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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article">
 <front>
  <journal-meta>
   <journal-id journal-id-type="publisher-id">
    ib
   </journal-id>
   <journal-title-group>
    <journal-title>
     iBusiness
    </journal-title>
   </journal-title-group>
   <issn pub-type="epub">
    2150-4075
   </issn>
   <issn publication-format="print">
    2150-4083
   </issn>
   <publisher>
    <publisher-name>
     Scientific Research Publishing
    </publisher-name>
   </publisher>
  </journal-meta>
  <article-meta>
   <article-id pub-id-type="doi">
    10.4236/ib.2025.173011
   </article-id>
   <article-id pub-id-type="publisher-id">
    ib-145673
   </article-id>
   <article-categories>
    <subj-group subj-group-type="heading">
     <subject>
      Articles
     </subject>
    </subj-group>
    <subj-group subj-group-type="Discipline-v2">
     <subject>
      Business 
     </subject>
     <subject>
       Economics
     </subject>
    </subj-group>
   </article-categories>
   <title-group>
    Designing Effective Multichannel Advertising Strategies in Competitive Markets: A Case-Based Framework
   </title-group>
   <contrib-group>
    <contrib contrib-type="author" xlink:type="simple">
     <name name-style="western">
      <surname>
       Simin
      </surname>
      <given-names>
       Latifi
      </given-names>
     </name>
    </contrib>
   </contrib-group> 
   <aff id="affnull">
    <addr-line>
     aIranian Scientific Marketing Association, Tehran, Iran
    </addr-line> 
   </aff> 
   <pub-date pub-type="epub">
    <day>
     06
    </day> 
    <month>
     08
    </month>
    <year>
     2025
    </year>
   </pub-date> 
   <volume>
    17
   </volume> 
   <issue>
    03
   </issue>
   <fpage>
    185
   </fpage>
   <lpage>
    190
   </lpage>
   <history>
    <date date-type="received">
     <day>
      26,
     </day>
     <month>
      April
     </month>
     <year>
      2025
     </year>
    </date>
    <date date-type="published">
     <day>
      14,
     </day>
     <month>
      April
     </month>
     <year>
      2025
     </year> 
    </date> 
    <date date-type="accepted">
     <day>
      14,
     </day>
     <month>
      September
     </month>
     <year>
      2025
     </year> 
    </date>
   </history>
   <permissions>
    <copyright-statement>
     © Copyright 2014 by authors and Scientific Research Publishing Inc. 
    </copyright-statement>
    <copyright-year>
     2014
    </copyright-year>
    <license>
     <license-p>
      This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/
     </license-p>
    </license>
   </permissions>
   <abstract>
    In a marketplace where consumer attention is fragmented across platforms and devices, single-channel advertising strategies have become increasingly ineffective. This paper presents a case-based framework for designing and executing multichannel campaigns that deliver measurable performance gains in competitive environments. Drawing on a real campaign spanning eight channels—search, social, video, display, and connected TV—the study explores the tactical choices, creative strategies, and measurement practices that turned underperforming single-platform results into a high-ROI, cross-channel system. Core components include behavioral segmentation, platform-specific KPI tracking, integrated sales feedback loops, cross-channel retargeting, and multi-touch attribution (with enhanced conversions) to resolve last-click bias. Findings highlight how coordinated messaging, advanced tracking, and agile budget allocation increase conversion efficiency and lead quality. For marketing leaders, the implication is direct: multichannel orchestration is no longer an edge—it’s the operating model for sustainable growth. 
   </abstract>
   <kwd-group> 
    <kwd>
     Competitive Market Strategy 
    </kwd>
   </kwd-group>
  </article-meta>
 </front>
 <body>
  <sec id="s1">
   <title>1. Introduction</title>
   <p>The way customers discover, evaluate, and buy today is messy. In a good way if you know how to work with it. The neat “see ad → click ad → buy” path has been replaced by a jumble of micro-interactions: a branded search, an Instagram scroll, a YouTube review binge, an email nudge, maybe even a podcast mention during a commute. In this environment of competitive markets, betting your entire ROI on a single platform’s algorithm is a fragile strategy. This paper grew out of a real campaign in a saturated category. The brand’s single-platform plan had flatlined: weak conversions, rising acquisition costs, and attribution gaps. Shifting to a coordinated mix of multichannel advertising, spanning search, social, video, display, and audio with integrated digital attribution and thoughtful retargeting wasn’t just a tactic; it was a survival move that produced faster conversions, broader reach, and materially better ROAS. What follows is a practical, case-based framework marketing leaders and operators can adapt to orchestrate high-intent customer journeys across platforms.</p>
  </sec><sec id="s2">
   <title>
    <xref ref-type="bibr" rid="scirp.145673-"></xref>2. Market Context and Challenges</title>
   <p>Note: Today’s CPC inflation means agility isn’t optional, reallocate fast, because most categories are paying more per click than last year.</p>
   <p>Digital media has become Manhattan real estate, crowded and pricey. Benchmarks show cost-per-clicks rose across most industries year-over-year, with an average CPC increase of ~10% across Google Ads in 2024, and 86% of industries experiencing higher CPCs. That inflation makes “single-pond” fishing risky: you need coverage where decisions actually happen, not just where it’s cheapest to buy impressions.</p>
   <p>At the same time, consumers expect consistency as they hop between channels and devices. In Salesforce’s latest Connected Customer study, 79% of customers expect consistent interactions across departments, 70% expect every rep to have the same information, yet 55% say their experience still feels siloed, like talking to separate departments, not one company (<xref ref-type="bibr" rid="scirp.145673-5">
     Salesforce, 2023
    </xref>). That expectation gap punishes brands that manage channels in isolation.</p>
   <p>And while streaming/CTV is now mainstream in plans (84% of marketers include it), confidence and measurement still lag another reason you need integrated analytics and attribution rather than “last-click or bust.”</p>
   <fig id="fig1" position="float">
    <label>Figure 1</label>
    <caption>
     <title>
      <xref ref-type="bibr" rid="scirp.145673-"></xref>3. Case Overview and Methodological ApproachThe eight channels were selected based on media consumption studies in the category, balancing reach (CTV, YouTube), intent (Search), discovery (Pinterest, Facebook), and cost-effective scale (Display, native, radio). Channels such as TikTok were tested but excluded after showing low lead quality in preliminary runs (<xref ref-type="table" rid="table1">
       Table 1
      </xref>).<xref ref-type="bibr" rid="scirp.145673-"></xref>Table 1. Results summary.
      <table class="MsoTableGrid custom-table" border="0" cellspacing="0" cellpadding="0"> 
 
