Rethinking the Value Proposition of DSPs in Today's Programmatic Landscape

Jude O’Connor

March 17, 2025

5

minutes read

The programmatic advertising ecosystem is experiencing a significant shift in how DSPs deliver value. As AdTech stocks experience volatility and market dynamics evolve, it's time to move beyond asking whether organizations need a DSP and instead focus on defining precisely what functions these platforms should serve in modern media strategies.

The END of one-size-fits-all programmatic solutions

Traditional DSP selection processes have historically followed a predictable pattern - especially considering the high cost of entry.  The typical approach has been: Which single partner or solution can I choose that will check most of my boxes while unfortunately still not being a perfect fit for all of my needs or use cases?  The decision is normally driven by a combination of “What are the platform fees and minimums?” versus an analysis of a features checklist and functionality comparison. 

What you end up with is a platform that is “good” across a large swath of potential use cases, offering versatility at the expense of specialized excellence. Or the opposite - the best in-class platform for a specific high-value need that may fall short of your needs across other, albeit less important priorities.

More often than not, today's sophisticated media buyers are increasingly focused on specific programmatic applications rather than general-purpose capabilities. This shift challenges the conventional wisdom about what makes a DSP valuable.

The NEXT ERA of programmatic solutions - centered around specialized solutions

The already fragmented programmatic buying landscape appears to be fragmenting further - this time into purpose-built solutions designed to address specific advertiser needs. Tomorrow's successful programmatic platforms will likely be defined less by comprehensive feature sets and more by their ability to solve specific advertiser challenges with precision. 

This evolution represents a natural progression as the digital advertising ecosystem matures:

  • Increased emphasis on specialized capabilities over general-purpose tools
  • Greater focus on measurable business outcomes rather than technical specifications
  • More transparent value exchange between platforms and advertisers
  • Elevated importance of integration capabilities over all-in-one solutions

Strategic implications

For media buyers, this evolution demands a more thoughtful approach to platform selection. Rather than seeking the most feature-complete solution, organizations should:

  1. Identify their primary programmatic objectives with precision
  2. Evaluate platforms based on specific use-case excellence
  3. Consider unbundling programmatic functions across specialized providers
  4. Prioritize platforms that deliver measurable value against specific KPIs

For technology providers, opportunity lies in moving beyond the undifferentiated DSP model toward purpose-built solutions that address clearly defined advertiser needs.

What comes next?

Tier 1 DSPs, or the core walled garden platforms, are incredibly entrenched at the holding company level, and the DSP market has been called “over-saturated” by many people, many times, for many years.  With that said, I feel like this may be a unique point in time for existing Tier 2 DSPs to build and innovate, or for some all together new players to make their way on to the scene that can solve for real problems that modern media buying teams face.

Rather than relying on a single, all-in-one DSP, the industry is moving toward an open garden ecosystem, one that prioritizes neutral access to inventory, AI-powered intelligence, and media strategies built around business outcomes, not platform constraints.

Other important developments could include:

  • Bespoke DPSs powered by AI only features - needed specifically to achieve a brand’s KPI’s without expensive features and tools that would go unused by them
  • The return of ‘official’ agency trading desks or brands getting even more aggressive in taking programmatic media buying in-house
  • Curation platforms redefining the role of DSPs - shifting DSPs from the role of "brain" of the operation to that of "plumber," enabling advertisers to move away from black-box decision-making while SSPs become more demand-focused, blurring the traditional lines between DSPs and SSPs.

There is never a dull moment in AdTech! We seem to be approaching a crossroads moment in time where the legacy definitions and roles of platforms—established nearly two decades ago—are being forced to evolve or potentially watch the industry they helped usher in move forward without them. Interesting times!

Let’s keep the conversation going, feel free to reach out: 

📩 Jude O’Connor, VP of Growth

📧 jude.oconnor@aidigital.com 

🔗 LinkedIn

Inefficiency

Description

Use case

Description of use case

Examples of companies using AI

Ease of implementation

Impact

Audience segmentation and insights

Identify and categorize audience groups based on behaviors, preferences, and characteristics

  • Michaels Stores: Implemented a genAI platform that increased email personalization from 20% to 95%, leading to a 41% boost in SMS click through rates and a 25% increase in engagement.
  • Estée Lauder: Partnered with Google Cloud to leverage genAI technologies for real-time consumer feedback monitoring and analyzing consumer sentiment across various channels.
High
Medium

Automated ad campaigns

Automate ad creation, placement, and optimization across various platforms

  • Showmax: Partnered with AI firms toautomate ad creation and testing, reducing production time by 70% while streamlining their quality assurance process.
  • Headway: Employed AI tools for ad creation and optimization, boosting performance by 40% and reaching 3.3 billion impressions while incorporating AI-generated content in 20% of their paid campaigns.
High
High

Brand sentiment tracking

Monitor and analyze public opinion about a brand across multiple channels in real time

  • L’Oréal: Analyzed millions of online comments, images, and videos to identify potential product innovation opportunities, effectively tracking brand sentiment and consumer trends.
  • Kellogg Company: Used AI to scan trending recipes featuring cereal, leveraging this data to launch targeted social campaigns that capitalize on positive brand sentiment and culinary trends.
High
Low

Campaign strategy optimization

Analyze data to predict optimal campaign approaches, channels, and timing

  • DoorDash: Leveraged Google’s AI-powered Demand Gen tool, which boosted its conversion rate by 15 times and improved cost per action efficiency by 50% compared with previous campaigns.
  • Kitsch: Employed Meta’s Advantage+ shopping campaigns with AI-powered tools to optimize campaigns, identifying and delivering top-performing ads to high-value consumers.
High
High

Content strategy

Generate content ideas, predict performance, and optimize distribution strategies

  • JPMorgan Chase: Collaborated with Persado to develop LLMs for marketing copy, achieving up to 450% higher clickthrough rates compared with human-written ads in pilot tests.
  • Hotel Chocolat: Employed genAI for concept development and production of its Velvetiser TV ad, which earned the highest-ever System1 score for adomestic appliance commercial.
High
High

Personalization strategy development

Create tailored messaging and experiences for consumers at scale

  • Stitch Fix: Uses genAI to help stylists interpret customer feedback and provide product recommendations, effectively personalizing shopping experiences.
  • Instacart: Uses genAI to offer customers personalized recipes, mealplanning ideas, and shopping lists based on individual preferences and habits.
Medium
Medium