Canonical URL: https://elacelerador.es/en/acquisition/gioseppo-ai-max-brand-search-roas-order-value/ --- description: Product Hackers combines AI Max, dynamic destinations and value-based bidding in Gioseppo’s brand campaigns. The case reports higher conversion value and average order value with lower investment. title: Gioseppo raises brand-search ROAS by 23% with 13.36% less advertising investment image: https://elacelerador.es/images/social/v1/gioseppo-raises-brand-search-roas-23-13-36-less-advertising-investment-f0dd1c43f618deba12fcba1c61b09705.jpg --- 1. [Home](https://elacelerador.es/en/) 2. [Acquisition](https://elacelerador.es/en/acquisition/) # Gioseppo raises brand-search ROAS by 23% with 13.36% less advertising investment Product Hackers combines AI Max, dynamic destinations and value-based bidding in Gioseppo’s brand campaigns. The case reports higher conversion value and average order value… By [Marian Vasile](https://elacelerador.es/en/author/marian-vasile/) 18 October 2026 at 21:34 CEST 12 min read [ Add as preferred source ](https://www.google.com/preferences/source?q=elacelerador.es&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) Share [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Felacelerador.es%2Fen%2Facquisition%2Fgioseppo-ai-max-brand-search-roas-order-value%2F%3Futm%5Fsource%3Dlinkedin%26utm%5Fmedium%3Dsocial&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on LinkedIn") [ ](https://wa.me/?text=Gioseppo+raises+brand-search+ROAS+by+23%25+with+13.36%25+less+advertising+investment+https%3A%2F%2Felacelerador.es%2Fen%2Facquisition%2Fgioseppo-ai-max-brand-search-roas-order-value%2F%3Futm%5Fsource%3Dwhatsapp%26utm%5Fmedium%3Dsocial&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on WhatsApp") [ ](https://t.me/share/url?url=https%3A%2F%2Felacelerador.es%2Fen%2Facquisition%2Fgioseppo-ai-max-brand-search-roas-order-value%2F%3Futm%5Fsource%3Dtelegram%26utm%5Fmedium%3Dsocial&text=Gioseppo+raises+brand-search+ROAS+by+23%25+with+13.36%25+less+advertising+investment&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on Telegram") [ ](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Felacelerador.es%2Fen%2Facquisition%2Fgioseppo-ai-max-brand-search-roas-order-value%2F%3Futm%5Fsource%3Dfacebook%26utm%5Fmedium%3Dsocial&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on Facebook") ![The façade and display windows of the La Alicantina shoe shop in Zaragoza](https://elacelerador.es/images/variants/v1/gioseppo-raises-brand-search-roas-23-13-36-less-advertising-investment-facade-w1280.webp) The La Alicantina shoe shop in Zaragoza, December 19, 2018\. Archive photograph by SimónK, CC BY-SA 4.0\. Framing, size and format adapted. Photo: [SimónK · CC BY-SA 4.0](https://commons.wikimedia.org/wiki/File:Shoe%5Fshop%5FLa%5FAlicantina%5Fin%5FZaragoza.jpg?utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) [Gioseppo](https://elacelerador.es/en/company/gioseppo/) improved the return on its brand-search advertising by 23% while cutting investment by 13.36%, according to the case published by [Product Hackers](https://elacelerador.es/en/company/product-hackers/) on July 3\. The combination has a specific commercial meaning: the conversion value recorded by those campaigns increased by 6.75% with lower advertising spend. The work focused on a part of marketing that companies often consider mature, searches by consumers who already know the brand, and changed how [Google](https://elacelerador.es/en/company/google-company-43a3948c/) Ads connected each query with an advertisement, a destination and a bid. The footwear company worked with Product Hackers' Acquisition team and Google's Accounts team. They activated AI Max for Search and combined its search-term matching, text customisation and final URL expansion features with value-based bidding. Their objective was to find higher-value purchases within demand that manual configurations were already serving, but where the scope for further improvement had become limited. The case, written by Nicolás Jacob, also reports a 13% increase in average order value, a 15.51% improvement in click-through rate and reductions of 5.50% in cost per acquisition and 18.75% in cost per click. These are changes for the Search Brand project described by the agency. Together, they trace a complete intervention: which searches enter the campaign, what promise the user sees, which page they reach and how much the advertiser should pay for that opportunity. ## The opportunity inside an established brand-search account Brand search occupies a particular position in the funnel. Consumers who include Gioseppo in their query arrive with prior awareness that a discovery campaign still needs to build. Product Hackers describes this activity as an established engine performing two functions for the company: defending its presence in search and capturing demand with a high intention to purchase. The commercial challenge was to improve an account that was already generating conversions regularly. The agency's diagnosis focused on rigidity. Strict targeting, manually configured keyword matching and preset bidding limits had helped organise the activity, but reduced its ability to adapt to