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Should you build or buy a customer data platform?



Should you build or buy a customer data platform?

“Build versus buy” in the context of technology marketplaces is a long-running debate. At Real Story Group, we see this debate getting revisited for marketing tech stacks, particularly for customer data platforms (CDPs).

Is there a single right approach? I don’t think so, but the details matter here.  So let’s dig in.

Build vs. buy

Traditionally, two main approaches for obtaining enterprise functionality have been:

  1. Buying an off-the-shelf package and then customizing it for specific needs.
  2. Building a platform in-house, specifically for your requirements, sometimes via packaged piece parts.

Both approaches have valid rationales, and over the past two decades as an industry analyst, I’ve seen this choice emerge in pretty much all technology marketplaces. However, the boundaries between build and buy in CDPs can become fuzzier.

Part of the challenge is that packaged CDPs can vary substantially in scope. Some have great vertical depth, reaching back into the enterprise to perform upstream data processing or extending forward to the engagement tier to provide real-time interaction. Some packaged CDPs offer lateral services around orchestration, campaign management and even outbound messaging.

So before deciding on the right approach, it is important to answer what a CDP will do specifically for your enterprise.

What does a CDP do (for you)?

RSG’s enterprise service model for customer data.  Source: Real Story Group

The model shows different stages in a data life cycle, regardless of specific technology platform. Your customer data probably goes through all these stages:

  1. You need to obtain data from various online and offline data sources before you can do anything with it. Therefore, you need some mechanism to ingest data, clean it, perform some transformations and aggregation, and ensure quality.
  2. Once the data is collected or ingested from different sources, you need to tie it to user profiles. That includes activities such as identity resolution and profile unification. You also enrich your profiles with additional data while ensuring data governance and compliance.

In a larger organization, these two initial phases typically transpire within part of a broader enterprise data “fabric” or “mesh.” The typical enterprise already possesses data management tooling to handle these services – like data lakes, warehouses, ETL tools, quality and governance, etc. – and applies them to customer data. However, as we’ll see below, many packaged CDP tools also provide some of these services. In any case, enterprise IT and Data teams become important stakeholders in these first two stages.

  • The next stage is where you use all this cleaned-up, aggregated, unified profile data for your business objectives. For example, now that you have profiles or 360-degree views of your users or customers, you can segment them based on different attributes. You can slice and dice the profiles, create cohorts, group similar data, create audiences and so forth – and then, critically, activate that data through various channels.
  • This stage is the last mile where you engage with your customers via e-commerce, email, web, mobile, chat or other channels, using personalized content and product recommendations.

You see considerably higher marketing and customer experience teams’ involvement in these latter two stages.

In theory, all these services can be potentially addressed by a CDP. You will often find CDP vendors boasting they can perform all these stages equally well.

In practice, though, we see several variations of this model. See, for example, the different scopes for Company A, B and C in the diagram. Rarely do large, complex enterprises deploy a single platform for all these stages. There are at least two reasons for that:

  1. As you can see, the overall potential functionality is quite broad, and large enterprises already have existing initiatives outside of CDP for several of the stages (or functionalities within those stages) identified above. These functionalities often include data pipeline management, machine learning ops, and identity resolution, to name just a few.
  2. Despite what vendors claim, the truth is they are never equally good at all these stages. They can usually do only one or two of these stages well.

Therefore, where a CDP fits in your martech stack could differ from where it fits for another company. This then affects any build versus buy decision since the question initially becomes: build or buy precisely what? Even if you license an off-the-shelf CDP for some functionality within the model above, you will likely build extensions for missing capabilities.

So the first lesson: you will likely do some build and some buy, regardless of the overall strategy. The question then becomes: in what proportions?

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Assembling from piece parts

One approach potentially open to you is assembling components to build CDP capabilities instead of developing from scratch or buying a more wide-ranging, general-purpose CDP off-the-shelf.

This approach has some appeal because you may already possess some powerful data management capabilities as part of your broader customer data fabric.

