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2024³â¿¡ 21¾ï ´Þ·¯·Î ÃßÁ¤µÇ´Â ¸ÖƼ ÅÍÄ¡ ¾îÆ®¸®ºä¼Ç ¼¼°è ½ÃÀåÀº 2024³âºÎÅÍ 2030³â±îÁö CAGR 12.4%·Î ¼ºÀåÇÏ¿© 2030³â¿¡´Â 42¾ï ´Þ·¯¿¡ ´ÞÇÒ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. ÀÌ º¸°í¼­¿¡¼­ ºÐ¼®ÇÑ ºÎ¹® Áß ÇϳªÀÎ ¼Ò¸Å ¹× E-Commerce ÃÖÁ¾»ç¿ëÀÚ´Â CAGR 15.7%¸¦ ±â·ÏÇÏ¸ç ºÐ¼® ±â°£ Á¾·á±îÁö 12¾ï ´Þ·¯¿¡ ´ÞÇÒ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. BFSI ÃÖÁ¾»ç¿ëÀÚ ºÐ¾ßÀÇ ¼ºÀå·üÀº ºÐ¼® ±â°£ µ¿¾È CAGR 9.9%·Î ÃßÁ¤µË´Ï´Ù.

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¼¼°èÀÇ ¸ÖƼ ÅÍÄ¡ ¾îÆ®¸®ºä¼Ç ½ÃÀå - ÁÖ¿ä µ¿Çâ°ú ÃËÁø¿äÀÎ Á¤¸®

¸ÖƼ ÅÍÄ¡ ¾îÆ®¸®ºä¼ÇÀÌ µðÁöÅÐ ¸¶ÄÉÆÃ ºÐ¼®ÀÇ ÇÙ½ÉÀÌ µÇ´Â ÀÌÀ¯´Â ¹«¾ùÀϱî?

¼¼ºÐÈ­µÈ µðÁöÅРȯ°æ¿¡¼­ ¸ÖƼ ÅÍÄ¡ ¾îÆ®¸®ºä¼Ç(½ÃÀå »óȲ)ÀÇ µîÀåÀº ¸¶ÄÉÅ͵éÀÌ Ä·ÆäÀÎÀ» Æò°¡Çϰí ÃÖÀûÈ­ÇÏ´Â ¹æ½ÄÀ» ¹Ù²Ù°í ÀÖ½À´Ï´Ù. MTA´Â ´ÜÀÏ ÀÎÅÍ·¢¼Ç(ù Ŭ¸¯ ¶Ç´Â ¸¶Áö¸· Ŭ¸¯ÀÇ ±â¿©µµ)À» Æò°¡ÇÏ´Â ±âÁ¸ ¸ðµ¨°ú ´Þ¸®, °í°´ ¿©Á¤ÀÇ ¸ðµç ÅÍÄ¡Æ÷ÀÎÆ®¿¡ °ÉÃÄ °¡Ä¡¸¦ ¹èºÐÇϰí, ä³Î°ú µð¹ÙÀ̽º¿¡ °ÉÃÄ Ä·ÆäÀÎ ¼º°ú¿¡ ´ëÇÑ ¼¼ºÐÈ­µÈ ºä¸¦ Á¦°øÇÕ´Ï´Ù. ºä¸¦ Á¦°øÇÕ´Ï´Ù. ÀÌ ¹Ì¹¦ÇÑ ¾îÆ®¸®ºä¼Ç ±â¹ýÀ» ÅëÇØ ºê·£µå´Â µð½ºÇ÷¹ÀÌ ±¤°í¿Í À̸ÞÀÏ¿¡¼­ ¼Ò¼È ¹Ìµð¾î Ŭ¸¯°ú ÀÎÇ÷ç¾ð¼­ Âü¿©¿¡ À̸£±â±îÁö °¢ ÀÎÅÍ·¢¼ÇÀÇ ½ÇÁ¦ ¿µÇâÀ» ÆÄ¾ÇÇÒ ¼ö ÀÖÀ¸¸ç, À̸¦ ÅëÇØ º¸´Ù Á¤È®ÇÑ ¿¹»ê ¹èºÐ ¹× Ä·ÆäÀÎ Àü·«À» ¼ö¸³ÇÒ ¼ö ÀÖ½À´Ï´Ù. °¡´ÉÇÕ´Ï´Ù.