different queries. The previous strategy depended on decisions made in advance about what consumers would search for and which combination of advertisement and page would answer that search. As the account matured, extending that manual map produced diminishing returns. The team set two simultaneous objectives: reduce the cost of acquiring a purchase and raise the value of the orders it attracted. The second part changes the advertising system's task. Two conversions can count equally in a volume report while contributing different amounts to the business. When a campaign is directed towards value, an opportunity is assessed through both its likelihood of becoming a purchase and how much that purchase could represent. The intervention retained its focus on Search Brand. That scope matters when reading the outcome: the reported improvement concerns campaigns serving brand searches, rather than all Gioseppo's digital sales. The new phase of demand generation comes later, as the planned destination for the savings. The project's first decision was to extract more value from investment that was already close to the moment of purchase. ## A catalogue broader than a brand name Gioseppo's own store helps explain why the destination matters. The catalogue consulted in October separates women, men and children and displays products including ankle boots, loafers, ballet flats and barefoot trainers, as well as accessories such as bags. Different colours, materials and finishes appear within the same product class. The brand name brings that offer together, but does not by itself express which part interests the person searching. That commercial range gives the final URL work a practical purpose. A campaign can reach someone who recognises Gioseppo and still need a more precise response to continue the purchase. The intervention described by Product Hackers brings page selection into the same adaptation process used for the query and the advertisement. The current catalogue illustrates the range of possible destinations; it does not identify the particular products that generated the case's improvements. It also puts the higher-average-order objective in context. The bidding strategy was formulated around purchase value inside a store with different product families and propositions. The agency does not describe a uniform promotion or attribute the result to a particular collection. Its explanation focuses on recognising higher-value opportunities and adjusting participation in the auction, keeping consumers' intention connected with the offer they encounter after clicking. ## Three decisions that AI Max began to coordinate Product Hackers describes three features activated alongside AI Max. The first was final URL expansion. Instead of retaining a fixed assignment between an advertisement and its destination, the system could direct consumers to a page on Gioseppo's website that better answered their detected intention. The campaign stopped treating the landing page as a decision settled in advance for every variation of a search. This changes the continuity of the journey. The query expresses a need; the advertisement proposes an answer, and the page develops it. When the three elements are planned separately, a specific search can lead to a destination that is too broad. URL expansion introduces a selection capability into that final step. In Gioseppo's project, the agency explicitly connects it with page relevance and search intention, in contrast with the previous static mappings. The second feature was text customisation. The team allowed Google Ads to generate combinations of messages adapted to the motivations detected in a query. The commercial change went beyond producing more variations of a sentence. The text could accompany the dynamic selection of a destination, creating a closer connection between what consumers had typed, what the advertisement promised and the page that opened when they clicked. The third feature was more flexible search-term matching. The agency wanted to enter complex or long-tail queries that manual models were not capturing with sufficient agility. In search advertising, long-tail describes combinations that are more specific than a general term. The significance here is that expansion was applied to an advanced brand account: further opportunities existed within apparently well-covered demand, without having to begin with an entirely new market. These features operate on different parts of the journey. Matching determines which queries the advertisement can compete for; the text adapts the response, and URL expansion selects the destination. Product Hackers presents them as a combined intervention, rather than three projects with separately measured results. The commercial logic lies in their coordination: widening search coverage while giving the message and the page greater capacity to answer an individual consumer's intention. ## More automation inside Search, with brand controls Google's current documentation puts the chosen technology in context. AI Max is an optimisation layer activated inside Search campaigns, rather than a new campaign type or another name for Performance Max. It retains the search