Trading used heavy equipment with a click

How we helped Spectinga get from lean experiments to a £ 100k+ monthly gross marketplace value in 4 months




Heavy equipment



Founder's Challenge

In 2021 Michael and Bertie, founders of Spectinga, had a realisation: technological innovation had affected nearly every consumer experience except one — how second-hand equipment is sold today. Michael had previously worked in a dealership, on a farm, and then for a global machinery manufacturer, and Bertie had previously founded two startups and was leading product & sales teams in data-businesses. Opportunity called, so they packed up their experience and headed into their first joint venture.

They spent their first few months running a series of lean experiments to validate their most critical business hypotheses. The Concierge MVP approach gave them the confidence to take their business to the next stage. They were at a point where they needed to answer pivotal questions for their business: Who will be our first customers? What should be our business model? And, what kind of product should we build? So, when Michael and Bertie came to us for help, we jointly asked ourselves:

How might we take a service that hasn't changed in over 50 years, to the 21st century, with a seamless digital experience that’s fast, easy, and transparent for all?

Scope of Work

Step 1

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Business model hypothesis

Value proposition

Minimum Viable Brand


Solution validation

Step 2

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Product build


User-facing application

Admin application

Quality Assurance

KPI Analytics dashboards

Step 3

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Landing page

Explainer video

Social media templates

Email templates

Communication strategy

Discovery Process

We started our discovery phase by immersing ourselves into the problem. The team deep dived into the market of machinery, its challenges throughout the value chain, and studied them thoroughly. Through a series of collaborative workshops and our own research, the team downloaded a lot of information. Not only have we learned that John Deere 6150R is the most powerful model of the smaller-chassis 6R range, but we generated insights, which helped us ideate on solutions that focused on solving actual problems for Spectinga's customers.

After initial problem exploration we split into two streams:

Product was trying to figure out what the solution should look like. We were focused on defining key user flows, product features, the business logic and system architecture. Through wireframe iterations we prepared an end-to-end prototype, which was a visual representation of the final product and how customers will interact with it. The prototype was tested with customers and polished based on their feedback.

Business was figuring out a viable the business model. We held several workshops with the founders where we analysed the value chain and mapped out key hypothesis for 3 viable business models. This helped clarify who Spectinga's first customers will be, how to harness the most value going to market and what the key business KPIs were.

Key Insights

Selling to local dealers is fraught with uncompetitive prices. Also, the transaction process is inefficient with back-and-forth emails (and sometimes Whatsapp messages), detailing and aligning on the machine’s condition & photos.
Equipment auctions are lengthy (getting cash can take up to 6 weeks) and pre-sale inspections are a hassle.
Equipment trade is expensive with buying and selling fees coming up to a total of 20% at major auction houses.

Design Highlights

Our design process started with our Minimum Viable Brand Workshop, a 2-hour collaborative session, that helped us define a preliminary brand strategy and design a brand that will both resonate with trade buyers and embody the identity of Spectinga. The brand needed to be friendly, trustworthy, innovative, casual, reliable and simple.

We prepared 4 different brand concepts that we presented to the founders. Making the final call was difficult for them, because several concepts managed to capture the essence of the brand. The "World of machinery" was ultimately the winning one, with an approachable logo, professional typography, and a colour palette where the primary blue colour signifies trust and reliability, and the secondary orange balances it out with positivity and change.

> Learn how to design a Minimum Viable Brand

The UI of the user-facing application aimed to be clean and simple for the user, as the content itself (machinery listings) was the main focus of the marketplace. When we were designing the web application we only had two guiding principles:

  1. Make listing for sellers as easy as possible, with only the necessary steps and information needed to get buyers interested and ready to bid, and
  2. Build a habit of regular browsing and bidding on machinery for buyers.

Buyers and sellers on Spectinga reported the UI being friendly and analytics data confirmed that users visit the site daily to see new listings and browse the site for over 3 minutes on average.

Product Build

After the Discovery phase we proceeded with designing and building the product that was soon to be launched on the market. In this stage we moved a step further from an MVP, as it was necessary not only to incorporate a core set of "must have" features with the desired user experience, but also start creating quantifiable value for the Spectinga. Building a more foundational, marketable product meant that that we needed to balance getting the product on the market quickly with designing a scalable system. The fact that the product will need to allow for incorporating customer feedback and adding new features in a hassle-free way post-launch, was considered when designing the system architecture and choosing the tech stack.

The team that designed, built & launched the product consisted of:

  • Product Design Lead
  • Product Designer
  • Product Manager
  • Tech lead & front-end engineer
  • Back-end engineer
  • QA engineer
  • DevOps engineer
  • Digital Marketer

We delivered a fully-functioning product over the span of 10 weekly sprints, allowing for short feedback loops, quick iterations and guaranteed quality and usability.

Technology Stack


  • Using the neo4j graph database
  • Hosted on GCP (Google Cloud)

API application (back-end)

  • Written as a stateless API application in Python, using the django-rest-framework
  • Hosted on AWS EC2, configured with highly scalable and flexible ElasticBeanstalk

Client applications

  • Written as a single-page-web-application using the Vue.js framework and XState for state management via state machines
  • Hosted on AWS S3 Buckets, behind AWS CloudFront CDN

Marketing pages

  • Built and hosted with Webflow


  • for event based analytics
  • Plausible for view based/visitor analytics (cookie-less)
  • Hotjar for user recordings
  • Google analytics via Google Tag manager on marketing pages


  • Intercom: Support chat
  • Iubenda:  Cookie Policy, Privacy Policy and T&C management
  • SendGrid: Transactional emails and campaign emails
  • Twilio: SMS and Whatsapp message sending

> Read more about metrics for early-stage startups

The Outcome

During the Discovery & Product build phases the founders were able to focus on their concierge services between dealers and trades to understand the attractiveness of what we wanted to achieve and preliminary unit economics. Before launching the product they were able achieve multiple £ 100k of GMV and built a small initial network of dealerships that have become their early-adopted clients.

In 4 months we:

  • Designed a Minimum Viable Brand strong enough to make a statement and get attention of trade buyers
  • Designed a product to validate bidding platform as a solution for trade buyers
  • Automated several manual processed by building tools to support supply & demand side
  • Designed a user experience that encourages sellers to list their own machinery in an intuitive, user-friendly way
  • Shipped a marketable product that allowed for natural supply and demand to come through
  • Reduced manual matching of supply and demand from 100% to 50%
  • Started to build a dataset from auction data and transactions on the platform that will enable automated valuation in the future
  • Minimised uncertainty around the business model that helped the founders raise their next investment round
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