MTA¿¡ ´ëÇÑ ¼ö¿ä´Â ¸¶ÄÉÆÃ Ã¤³ÎÀÇ ±ÞÁõ°ú ¿È´Ïä³Î ¼ÒºñÀÚ ÇൿÀÇ ºÎ»ó°ú ÇÔ²² ±ÞÁõÇϰí ÀÖ½À´Ï´Ù. »ç¿ëÀÚ°¡ À¥, ¸ð¹ÙÀÏ, OTT, À½¼º, ÀÎ¾Û Ç÷§ÆûÀ» ³Ñ³ªµå´Â °¡¿îµ¥, ¸¶ÄÉÅÍ´Â ¼­·Î ´Ù¸¥ µ¥ÀÌÅÍ ½ºÆ®¸²À» ¿¬°áÇÏ°í °¢ ÅÍÄ¡Æ÷ÀÎÆ®ÀÇ º¹ÇÕÀûÀÎ ¿µÇâ·ÂÀ» Æò°¡ÇÒ ¼ö ÀÖ´Â °ß°íÇÑ ¸ðµ¨À» ÇÊ¿ä·Î ÇÕ´Ï´Ù. ÄíŰ ±â¹Ý ÃßÀû¿¡¼­ ¾ÆÀ̵§Æ¼Æ¼ ±×·¡ÇÁ¿Í ÆÛ½ºÆ® ÆÄƼ µ¥ÀÌÅÍ Àü·«À¸·ÎÀÇ ÀüȯÀº »ç¿ëÀÚ ¼öÁØÀÇ ÇൿÀ» ¸ÅÇÎÇϰí Àüȯ µ¿ÀÎÀ» ÇØµ¶ÇÒ ¼ö ÀÖ´Â MTA Åø¿¡ ´ëÇÑ °ü½ÉÀ» ´õ¿í ³ôÀ̰í ÀÖ½À´Ï´Ù. ÀÌ¿¡ µû¶ó ¼Ò¸Å, ±ÝÀ¶, Åë½Å, ¼ÒºñÀç µî ´Ù¾çÇÑ ºÐ¾ß¿¡¼­ ÷´Ü MTA Ç÷§ÆûÀÇ Ã¤ÅÃÀÌ È®´ëµÇ°í ÀÖ½À´Ï´Ù.

¸ÖƼ ÅÍÄ¡ ¾îÆ®¸®ºä¼Ç ¸ðµ¨À» µÞ¹ÞħÇÏ´Â ±â¼ú ¹× ±â¹ýÀº ¹«¾ùÀΰ¡?

MTA ½Ã½ºÅÛÀÇ ÇÙ½É ±â¼ú ¹éº»Àº ¸Ó½Å·¯´× ¾Ë°í¸®Áò°ú È®·ü·ÐÀû ¸ðµ¨¸µ¿¡ ÀÖ½À´Ï´Ù. Àαâ ÀÖ´Â ±â¹ýÀ¸·Î´Â ¸¶¸£ÄÚÇÁ üÀÎ ¸ðµ¨¸µ, »þÇø® °ª ºÐÇØ, ½Ã°£ °¨¼è ±â¿©µµ, ¾Ë°í¸®Áò ȸ±Í ±â¹Ý ¸ðµ¨ µîÀÌ ÀÖ½À´Ï´Ù. ÀÌ ±â¹ýµéÀº °ú°Å »ç¿ëÀÚ °æ·Î¸¦ ºÐ¼®ÇÏ¿© ¿µÇâ È®·üÀ» ±â¹ÝÀ¸·Î ÅÍÄ¡Æ÷ÀÎÆ®¿¡ µ¿Àû °¡ÁßÄ¡¸¦ ºÎ¿©ÇÏ´Â ¹æ½ÄÀÔ´Ï´Ù. ÀÌ·¯ÇÑ ¸ðµ¨À» Ȱ¿ëÇÏ¸é ¸¶ÄÉÆÃ ´ã´çÀڴ ä³Î °£ ½Ã³ÊÁö È¿°ú, Æ÷È­Á¡, ÃÖÀûÀÇ ¼ø¼­¸¦ °¨ÁöÇÒ ¼ö ÀÖ½À´Ï´Ù.

