The Future of Cross-Media Measurement in a Privacy-First Economy
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5 CDP Shortcomings Ad Tech Brands Face & How to Fix Them

By Scuba Insights

Customer data platforms (CDPs) help businesses aggregate and analyze customer data from multiple channels. As brands interact with consumers through various touchpoints, the CDP cleans and unifies the data to build more complete customer profiles.


But getting a true 360° view of user behavior remains a challenge. As we move beyond business process automation (Digital Transformation 1.0) and dive into emerging technologies like artificial intelligence, machine learning, and the Internet of Things (IoT), CDPs don't always live up to expectations. 


Despite the promise of building a single customer view, most CDPs can’t handle the high-speed, high-volume analytics associated with the new wave of digital transformation (what many are calling DT or DX 2.0).  As a result, both business goals and customer experiences suffer.


Read on to understand where CDPs fall short, and how combining your existing CDP with real-time customer intelligence will help launch you into the modern digital economy.


What ad tech brands need in a DX 2.0 economy

As we mentioned, CDPs are platforms that help businesses collect and store information about customers. They enrich customer profiles for better segmentation and personalization.  


But ad tech brands have needs that CDPs have a challenge providing in a DX 2.0 economy:

1. Cross-media measurement

Ad tech brands want to understand customer exposure and track user activity across all channels, with the end goal of getting the most bang for their ad buck. Before you can optimize your ad reach, frequency, and ultimately ROI, cross-media measurement is critical. 


For instance, let’s say that based on testing, you know your ad needs to be seen by your target audience at least three times to spark a conversion. How can you ensure your marketing campaigns achieve this reach and frequency across multiple channels? And how can you do it without bombarding the user or overspending on platforms that don’t convert?


In theory, a CDP should support cross-media measurement through fast and simple omnichannel data tracking. It should combine data sources to create a personalized, consistent customer journey, even across multiple touchpoints and timelines. With that single view across channels, ad tech marketers can better optimize for marketing goals and avoid bloated ad spend. 

2. Unified customer 360° view

Without a single customer view (SCV), you might have skewed ideas about what your audience actually wants, or who is even a potential customer. For this reason, brands need a solution that can process user data gathered into a singular, unified view, based on insights like behavior, interests, and purchase history. 


CDPs should consolidate any and every form of customer interaction with a brand—across sales, marketing, support, or transactions—to give them a unified view of customer behavior. However, this isn’t a guarantee from CDPs—despite the need for brands to serve customers better and deliver more personalized experiences.

3. Hyper-personalization

The marketplace is evolving far beyond the typical personalization strategies of the recent past. It's now a matter of matching customers with their unique preferences through hyper-personalization. 


For example, say a user spent 15 minutes on a Saturday afternoon browsing a site for olive green shoes, then left without purchasing. Personalization might look like sending an email to the user, adding their first name on the subject line, and encouraging them to return to shopping on the site. 


Hyper-personalization takes it a step further—which might look more like sending an email or app notification within 30 to 60 minutes, advertising a discount on olive green shoes. Hyper-personalization also uses AI and real-time data to address specific needs and deliver fine-tuned messages. This is not a capability CDPs have.

4. Predictive customer analytics

Customer-centric teams need a CDP that can adopt machine learning to generate predictive models from customer data. Predictive customer analytics uses existing data to help advertisers and publishers better target their ad campaigns to the right audience.


For example, if you’re in ad tech that focuses on sports gaming and entertainment, you may want to promote timely content and ads before and after a football big match, like the Super Bowl. Predictive customer analytics can help identify which customers are most likely to engage with those ads and content pieces, based on their past interactions with relevant articles and ads–as well as demographics, online behavior, and past purchases.


But none of these use cases can be fully realized with the currently limited capabilities of CDPs, which we'll dive into below.

5 ways CDPs are falling behind in the DX 2.0 era

While CDPs have their strong suits, they were built for a different time, before privacy became more important than ever and the death of third-party cookies. Simply put, CDPs weren’t built for the needs and new capabilities of tools in the DX 2.0 world. There are several critical shortcomings CDPs have:

1. Real-time analytics

Unfortunately, CDPs don't often provide the full control you need to analyze, iterate and act on user data as it happens. Most CDP implementations fail to provide comprehensive or integrated analytics across first-party data or connect to multiple data sources in real time. At best, you might only be seeing partial, near-real-time information, creating missed opportunities every second, every minute, every hour. In other words, ad tech brands won’t get that 360 customer view they need to optimize their campaigns and ad spending.


Non-real-time data impacts customer experiences across the entire lifecycle and delays decision-making. Since CDPs lack real-time analytics to pull in data about your customers, you miss out on ROI-driving opportunities. You fail to understand what your customers want as they’re browsing your product catalog or at the moment of purchase. Without that in-the-moment visibility, you can forget about using well-timed discounts to push conversions, upsell opportunities, or retargeting to recoup a missed sale.  

2. Direct data connection

Direct data connection refers to plugging directly into various data sources (like your company’s CRM or website analytics tool) to collect and process customer data, rather than relying on an intermediary or third party to access it. 


