Selling data is all about finding the match between use case and data product. Data vendors sell their most valuable data products where they own the customer relationship, because this is how they can get the deepest understanding of the customer’s use case.

Over the last few years, we’ve increasingly seen that the best data businesses center the customer relationship by developing their core channel as part of a multichannel approach. A core channel refers to the seller owning the means of distribution. A perfect example of a strong core channel from the consumer world is the Apple Store. For data companies, this means dealing directly with data buyers. Bloomberg, Experian, and Moody’s Analytics are examples of data companies with strong core channels.

Characteristics of a strong core channel

Data vendors with a strong core channel tend to have several things in common:

  • Control of the customer experience: branding, packaging, placement, promotion, and pricing
  • Governance and control of their data products, with rules dictating their distribution, terms and conditions, and use
  • Ability to deliver customized data products tailored to customer needs

These capabilities lead to several key benefits:

  • Ability to cross-sell and up-sell complementary data products and services
  • Discovery of customer use cases, leading to more refined data products
    • Over time, the product-market fit for your data products gets refined, allowing more valuable use cases to be solved
  • Superior margins due to a lack of competitors and a direct relationship with customers
  • Ability to serve customers in their preferred format, mechanism, and geographic location. The most advanced data commerce platforms are multi-cloud and allow distribution of data products independent of traditional constraints.

Making it better

Developing the core channel can take a great data business and make it even better. For example, a major data provider for financial services was already seeing double digit growth for one of its leading business units via the traditional approach. However, by developing their own core channel, which they launched in 2021, they’ve been able to drive millions of dollars of extra revenue to this business unit. One key factor has been the possibility of on-platform data evaluations — a cumbersome process with traditional direct sales — which are key to reducing friction in the sales cycle. Creating a self-service customer experience was only possible once this core channel had been developed.

Upgrading the traditional process

The major data companies have historically built strong businesses based primarily on two things: high quality, reliable data products; and a traditional go-to-market process. Yet this approach has some limitations which are holding them back. The emergence of a strong core offering helps them address the following limitations of the traditional go-to-market process.

Discoverability

Customers can’t buy products they don’t know about. Because the sale relies on a match between the use case and the data product, a lack of product discoverability is a problem. This can be exacerbated for data vendors with vast, diverse offerings. A salesperson may not be knowledgeable about certain products, thus missing an opportunity to find a match for a customer’s use case.

In contrast, a strong core channel is built for data product discovery. By creating an ecommerce-like experience, data vendors give their customers the chance to browse, compare, learn about, and view recommendations for data products. The vendor can also control which products and plans are presented to certain organizations or users. This tailored customer experience allows for up-sell and cross-sell in a completely self-service environment.

Product evaluation

If it’s wise to try on shoes before you buy them, then it’s unthinkable to buy a data product without understanding what it includes. This usually involves the customer accessing a trial or sample of the data in order to know if it will fit their use case. Getting to this trial stage can take weeks or even months, with the outcome still uncertain. Updating sample data sets creates additional work for data vendors, who can be reluctant to lose custody of valuable data products, even if they’re just for evaluation.

Self-service product evaluation is one of the key advantages of a strong core offering. Evaluation covers everything from the most basic things like descriptions and embedded data previews and visualizations, to sample data sets, to time-limited access to full data products.

For example, one key thing that sets Moody’s Analytics core offering, DataHub, apart from its competitors is the ability for customers to do on-platform data trials without data leaving Moody’s cloud environment. In practice, this is enabled by a secure sandbox environment. But from a customer’s perspective, they’re getting a chance to investigate the data product in a really detailed way, without Moody’s losing custody of the data itself.

Lack of digital footprint

While traditional sales actions will be tracked in the CRM, because data products are trialed, customized, and integrated “off-platform” — i.e. on the customer’s systems — the data vendor isn’t able to get more granular insight into the data product. Therefore, data vendors are missing out on valuable insight into how their data products are really providing value. This lack of insight affects both the sales team and the data product managers.

A key advantage of a core data commerce offering is the ability to collect and analyze customer behavioral data. Insights gathered might help reduce customer churn, making it possible to identify issues that are preventing sales. Data product managers may learn to create more useful metadata so that customers can easily see “what’s in the box”. Platform operators may see that seemingly minor functionality is extremely important for certain types of users, thus helping prioritize the development roadmap. The point is, when you’re able to see much greater detail into how your customers are actually buying and integrating your data products, it’s much easier to improve on this experience.