       <tr> 
  
        <td class="custom-bottom-td acenter" width="14.39%"><p style="text-align:center">Metric</p></td> 
  
        <td class="custom-bottom-td acenter" width="14.39%"><p style="text-align:center">Pre-Campaign</p></td> 
  
        <td class="custom-bottom-td acenter" width="14.39%"><p style="text-align:center">Post-Campaign</p></td> 
 
       </tr> 
 
       <tr> 
  
        <td class="custom-top-td acenter" width="14.39%"><p style="text-align:center">CPC </p></td> 
  
        <td class="custom-top-td acenter" width="14.39%"><p style="text-align:center">$2.10</p></td> 
  
        <td class="custom-top-td acenter" width="14.39%"><p style="text-align:center">$1.65</p></td> 
 
       </tr> 
 
       <tr> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">CVR </p></td> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">2.40%</p></td> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">4.10%</p></td> 
 
       </tr> 
 
       <tr> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">ROAS</p></td> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">1.8×</p></td> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">3.2×</p></td> 
 
       </tr> 
 
       <tr> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">Lead Quality Score</p></td> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">62/100</p></td> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">84/100</p></td> 
 
       </tr> 
 
       <tr> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">Time Frame</p></td> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">6 months</p></td> 
  
        <td class="acenter" width="14.39%"><p style="text-align:center">6 months</p></td> 
 
