On Selling Products in the Data World
I’d like to explore the idea of who you can sell your data products to. But before we look into the archetypes of potential customers, let’s first define what I mean by data products here specifically.
This might be a very simplistic way of classifying but I think it’s a useful way to classify if you are thinking about the users/customers of the products you build and sell in the data industry.
So here’s one way of thinking about products in the data industry. There are basically two kinds of products you can sell. One group of products are what I like to call data processing products. The other group of products can be named as processed data products. It’s also simple to remember — Data Processing and Processed Data (DP and PD).
Now we move on to the question, what are the sets of products that come under the Data Processing category? Simple! All the products that ‘enable’ Data Processing. These maybe your data processing cloud tools (eg: Modal, Vast AI etc), data engineering and etl tools (eg: Airflow, dbt and many others), batch and streaming data processing tools (eg: Kafka, Redis etc), data science and machine learning tools (eg: Sagemaker, Azure ml etc) and so on. Basically whenever the use cases of the tool involve using the tool to build and well, process data, then these type of products come under the Data Processing category.
Then the next question is, what are the sets of products that come under the Processed Data category? All the products that produce processed data for the consumption of the user/customer. These could be products like Amplitude, Segment, ChatGPT, Gemini etc where on using the product you actually directly derive an answer. In other words, based on your inputs, you trigger some data processing activity that takes place behind the scenes and then outputs a final answer. Since these kind of products are providing the users the capability to consume information directly, these fall under the Processed Data category.
Let’s now come back to our original question. We wanted to understand who could be potential customer/user for each of these categories. For the sake of simplicity let’s assume that the difference between the user and the customer is not more than 1 or 2 levels.
We find that the customer archetype for the data processing products are typically Developers, Software Engineers, Software Architects, Engineering Managers and CTOs. Since these tools further enable the developers/engineers in their workflows, they’re often the users of these tools. Sometimes they’re also the customers but even if they cannot directly make the buying decision they have enough influence (specially if it’s a senior engineer) to get it done. If you are building a product that is in this space, then the positioning and marketing of your product should perhaps be more focused on the features and how using them will benefit your user groups. You might also want to focus on how it improves the overall development process by enabling either speed or reliability or scalability or maintainability or all of them.
For the processed data products, the customer archetype can be slightly different. These might include Product Owners, Product Managers, Business Analysts, Business Leads, Data Analysts and Data Scientists. While all of these roles might be users of processed data products, not all might have the ability to make a buy decision. The organisation might have empowered the Business Leads, Senior Product Leaders and Senior Data Analysts/Scientists to take the call on integrating new tools that provide processed data. While positioning and marketing for such an audience, it is important for you to focus less on the engineering capabilities of your product, but rather the business outcomes the product would enable. This is because the language that the stakeholders speak in their day-to-day life is different from engineering jargon and your marketing will have to be aligned to their language for them to be able to understand the value that your product would give them.
This distinction, is of course, not set in stone. Sometimes you’ll find one archetype of customers/users interested in the other. Sometimes they might just be users and sometimes they might even have the ability to make purchasing decisions. However, I think it’s useful to understand the potential use cases of the product you are building in the data space and who is actually going to benefit from them. Based on these, you might want to refine your strategy of who to reach out and at what level of data maturity.
That’s all for now! If you enjoyed reading this, you will also enjoy reading my posts on LinkedIn. Take a look at them here:
https://www.linkedin.com/in/imaad-mohamed-khan/