Efficiency

The best salespeople have a knack for focusing on the deals most likely to close. Yet with any manual sales process, it can be difficult to optimize the effort put into a particular deal. Qualifying leads and matching data products with their use cases can feel more like art than science. Even when there is a good match between product and use case, closing the deal and actually getting the data in a customer’s hands is another issue entirely.

Self-service data commerce experiences offer a massive opportunity to increase the efficiency of the sales process. Through product discovery and evaluation, some of the most time-intensive steps can be self-served by the customer with minimal hand holding. It also provides a more efficient way for sales teams to qualify leads and put their attention into those most likely to produce a good outcome.

Technological and geographic diversity

Buyers want their data where they need it to be. And despite the adoption of cloud databases and storage, data consumers still tend to stick with more old-fashioned methods. From our experience managing hundreds of commercial products and thousands of data pipelines, the majority of data consumers continue to consume and integrate data products via SFTP and desktop downloads.

There is also demand to consume data directly into the three major clouds, with a small but growing segment of data buyers that want direct integration into cloud databases like Snowflake and Databricks. This presents a significant operational challenge for both existing and emerging data businesses. Vendors need to continue facilitating the bulk of integrations via SFTP and downloads while simultaneously enabling new modes of integration across an increasingly diverse range of cloud-based destinations.

So while there is customer appetite for delivery via cloud services, it takes a lot of time and resources to move data workloads into the cloud. Therefore, in order to serve customers in dispersed geographies and with varied requirements, data vendors continually need to set up new mechanisms to facilitate secure transfer of data to customers. These considerations are far from trivial in terms of operational overheads, so data vendors must carefully consider the consequences of the delivery mechanism of serving customers.

To avoid these spiraling operational costs, complexity, and manual overhead, data vendors are turning to data commerce platforms. This move allows them to better serve data consumers across organizational boundaries while keeping costs down. Data consumers want flexibility, and they want to be able to adjust how they integrate data products over time as they develop their own tech stack. Data commerce platforms should enable self-service, fully-automated data integration across SFTP, desktop download, the major cloud providers, and cloud databases. Ultimately, this helps data vendors to maximize the addressable market and future-proofs their data business when new technologies and destinations inevitably emerge. 

But what about public data marketplaces?

For selling data, an emergent alternative to the core channel are public data marketplaces. These third party platforms provide an ancillary channel for selling data. Data products are stored on technology owned and operated by the marketplace, and can be browsed or searched for much like consumer marketplaces such as ebay, Etsy, or Amazon.

Advantages of public data marketplaces

Public data marketplaces offer several advantages:

  • Fast route to market for selling data products to a large user base
  • Low-cost route to market for serving low-value customer segments
  • Useful for lead generation for the core channel

Therefore, public data marketplaces can be a good fit for data products with a more broad appeal. And given the exposure to a potentially wide set of customers, they can offer good exposure to new customers that can then be nurtured into the core channel.

Disadvantages of public data marketplaces

Unfortunately, a few key factors hold back public data marketplaces from sufficiently transforming the fortunes of data companies:

  • No control over the customer relationship, experience, or brand; the marketplace operator, not the data vendor, owns the relationship.
  • Lack of collaboration or product customization to create higher value data products
  • Loss of margin due to competitive pressure and revenue sharing with marketplace operator
  • A limited subset of customers will use this marketplace. This inherently limits the total addressable market (TAM), an effect which cannot be fully overcome by simultaneously servicing multiple marketplaces. Doing so also requires the mass movement or copying of data onto third party systems, with the knock-on effect of figuring out how to update these data products in a scalable way.

Putting the core channel at the center of a multichannel approach

Central to any multichannel data commerce strategy should be your direct, core channel. This is where you give your customers the ability to discover, evaluate, and integrate your data products in a self- service manner. Doing so gives you greater control over the customer relationship, resulting in better value for them, and higher margins for you. Again, data companies can make a lot of money doing what they’ve always been doing. But without a sufficient focus on the core channel, there is a risk of diluting value and not delivering on potential.

If you’d like to learn more about how market leading data companies are building this core offering, get in touch.