Project Overview

As Insites is primarily a product that offers SEO audits of small businesses, it’s important for a digital marketing agency to recognise how they can improve an SMB’s online presence with the help of these audits. With the emergence of AI, and its increasing influence on the tech world, we wanted to utilise its capabilities to tackle our users’ primary pain points regarding selling to prospect SMBs. This project covers the product process from market research, through to the development and launch of two AI features from our product suite.

Role
Product Manager, UX/UI Designer

The Problem

Sales teams need to increase their productivity and improve workflow, as they often spend excessive time understanding and summarising the detail of prospect audits. How might we aid users in achieving increased productivity on the platform?

Market Research

Heading into the user research phase of this project, we already had an idea of who we wanted to target - sales teams who utilise the SEO audits we produce to help them sell to prospects. We ultimately wanted to discover what core pain points this user segment had when selling to prospects, and how we could address these pains with an AI solution. With this in mind, we held stakeholder interviews with various sales agents who use Insites on a daily basis.

With the qualitative and quantitative data we’d collected, we synthesised this data into distinct categories representing the most prevalent topics of this discovery phase. From this, we discovered some significant pain points that were centre to a lot of our conversations, and that a number of our interviewees had in common.

Understanding the User

With the market research we’d conducted, we then crafted a user persona representing who we aimed to build our solution for. This helped us empathise with our users’ pains that hindered their current user experience, and largely influenced how we approached the ideation process.

At this stage, we had a clear understanding of who we were building for, and what primary pain points these people faced. From the qualitative data we’d collected from our stakeholder interviews, followed by our user persona, we felt that these were the main problems sales reps voiced to us:

In identifying these standout pain points, it helped narrow our focus in what we needed to tackle and how. It was clear to us that sales agents find difficulty in understanding our SEO audits, which ultimately has a knock-on effect on the length of time it takes them to fully digest the audit, and hinders the quality of conversations they hold between themselves and small business owners. All of these factors ultimately result in sales agents having a harder time selling to prospect SMBs, which can be detrimental to the overall success of their agencies.

Knowing the importance of this issue, we were sure that we wanted to improve sales agents’ ease of use, speed of understanding, and improved confidence in conversation with SMBs to help them make more sales.

Ideating the Solution

In our ideation phase, we mapped out a value proposition canvas, allowing us to brainstorm various ideas as to how we were to relieve user pains, and create valuable gains for them using AI. This canvas helped us ensure that our solutions were user-centric, and would have a genuine impact on real user problems.

From the ideas we’d brainstormed, we felt that a few in particular stood out as viable and valuable options. One of those ideas was to propose an AI-generated list of “audit recommendations” which would highlight and summarise the most standout problems with an SMB’s business in any given audit. This would tackle our issue of sales agents struggling to quickly identify the important talking points of their audits, and would help them to communicate these talking points with greater efficiency.

Another solution we leaned towards was to use AI to generate a cold outreach email to SMB prospects, summarising the key issues of their audit, and how the agency could help fix these issues with the solutions they offer. This would combat our sales reps’ pain points of both speed and understanding - the AI would understand and summarise their audit for them, and they could do it all in the click of a button.

Launch & Outcomes

In launching this beta, we made some key discoveries in analysing the usage data from the launch. This data helped guide our decision-making as to how we would improve these features moving forward.

In assessing this quantitative data, we wanted to use these statistics as benchmarks to improve upon and dig deeper into why the features performed how they did. For instance, we wanted to know why 20% more of our users were drawn to the AI recommendations rather than AI emails, and look into ways in which we could enhance the email feature to increase it’s usage. Not only that, but we wanted to hold talks with various existing clients who were frequently using the beta, and obtain some valuable qualitative feedback about their overall experience thus far.

Upon speaking to our customers using the beta, we discovered some key issues they were having with the features, despite having an overall positive experience using them. Sales reps found that they struggled to make reference back to the audit itself in conversation with SMB prospects, when talking through their top recommendations. To relieve this pain point, we made it possible for users to click through to the relevant audit test section directly from the recommendations list, as demonstrated above. This is just one of many user-driven iterations we have made thus far, as we continue to refine our suite of AI features.