structure and brings matching and asset-adaptation features together. That distinction explains Gioseppo's intervention: the team was changing how its brand-search campaigns worked, rather than automatically moving all its budget into a multichannel product. Google explains that matching learns from existing keywords, creative assets and URLs, combining broad match with technology that can find searches without depending on a previously added keyword. The website and advertisements form part of the information the system uses to decide where to compete. The expansion into complex queries described by Product Hackers rests on this change: the manual catalogue of terms ceases to be the only entry point into a relevant auction. Text and destination also have a technical dependency. Final URL expansion requires text customisation to be enabled so that the message can accompany the content of the chosen page. According to Google, the system can replace the final URL and generate a headline, a description and further assets that fit it. If a more relevant destination is selected, pinned assets in a responsive search advertisement may not be used. Adaptation therefore affects the combination consumers see, rather than only the link behind the advertisement. That flexibility exists alongside specific controls. The platform offers brand inclusions at campaign and ad-group level, campaign-level brand exclusions, and rules for including or excluding URLs. These are the tools Google provides to delimit an automation system with wider selection capabilities. In a Search Brand project, the distinction is central: giving the system more room to interpret queries does not make every available search its target. Gioseppo's case details the three activated features; these options explain the product's control framework. The documentation also describes reports combining search terms, headlines and URLs. They include an AI Max match type and a source column distinguishing broad-match expansion from keywordless matches. Asset reports contain spending and conversions alongside impressions. This information makes the query, message and destination journey visible in more detail than a total click count. It is Google's current description, consulted in October and used here to explain the technology in the case published in July. ## Bidding followed the value of the purchase The team added value-based bidding to that search and advertisement-adaptation system. According to its account, Google Ads used contextual signals to anticipate which users might make higher-value purchases. The campaign could raise bids for opportunities it identified as more valuable and restrain them where the potential value was lower. The account moved from treating outcomes uniformly towards distinguishing the expected commercial value of each opportunity. That direction connects directly with the average-order-value objective. If the system treats every purchase as equivalent, finding more conversions does not necessarily mean attracting better baskets. The strategy described sought to combine budget efficiency with higher-value orders. The 13% increase in AOV reported by Product Hackers fits that initial goal: the team presented more than a traffic improvement, documenting a change in the average value of the purchase it acquired. The distinction between value and volume also explains why the case brings together metrics from several layers. CTR observes the response to an advertisement; CPC records the average price of a click; CPA summarises cost per acquisition; AOV describes the average order amount, and ROAS relates conversion value to advertising investment. Each indicator illuminates part of the same activity. Reading them together explains the intervention more clearly than selecting only the most striking percentage. Value-based bidding turns conversion information into a signal for subsequent spending decisions. In this project, that decision was combined with less rigid search-term coverage and adaptable advertisements and destinations. The system had more opportunities to compete for and a commercial criterion for assigning a bid to them. The strategy described by the agency therefore links demand selection with the economic valuation of the purchase that could follow. ## Seven indicators explain the outcome The table published by Product Hackers places ROAS 23% above the reference used in its comparison. This is a relative improvement in advertising return, rather than a claim that every euro spent generated exactly 23 euros. The figure becomes clearer alongside its two components: total conversion value increased by 6.75%, while advertising cost fell by 13.36%. The relationship between those movements explains why return rose faster than conversion value. Taking a base of 100 solely to represent the comparison, value becomes 106.75 and investment 86.64\. Dividing those changes produces an approximate 23.2% improvement in the relationship between value and cost, consistent after rounding with the 23% reported by the agency. This explains the indicator; it is not an estimate of the euros billed by Gioseppo. The data behind the story ### Higher value and response