How Social Listening Platforms Are Built: From Foundations to Evolution

The global social listening market is an 8 billion dollar market today, with Grand View Research projecting a 14.1% year on year growth!

With such a rapidly growing market, there are new social listening platforms appearing every day. With so many new social listening platforms appearing, brings some questions:

1. How are social listening products built?
2. What are their challenges in growth?
3. How do they stick out?

I can’t answer every question, but I’ll try to help. First off, my background is in building products that either use or power social listening platforms. Today, I lead the product management team at Datastreamer. Datastreamer is an integration platform whose data pipelines power many social listening platforms, and I’ve also led the building of products in competitive intelligence, anti-cyber bullying, and threat intelligence.

A big thanks to those who shared their insights: Justin Wyman , David Soderberg , and Martin Miliev .

TLDR (A quick summary of the article below)

• Social listening platforms are built in stages. They often begin with external APIs to get to market quickly (Stage 1), but as customer demands grow and operational challenges mount, they must evolve to maintain their competitive edge (Stage 2)
• Moving from Stage 1 to 2 has a big challenge. Going relying on external APIs (often from competitors in the social listening space), to "catching up” to the market. We like to call that the “Pipeline Plateau” because its a data pipeline problem at heart.
• There are easy ways to avoid innovation stall. Companies risk stalling innovation while struggling to replicate market capabilities. Breaking through this plateau requires a combination of strategic planning, infrastructure investments, and leveraging platforms that enable scalability and flexibility.

Appetizer done? Let's dive into the details.

Introduction: There are really two stages of Platform Development

Social listening platforms typically follow a two-stage development process. These stages are often based on a combination of speed to market, cost, and background. Most social listening products start from a consulting background, as an example. These companies will traditionally have relied on the social listening products that they (or their customers) were already using.?

As a result, most newly born social listening platforms will initially rely on using the APIs of those social listening platforms (like Talkwalker, Brandwatch, etc), to get started quickly. This provides a speed-to-market advantage, where they can leverage existing systems to aggregate data and generate insights. As these platforms grow, they often encounter the limitations of their reliance on external platforms and must evolve to meet new demands.

This evolution isn’t without challenges. Many companies face a critical hurdle—what I call the “Pipeline Plateau”. This is when their efforts to build and scale in-house systems stall their ability to innovate. Below, we will explore the two stages of platform development, the challenges encountered at each stage, and strategies for overcoming them.

Stage 1: Relying on A Competitors' Platform APIs

Using APIs from established platforms, such as Talkwalker or Brandwatch, to access aggregated data and basic enrichments. This reliance allows them to get a product off the ground quickly without the need for a custom-built data pipeline. However, this approach often limits differentiation. While early platforms may create unique insights by optimizing queries or focusing on niche markets, they remain constrained by the foundational capabilities of the external platforms they rely on. As a result, the creativity of their insights is capped by the data foundation underneath.

Many of the largest social listening platforms (there are 15-25 well known ones), have built their APIs to be extensions of their own platform. Some often have very strict requirements in terms of capabilities, access to data sources, and what can be interacted with. While some do offer the capability to utilize their APIs without any usage of the platform’s front end, most have some restrictions that require using the front-end. This leads to issues with scale and differentiation later on.

Expert advice on winning the early stages

A key point in winning early is by ensuring that you give high priority to providing transparency to your customers, and being creative! Here is an expert insight from Martin Miliev (VP Social Intelligence, Publicis Groupe):

"Newer social listening platforms sometimes over-promise, and do too much "yes, we can do it" during meetings which sinks them quickly. To see how the tool holds up, I use some old use cases that are a bit complicated and require creative approaches."

So, how do you differentiate in the early stage?

To stand out in the early stages, companies often rely on their in-house expertise to carve out niche markets. While many of the platforms use the same underlying data and APIs, they can apply unique methodologies to produce distinct insights. These differentiators can include:

• Specialized Querying: Optimizing boolean searches to target specific industries, languages, or geographies. Often using the experience and expertise of the consulting arm of the company to know exactly what to look for (and what to avoid).
• Focused Niches: Delivering insights tailored to underserved markets or customer segments.

However, even at this stage, gaps in data coverage and enrichment capabilities often drive companies to start supplementing their external platforms by integrating additional data providers or experimenting with custom enrichments. This is often the launching point into Stage 2.

Stage 2: Removing the Reliance on Competitors' APIs

Recognizing the tipping point

This might get a little technical, but the tech is where the tipping point appears. The shift to more advanced systems typically begins when API constraints—such as rate limits, data gaps, or lack of scalability—become too restrictive. Companies often reach this tipping point when:

• Customer demands require data or insights beyond what existing platforms can provide.
• Engineering challenges arise as teams spend more time managing integrations and “duct tape” fixes.
• Operational bottlenecks emerge, with ongoing maintenance of disparate systems consuming resources.

