Vinterior is the UK’s largest online marketplace for pre-loved antique & vintage furniture, lighting, art and home décor. With over 400,000 products from 2,000 sellers, every piece has its own story.

Out with the new, in with the old.

Increase in AOV
Increase in Revenue
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Vinterior Boosts Revenue and Average Order Value with Data-Driven Paid Search & social Strategy

Vinterior is a UK based online marketplace specialising in pre-owned antique and vintage furniture, lighting, home décor, art and more. Vinterior enlisted performance marketing agency outbloom to scale orders and revenue via paid search, whilst focussing on average order value to drive efficient growth across key categories.

The Challenge

At any given time, Vinterior has over 400,000 live products on site and there’s only ever one of every item, once it’s sold, it’s gone. In addition, the purchase path can vary from days to years as consumers often speak with individual sellers whilst taking time to find the perfect piece for their home.

Objectives and aims:

Vinterior set the following business objectives across their three top focus categories (storage, tables, and, lighting):

  • Increase average order value +10% within H1 2024
  • Increase revenue +20% within H1 2024   


The strategy was based around data segmentation, analysing product performance using identifiers such as; style, material, era, designer, etc, then using this to form a base to map the campaign structures that provided control whilst allowing Vinterior to grow sales and revenue whilst targeting relevant high AOV products.

The Change

Vinterior wanted to increase the average order value for new customers, using this as the foundation for driving efficient and scalable growth through paid search. Historically, Vinterior invested heavily in paid search using a ‘catch all’ approach, leaning into automation to decide which products to serve from their >400k inventory with little control.

Gaining control and setting up for scale started with in-depth data analysis to answer several questions:

  • How are users searching? outbloom built a script that analysed over 5 million unique paid search queries which were grouped into defined categories, such as: top level generics (e.g. ‘antique bookcases’), designers (e.g. ‘willy rizzo tables’), era (e.g. ‘mid-century table lamps’), etc.

  • How does this data marry up to overall performance? With a ‘catch all’ approach ran for many years, it was essential to cross-reference paid search performance with all channel data to identify gaps and share learnings.

  • Which themes within the target categories have the potential to drive higher average order values, and which do not? Once all performance data was mapped across defined themes, areas of opportunity were identified where average order value was above average and conversion rates consistent, also, areas where budget was being spent on low average order value items that had impacted efficiency (ROAS).

Segmenting the performance data not only allowed historical performance to be viewed through a granular lens, it formed the basis for an account rebuild that began with the three focus categories. 

This uncovered many inefficiencies, over a 12-month period, for example, a large percentage of budget was allocated to queries containing ‘vintage’ however when compared to queries within the antique and product/styles categories, ROAS was stronger owing to a higher average order value. The result of this segmentation defined two strategic changes that needed to happen:

  • Campaigns were to be split by category > best-selling product categories OR all products > eras/styles (dependent on the category).

  • Query funnelling was essential across all categories, the target three (storage, tables, and, lighting) in particular, to ensure Vinterior entered auctions that would drive efficient growth and scaled back on those that didn’t.

Once implemented, dynamic labels were added to the product feed that allowed for further segmentation using average order value, pushing below average items into a catch-all grouping with a lower budget to give visibility to the categories that drove the highest average order values and fell within the best-performing groupings.

One final strategic piece was required to enable us to accurately plan and forecast paid search budget and revenue. Our analysis showed varying purchase path lengths across categories. For example, we pay for a click one day, but revenue is recorded on the day of purchase which could be day/week/months apart, creating a misalignment of true ROAS. Having the category-level insights enabled us to forecast future revenues, enabling us to assign the correct budget per category.

The Growth

  • 16% increase in AOV
  • 35% increase in Revenue

Reporting Dates: 01/04/24 – 31/05/24
Compared To: 01/02/24 – 31/03/24
Data Source: GA4