Online advertising has made marketing strategies transparent in ways that would have seemed unimaginable a few decades ago. Today, you have the tools available to trace just about any conversion or purchase back to the content that first caught the customer’s eye. You can track and measure the impact of every variation in messaging. You can see exactly how many people saw or clicked an ad. You can calculate the precise ROI of every dollar you spend.
That level of granular understanding and accountability is fantastic for keeping marketers on their toes, but it also brings enormous pressure. When you’re working with multiple channels and a finite budget, you constantly have to make tricky decisions about where to direct your resources. Every misstep is glaringly clear to you and to your colleagues.
In a time where every penny a marketer spends must be justified, high CPLs and over-spending will certainly raise an eyebrow. One major area where costs often balloon is in ad-bidding and display ads. If you get your strategy slightly wrong or fail to react fast enough to changing external factors, you may end up massively overpaying for each lead. Or failing to get the right kinds of leads. Or getting very few conversions at all. In any case, the specter of poor results is enough to keep many digital marketers up at night.
Wouldn’t it be great if you didn’t have that agonizing uncertainty before making each spending decision? If you could predict with confidence exactly what will work, right down to the best time of day and location of each ad? If you could optimize each ad based on the specific outcomes you’re hoping to achieve?
Well, the good news is that all of this is possible — with the right tools and approach.
This where data science comes in. AI and machine learning will boost your strategy across the funnel, but one of its most effective uses for marketers is optimizing ad spend.
When we talk about data science for online advertising, we’re usually referring to ad allocation technology. Or to be precise, how this technology uses machine learning to take in and analyze data in order to draw conclusions that improve the way it performs a task. Or, to put it simply, the ways that ad tech continually learns and improves over time.
What is being learned depends on what goals have been set and what indicators of success the machine learning program has been trained to look out for. The technology may be trying to predict where an ad should be placed on the page to get the most clicks. Or it could be trying to match the most relevant individual ads with the interests of audience members to improve clicks. It could be trying to ascertain which variations of an ad get more clicks, or which ad slots deliver the lowest CPL, or which channels ultimately deliver the highest quality leads.
It may even be calculating and offsetting all these measurements simultaneously to ensure you don’t push too far with a strategy that delivers on one metric at the expense of another, more important one. The overarching goal is to help you build the best possible ad-bidding model — one that evolves continuously as it receives new data or as external factors change so that you roll with the punches and keep optimizing your ad spend.
Some of the primary ways that data science and machine learning can improve your ad bidding model are:
What times of day, or days of the week, do your ads gain the most traction? If you have an international presence, finding the sweet spot may be quite complicated. Machine learning helps you sift through huge swaths of data to identify the most successful times overall, as well as splicing and dicing the data to work out which times work best in particular locations.
People live their lives online, but figuring out exactly where your potential customers are most likely to interact with your ad content can be an expensive gamble. Is your CPL better on Facebook or LinkedIn? Do you get a better ROI from Instagram or Twitter ads? Do the platforms that yield the most interest also deliver the best-quality leads? Data science projects help cut through the confusion to give you a clear picture that informs your decision-making going forward.
Real-time bidding for display advertising allows marketers to purchase ad impressions by auction, bidding to place their ad in front of a particular user in the blink of an eye.
Some publishers and intermediaries already use a machine learning-driven algorithm to optimize this process, both to improve the conversion rates and ensure the system delivers value to advertisers and to manage the bidding process for maximum profit. Google Ads, for example, allows advertisers to bid for ad slots based on priorities like improved CPA, ROAS, or CPC. However, as we’ll talk about in a moment, the signals and algorithms they use to calculate these predictions tend to be carefully guarded secrets.
Many platforms and publishers make their own data science-backed systems and tools available to advertisers. After all, helping advertisers to derive more value from the system and improve the ROI of their campaigns makes solid business sense. However, there’s a catch.
If you’re using Google’s Smart Bidding application or Facebook’s ad center, you kind of have to take their word for it that the algorithm is getting you the best results it can. You can’t access the raw data yourself. You can’t examine or tweak the machine learning engine. You can’t create new priorities for it to focus on. You’re acting within the other party’s perimeters — and if they discontinued the product tomorrow, you’d be back to square one, with no way to reach the end-users yourself.
This is why it’s so important to do your own data science and build your own predictive models, too. Note that this doesn’t necessarily mean you have to have your own team of data scientists — you don’t need any machine learning knowledge at all if you’re using data science-as-a-service for marketing, for example. But you do need to own the data and understand how the model is reaching its conclusions to gain true visibility over your ad bidding strategy.
Another challenge is getting hold of enough data to train and feed your machine learning model that it has enough to, well, learn from. Sure, you may have mountains and mountains of historical data at your disposal, but how up-to-date is it? If the context is changing rapidly, can you reliably extrapolate future conditions from the historical data you have?
For example, Google’s Smart Bidding application can take a while to generate useful insights if you’re using it for a new campaign. For target ROAS bidding, it needs a minimum of 15 conversions over 30 days to analyze, while for CPA bidding, it needs at least 30 conversions.
This puts marketers in an awkward position. Do you experiment for the first month and potentially waste a lot of your valuable budget just seeing what works? Or do you play things really safe but never generate the data you would need to see if a more ambitious strategy could have paid off?
If you use a sophisticated data science tool or data-science-as-a-service platform, you potentially solve this conundrum by introducing a third option: incorporating up-to-the-minute data from external sources. This adds more context to your internal data, allowing you to fill in the gaps, build stronger models, and get a clearer picture of how to bid without needing to learn from your own mistakes first.
A machine learning or data-science-as-a-service platform will help you hit the ground running and scale up fast in a number of marketing machine learning use cases, including optimizing your ad bidding strategy. It gets you from the concept stage to deployment very quickly, allowing you to start deriving ROI much sooner, making the most of your budget and getting stronger results from your campaigns.
All of which leaves you with more time to focus on the things that machine learning can’t do: designing creative, engaging, innovative marketing ads and content your audience will love. That’s what you bring to the table. Data science takes care of the mechanics — so marketers can focus on perfecting the human side of the equation.