At this stage, companies usually start by supplementing their existing platforms, gradually transitioning toward building or adopting more and more.

However the demands of engineering expertise and technical requirements starts to rise exponentially.

Expert advice on product differentiation in stage 2

Excelling in Stage 2 relies on capabilities more than creativity. Different use cases of your social listening product will also be looking for different capabilities. Here is what David Soderberg shared:

"How newer social listening platforms stick out depends very much on the use case. As an example, trend detection and prediction requires a high volume of sources, and coverage within the sources. Brand monitoring, on the other hand, requires better identification and filtering.”

The Risk in Stage 2: Pipeline Plateau

I don’t want to scare you, but this is a very tricky spot in building a social listening product. Up until now, you’ve been able to rely on APIs of bigger players (or direct integrations), but now you are going past early adopters. A big risk here is the “Pipeline Plateau”.?

The Pipeline Plateau is a critical juncture where companies attempting to build their own platforms find themselves stalled. Instead of focusing on new features or innovative insights, they spend the majority of their resources catching up to market standards.

Companies that come from a consulting background tend to find themselves easing into Stage 2 and the potential “Pipeline Plateau” very subtly. However, companies that are directly integrating with data sources may find themselves accelerating through Stage 1 and hitting this plateau almost instantly.

Common symptoms are:

1. Replicating Baseline Features: Rebuilding foundational capabilities, such as sentiment analysis or translation, that competitors already offer out of the box. However if you are moving away from (or never used) their APIs, you suddenly find this as a gap in your capabilities.
2. Scaling Struggles: Integrating new data sources or handling higher data volumes becomes? increasingly complex, requiring specialized expertise. Integration of one source is easy, two is tricky, 3+ is a nightmare. That is also not counting the limitation of languages, enrichments, availability that comes with expansion.
3. Maintenance Overload: Constantly updating integrations to keep pace with API changes and schema updates drains engineering resources.

Which leads to:

1. Technical Debt Accumulation: Quick fixes and piecemeal solutions result in inefficiencies and instability.
2. Resource Strain: Engineering teams focus on maintenance instead of developing new features. With one of the companies I worked with, they had two engineering teams focused on data pipelines, and one engineering team focused on the product. Talk about a focus loss!
3. Customer Frustration: A lack of visible innovation leads to stagnation, putting platforms at risk of losing customers to more advanced competitors. Churn, combined with rising costs can scare many investors and customers.

But can be solved by:

1. Expanding Data Ingestion: Integrating 8–20 sources into a common schema, focusing on customer demands for broader data coverage. Sources generally include (up to) 5 social media networks, blogs, forums, news sources. Those 8 are generally the core expectation. For platforms that have used other social listening platforms’ APIs, they need to find a comparable data provider. For those integrating sources directly, less effort may be required.
2. Rolling out Basic Data Enrichments: Matching market capabilities like sentiment analysis, entity detection, and translation are a basis. With rapid expansion of AI capabilities, this “foundational” list is also rapidly expanding.
3. Complex Routing: With the above two items appears a massive increase in risk of cost explosion. To manage it, filtering and dynamically managing content in pipelines to improve efficiency? and control costs is needed. You don’t need to translate English to English!

The issue is the lack of a data pipeline problem at its core. Companies reaching this stage have a need for rapid, low-cost growth in features. They need to pass beyond the “catch up” stage, and get into a market leading position.

Data pipelines as a critical part of Stage 2

I have chatted with dozens of companies about to enter the Plateau (or in it). Of those that choose to power through by themselves, some often reach out 6 months later looking to rapidly bypass as fast as possible. Others have experienced dysentery on the Oregon Trail.

I mentioned about a bit about data pipelines as a way to bypass this plateau. Another approach is to adopt an integration and orchestration platform to handle the complexities of data integration, enrichment, and delivery. These platforms provide:

The option of data pipelines is to build or adopt an integration and orchestration platform to handle the complexities of data integration, enrichment, and delivery. These platforms provide:

• Prebuilt Infrastructure: Removing the need to build and maintain systems in-house.
• Scalability: Allowing companies to scale seamlessly as data sources and volumes grow.
• Modularity: Supporting the addition of new enrichments and insights without disrupting existing pipelines.

I know them well because Datastreamer (where I lead the product) is one of them. Back in the earlier anecdote I mentioned 2 engineering teams on data pipelines, and 1 engineering team on product? This is part of the inspiration behind Datastreamer’s platform.?

By offloading the data pipeline and integration part of the equation, you can avoid the “Pipeline Plateau”. It also allows you to speed up breaking past that stage and past the “building” part of your product.

The Goal: Build for the future!

Ultimately, the goal is to move beyond reliance on external platforms, creating systems that not only meet customer demands but also position the platform as a market leader in insights and innovation. I hope this brief and high-level insight into how social listening platforms were built is helpful.

If you'd like to dive into any of this more, my inbox is always open!