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Great research doesn’t just sit on a shelf. It turns raw data into actionable insights, transforms those insights into tangible recommendations, and ultimately, bridges the gap between what you know about your users and what you do to enhance their experience.
In this post, we will provide a clear, step-by-step guide to help you make your research insights actionable, empowering you to create products and services that truly resonate with your target audience.
Before we delve into the practical steps, let’s take a look at why actionable insights are so crucial. They empower you to make informed decisions, solve user problems, and ultimately, create experiences that delight and foster loyalty.
Without actionable insights, your research risks becoming a collection of interesting but ultimately inconsequential observations.
Let's dive into a three-step approach to shape your research insights into game-changing recommendations using triangulation.
The first step is identifying the most important insights within your research data. This involves carefully sifting through the raw information and pinpointing patterns, trends, and themes that truly matter.
Think of it as panning for gold—you need to separate the valuable nuggets from the surrounding silt.
Here are some powerful techniques to aid your quest:
This collaborative approach involves grouping similar pieces of data together to reveal common themes and connections. It’s a fantastic way to visualise and make sense of large volumes of qualitative data.
This method involves identifying recurring ideas or concepts that surface from your data. It helps you uncover the underlying meanings and motivations behind user behaviour.
By analysing the emotional tone of your data, you can gain valuable insights into user attitudes and perceptions towards your product or service.
Remember, not all insights carry the same weight. Focus on those that are surprising, impactful, or directly relevant to your research goals.
These are the insights that hold the potential to truly transform your product.
Once you’ve identified your key insights, it’s time to prioritise them strategically. This is where you separate the ‘must-haves’ from the ‘nice-to-haves’, ensuring your efforts are focused on the insights that will deliver the greatest value.
Consider the following factors during prioritisation:
How significantly will addressing this insight improve the user experience or business outcomes? Will it solve a major pain point, open new opportunities, or increase customer satisfaction?
How easily can this insight be translated into actionable recommendations and implemented? Consider the resources, time, and technical capabilities required.
How well does this insight align with your overall business or product strategy and goals? Ensure your actions support your broader vision. Prioritisation acts as a filter, ensuring your resources are allocated to the insights that matter most.
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Let's chatAs you prioritise your insights, consider employing the power of data triangulation. This involves cross-referencing and validating your findings from multiple data sources or research methods.
Triangulation acts as a quality assurance check, ensuring your insights are robust and reliable. By comparing and contrasting data from different perspectives, you can:
Identify subtle patterns and connections that might be missed when looking at a single data set.
Mitigate the potential for individual biases or limitations associated with any single research method or data source.
Build greater confidence in your insights and recommendations, leading to more decisive action.
Now comes the pivotal step of translating your prioritised insights into clear, actionable recommendations. This is where you bridge the gap between data and decision-making, transforming knowledge into tangible action.
Here are some key principles to keep in mind:
Vague recommendations are rarely helpful. Clearly outline the specific actions that need to be taken. Provide concrete examples and details whenever possible.
Ensure your recommendations are feasible within the constraints of your resources and timeline. Avoid setting unrealistic expectations that can lead to frustration and disappointment.
Include metrics or key performance indicators (KPIs) to track the success of your recommendations. This allows you to evaluate their impact and make further refinements if necessary.
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Contact salesLet’s explore a few examples of how insights can be translated into actionable recommendations.
New users are dropping off during the onboarding flow, particularly at the account creation stage. Analytics data shows a significant decrease in user engagement after the initial sign-up screen, and qualitative feedback indicates that the process feels lengthy and cumbersome.
Streamline the onboarding process by reducing the number of required fields during account creation, offering social login options, and providing clear progress indicators. Consider progressive profiling, where additional information is collected gradually as the user engages with the product.
Track the onboarding completion rate, time spent on each step of the flow, and user engagement metrics (e.g. number of actions taken, features explored) after onboarding. An increase in completion rate, reduced time spent, and higher post-onboarding engagement would indicate success.
Users express a desire for more personalised product recommendations, expressing frustration with generic suggestions. This insight is reinforced by analysing both user surveys and website analytics data, showing high engagement with personalised content.
Implement a recommendation engine that suggests products across key touch points based on user browsing and purchase history, as well as demographic information and preferences.
Track click-through rate (CTR) and conversion rate of recommended products, alongside overall revenue generated from these suggestions. Gather user feedback on recommendation relevance to guide further refinement.
Customers who primarily use in-person payment channels (e.g. cash, in-store card payments) express frustration with long wait times and limited payment options during peak hours. This insight is corroborated by observational data showing queue build-up and customer complaints during busy periods.
Implement self-service payment kiosks or mobile point-of-sale (mPOS) systems to expedite transactions and alleviate congestion during peak times. Additionally, expand accepted payment methods to include contactless options like mobile wallets and tap-to-pay cards.
Track the average transaction time during peak hours, customer satisfaction scores related to payment experience, and the adoption rate of new payment options. A decrease in transaction time, an increase in satisfaction scores, and a high adoption rate of new payment methods would indicate the success of the recommendation.
Users are abandoning the booking flow at the payment confirmation stage. Heat maps and clickstream analysis reveal hesitation and drop-offs on the final page, and user feedback suggests concerns about payment security and lack of clarity on cancellation policies.
Enhance trust and transparency in the booking flow. Display security badges and clear information about payment protection. Provide a concise summary of the booking details and cancellation policy before the final confirmation. Consider offering multiple payment options and a clear “Book Now” call-to-action.
Track the booking conversion rate, cart abandonment rate at the payment stage, and customer support inquiries related to bookings. An increase in conversion rate, a decrease in abandonment, and fewer support inquiries would suggest improved flow effectiveness.
Creating user-centric products and experiences hinges on transforming research data into actionable recommendations.
By following these steps and leveraging data triangulation, you’ll empower your research to drive meaningful change, ensuring your product truly delights users and delivers real business results.
Remember, data is only as valuable as the actions it inspires.