You can also license specific products for these different functionalities. Several vendors offer components for such functionality. For example:

  • Data ingestion: There are specialized data ingestion vendors and modules from CDP vendors themselves. Vendors such as Stitch (acquired by Talend), Snowplow, Fivetran, Matillion and others provide modules for data ingestion, data pipeline management, transformations and other relevant functionality.
  • ETL and ELT: Many vendors target Extract-Transform-Load (ETL), Extract-Load-Transform (ELT) and Reverse-ETL/ELT for different types of transformations that you can do with your raw data. Examples of vendors in this category are Hevo Data, Hightouch, DBT and Census.
  • Data warehouses and Data Lakes: Several data warehouses and data lakes, including Snowflake, Google and others, include data management and processing functionality. Many packaged CDP architectures already assume that source data will come from this environment.
  • “Virtual” CDPs: Some vendors, such as Aqfer, Rudderstack, and some other players, offer some services for cobbling together a CDP with a decoupled data layer.
  • Identity Resolution: Several vendors target identity resolution. Many CDPs have now given up their own identity resolution efforts instead of partnering with vendors such as Neustar, Infutor,  LiveRamp, and others.
  • Engagement: The marketplace for engagement-oriented products remains quite vibrant. You can find many point solutions that target journey orchestration, campaign management, personalization, recommendations and other engagement use cases. Several packaged CDPs are also strong in this area.

This isn’t an exhaustive list of services, and you can find many other specialized vendors (e.g., those providing governance solutions). The key point is that it is possible to assemble these services to have a composable data ecosystem instead of doing everything using a single CDP.

Read next: Deep changes in the CDP space

What you might miss

By now, you’ve probably figured out that a couple of key CDP services are missing from that list above: business-friendly segmentation and activation. These are more challenging capabilities to purchase off the shelf, and at RSG, when we’ve seen home-grown CDPs, typically, the enterprise will build these business-user interfaces from scratch. When we hear enterprise developers arguing, “let’s just employ our data warehouse as the data layer instead of a CDP,” this is typically where they are headed.

I would caution you about this approach, though, because custom segmentation and activation tooling could prove fragile, and advanced UX design is a big part of what you pay for in a CDP (though to be sure: not all CDPs are equally good at this).

What you should do

Recognize that your CDP effort will undoubtedly include some measures of both build and buy. It’s just a question of proportion and location. Even if you license a packaged CDP – and there are good reasons to do so – you will need ample development work to stitch it into the rest of your customer data fabric, let alone your front-line engagement systems.

The jury remains out on a single best approach for this, but design patterns are emerging. Consult this briefing for more details.

In the meantime, as you look to build your customer data management muscles over the next year, keep your data scientists close but your developers even closer.

Customer data platforms: A snapshot

What they are. Customer data platforms, or CDPs, have become more prevalent than ever. These help marketers identify key data points from customers across a variety of platforms, which can help craft cohesive experiences. They are especially hot right now as marketers face increasing pressure to provide a unified experience to customers across many channels. 

Understanding the need. Cisco’s Annual Internet Report found that internet-connected devices are growing at a 10% compound annual growth rate (CAGR) from 2018 to 2023. COVID-19 has only sped up this marketing transformation. Technologies are evolving at a faster rate to connect with customers in an ever-changing world.

Each of these interactions has something important in common: they’re data-rich. Customers are telling brands a little bit about themselves at every touchpoint, which is invaluable data. What’s more, consumers expect companies to use this information to meet their needs.

Why we care. Meeting customer expectations, breaking up these segments, and bringing them together can be demanding for marketers. That’s where CDPs come in. By extracting data from all customer touchpoints — web analytics, CRMs, call analytics, email marketing platforms, and more — brands can overcome the challenges posed by multiple data platforms and use the information to improve customer experiences. 

Read next: What is a CDP and how does it give marketers the coveted ‘single view’ of their customers? 

Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.

