Analytics seem overly complex and not fully trustworthy. 

Perceptions around analytics can be a hurdle. Many view it as a black box, lacking transparency and trustworthiness, and may think it’s merely retrospective with no actionable insights.

This is not the case. In fact, analytics can be highly effective when approached correctly. What you need is the right supplier. Look for one that offers transparency, expertise in developing robust MMM, and the ability to translate these models into actionable recommendations. Ultimately, the value lies in insights and recommendations rather than the models themselves.

Insufficient budget or resource allocation. 

The cost associated with MMM can often seem overwhelming. Many businesses do not allocate sufficient funds in their annual budgets, which can stall or completely halt the implementation of an MMM program.

To overcome this, start small and focus on your main objectives initially. Use this to build a case for increased budgets over time. Leverage existing resources and identify any open-source tools to build MMM, and work with suppliers to provide data in a pre-agreed format, which can often help reduce fees.

Uncertainty of when is the right time to start.

Timing is critical. With new campaigns always on the horizon, it's tempting to delay MMM analysis to include the next campaign, wait for full-year numbers, or incorporate the upcoming product launch. There is always something upcoming and this can lead to a cycle of postponement.

In short, don’t delay. Begin the process as soon as possible. Early insights and recommendations can lead to impactful business changes. Over time, your MMM will evolve and you can always update the analysis to include future activities. The value from initial findings often outweighs the reasons for waiting.

Concerns about data quality and accuracy. 

Quality data is the foundation of any successful MMM program. Questions about data availability can cause hesitation, as the adage "garbage in, garbage out" holds true.

Conduct a data audit, as the quality of the data remains unknown until it’s reviewed. Set up checkpoints during the analysis to determine data fitness, and if it falls short, create a plan to collect fit-for-purpose data. Focus on using the data that is available. Generate initial insights with whatever data you have, then enhance the MMM with more data over time. Don't wait for perfection to begin gaining insights. Look for continuous improvements, and remember that better data leads to better decisions. Improving data quality is an ongoing process.

Fear of undesirable results.

Fear of discovering negative outcomes can be paralyzing. Concerns about campaign performance or potential budget reallocations may deter businesses from pursuing an MMM program.

Acknowledge the risks and treat this as an education. Working in ignorance is one thing, but remaining deliberately uninformed is unacceptable. Embrace the process and be prepared to make data-informed decisions.

Addressing these common blockers is essential for the successful implementation of an MMM program. Finding the right scope to meet your budget and a trusted MMM partner are the best next steps to creating valuable insight and actionable recommendations to drive business performance.

These techniques are exciting, as Google has published data demonstrating that double-digit percentage uplift in conversion value is possible. Results clearly depend on having the very best data, the best modeling capabilities and the best activation strategy, which is where Media.Monks teams play an essential role.

Who is this for? Everyone!

Are you in digital marketing as an “analytics person”? Primarily data focused? Technical? You’ll know about enhanced conversions (EC) and value-based bidding (VBB), but beyond the tagging, do you know what’s going on in the media systems and what it’s actually for?

Or are you a “non-technical” marketer? Your talents for campaign setup and management don’t overlap with tagging. Again, you’re across EC and VBB but where does the data come from? Why’s it so tricky to get right? What’s the hold up with the tags?

Regardless of our role specifics, we all need to have as full understanding of the solutions as possible. We need to get a handle on what happens “on the other side” so we can deliver the very best solutions for clients, and for users. Here’s the scoop you need. This is relevant to people on the Search Ads/Display & Video/Campaign Manager side as well as those on the Google Analytics/Google Tag Manager side. Here’s an opportunity to share knowledge… LFG.

Set the scene.

Cookie atrophy is a poorly kept secret. Browser tech continues to erode cookie usage. Third-party cookies are being deprecated from Chrome in 2024, which holds a dominant market share that’s significant for marketers. That doesn’t mean we are on safe ground when it comes to first party cookies though; just check through the details on Cookie Status to see the reality.

As data volume diminishes with sufficient signal quality, we can still use modeling techniques to mitigate for gaps in data, but that’s not a robust solution in isolation. We continue to make every effort necessary to maintain data volume, whilst evolving our tactics to improve efficiency.

This is where EC becomes a playbook entry to maximize observable conversions, while VBB drives greater efficiency by enabling optimization for value rather than volume.

Maximize observable data.

If we have less data, we must have better data quality. By that, we mean clean and clear data where we can clearly see conversions and channels. This means that the data still has utility even if it’s not complete. Where we may have holes due to browser tech and cookie loss, for example, we can still use first-party data to get better conversion accuracy. Enhanced conversions help us see more conversion data, but in a privacy-safe manner.

What it does.

