2022 has already been identified as a year of digital transition and evolution. Highlights include the emergence of new technologies and the (r)evolution toward improved user privacy on the Internet. These indicate a present and future moment of change that will affect both people and brands.

The metaverse, cryptocurrencies, NFTs (Non-Fungible Tokens), are some trends that brands must understand to best reach their target audiences.

But before looking to the future, we need to remember our homework from 2021: adapting our digital activity to fit the new reality focused on privacy.

We estimate that companies that do not adapt to the current changes in privacy will lose up to 25% of their data if they do not implement new Privacy solutions.

User privacy online is something that fully affects both consumers and companies. With the disappearance of third-party cookies, companies and brands must find new ways to ethically collect and measure user data, while continuing to offer personalized experiences to each user.

Today, on Data Privacy Day, we tell you how you can best adapt and operate with the new privacy rules in order to keep making the most of your data. 

2022, privacy’s peak

Companies that utilize customer data must rethink the way they operate online with the disappearance of third-party cookies. User privacy regulations (like the California Privacy Rights Act) affect:

• what data we collect
• how we collect it, and
• how we activate it in our strategies

In the words of Paula Gómez, Data & Adtech Director at Making Science, “If we don’t adapt to this new scenario, it will lead to two major errors. First, it means that we are not respecting the relevant regulations, nor our customers. Secondly, it means our activity cannot be correctly measured, leading to errors in measurements and activations that will affect our business.”

To mitigate these effects, companies must integrate First-Party Data: customer data that the company owns directly. It’s crucial to make this data central to the marketing strategy. Then you can have control over the platforms that capture, process, and activate this data, working within an integrated ecosystem.

Along with this new strategy, we recommend implementing solutions that can help us develop campaigns and actions with the same normality as in the past.

Our most recommended solutions for a cookieless future include: 

• Google Analytics 4
• Consent Mode
• Server to Server (S2S)

Google Analytics 4

Google Analytics 4, or simply GA4, has new capabilities that improve user identification across devices and platforms; new capabilities to measure and gain compliant insights; and machine learning-based capabilities for new and advanced insights.

Consent Mode

Another option is Google’s Consent Mode. This solution allows Google tags (analytics and advertising) to adjust their behavior based on the user’s consent. It can send the information as usual if the user consents. If not, it will send a cookieless ping.

Server to Server (S2S)

Server to Server uses server-side tracing, where a single tag sends information to the server and from there redirects the strictly necessary information to each platform, creating a more secure data flow.

Which option is best for you?

These days, privacy is a very serious matter. Companies must keep their online experience relevant and personalized without compromising the privacy of their customers. 

If you haven’t gotten started yet, don’t wait until it’s too late. Our team of Google and first-party data experts would love to help you and your team prepare for this and the many other upcoming changes in the market.

Reach out to us at info@makingscience.com to book a call and see how we can help.

BigQuery is a Google Cloud database that allows you to process huge amounts of data in just a few seconds. When used in conjunction with Google Analytics 360, you can import Analytics data automatically, without needing to process it. This is where BigQuery really shines. You can utilize the same data that is in Google Analytics 360 without any limitations, making BigQuery the perfect analysis tool.

What are the advantages of BigQuery?

No Sampling

One of the limitations that our Google analysts have found inside the Analytics interface is  “sampling.” This most commonly occurs in personalized reports or a wide range of dates. With sampling, Google extrapolates the data it receives – counting only some of its data points and modeling the other information. As a result, the data that appears is approximate. With BigQuery, we can avoid sampling. We can include all unprocessed data, allowing us to consult, divide, or modulate it using 100% real data. It’s truly powerful!

Users as protagonists 

In Google Analytics, objectives are based on sessions. If a user completes an objective after multiple visits, it will not be observable in the Google Analytics funnel. On the other hand, BigQuery has the data as well as the capacity to show user-level information. This can have a great impact for ecommerce focused companies. If you’ve used the improved e-commerce reports inside Google Analytics, you’ll know that these are based on sessions. However, in the real world, a person may add an article to his cart during one visit and wait to complete the purchase on another visit. BigQuery allows you to monitor the purchasing behavior of users that need more than one session to convert.