About The Author

Apoorv Durga is Vice-President, Research & Advisory at analyst firm Real Story Group, where he covers CDPs, e-commerce, Web CMS, and technologies. He is a two-decade veteran in the marketing technology space.

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2022 YouTube and Video SERP Result Changes



2022 YouTube and Video SERP Result Changes

When you think of video results on Google in 2022 (and video optimization), you might think of something that looks like this (from a search for “flag football”):

In mid-October, we noticed a drop in this type of video result, and that drop became dramatic by late-October. Did Google remove these video results or was our system broken? As it turns out, neither — video results have split into at least three distinct types (depending on how you count).

(1) Video packs (simple & complex)

The example above is pretty simple, with the exception of “Key Moments” (which debuted in 2019), but even the familiar video packs can get pretty complex. Here’s one from a search for the artist Gustav Klimt:

All three of the videos here have Key Moments, including a pre-expanded section for the top video with thumbnails for each of the moments. Some specific SERPs also have minor variations, such as the “Trailers & clips” feature on this search for “Lion King”:

Video packs are still often 3-packs, but can range from two to four results. While only the header really changes here, it’s likely that Google is using a modified algorithm to surface these trailer results.

(2) Branded video carousels

Some videos are displayed in a carousel format, which seems to be common for branded results within YouTube. Here’s an example for the search “Dave and Busters”:

While the majority of these “brand” (loosely defined) carousels are from YouTube, there are exceptions, such as this carousel from Disney Video for “Lightning McQueen”:

Like all carousel-based results, you can scroll horizontally to view more videos. Google’s mobile-first design philosophy has driven more of this format over time, as the combination of vertical and horizontal scrolling is more natural on mobile devices.

(3) Single/thumbnail video results

Prior to breaking out video into separate features, Google typically displayed video results as standard results with a screenshot thumbnail. In the past month, Google seems to have revived this format. Here’s an example for the search “longboarding”:

If you hover over the thumbnail, you’ll see a preview, like this (edited for size):

In some cases, we see multiple video results on a single page, and each of them seems to be counted as one of the “10 blue links” that we normally associate with standard organic results from the web.

There’s also a variant on the single-video format that seem specific to YouTube:

This variant also shows a preview when you hover over it, but it launches a simplified YouTube viewing experience that appears to be new (and will likely evolve over time).

(4) Bonus: Mega-videos

This format has been around for a while and is relatively rare, but certain niches, including hit songs, may return a large-scale video format, such as this one for Taylor Swift’s “Anti-Hero”:

A similar format sometimes appears for “how to” queries (and similar questions), such as the one below for “how to roundhouse kick.” Note the text excerpt below the video that Google has extracted from the audio …

While neither of these formats are new, and they don’t seem to have changed significantly in the past month, they are important variants of Google video results.

(5) Bonus: TikTok results

Finally, Google has started to display a special format for TikTok videos, that typically includes a selection of five videos that preview when you hover over them. Here’s an example from one of my favorite TikTok personalities:

Typically, these are triggered by searches that include “TikTok” in the query. While it’s not a standard video format and isn’t available outside of TikTok, it’s interesting to note how Google is experimenting with rich video results from other platforms.

Does YouTube still dominate?

Back in 2020, we did a study across 10,000 competitive Google searches that showed YouTube holding a whopping 94% of page-one video results. Has this changed with the recent format shuffling? In a word: no. Across the main three video formats discussed in this post, YouTube still accounts for 94% of results in this data set, with Facebook coming in at a distant second place with 0.8%. This does not count specialized results, such as the TikTo results above.

What does this mean for you?

If you’re tracking video results, and have seen major changes, be aware that they may not have disappeared – they more likely morphed into another format. This is a good time to go look at your SERPs in the wild (on desktop and mobile) and see what kind of video formats your target queries are showing. Google is not only experimenting with new formats, but with new video-specific markup and capabilities (such as extracting text directly from the soundtracks of videos and podcasts). You can expect all of this to continue to evolve into 2023.

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