In today’s data-driven world, companies are increasingly looking to third-party data providers (Data as a Service companies) for new and unique datasets to train their AI models, power analytics, or aid in product development. It’s therefore no surprise that the alternative data market is set to grow at a whopping 50%+ annual growth rate over the next decade. So as DaaS providers rush to fill this growing demand, how do they, as content providers, stand out from the crowd? The answer is personalization - both in content and delivery, and in this post we’ll explore why every DaaS business should add personalization to their strategic roadmap.
The best way to understand the power of personalization is to look at another content business that does it incredibly well: Netflix. The 20+ year success story of Netflix is undisputed, but interestingly one of their key drivers of success was not just to serve the biggest library of content, but rather serve the most personalized content for every viewer. Where in the beginning, members would watch only 2% of the movies that Netflix suggested, now members typically watch 80%+ of what Netflix suggests. This journey wasn’t easy with many internal experiments and ultimately even the realization that they might need to buy vs. build: in 2006, they launched the $1M Netflix Prize for any outside team that could improve their recommendation system by 10%.
Along the way, Netflix realized that content personalization was not enough, the form factor of content consumption mattered to users as well. The most notable example of this was their key switch from mail-based DVD rental to streaming in 2007. But since then, they’ve launched Netflix support across all mediums of consumption from browser-based viewing, to iOS/Android apps, gaming consoles, native TV applications, and now even offline watching. It wasn’t enough to just serve the right piece of content, how and where they served that content was just as important.
Gone are the days where a company could build a thriving business by reselling the same database to everyone. Sure, there are some quant hedge funds that would prefer to consume everything and then figure it out, but the typical data buyer, whether at an investment firm, tech company, or corporate, has specific needs.
Let’s say a marketing executive in the cosmetics division of Unilever wants to use third-party data to understand competitive pricing. She doesn’t want the whole universe of credit card transactions, instead she wants only the data for the relevant SKUs at P&G, L’oreal, and Reckitt (her competitors). Not only is it easier to analyze a smaller dataset, it is much easier to manage from an infrastructure standpoint! Giving customers only what they need improves stickiness and customer satisfaction.
In pretty much every industry, when it comes to sales, time kills deals. The same is true in the DaaS world, where data trials and evaluations are painfully long. We recently heard this candid example from a data buyer at a hedge fund that traded fast-casual restaurant stocks (paraphrased):
“I essentially just needed to know how many burritos Chipotle sold last quarter, so I asked the data provider if they had something that could help me figure that out. What I got was a list of 100 consumer retail stocks covered by their dataset. Not helpful. So I asked again about Chipotle, and this time I got a sample dataset with a ton of irrelevant data from other chains… I’m moving on.”
Personalization doesn’t just need to apply post-sales, personalized datasets can be used to more effectively showcase data during the sales process and help speed up the sales cycle.
When DaaS companies sell large standardized sets of data, in order to make the economics work, they only sell to larger buyers that both have the need for and can afford to work with big data volumes. The smaller potential customers that only want to pay for what they want, are unfortunately turned away.
We recently heard from the head of sales at a data provider that they estimated turning away millions of dollars in revenue last year because they couldn’t serve the smaller customers. That’s a shame - data personalization opens up many possibilities with consumption based pricing and lower price points to convert more pipeline.
Most data buyers have already standardized on a modern tech stack, that usually involves one of the three major cloud providers (AWS, GCP, and Azure) and a data warehouse of some kind (Snowflake, Redshift, BigQuery, Databricks, etc.). As such, whatever data they buy ultimately needs to find its way into their ecosystem before they can use it. If data providers deliver data in a one-size-fits-all manner (which scarily, is still FTP in many cases), the customers need to do extra work to integrate that data, increasing the friction pre and post sales.
“I’d definitely prioritize buying data from a vendor that could serve their data directly into Snowflake over one that could not.”
- Data Buyer at DoorDash
This friction can in itself be a blocker for deals, with modern buyers requiring data to be delivered in the format of their choosing. Data providers therefore need to be able to deliver data wherever their clients want, regardless of their own tech stack. This is easier said than done, but companies like Amplify can help!
Each major cloud provider has a data marketplace that they are investing heavily in, like AWS Data Exchange (300+ providers), Snowflake Data marketplace (500+ providers), or the Databricks marketplace (200+ providers). As a result, when data buyers need data, they turn to search their own ecosystem’s marketplace first. It is therefore in the DaaS provider’s best interest to expand their footprint and list on as many marketplaces as they can so as to capture customer demand. The catch: each marketplace requires providers to deliver data via the marketplace providers own cloud technologies and protocols.
Once again, this is easier said than done, but data providers that can personalize data fulfillment across multiple channels can leverage each channel’s data marketplace to amplify their demand-gen efforts.
Delivery is ultimately where the rubber meets the road and the customer is supposed to get the value that they are promised. Unfortunately with today’s one-size-fits-all approaches to data delivery, the onboarding process for new customers is a long, drawn-out affair with multiple emails, test connections, internal tickets, and credential swapping. This leads to frustration on both provider and customer sides.
Data providers that can personalize data delivery for each customer can greatly speed up the onboarding process and accelerate time to value for both sides. Here modern DaaS companies are even offering UI driven, self-serve capabilities for customers to consume data on their own terms!
In summary, personalizing both the data and the delivery mechanism for each customer is not just a value-added service; it is a necessity in today’s growing DaaS market. Through personalization DaaS providers can increase customer satisfaction, speed up sales cycles, grow their addressable market, outcompete the market, and increase their brand awareness.
While the opportunity is wide open, true data and delivery personalization is not easy to achieve as it requires changes at the data, platform, people, and process layers. Fortunately that is where companies like Amplify come in. If personalization is on your roadmap or you are curious in learning how we can help, please reach out!
