marketing… do consumers understand how it works. Is this or isn’t this a problem?

Great question, for an average user not in the industry it’s very low, most don’t realise ads are bought and sold on exchanges similar to stock markets. They are aware their data is being used in some capacity, but I think most misunderstand what’s available and in what capacity their data being used, this creates a level of mistrust that I believe is unwarranted. The reality is all data is hashed, anonymous data with no personally identifiable information that gets grouped into large pools.

While there’s absolutely cause for concern with the amount of data certain entities possess internally and the potential risks to individuals that poses, the way I see data being used in day-to-day programmatic advertising is far off what the average layman believes is going on. Other problems include things such as users believing a publisher deliberately uploaded a rude or scam creative for money when the reality is all New Zealand publishers work hard to prevent ads that could hurt their brand or users.

From a marketing perspective I still hear a lot that digital ads are less premium, with the decision to focus spend on more traditional channels.  Obviously I have skin in the game but personally it seems disingenuous how people can be happy to take a hazy, vague and inferred stat about the number of people that saw a non-digital ad at face value, but completely discredit the reams of data available around nearly every aspect of digital ads.

To say digital is a less premium environment would suggest Netflix is less premium TV or Amazon is less premium shopping.  I see digital as the new norm, and a digital environment can be premium just as a non-digital environment can be second-rate.

  • With the current landscape, what are people not wanting to advertise around?

For us, and from what I have heard of other publishers here and in Australia, the main blockers are any COVID-19 related news. Some buyers for government will specifically target towards COVID-19 news, but most advertisers want to avoid it.  Most buyers have extensive blocklists in place for their clients which are always on, using platforms such as IAS, DoubleVerify or Grapeshot to categorise page risk levels.  In terms of what industries are spending, most Retail and Telecoms are above previous levels, making use of eCommerce opportunities which programmatic advertising excels at, while Finance, Autos, Travel and Tourism are still lagging, understandably.

  • is there a solution to the problem of… “I have already brought the shoes, but they continue to follow me – I don’t need that ad anymore”. How does advertising know when to stop?

Technically it is possible, in the same way an advertiser adds you to a list to target to after viewing the shoes, they can add you to another list after you land on the thank you page of the purchase to exclude you from future targeting.

As to why they do not, that is subjective depending on the advertiser, in the case of shoes or online retail, I would imagine it is less effective for eCommerce stores to remove you after purchase. The fact that you’ve made a purchase on their site makes you significantly more likely to re-purchase than a random user, you’ve probably already got an account, card details saved, and trust them enough to deliver the goods etc.

Most eCommerce shops use dynamic banners now that rotate what goods they are advertising, so it is less likely they will continue to serve the same shoes but continue to serve you shoe ads on the assumption you’re still more likely to buy another pair than a random individual.

That said, there is still room for improvement, for example data science could be done to figure out exactly how long between shoe purchases the average user makes on the site, if the average user buys a pair of shoes every 16 weeks there may be better results downweighing spend for a few weeks to prevent wastage and ramp up spend again as you approach the 16th  week.  Likewise with subscription-based services it would make sense to significantly reduce spend on users you know are already subscribed. I get a lot of ads from services I’m subscribed to which just feels like wasted ad spend, and in some cases the ad has even reminded me to cancel the subscription.  I cannot comment on why they do not, it could be lack of knowhow, or an unwillingness to share that data with their agency out of security/privacy concerns, as an example.  I do think the industry has a lot of room for improvement in the data science and optimisation space.

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