Combined dimensions 

The data model in Google Analytics is structured so that session based dimensions (like campaigns) don’t work when combined with user or hit level dimension and metrics. There’s a limit to the number of dimensions we can see: usually only two within Google Analytics, or five within personalized reports. Using an API, we can achieve up to seven dimensions. But with BigQuery, there are no limits. For example, ecommerce companies may have trouble getting exact statistics about the quantity of social media users that saw a product page and then bought that product. In BigQuery, we can get that data with just one query.

Apply changes retroactively

Google Analytics has a specific way of processing data, for example with objectives or filters. When we gather data in Google Analytics, whether by web or app, Google will apply our configurations and store this data for future reports. As such, it’s not possible to go back and change data in Google Analytics. But with BigQuery this all changes. If you made a mistake when configuring your reports and you wish to filter or modify data (settings of objectives, channel groupings, etc.) you can adjust your consults in BigQuery to correct those issues retroactively. 

Now what??

If you’re not sure how to get started with BigQuery, get in touch with our team by shooting us an email at info@makingscience.com.

What is a dataLayer?

A dataLayer is where page view data (hits, users, sessions, and products viewed) is stored. We can use this data to improve the performance of a website. 

What kind of variables can find in dataLayer?

• Static: The data stored doesn’t change – the values that we get are always the same. For example, the version of the dataLayer.
• Dynamic: This is the most common way to get data into the dataLayer. For example, we can use the same dataLayer for every single product on our website, but the variable’s value changes depending on the product that the user is viewing. 

What are the benefits of having a dataLayer?

The data that we get from the dataLayer gives us more information about a user’s behavior on our website. We can create and adjust our business strategy based on the user data that we get from the first visit until the moment he/she purchases something on our website.

Some other benefits from implementing dataLayer on our website include:

• Content grouping: It allows us to have a standard system for exposing data, making it easier to detect changes. In addition, we can configure conversion funnels.
• Ecommerce: Ecommerce variables are the most reliable and secure way to measure conversions (transactionId and transactionTotal). When using the dataLayer, you are less subject to errors if the website is modified.
• Audiences: By collecting more valuable data through the dataLayer, we can create better segmented audience lists. These lists can provide us insights to improve the conversion rates of our campaigns.
• Ease of creating activation rules in pixels: A label can also be triggered based on the information that appears in our dataLayer.

What variables could you collect in the dataLayer depending on the business industry?

• Travel sector
  • Number of nights that the user has selected
  • Number of people who have purchased the service
  • Destination chosen in the conversion
  • How long in advance the user has purchased the service
• Retail sector
  • Product discounts
  • The number of reviews of certain products
  • Number of photos of a product on the product page
  • If the product was on sale or not
  • Number of previous purchases by a certain user
• Automotive sector:
  • Type of engine: 135cv, 150cv… 
  • Car color color: blue, red, green…
  • Vehicle fuel: diesel or gasoline
  • Name of the dealer: Enterprise, Chevy, etc.
  • Type of car: new, semi-new, or used car.

In short, it is very important to define a good measurement strategy to be able to extract more information from our users. In the long run, this will help us make strategic decisions within our company.

What is a Customer Data Platform (CDP)?

Before we jump in, we first need to understand its current context, the issues we currently face in terms of capturing data, and the diverse solutions for moving forward. Let’s first discuss the current context:

• Regulatory changes: the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) have impacted how data can be stored, collected, and used.
• Updates: companies are no longer able to harness traditional data collection methods (for example: the third-party cookies and device identifiers) due to the increase in control.
• Privacy control: Today, users demand more control and transparency over the data collected about them, which is then used for ad personalization.

The main and direct consequence in today’s digital ecosystem is clear:

• Cookieless: we are no longer able to track our users nor their activity, behaviors, nor interests
• Cookieless world third-party: limitations like Intelligent Tracking Prevention (or ITP, a Safari feature) – or Enhanced Tracking Protection (or ETP, a Mozilla feature) mean that our marketing strategies based on third party audiences and cookies will be the most affected.

There are diverse solutions for solving the cookieless puzzle. Users now have the option to choose how their data is collected. However, with Consent Mode and GA4, we can mitigate the loss of data from our users through data modeling. For example, we can use ServerToServer measurement (which means more first party data). Most importantly, this grants us ownership of how this data is stored.

Here, as we confront the concept of first party data, is where the concept of CDP is reasonable, because it is a technological solution for the management of our data as well as its subsequent activation. CDP should be understood as a solution – not just as a tool. 

Why use a CDP?