Basically, on the conversion/sale/thank you page, a tag will fire—let’s say a floodlight tag for simplicity. The user’s email address is hashed (encoded using the SHA-256 algorithm), and then added to the tag data which is then sent to Google. This hashed value is then used to match the user with Google’s data to recover conversions that are absent from your data set.

You can use a range of values in addition to, or instead of, the email address. The email address is normally fine. It’s hashed, so no third party (not even Google) sees the data and it’s deleted after use. Google has published in-depth details on how the data is used, and this is essential reading for your teams.

Use best practices for tagging.

Ideally, you’d expose pre-hashed personal identifiable information (PII) on the dataLayer variable which can be picked up easily by Google Tag Manager (GTM) and added to the floodlight.

You can scrape the Document Object Model (DOM) to extract the data, but this is not a robust, long-term solution. You can use Google tag instead of GTM if a tag management system is not available. For offline conversions (leads), you can also upload conversion data via an API.

Collaboration is key.

Tech, data media and legal teams should work closely in order to correctly implement and then validate changes in data volumes.

This is not legal advice, so you need to get buy-in early from your legal team. Advise your teams to make sure EC usage is covered in your privacy policy and cookie policy and that consent is fully informed with a clear opt-out option.

Make sure you know the conversion page path, and that the PII variable is available. Scraping the DOM might be okay for a proof of concept, but don’t rely on it as a permanent solution.

Media teams need to make simple configuration changes and then report accurately on conversion volume changes. Use your data science teams to establish causality and validate EC is working. Liaise with your media teams regularly after rolling out EC to maintain scrutiny on the data volumes and changes. Be impatient for action (get it done!), but patient for results—manage expectations regarding timing, change may take weeks.

Using value-based bid optimization.

As we progress along the path of digital maturity, our tactics adapt and evolve. Where it’s normal and fine to optimize for click volume in the early days, the optimization KPI changes as our business grows. We aim to reduce cost, grow revenue, build ROI and ultimately optimize for long-term profit.

Optimizing a campaign for click volume was a brute-force budget tactic. Optimizing for value (profit stems from value) is a more precise allocation of budget. How the budget is allocated is the clever part.

Optimize for value.

Consider an ecommerce site where the obvious valuable outcome is a sale. There are other outcomes that serve as signals to indicate a user may be a valuable customer: viewing a product detail page, adding to cart, starting a checkout. All actions lead to the conversion, all with varying degrees of value. As each outcome is completed, fire a floodlight to inform GMP that the user has performed a “high-value action” worth €x. These actions and values are then used to automatically optimize the bid for the user.

Previously, defining the values associated with an action was a matter of experimentation. Now you can use an online calculator to refine these numbers.

This approach to value-based bidding needs a level of data volume and quality that is delivered by using EC with VBB—and is extremely powerful. It has few moving parts, but the values are static, commercial values that don’t always reflect the user’s likely behavior. To address this, let’s look back at an older solution to see how we can level up this approach.

Using coarse-grained optimization.

Previously, we’ve used machine learning to build a predictive model that will output an answer to “how likely is it for user X to convert”? At scale, the data is imported into GMP as an audience, and we use this to guide where the budget is spent. A simple approach here is to build a set of audiences from the model output to drive bid optimizations:

• “No hopers” with the lowest propensity to convert: €0.
• “Dead certs” with the highest propensity to convert: low or €0
• “Floating voter” with medium propensity; needs convincing: €maximum

This technique has delivered great results in the past. There are shortcomings, however. With three audiences, the segmentation by propensity is quite coarse. As the number of audiences ramps up, there is more to compute and more to maintain in terms of infrastructure. The user needs to revisit the site to “get cookied” and be included in a remarketing audience.

There is a more modern approach that addresses the shortcomings from these techniques.

Modeled VBB optimization goes even further.

We’ll now blend these two solutions with server-side data collection (sGTM). Server-side data collection has a number of key features that make it very appropriate for use here:

• First, it allows data enrichment in private—we can introduce profit as a value for optimization without exposing margin data to third parties.
• Additionally, first-party cookie tenure is enhanced by server-side data collection. Your first-party cookies are set in a way that prevents third-party exposure—browsers like this and take a less harsh view of them. This is better for your first-party data quality.
• There is no need to revisit the site to establish audience membership; all cookie-ing is done in the pixel execution.

So now, we can fire floodlights for our sales conversions, attach per-item profit data at the server level and optimize bids based on user profitability. Awesome, but what about the predictive model output?

At the server-side data collection point, sGTM can integrate with other Google Cloud Platform (GCP) components. As well as extracting profit data, we can interrogate a propensity model, and for each high-value action per user, ask what the propensity is for the user to convert. The predictive score is then attached to the floodlight to drive VBB.