°í°´ µ¥ÀÌÅÍ Ç÷§Æû(CDP), µ¥ÀÌÅÍ ·¹ÀÌÅ©, ¸¶ÄÉÆÃ ÀÚµ¿È­ Á¦Ç°±º°úÀÇ ÅëÇÕÀ» ÅëÇØ MTA Ãâ·ÂÀÇ °íµµÈ­°¡ ÁøÇàµÇ°í ÀÖ½À´Ï´Ù. Ŭ¶ó¿ìµå ±â¹Ý MTA ÅøÀº ÇöÀç DSP, CRM Ç÷§Æû, ºÐ¼® ´ë½Ãº¸µå, ±¤°í ¼­¹ö¿Í ¿¬°áµÇ´Â API¸¦ ÅëÇØ ½Ç½Ã°£ ¾îÆ®¸®ºä¼Ç ¹× ¿¹Ãø ÀλçÀÌÆ®¸¦ Áö¿øÇϰí ÀÖ½À´Ï´Ù. GDPR, CCPA, ¾ÖÇÃÀÇ ATT(App Tracking Transparency) ÇÁ·¹ÀÓ¿öÅ©¿Í °°Àº ±ÔÁ¦ °­È­¿¡ ´ëÀÀÇϱâ À§ÇØ Â÷µî ÇÁ¶óÀ̹ö½Ã ¹× ¿¬°è ÇнÀ°ú °°Àº ÇÁ¶óÀ̹ö½Ã º¸È£ ¸ÞÄ¿´ÏÁòÀÌ MTA ½Ã½ºÅÛ¿¡ ³»ÀåµÇ¾î ÀÖ½À´Ï´Ù.

½Ã°¢È­ ·¹À̾î¿Í AI Áö¿ø ÀÎÅÍÆäÀ̽º´Â ¸¶ÄÉÅͰ¡ µ¥ÀÌÅÍ °úÇÐ Àü¹® Áö½Ä ¾øÀ̵µ º¹ÀâÇÑ ¾îÆ®¸®ºä¼Ç ÆÐÅÏÀ» ÇØ¼®ÇÒ ¼ö ÀÖµµ·Ï µ½°í ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ÇÁ·ÐÆ®¿£µå´Â ¸¶ÀÌÅ©·Î ¸ð¸àÆ®, ¿©Á¤ÀÇ º´¸ñÇö»ó, ä³Îº° ROI¸¦ °­Á¶Çϰí Àü¼úÀû Á¶Á¤À» °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù. A/B Å×½ºÆ® ÇÁ·¹ÀÓ¿öÅ©´Â Á¾Á¾ MTA ÆÄÀÌÇÁ¶óÀΰú ÅëÇÕµÇ¾î ¸®ÇÁÆ®¸¦ ÃøÁ¤Çϰí, ¶óÀ̺ê ȯ°æ¿¡¼­ ¾îÆ®¸®ºä¼Ç Ãâ·ÂÀ» °ËÁõÇϰí, ÀÇ»ç°áÁ¤ÀÇ Åë°èÀû ½Å·Ú¼ºÀ» º¸ÀåÇÕ´Ï´Ù.

MTA µµÀÔÀÌ È®´ëµÇ°í ÀÖ´Â ºÐ¾ß¿Í Áö¿ª, ±×¸®°í À庮Àº ¹«¾ùÀΰ¡?