There are many different tools in the digital market that store and activate data. However, from a Making Science perspective, we recommend CDP. This is how it can be approached from a client’s perspective.

This is a simplified diagram of how a CDP works:

Let’s first recognize that we presently have a lot of information (much more than we can usually analyze and activate). Additionally, this information is stored and created by multiple data sources, creating silos which rarely interact with each other. This causes data duplication, because there could be different sources with the same user information. As it’s processed, the data can become blurred, generating a lack of information about attribution and purchase data. This makes it difficult to administer the marketing mix budget and nearly impossible to optimize the investment.

A CDP will allow us to have one repository for all our data, whose initial purpose is to standardize and unify customers’ information according to all the user touch points generated. This will allow us to activate the data on a per-customer basis, so we can meet their needs according to the time of purchase or the point of maturity of the customer with the brand.

The significant advantage of a CDP is that it allows us to work with the data as if it were First Party Data (as if we were the owners of the data). It is important to have a measurement strategy related to solutions such as the aforementioned Consent Mode or ServerToServer. Since a CDP requires several development iterations we’ll have to question ourselves the following in this second stage:

1. What goals do we hope to achieve working along with a CDP? What kind of problems could we face with the platform? At the end of the day, it is just a technology solution, however we must first identify what problems we want to solve.
2. Which CDP should I use? How can I get it?

Factors to consider

For companies with a high level of technological development, it may be feasible to generate a proprietary CDP from their stack of tools. Others may prefer to go to the market in search of solutions that have already been developed. In any case, we suggest the following four factors to take into account when making this decision:

• Timing: What’s the fastest route? When will my CDP be functional? For marketplace solutions, it will depend on the connection options with the tools of your digital ecosystem. For an ad hoc option, one must consider what kind of connection options exist as well as the effort needed to create them. This leads us to the second factor:
• Connections: which option will allow us to link easily with our current stack of tools? If your company already works with a market solution, this embedded connection should be easier; otherwise you should evaluate the effort to generate those connections out of the box.
• Scalability: Which connection option will grant the most flexibility to adapt to changes in your company? For market solutions, it will depend on the evolution of their adaptations. An ad hoc one should be easier for you in this respect.
• Cost: What is the most cost-effective solution? Consider not only the initial monetary cost, but also the cost of CDP evolution according to your main objectives and stack of tools.

In conclusion, these are just a few hints of what CDP implies as another solution in the current digital landscape. It is not a simple decision to make for companies. At Making Science, we recommend CDPs because we understand how this solution should be approached: always focus on generating solutions according to the current market and the company’s particular needs.

In early 2020, Google announced that it would remove Third Party Cookies from its Chrome browser by 2022. Chrome will not be the first or the last browser to remove these cookies, but in this specific case, it will have a major impact on the campaigns we run. In this sense, Google has communicated a very important point for all of us who operate digital marketing campaigns: they will not limit third-party cookies until they ensure that the market can continue to work in a qualified manner.

That is why, taking into account the current and upcoming situation, and having the horizon of a #cookielessworld in 2022, brands should start looking for a plan B to carry out their digital activity without being affected by the disappearance of third-party cookies.

At Making Science, we help our clients tackle these major changes. We base our clients’ strategy on their first-party data. Every company possesses proprietary data and with this, we can accurately profile groups of users that are going to impact with our campaigns.

Failure to change the philosophy of our digital strategy can be very costly. If we do not adapt, we will not be able to operate or measure our campaigns properly – thus losing efficiency, data, and revenue. In fact, according to an internal study conducted by Making Science, we estimate that companies that do not adapt to the current changes in privacy, specifically in relation to the disappearance of third-party cookies, will lose up to 25% of their data if they do not implement new Privacy solutions.

Among the multitude of current solutions in relation to these privacy updates, we are mainly committed to one, Google’s Consent Mode. Which will allow you to continue measuring your ROI while respecting the consent of your users.

Consent Mode: Google’s new solution to continue measuring conversions while respecting consent rules

Asking users for explicit consent before installing cookies in their browser may lead to the loss of conversion measurement for those users who refuse to give permission.

Google’s new “Consent Mode” functionality, seeks to mitigate this impact. Therefore, it is important that we understand.

Consent Mode currently focuses on two types of consent; analytics and advertising personalization. Introducing two new variables, ad_storage and analytics_storage in the configuration will allow you to inform your tags (Google Analytics, GMP, and Google Ads) of the user’s consent status, adjusting their behavior accordingly.