This has fewer moving parts than the older solution. It solves for the coarse-grained audience feature by delivering per user scoring as the data is collected. Again, we team this up with EC to maximize conversion visibility and drive powerful marketing optimizations.

Optimize your marketing with EC and VBB.

These techniques have existed in isolation for some time. With a broader understanding of the data requirements, and the activation of the data, we’re all in a better position to use privacy-first marketing optimizations to deliver efficiencies for clients, and ultimately, a better, more useful online experience for consumers.

What is geomarketing?

Geomarketing is a technique that uses location data to optimize campaigns, helping you engage with customers at the right place and time. Geomarketing can be used for both online and offline touchpoints, making it a versatile part of your toolkit. It can take several forms: a set of information that helps you with decision-making, an analytical approach to building campaigns, or a strategic channel that helps you gather demographic data. It can even be a combination of these tools.

Why do brands use geomarketing?

Demographic surveys have long been used by brands to learn more about existing and prospective customers, and historically geomarketing has been used to help retailers choose the right region to open a physical store based on that data. Now, geomarketing is continuing to evolve along with demand for services within specific geographic areas. For example, an estimated 97.1% of users in Brazil access the internet via smartphones—and with so many customers always on the move, the need for geographically relevant messages and services has increased. There are three main advantages of geomarketing:

Audience segmentation. Geomarketing is a great way to segment your audience. This way, your campaigns can extract greater results from specific locations. Use this data to drive better placement in local searches, like “pharmacies in Rio de Janeiro.”

Increased ROI. Without a geomarketing strategy, it’s possible that your campaigns will reach people located far away—who might have no use for your services. For example, that pharmacy in Rio de Janeiro won’t want to advertise to people several cities away. By employing geomarketing, brands have the power to choose exactly where their campaigns run, meaning they’ll spend less for more effective results.

More qualified leads and higher conversion. The previous point shows how targeting more specific, engaged audiences is more cost-efficient. But it can also earn you more leads, because you’ll be reaching an audience likely to have a greater interest in your product or service—especially when taking other data, like purchasing behavior or interactions on social media, into account.

If you own an ice cream parlor in Brasília, for example, and you're on a tight marketing budget, geomarketing will help you to get leads who are in Brasília, close to the neighborhood and interested in ice cream. This way, you'll get more conversions at a much lower cost than advertising to the whole city, or to all of Brasília, the state.

Here’s how to use geolocation marketing in your business.

Once you understand the concept of geolocation marketing and how important it is, you can use one of many pieces of software available to manage data and optimize your geomarketing efforts, like Google Analytics or Meta Ads. Here are three tactics to get the most out of geomarketing.

Geotargeting. Geotargeting is a way of showing users content based on their location. With a database that maps IP addresses onto specific locations, you can target by country, state or even ZIP code depending on your platform of choice.

Geofencing. Geofencing is the use of technologies such as the Global Positioning System or radio frequency identification to create a virtual geofence. In other words, it involves collecting location data from electronic devices in order to take action based on it. You can use geofencing to deliver real-time content to your customers based on their GPS data. Note that geofencing requires the use of a branded application that your audience has already downloaded and authorized to track location data.

Another way to serve content to customers is by leveraging third-party platforms like Waze, a collaborative traffic and navigation app. By using Waze Ads, your content can be shown to drivers within a certain vicinity.

Geotagging and check-in. Another interesting geomarketing tactic is the strategic use of the check-in feature. For example, if you create a Facebook and Instagram page that includes your business address, both apps will allow customers to check in. Marking the location helps others easily find the profile of the business, along with other useful info.

Geotagging is similar, in which users tag the business location to a photo or other piece of content when sharing it to social media. Again, this helps people discover the business and generates publicity for the brand. Because people tend to be influenced by their peers, this can be a great factor in analyzing consumer behavior.

You can leverage geomarketing alongside other marketing strategies, too.

Geomarketing becomes even more useful when tied to other marketing strategies. Having access to customers' location is a great way to build efficiency across your brand’s actions. You can analyze market competition in your region of choice as well as the behavior of your target audience.

Geomarketing involves large volumes of information, and you can use that additional info to optimize your processes and improve business strategies overall—like directing investments to regions with the greatest potential for conversion, or identifying areas with high demand for your products or services.  

Geomarketing truly shines when you look for quality information that can provide insights into consumption patterns or other data obtained through studies, thus improving your geomarketing performance. For example, you can look into public databases of sociodemographic data. My team in Brazil uses IBGE, PNAD and Ipea.

With that, you should be ready to begin supporting geomarketing. For my team, geographical diversity is a big part of what we do, and leveraging insight into the interest and behaviors across different regions, cities and places is a fascinating way to deliver content to build your business. By using the strategies above, you’re well on your way to meeting the diverse needs of your own customers.