E-Commerce¿Í ¼ÒºñÀÚ Á÷Á¢ ÆÇ¸Å(DTC) ºÎ¹®Àº MTA µµÀÔÀÇ ÃÖÀü¼±¿¡ ÀÖÀ¸¸ç, Á¤È®ÇÑ ROAS(±¤°í ºñ¿ë ´ëºñ È¿°ú(ROAS)ÀÇ ÃøÁ¤ÀÌ ¼ºÀå¿¡ ÇʼöÀûÀÔ´Ï´Ù. ÆÐ¼Ç, ºäƼ, °¡Àü, ¶óÀÌÇÁ½ºÅ¸ÀÏ µîÀÇ ºê·£µå´Â °Ë»ö ¿¬µ¿ ±¤°í, ¼Ò¼È ¹Ìµð¾î, µð½ºÇ÷¹ÀÌ ³×Æ®¿öÅ©, Á¦ÈÞ ÇÁ·Î±×·¥, ÀÎÇ÷ç¾ð¼­ ¸¶ÄÉÆÃ µîÀÇ µðÁöÅÐ Àü·«À» Á¶Á¤Çϱâ À§ÇØ MTA¸¦ Ȱ¿ëÇϰí ÀÖ½À´Ï´Ù. ±ÝÀ¶ ¼­ºñ½º ±â¾÷, ƯÈ÷ ½Å¿ëÄ«µå, º¸Çè, °Å·¡ ¾ÛÀ» Á¦°øÇÏ´Â ±â¾÷µéÀº ȹµæ, ¿Âº¸µù, ¸®ÅÙ¼ÇÀÇ °¢ ´Ü°è¿¡¼­ Àüȯ °æ·Î¸¦ Á¶Á¤Çϱâ À§ÇØ MTA¸¦ µµÀÔÇϰí ÀÖ½À´Ï´Ù.

Åë½Å ¹× ¹Ìµð¾î ±â¾÷µéÀº À¥, ¸ð¹ÙÀÏ, ½º¸¶Æ®TV, ¿þ¾î·¯ºí µî ¸ÖƼ µð¹ÙÀ̽º ¿©Á¤À» ÃÖÀûÈ­Çϱâ À§ÇØ MTA¸¦ äÅÃÇϰí ÀÖ½À´Ï´Ù. B2B ±â¼ú ¹× SaaS ȯ°æ¿¡¼­´Â ¿µ¾÷ ÁֱⰡ ±æ°í ÀÇ»ç°áÁ¤±ÇÀÚ°¡ ¿©·¯ ¸íÀ̱⠶§¹®¿¡ ¿þºñ³ª, À̸ÞÀÏ, ¹é¼­, ¿µ¾÷ Áö¿ø µî ¼ö°³¿ù¿¡ °ÉÄ£ »óÈ£ÀÛ¿ëÀ» ¸ÅÇÎÇÏ´Â ¾îÆ®¸®ºä¼Ç ¸ðµ¨ÀÌ ÇÊ¿äÇÕ´Ï´Ù.

ºÏ¹Ì´Â ¼º¼÷ÇÑ µðÁöÅÐ ±¤°í »ýŰè, ºÐ¼® ÀÎÇÁ¶ó, µ¥ÀÌÅÍ ÇÁ¶óÀ̹ö½Ã ÇÁ·¹ÀÓ¿öÅ©·Î ÀÎÇØ ±¤°í äÅÃÀ» ¼±µµÇϰí ÀÖ½À´Ï´Ù. À¯·´Àº GDPR Áؼö°¡ µ¿ÀÇ ±â¹Ý°ú ÇÁ¶óÀ̹ö½Ã¸¦ ÀǽÄÇÑ ¸ðµ¨¸µÀ¸·Î ¸¶ÄÉÅ͵éÀ» ¾Ð¹ÚÇϰí ÀÖ´Â °¡¿îµ¥, ±Ù¼ÒÇÑ Â÷ÀÌ·Î µÚµû¸£°í ÀÖ½À´Ï´Ù. Àεµ, Áß±¹, µ¿³²¾Æ½Ã¾Æ µî ¾Æ½Ã¾ÆÅÂÆò¾ç ½ÃÀåÀº ¸ð¹ÙÀÏ ÆÛ½ºÆ®(Mobile First) Çൿ°ú ÆÛÆ÷¸Õ½º ¸¶ÄÉÆÃÀ¸·Î ÀÎÇØ È®Àå °¡´ÉÇÑ ¾îÆ®¸®ºä¼Ç ¸ðµ¨¿¡ ´ëÇÑ °ü½ÉÀÌ ³ô¾ÆÁö¸é¼­ Ãß°ÝÇϰí ÀÖ½À´Ï´Ù.