It should be clarified that Consent Mode is not a CMP (Consent Management Platform), but complements the system or solution installed to collect the user’s consent.

The tags will always be executed, regardless of whether the user consents or not. The real change comes in the information they collect:

If the user has given consent, the tag will run normally, while if the user has refused consent, the tag will run without collecting or sending cookie information. In this way, you can still have basic, aggregated measurements instead of losing this information completely.

With the information collected from users who have consented, Google’s platforms will also be able to model conversions for those users who have not consented. This modeling is currently not active but is planned in their roadmap.

Additionally, functionality that can be activated with Consent Mode is the so-called “URL Passthrough”. This option enables the process of sending the click identifier in the URL so that it can still be associated with a conversion in the absence of cookies.

The implementation requires GTM or gtag to be in use and consists within the inclusion of some lines of code for the configuration.

Consent Mode arises because of the need to comply with the user’s consent without completely losing the measurement. Such a basic and necessary element allows us to analyze, improve and optimize ad activation.

It is currently a beta tool that is still being developed and will progressively include more functionalities and options, such as the aforementioned conversion modeling.

Personalization in digital marketing & machine learning

Machine learning is indispensable when it comes to personalization in digital marketing. Period.

Thanks to incredible advancements in terms of machine learning capabilities and analytics, companies can offer unique digital experiences to specific customers with virtually nothing more than a “flip of a switch.” So, instead of having a single experience for all of your users, your site can be personalized to fit the needs of each and every one of them.

This practice, known as personalization, involves categorizing customers as unique individuals rather than members of a broader customer segment.

What does this process look like?

Firstly, data is rapidly aggregated and then analyzed. Then, machine learning does what it does best. Learn. By deep analysis of the data it is being fed, relevant insights can be extracted about customers on a granular level. These insights can then be the driver in terms of deploying the optimal personalized website or app layout to the right person—in the right way.

Why should I implement personalization in my digital marketing strategy?

Each customer is unique—they want you to know what they want, when they want it and how they want it.

Personalization allows you to build deeper relationships with your users. It improves your conversions by convincing prospects that you know and understand their needs.

Furthermore, personalization allows you to micro-target prospects based on probability to convert, improve your engagement levels and grow your user community via features such as social sharing, comments and likes.

It is also important to note that combining your personalization strategy with CRO (conversion rate optimization) is a revenue-generating machine—.

How do you implement personalization on your website?

Before you can implement a website personalization program, you need to understand everything about your users. This may sound straightforward, but it can be incredibly difficult if you don’t have a clear strategy or roadmap from the beginning.

The “Who”

To get your 1-to-1 personalization program right, first establish the “who.” Who do you want to target with a unique experience? Here are some examples on how to categorize the “who”:

• Demographic attributes such gender, location, income & profession.
• Behavior of the users on-site and other marketing channels—i.e., browses awareness pages before making a conversion.
• First-time visitors vs. returning visitors, logged-in vs. not logged-in etc.
• Device types—desktop, mobile or tablet? What browser or do they use?

The “What”

When it comes to the “what”—what type of experience or content do you want to show this segment of your audience? Consider the following:

• How much of your page can you personalize? Do you need to change anything on the backend before you are able to make the desired changes?
• What part of your page do you want to personalize? Popular options include headlines, images and calls to action.
• What value will personalization create? Don’t personalize just because you can. Think of what you are trying to achieve.

Things to keep in mind

Once you’ve done the above and know the “who” and the “what”, you need to keep the following in mind:

• Don’t slice your audience too thin. Allow them to discover new things should they choose to do so.
• Make sure to measure the impact of changes before and after you make them so that you have more insight into what worked, what didn’t and why—always test.
• Use automated data collection and personalization technology (to be discussed below) whenever possible for maximum efficiency.

Tools that can help with personalization

Monetate is a global leader in personalization software for consumer-facing brands. Their technology allows brands to create a more relevant and personalized customer experience.

CommerceStack is an add-on that works with Magento and automatically displays relevant products based on shopper behavior. This is accomplished by tapping into data already stored by Magento to make its recommendations.

CNET Intelligent Cross Sell handles cross-selling and up-selling based on behavioral data and can also be customized to use data points such as product profitability or customer reviews before displaying items to specific people.