in brand-search campaigns Relative changes reported by Product Hackers in the Gioseppo case published on July 3, 2026. Relative change % relative change Tap or hover for details. All values are also available in the data table. Expand notesCollapse notes **Source:** Product Hackers · Gioseppo AI Max case Each bar expresses the relative change in the named indicator, not percentage points. Explore the data table Scroll left or right to see all columns __Higher value and response in brand-search campaigns (% relative change)__ | Category | Relative change | | ------------------------- | ----------------------- | | ROAS | 23.00 % relative change | | Conversion value | 6.75 % relative change | | Average order value (AOV) | 13.00 % relative change | | Click-through rate (CTR) | 15.51 % relative change | The 6.75% increase in conversion value adds another dimension. In the comparison presented, the spending reduction was not accompanied by a contraction in that value. The campaign recorded more with less investment. At the same time, AOV rose 13%, giving the average order amount a greater role in the evaluation. The agency's account does not reduce the improvement to buying cheaper clicks: it connects spending efficiency with the value of the purchase. CTR increased by 15.51%. This expresses a relative change in the rate, rather than an increase of 15.51 percentage points. It describes an improvement in the relationship between advertisement clicks and impressions in the reported comparison. Within the search journey, it is the indicator closest to consumers' response to the message appearing on screen, before a visit becomes a purchase. On the cost side, CPC declined 18.75% and CPA 5.50%. The first concerns the average cost of a click and the second the cost of the acquisition being counted. Their different movements reflect different stages. A click starts the visit; an acquisition represents a later outcome. The table is useful because it shows improvements at both levels alongside higher conversion value and average order value. Total investment fell 13.36%. Product Hackers rounds that saving to 13% when explaining the project's next commercial step. Retaining the precise figure in the results distinguishes the reported change from its narrative summary. All the percentages belong to the performance comparison published by the agency; July 3 is the date of the case's publication, rather than the identification of a month of campaign results. The data behind the story ### Cost reductions in Search Brand Relative changes reported by Product Hackers in the Gioseppo case published on July 3, 2026. Relative change % relative change Tap or hover for details. All values are also available in the data table. Expand notesCollapse notes **Source:** Product Hackers · Gioseppo AI Max case Each bar expresses the relative change in the named indicator, not percentage points. Explore the data table Scroll left or right to see all columns __Cost reductions in Search Brand (% relative change)__ | Category | Relative change | | ---------------------- | ------------------------- | | Advertising investment | \-13.36 % relative change | | Cost per click | \-18.75 % relative change | | Cost per acquisition | \-5.50 % relative change | ## From savings in brand search to the next part of the funnel The agency plans to reinvest the efficiencies in upper-funnel campaigns. This changes the budget's function. Search Brand operates close to demand that already includes the brand in its query. Discovery investment seeks to enlarge the group of people who will know or consider the offer before reaching that search. The plan connects improvement in an established activity with funding for the next phase of acquisition. This is an advertising-capital allocation decision. The 13.36% saving is not presented simply as a spending reduction that closes the project. Product Hackers describes it as resources available for further commercial activity. The sequence is concrete: first adjust the capture of existing demand, then use the resulting efficiency to extend the brand's reach. In the published case, that reinvestment is expressed as a plan developed with Gioseppo. Keeping the phases distinct separates measured results from commercial ambition. The seven indicators describe the transformed brand-search campaigns. Expansion into new audiences represents the next planned use of the budget. The project is interesting because it connects those decisions: automation is presented as more than an isolated change inside the Google Ads interface, becoming an intervention that alters how much value a brand obtains from its money and where it can invest next. ## Who's who and how to read the figures Gioseppo is the footwear brand at the centre of the case. Product Hackers is the firm describing the work of its Acquisition team in collaboration with Google's Accounts team. Nicolás Jacob wrote the publication. AI Max for Search is the set of Google Ads features used to expand query matching and adapt text and destinations; value-based bidding directs spending towards the expected value of conversions. Search Brand refers here to brand-search campaigns. ROAS is the relationship