A direct data connection ensures that your data is accurate, up-to-date, and comprehensive, helping you better understand customer behavior and preferences. All of this equals faster and more informed decisions based on the insights gained from the customer data.


While many CDPs offer some direct data connection and integration capabilities, they may fall short in a few ways. For example, some CDPs may have limited connectivity options or may not be able to integrate with certain data sources—and there’s no telling how quickly the data will flow or be able to generate timely and accurate insights. 


Additionally, setting up direct data connections can be a complex and time-consuming process, often requiring technical expertise and resources. Finally, some companies may prefer to use third-party tools or intermediaries to collect and process their customer data—another product of CDPs unable to do the job on its own.

3. Ad hoc exploration

Ad hoc exploration is key for ad tech companies to stay agile and responsive to changing market conditions and optimize ads in real time. 


For example, let’s say you’re running a campaign for a sports gambling and entertainment site, again targeting users interested in basketball’s March Madness. A few weeks into a campaign, you notice that the click-through rates (CTR) are not as high as you’d hoped. By using ad hoc exploration to dive deeper into the data, you find that users who’ve previously clicked on ads related to winning teams and high-performing players are more likely to engage with your campaign. Armed with this information, you can adjust the campaign to target this specific audience segment and improve CTRs.


CDPs may fall short when it comes to ad hoc exploration due to limited flexibility, limited data sources, and inferior data analysis capabilities. Often, ad hoc exploration within CDPs requires so much data science or programming expertise that it becomes useless to the marketing teams who need it.

4. Privacy-by-design architecture

Now more than ever, consumers—and governments—demand a higher level of security and privacy from customer-driven enterprises. You simply can’t ignore privacy control and compliance when it comes to your customer's data. 


Unfortunately, most CDP architectures weren't built with privacy in mind. Connecting first-party data to a CDP increases the risk of a data breach due to the movement of data outside the customer environment into the CDP. This creates a problem, as initiatives like hyper-personalized ad campaigns and real-time targeting can easily clash with data security, data control, and some aspects of data integration. 


Adherence to the ever-changing data privacy regulations across different nations and regions should be a top consideration when selecting a CDP.

5. Cost & time to value

In a DX 2.0 era, your ad and marketing agility has to keep pace with the lightning speed of digital development. CDPs are an essential component in your Martech stack, but as is, they don't enable you to reach your full potential. 


The limitations of CDPs (i.e. lack of real-time data, complex setup, limited data accessibility, the need for help from technical teams, and the inability to do spontaneous exploration) all negatively affect your cost and time-to-insights. Not to mention that CDPs can be expensive, and costs can increase with each addition, functionality, or more integrations requested by brands. 


There's more. Read the eBook: CDPs: A Misalignment of Expectations


The simple solution to supercharge your CDP strategy

For ad tech brands to successfully ride the wave of DX 2.0, they need to leverage and integrate advanced digital technologies to improve business goals. But, CDPs have struggled to adapt to the evolving digital economic landscape and can't deliver on these needs.


That's where Scuba comes in.


Scuba's customer intelligence platform works in tandem with CDPs to bridge the gap between the past and today's DX 2.0 world. The power of CDPs and Scuba together empowers you to: 


  • Seamlessly maximize your customer data 
  • Deliver hyper-personalized content
  • Manage your campaigns efficiently 
  • Achieve a higher ROI and return on ad spend


Scuba's privacy-by-design customer intelligence solution supercharges your CDP in the following ways:

1. Get actionable 360° customer intelligence across all touchpoints & profiles  

Scuba’s customer intelligence platform unifies and activates customer data from your CDPs and across channels at scale, with integrated real-time analytics. It also takes identity resolution to the next level with real-time 360° connected customer insights, and drives higher ROI with first-party data, for unmatched speed and limitless scale.

2. Cross-media measurement: optimize ads, track & aggregate user activity

Scuba's real-time multi-touch attribution insights empower your team to plan & deliver hyper-targeted campaigns with confidence—and in real-time—to optimize your marketing goals. You can also apply artificial intelligence and machine learning to tailor your content and campaigns and maximize impact, ad spend, and customer experiences.

3. Unlock hyper-personalization

Scuba activates your CDP data to personalize customer engagement across every touchpoint. It gives you ultra-granular insight into customer behavior and product interactions so you can identify areas of success and opportunities to engage and delight your users.

4. Multi-touch attribution & measurement

Scuba can help businesses conquer the challenge of multi-touch attribution and measurement. This includes performing behavioral journey analysis with unified data sources at scale to better understand past, present, and future user behavior. 


Scuba's robust A/B testing and predictive analytics capabilities allow for refined experimentation to maximize user experiments across segments and cohorts.

5. Privacy-Driven Analytics & Compliance by Design

Scuba enhances CDP compliance and privacy measures with encryption upon ingestion and throughout the entire data lifecycle, ensuring that your data—and user data—stay protected and secured. It also protects international customer data with global compliance to avoid violations and breaches. 


Ready to supercharge and future-proof your CDP? Request a demo today or talk to a Scuba expert.

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