SiteApps enables you to enhance WordPress websites with a wide range of apps, such as social media widgets, extensions for analytics, forms for feedback and comments and more, all of which can be added with a click.

Zendesk and LiveChat are great tools to use if you’re looking for customer help and support features on your site. These applications allow you to offer personalized support via your website, over the phone, on social sites, by email and by using live chat. This makes it easier to personalize customer service experiences for users.

Now to get a little more technical. You can use services such as Episerver and Kentico CMS to personalize content based on rules such as the physical location of your visitor and search terms. You can also use more advanced personalization features that use data on user behavior and profiles.

Finally, if you want to integrate marketing with your 1-to-1 personalization program, consider HubSpot, Marketo and Salesforce. With these tools, you can deliver the right content that you need to show a user based on where they are in the buying process.

As brands begin to re-open, there is a lot of talk about personalization and the importance of creating a positive customer experience. But, what does this really mean?

A personalized digital experience is about feeding your users the information they will be most likely to consume, a service tailored to their needs and desires. Before we can personalize the user journey, we need to understand who our customers are today, what are the new user traits, if these customers are willing to buy, and what are the new questions they need answered prior to considering your product.

We can define 3 key points to understand your personalization opportunities:

1- Analyze your data

It’s time to take a deep dive into your analytics and data platforms to see what data is available. This way, you can better segment your customers. Check your CRM for loyal customers and marketing channels flow for behavioral data points on prospects. The shift to a digitally dominated experience forces brands in build out conversion profiles such as deal hunters, last-minute buyers, and new online shoppers. They will do so to better understand how people navigate their sites and the repeat visits that will drive conversion.

2- Listen to your customers

This has always been a crucial part of the journey building to personalization. Emotions are the driving force. We need to take a fresh approach to understand how users are feeling now. Also, we should see how their emotions have changed post-Covid. Knowing what the current frustrations are, what health and safety guidelines customers need to feel safe and trusting, what is the #1 complaint in your call centers, what are they saying on social media about your brand.

3- Implement a testing tool  

Why is this important? With the need for faster execution and learnings, testing tools are an asset to any business. You will be able to test new features and understand their impact on the different segments (new vs. returning vs loyal customers, geo-location, 2nd visit, 3rd visit) and then be able to iterate quicker.

A personalized customer experience will be your ultimate brand differentiator. Brands should leverage technology such as AI and Personalization engines to create tailored, real-time experiences that will go beyond meeting customers expectations. The goal for any digital marketing team should be developing a cadence of iterative testing and learnings, to be able to update customer behaviors and prompt them with their preferred experiences.

What happened to all the data you gathered two months ago from interviews, workshops, and surveys? Users’ behaviors have drastically changed, so the hypothesis you were trying to prove in February is no longer valid.

Is more always better? Some still believe that extensive user research at the beginning of a project is the best approach, when in fact most of this information deprecates as you iterate in each sprint. Inevitably, your users tend to feel different about your product as their reality changes, and the world’s reality has changed. The faster you understand your users’ new problems and frictions with your product, the more effective your solution might be.

How to apply continuous delivery to CX research?

1. Simplify your scope based on priorities. Create short 15-30 minutes sessions rather than the standard 1-hour workshop or focus group for example.
2. Leave your opinions behind and wear your listener hat. You are not there to show the users how to use your product but to learn what they have to say about it.
3. Repeat interaction every two weeks if possible, but not necessarily the same activity. If you started with workshops, maybe you want to do interviews later.
4. Mix it up by engaging the same users from previous sessions and adding some new participants. By doing this, you will understand how (if) your users’ reactions evolved in this time period, while you also learn about new user trends.
5. Combine this qualitative data with quantitative data from Analytics (if available).
6. Convert users’ insights into actionable opportunities to improve your product.
7. Include AB testing if applicable and available.
8. Implement and launch.
9. Collect new feedback to validate your implementation.
10. Iterate and adjust the frequency of new CX research and testing to your business needs.

It is paramount to encourage user discussion during your sessions, and constantly try to appeal to the deepest honest emotions. This feedback will define the direction and success of your product. Rival groups and Love Stories are two of the techniques recommended to deeply connect with your existing or potential audience. More about these techniques in future articles.

Making erroneous assumptions should not be the norm. Allow yourself to make mistakes but, Fail fast and Recover faster.