between conversion value and advertising spending; AOV is average order value; CTR is click-through rate; CPC is average cost per click, and CPA is cost per acquisition. The seven figures are relative changes reported for the project, with different bases for each indicator. Read together, they describe the case's commercial proposition: more adaptable search, a higher average order and lower investment within the same acquisition activity. ## In this article ### Companies * [Gioseppo](https://elacelerador.es/en/company/gioseppo/) * [Google](https://elacelerador.es/en/company/google-company-43a3948c/) * [Product Hackers](https://elacelerador.es/en/company/product-hackers/) ### Sources and documents * [Product Hackers · caso Gioseppo AI Max](https://producthackers.com/es/blog/como-impulsamos-un-23-el-roas-de-gioseppo/?utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) · Product Hackers * [Google · funcionamiento de AI Max para Search](https://support.google.com/google-ads/answer/15910187?hl=en&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) · Google * [Google · expansión de URL final en Search](https://support.google.com/google-ads/answer/16230205?hl=en&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) · Google * [Gioseppo · tienda y catálogo actuales](https://gioseppo.com/en-ea?utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) · Gioseppo * [Gioseppo · identidad del vendedor y registro mercantil](https://gioseppo.com/en-ea/pages/legal-notice?utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) · Gioseppo Share [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Felacelerador.es%2Fen%2Facquisition%2Fgioseppo-ai-max-brand-search-roas-order-value%2F%3Futm%5Fsource%3Dlinkedin%26utm%5Fmedium%3Dsocial&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on LinkedIn") [ ](https://wa.me/?text=Gioseppo+raises+brand-search+ROAS+by+23%25+with+13.36%25+less+advertising+investment+https%3A%2F%2Felacelerador.es%2Fen%2Facquisition%2Fgioseppo-ai-max-brand-search-roas-order-value%2F%3Futm%5Fsource%3Dwhatsapp%26utm%5Fmedium%3Dsocial&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on WhatsApp") [ ](https://t.me/share/url?url=https%3A%2F%2Felacelerador.es%2Fen%2Facquisition%2Fgioseppo-ai-max-brand-search-roas-order-value%2F%3Futm%5Fsource%3Dtelegram%26utm%5Fmedium%3Dsocial&text=Gioseppo+raises+brand-search+ROAS+by+23%25+with+13.36%25+less+advertising+investment&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on Telegram") [ ](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Felacelerador.es%2Fen%2Facquisition%2Fgioseppo-ai-max-brand-search-roas-order-value%2F%3Futm%5Fsource%3Dfacebook%26utm%5Fmedium%3Dsocial&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on Facebook") ![Portrait of Marian Vasile](https://elacelerador.es/images/variants/v1/marian-vasile-autor-acelerador-retrato-w320.webp) [LinkedIn](https://www.linkedin.com/in/nairamelisav/?utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) About the author ## [Marian Vasile](https://elacelerador.es/en/author/marian-vasile/) Marian Vasile is a MarTech and growth professional. His work spans product marketing, SEO, paid advertising and deep online tracking, including behavioral analytics and attribution. ## Related articles [Acquisition](https://elacelerador.es/en/acquisition/) Analysis ### [Celeste and the eclipse: triple the reach at the same budget](https://elacelerador.es/en/acquisition/celeste-eclipse-reach-instagram-wine-searches/) [Marian Vasile](https://elacelerador.es/en/author/marian-vasile/) 20 October 2026 12 min [Acquisition](https://elacelerador.es/en/acquisition/) Analysis ### [Podcasts in Spain: iVoox links discovery, audience fit and creator support](https://elacelerador.es/en/acquisition/podcasts-spain-ivoox-discovery-affinity-creator-support/) [Marian Vasile](https://elacelerador.es/en/author/marian-vasile/) 20 October 2026 9 min [Acquisition](https://elacelerador.es/en/acquisition/) Analysis ### [Vocational training: EIGO times acquisition around enrolment](https://elacelerador.es/en/acquisition/vocational-training-eigo-sayonara-enrolment-acquisition/) [Marian Vasile](https://elacelerador.es/en/author/marian-vasile/) 20 October 2026 13 min ```json {"@context":"https://schema.org","@graph":[{"@type":["Organization","NewsMediaOrganization"],"@id":"https://elacelerador.es/#publisher","name":"El Acelerador","url":"https://elacelerador.es/","description":"Marketing and growth hacking with examples from Spanish companies.","email":"pr@elacelerador.es","publishingPrinciples":"https://elacelerador.es/en/editorial-policy/","correctionsPolicy":"https://elacelerador.es/en/editorial-policy/","verificationFactCheckingPolicy":"https://elacelerador.es/en/editorial-policy/","masthead":"https://elacelerador.es/en/about/","ownershipFundingInfo":"https://elacelerador.es/en/about/","logo":{"@type":"ImageObject","url":"https://elacelerador.es/icon-512.png?v=20261017-stacked"}},{"@type":"Person","@id":"https://elacelerador.es/author/marian-vasile/#person","name":"Marian Vasile","url":"https://elacelerador.es/en/author/marian-vasile/","description":"Marian Vasile is a MarTech and growth professional. 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