       </tr>

      </table>The brand began with an over-reliance on Google Search, high intent but rising click costs and stagnant awareness. We mapped real media habits and purchase touchpoints, then deployed eight channels: Google Search, Facebook, Pinterest, YouTube, Google Display Network, native, connected TV (CTV), and radio.Methodology combined weekly KPI reviews, qualitative sales feedback, CRM-based lead scoring, and behavioral analytics (UTMs, heatmaps/session replays). Crucially, we moved measurement beyond last-click using data-driven attribution and Enhanced Conversions for better identity matchback between CRM and onsite behavior both now recommended defaults in Google’s stack (<xref ref-type="bibr" rid="scirp.145673-1">
       Google, 2021
      </xref>; <xref ref-type="bibr" rid="scirp.145673-2">
       Google Ads Help, 2024-2025
      </xref>). The result wasn’t just better performance; it was an operating rhythm for running many channels as one system.<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/8601891-rId12.jpeg?20250917021330" /></p><xref ref-type="bibr" rid="scirp.145673-"></xref>4. Strategic Framework for Multichannel ExecutionAn effective multichannel plan gives each channel a clear job, stitches messages across touchpoints, and measures influence (assists), not just direct closures.We started with audience and business alignment, segmenting by behavior (comparison shoppers vs. impulse buyers), not just demographics. That guided channel roles and formats from day one. Segmentation details were derived from CRM and web analytics data, using variables such as frequency of site visits, product category depth, and recency of engagement. We applied rules-based clustering and validated these against closed-won conversion data in the CRM system.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/8601891-rId11.jpeg?20250917021330" />
   </fig>
   <p>On measurement, data-driven attribution (DDA) replaced rules-based models Google has since deprecated (first-click, linear, time-decay, position-based) (<xref ref-type="bibr" rid="scirp.145673-4">
     Play Media, 2024
    </xref>; <xref ref-type="bibr" rid="scirp.145673-3">
     Google Ads Help, 2025
    </xref>). Google’s Data-Driven Attribution was configured with a 30-day look-back window, while Enhanced Conversions used hashed first-party CRM emails to improve lead match accuracy. We paired DDA with Enhanced Conversions (including for leads) to improve match rates and bidding accuracy (<xref ref-type="bibr" rid="scirp.145673-2">
     Google Ads Help, 2024-2025
    </xref>). That combination surfaced true high-value paths (e.g., CTV → Search → Direct) that standard analytics would have missed and protected “assist” channels from short-sighted cuts.</p>
  </sec><sec id="s3">
   <title>
    <xref ref-type="bibr" rid="scirp.145673-"></xref>5. Why Multichannel Strategy Matters</title>
   <p>Two reasons: how people decide, and how memory works.</p>
   <p>First, omnichannel shoppers are simply worth more. A Harvard Business Review study of 46,000 shoppers found omnichannel customers spend ~4% more in-store and ~10% more online than single-channel shoppers, with spend rising as they use more channels (<xref ref-type="bibr" rid="scirp.145673-6">
     Sopadjieva et al., 2017
    </xref>). Google-commissioned research has likewise tied omnichannel engagement to higher lifetime value (IDC cited a ~30% LTV lift for omnichannel shoppers) (<xref ref-type="bibr" rid="scirp.145673-7">
     Think with Google/IDC, 2015
    </xref>).</p>
   <p>Second, repetition across contexts builds preference. The mere-exposure effect a foundational finding in psychology shows repeated exposure increases liking and choice propensity (<xref ref-type="bibr" rid="scirp.145673-8">
     Zajonc, 1968
    </xref>). In marketing terms, coordinated touchpoints (search, social, video, email) compound trust more than any one channel hammering away alone.</p>
  </sec><sec id="s4">
   <title>
    <xref ref-type="bibr" rid="scirp.145673-"></xref>6. Key Pillars of Multichannel Success</title>
   <p>Six pillars anchor execution: deep audience understanding; platform-specific content; cohesive messaging; cross-channel integration/retargeting; data-driven optimization; and automation/AI to scale personalization. When these lock together, your plan stops being a bag of tactics and starts behaving like a system.</p>
   <p>Multichannel marketing is now table stakes. The case here shows how orchestration—consistent promise, venue-native creative, cross-channel retargeting, and attribution that sees assists—turns fragmented attention into predictable revenue. The brands that win won’t just “be everywhere”; they’ll connect the moments that matter, prove it in the data, and adapt as fast as the customer moves.</p>
  </sec>
 </body><back>
  <ref-list>
   <title>References</title>
   <ref id="scirp.145673-ref1">
    <label>1</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Google (2021). The Future of Attribution Is Data-Driven (Data-Driven Attribution Becomes Default). &gt;https://blog.google/products/ads-commerce/data-driven-attribution-new-default
    </mixed-citation>
   </ref>
   <ref id="scirp.145673-ref2">
    <label>2</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Google Ads Help (2024-2025). About Enhanced Conversions and Enhanced Conversions for Leads.
    </mixed-citation>
   </ref>
   <ref id="scirp.145673-ref3">
    <label>3</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Google Ads Help (2025). About Attribution Models (Rules-Based Models Deprecated; DDA or Last-Click Remain). &gt;https://support.google.com/google-ads/answer/6259715
    </mixed-citation>
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     Play Media (2024). Studies Find Captions Can Improve Focus on Video Content (Notes Facebook Internal Finding ~12% Watch-Time Lift with Captions). &gt;https://www.3playmedia.com/blog/studies-find-captions-improve-engagement
    </mixed-citation>
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   <ref id="scirp.145673-ref5">
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     Salesforce (2023). State of the Connected Customer (6th ed.). &gt;https://ia800303.us.archive.org/12/items/state-of-the-connected-customer/state-of-the-connected-customer.pdf
    </mixed-citation>
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    <mixed-citation publication-type="other" xlink:type="simple">
     Sopadjieva, E., Dholakia, U. M.,&amp;Benjamin, B. (2017). A Study of 46,000 Shoppers Shows That Omnichannel Retailing Works. Harvard Business Review. &gt;https://hbr.org/2017/01/a-study-of-46000-shoppers-shows-that-omnichannel-retailing-works
    </mixed-citation>
   </ref>
   <ref id="scirp.145673-ref7">
    <label>7</label>
    <mixed-citation publication-type="other" xlink:type="simple">
     Think with Google/IDC (2015). Omni-Channel Shoppers: An Emerging Retail Reality (Omnichannel Shoppers ~30% Higher LTV). &gt;https://www.thinkwithgoogle.com/_qs/documents/3884/omni-channel-shoppers-an-emerging-retail-reality_5.pdf
    </mixed-citation>
   </ref>
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     Zajonc, R. B. (1968). Attitudinal Effects of Mere Exposure. Journal of Personality and Social Psychology, 9, 1-27. &gt;https://doi.org/10.1037/h0025848
    </mixed-citation>
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  </ref-list>
 </back>
</article>