µµÀÔÀÇ ¾î·Á¿òÀ¸·Î´Â µ¥ÀÌÅÍÀÇ ÆÄÆíÈ­, Á¶Á÷ÀÇ »çÀÏ·ÎÈ­, ÅëÇÕÀÇ ¾î·Á¿ò, ¾îÆ®¸®ºä¼Ç ¸ðµ¨ÀÇ º¹À⼺ µîÀ» ²ÅÀ» ¼ö ÀÖ½À´Ï´Ù. ¸¹Àº ±â¾÷¿¡¼­ MTA¸¦ È¿°úÀûÀ¸·Î µµÀÔÇϱâ À§ÇØ ÇÊ¿äÇÑ ºÎ¼­ °£ Çù¾÷°ú µ¥ÀÌÅÍ ÇÏÀÌÁö´Ï¾î¸µÀÌ ºÎÁ·ÇÕ´Ï´Ù. ¶ÇÇÑ, Ÿ»ç ÄíŰ Áö¿ø °¨¼Ò¿Í µð¹ÙÀ̽º ½Äº°ÀÚ ºñÃßõÀ¸·Î ÀÎÇØ »ç¿ëÀÚ ¼öÁØ ÃßÀûÀÌ º¹ÀâÇØÁö¸é¼­ ±â¾÷Àº Ŭ¸°·ë, ÇØ½ÃÈ­µÈ ½Äº°ÀÚ, ¾ÈÀüÇÑ ´ÙÀÚ°£ °è»ê¿¡ ÅõÀÚÇØ¾ß ÇÏ´Â »óȲ¿¡ Á÷¸éÇØ ÀÖ½À´Ï´Ù.

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¼¼°è ¸ÖƼ ÅÍÄ¡ ¾îÆ®¸®ºä¼Ç ½ÃÀåÀÇ ¼ºÀåÀº ¸¶ÄÉÆÃ Ã¤³ÎÀÇ Æø¹ßÀûÀÎ Áõ°¡, µðÁöÅÐ ±¤°íºñ ÃÖÀûÈ­¿¡ ´ëÇÑ ¾Ð¹Ú Áõ°¡, µ¥ÀÌÅÍ ±â¹Ý ÀÇ»ç°áÁ¤¿¡ ´ëÇÑ ¼ö¿ä Áõ°¡ µî ¿©·¯ °¡Áö ¿äÀο¡ ÀÇÇØ ÀÌ·ç¾îÁö°í ÀÖ½À´Ï´Ù. ¸¶ÄÉÆÃ ¿¹»êÀÌ Á¡Á¡ ´õ µðÁöÅзΠÀ̵¿ÇÔ¿¡ µû¶ó °æ¿µÁøÀº °¢ ä³Î°ú Ä·ÆäÀÎÀÌ ¸ÅÃâ ¼ºÀå¿¡ ½ÇÁ¦·Î ±â¿©ÇÏ´Â Á¤µµ¸¦ ÆÄ¾ÇÇÒ ¼ö ÀÖ´Â Á¤È®Çϰí Åõ¸íÇÑ ÃøÁ¤ ÇÁ·¹ÀÓ¿öÅ©¸¦ ¿ä±¸Çϰí ÀÖ½À´Ï´Ù.

