How Data-Driven Design Can Attract Investment
In our data-rich world, data-driven design (DDD) is rapidly becoming the standard approach to creating digital products. With its focus on analyzing user needs and continuous testing of design features, DDD dovetails with a wider data-driven approach to business decision-making. From this perspective, it becomes clear that data-driven design – when implemented correctly – can seamlessly link the development of digital products to business goals and insights. As such, DDD can be very attractive to investors interested in backing a successful product. For more on how data-driven design can appeal to investors and stakeholders, read on!
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The importance of data-driven design
In a nutshell, data-driven design can be defined as basing design decisions on information and data about user needs, behaviors, and attitudes. DDD brings an evidence-based approach to product design strategies and activities that keep the target user in mind at all stages.
A 2022 report from NewVantage Partners found that more than 90% of businesses see a return on investment in AI and data thanks to data-driven strategies linking product development to customer and user needs.
Using data in the design process
Data-driven design gathers data about and from users to inform the product design process. Data is utilized and applied at all stages of the design work, specifically including:
- Identifying patterns and trends – Data analysis can provide insights into the latest trends. Whether it’s user behavior and attitudes or market forces, knowing what appeals to your target users enables the creation of a more refined and engaging product design.
- The user journey – Data gathered through research and user interaction illuminates the user journey with your product (or potential product). Data can reveal any bottlenecks or sticking points in the user journey, allowing you to further iterate on the product.
- Prototyping – Inviting representative users to use and comment on early versions of the product and design (such as prototypes and even minimum viable products) tests the inevitable assumptions and hypotheses underpinning the design. As a result, designers can either proceed with greater confidence in the product’s suitability, or course-correct or pivot the design process to better align with what the market wants.
- Usability testing – The results of usability testing (interviews, observation, lab testing, hallway testing, etc.) can be used to optimize user experience design.
All of the above uses allow the design team to continuously refine the product for greater user appeal; and consequently, commercial potential.
Benefits of data-driven design for businesses
What’s clear is that one of the main benefits of data-driven design is the enhancement of the user experience, and therefore customer satisfaction. This comes down to DDD resulting in a product that is simply better than it would otherwise have been. Beyond design best practices and principles (which remain, of course, essential) data-driven design can give you a product calibrated to the exact needs and pain points of your target users.
Furthermore, while intuition and creativity remain a part of the design process (e.g. during ideation) data-driven design means that all design decisions can be justified in measurable terms by hard data; which is a distinct benefit when talking about product design progress with stakeholders and investors.
Similarly, the design team can be confident that when changes and refinements are made to the design, those changes will lead to a better product, more closely aligned with user needs; and any failures are corrected quickly and in a way that leads to improvement instead of compounding the problem.
Of course, what any potential investor or stakeholder wants to know about is the return on investment. Unsurprisingly, data-driven design practices can boost ROI in a number of ways:
- An optimized user experience based on observed user interactions and feedback.
- Personalization of UX to appeal to distinct user groups/needs.
- Improved conversion rates.
- A program of product maintenance and improvement with updates and upgrades in response to demonstrated user and market trends (or even in advance of such trends using AI and predictive analysis).
- A high-performing digital product in which the technical aspects (loading times, speed of execution, reporting, etc.) are optimized thanks to data gathered during the research stages.
- Better pricing strategies for commercial products based on what users are willing to pay.
All of these advantages derive from the systematic collection and analysis of data, leading to better-informed product design and development decisions and a potential competitive advantage in the market.
Data-driven design and decision makers
The key to using DDD to appeal to investors and other stakeholders is to ensure your data-driven design processes are aligned with the company’s overall business goals. The following is a simple process for doing just that:
- Identify the relevant business goals and KPIs – user satisfaction, increased revenue, expanding into new markets, or simply operational efficiency… the product design and data-gathering strategy can focus on any (or all) of these.
- Identify the necessary data to collect – the aspect of the business you expect the product to impact determines not only the nature of the product, but also the data that you gather; the focus of your research and user engagement activity.
- Current data capability – what is your current state of data usage in the business, and in product design? Aside from the data itself, do you have the necessary infrastructure: the storage, the management tools, the analytics, the skill sets?
- Data governance & security – if you are going to rely on data in your business and design activities, you must be able to rely on its integrity and security; and be compliant with the relevant data protection legislation (for example, in the EU, the GDPR).
- Embed data-driven design and decision-making processes in your organization – you have the tools, the methods, and the data… but do you have the culture? Are the necessary people sufficiently data-literate to fully exploit the possibilities? Do you have the right tools and practices in place to ensure data-driven cooperation in your design processes?
Investors need to see not only a commitment to data-driven practices but also an actual capability to follow through and implement data-driven design that will result in improved and optimized digital products.
However, the final aspect which you cannot neglect is to ensure that the investors and stakeholders themselves also understand how data can be leveraged in data-driven design. They may think of data as ‘numbers’ but in fact DDD depends on a broader range of gathered evidence than just statistics and figures, encompassing qualitative input such as target user views, opinions, and motivations.
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A data-driven design approach can be attractive to investors
The core principles of DDD include a user-centered process, all key design decisions backed up by gathered and analyzed data, and a continuous process of product iteration based on emerging data relating to users and the market. When this approach is aligned with a company’s business goals and KPIs, the outcome is a digital product that meets both the specific needs and requirements of users and those of the business. Successful digital products that also produce a measurable ROI for the company? That’s the kind of result that investors are drawn to.
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