The strategic use of data in conjunction with analytics and AI can offer companies a range of benefits — from contributing to the company’s top-line, for instance by driving subscriber growth and up/cross-sell opportunities using personalised customer experiences through to improving operational efficiency with initiatives like AI-assisted customer support and recommendation engines to replace manual content curation. On top of that, the capture and creative use of proprietary data to deliver sticky, hard-to-replicate user experience can also help build competitive defences.
However, as discussed in a previous 3-part series, value creation from data has not always been the most straightforward and the challenges can often be traced back to one or more of four root causes. In particular, we highlighted the importance of being strategic and timing the investment in establishing the necessary data infrastructure, capturing the right kinds of data and building data science teams. In this short article, we discuss the recommended investment timing for early-stage SaaS or platform business and the “why” of each move to improve the chances of value creation. Hence, this article will appeal to investors, founders and their advisors.
Timing of investment explained using the J-curve
We use the start-up J-curve as the backdrop to kick off our discussion. The J-curve outlines the six phases that entrepreneurs go through as they try to build out sustainable business models. The curve starts with the create and release phases, where teams and money come together to turn ideas into products and release them into the market. During the morph phase, the products and business models are iterated upon based on customer feedback, metrics and so on. If product-market fit can be achieved within this phase, the companies move on to optimise their business models and grow while keeping cost down and profit high in the model and scale phases. These phases are shown in blue in the diagram below.
The best time for entrepreneurs and companies to invest heavily into data infrastructure, analytics and AI capabilities is when their products are on the cusp of product-market fit, i.e., the period towards the end of the morph phase and throughout the model phase. In the world of “doing things that don’t scale”, this is often the best time to repay the debts incurred in the name of speed so that your infrastructure, for instance, does not crumble as you stack more features and more users on top of it. While many seasoned entrepreneurs get this point, what some people often miss is the fact that this period is also perfect for putting money into (1) data infrastructure to capture, process and store data and make it accessible, and (2) data science teams to explore the growing volume of data and build out analytics and AI capabilities. This period which is ripe for investment into all things data is marked as (A) in the diagram above.
Some things simply don’t automate. For everything else, build systems; they’re a lot easier to scale quickly than humans.
It is important to call out that there are AI capabilities that can be developed or utilised in products that will immediately be beneficial during the release and morph phases. These vanilla AI capabilities require next to nothing in terms of data about your users and how they behave with your products. Often, the purpose of these off-the-shelf solutions is to introduce basic automation and reduce bad or unnecessary friction points. For instance, let us assume your product involves the users uploading content. Instead of requiring the users to provide information about the content such as language or tags, they can be predicted and offered to the users as suggestion. Not surprisingly, such capabilities are unlikely to offer you any competitive advantage in the long run. This is why you need investment during period (A). Once you have started capturing what your users search for and the content they interact with, for instance, you can build AI capabilities to understand their preferences or intent for predictive features and personalisation.
By now you must be wondering, when will your investment during period (A) starts bearing fruits? The short answer is after product-market fit has been found and it can happen throughout the scale phase, which is indicated as (B) in the diagram above. How soon you can reap the reward depends on the nature of the problem you are solving and the solution that your team have picked. As more and more parts of your product become enabled by AI, especially the ones that are built on proprietary data, your business will be able to scale on top of that. This period of scaling with analytics and AI capabilities is marked as (C) in the diagram above. This period essentially offers your business an alternative path to the harvest phase along the J-curve. Your business will still grow and you will probably do fine without analytics and AI capabilities. However, the often neglected cost associated with retaining existing users and attracting new customers without a product that can automatically and continuously improve with more of the right kinds of data will become a major drag on your profitability.
Other benefits from analytics and AI capabilities
Before we end, let us look at the benefits of analytics and AI capabilities through two other different angles. The first angle is through the lens of the typical product lifecycle that involves products going through the introduction, growth, maturity and decline phases. AI capabilities give you the opportunities to extend the lifecycle of your product by allowing you to improve existing features or introduce new ones in ways that otherwise would not be possible, as shown in the diagram above in red.
The strategic use of data with analytics and AI can also help put a downward pressure on the cost of acquiring new users and servicing existing users who tend to expect more from your products as time passes. This is especially true if your business operates a digital marketplace and has to deal with constantly growing inventory or pool of content and match-making the different sides.