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Global Multi-Touch Attribution Market to Reach US$4.2 Billion by 2030

The global market for Multi-Touch Attribution estimated at US$2.1 Billion in the year 2024, is expected to reach US$4.2 Billion by 2030, growing at a CAGR of 12.4% over the analysis period 2024-2030. Retail & E-Commerce End-User, one of the segments analyzed in the report, is expected to record a 15.7% CAGR and reach US$1.2 Billion by the end of the analysis period. Growth in the BFSI End-User segment is estimated at 9.9% CAGR over the analysis period.

The U.S. Market is Estimated at US$565.3 Million While China is Forecast to Grow at 17.2% CAGR

The Multi-Touch Attribution market in the U.S. is estimated at US$565.3 Million in the year 2024. China, the world's second largest economy, is forecast to reach a projected market size of US$904.0 Million by the year 2030 trailing a CAGR of 17.2% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 8.8% and 11.3% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 9.9% CAGR.

Global Multi-Touch Attribution Market - Key Trends & Drivers Summarized

Why Is Multi-Touch Attribution Becoming a Cornerstone in Digital Marketing Analytics?

In an increasingly fragmented digital landscape, the rise of multi-touch attribution (MTA) is transforming how marketers evaluate and optimize their campaigns. Unlike traditional models that credit a single interaction-first-click or last-click attribution-MTA distributes value across all touchpoints in a customer’s journey, offering a granular view of campaign performance across channels and devices. This nuanced attribution method allows brands to understand the actual impact of each interaction, from display ads and emails to social media clicks and influencer engagements, enabling more informed budget allocation and campaign strategy.

The demand for MTA has surged in parallel with the proliferation of marketing channels and the rise of omnichannel consumer behavior. With users interacting across web, mobile, OTT, voice, and in-app platforms, marketers need robust models that connect disparate data streams and evaluate the combined influence of each touchpoint. The shift from cookie-based tracking toward identity graphs and first-party data strategies is further amplifying interest in MTA tools that can map user-level behavior and decode conversion drivers. This has led to growing adoption of advanced MTA platforms in retail, finance, telecommunications, and consumer goods sectors.

What Technologies and Methodologies Are Powering Multi-Touch Attribution Models?

The core technological backbone of MTA systems lies in machine learning algorithms and probabilistic modeling, which estimate the contribution of each interaction to conversion outcomes. Popular methodologies include Markov Chain modeling, Shapley value decomposition, time decay attribution, and algorithmic regression-based models. These techniques analyze historical user paths and assign dynamic weights to touchpoints based on their influence probability. By leveraging these models, marketers can detect synergy effects, saturation points, and optimal sequencing across channels.

Integration with customer data platforms (CDPs), data lakes, and marketing automation suites is enhancing the sophistication of MTA outputs. Cloud-based MTA tools are now supporting real-time attribution and predictive insights through APIs that connect with DSPs, CRM platforms, analytics dashboards, and ad servers. Privacy-preserving mechanisms such as differential privacy and federated learning are being embedded into MTA systems to comply with tightening regulations like GDPR, CCPA, and Apple’s App Tracking Transparency (ATT) framework.

Visualization layers and AI-assisted interfaces are helping marketers interpret complex attribution patterns without requiring data science expertise. These front-ends highlight micro-moments, journey bottlenecks, and ROI by channel, enabling tactical adjustments. A/B testing frameworks are often integrated with MTA pipelines to measure lift and validate attribution outputs in live environments, ensuring statistical confidence in decision-making.

Which Sectors and Regions Are Scaling MTA Deployment, and What Are the Barriers?

The e-commerce and direct-to-consumer (DTC) segments are at the forefront of MTA deployment, where precise measurement of return on ad spend (ROAS) is vital to growth. Fashion, beauty, electronics, and lifestyle brands are using MTA to calibrate digital strategies across paid search, social media, display networks, affiliate programs, and influencer marketing. Financial services firms, especially those offering credit cards, insurance, or trading apps, are deploying MTA to align conversion paths across acquisition, onboarding, and retention phases.

Telecommunications and media firms are employing MTA to optimize multi-device journeys, including web, mobile, smart TVs, and wearables. In B2B technology and SaaS environments, longer sales cycles and multiple decision-makers necessitate attribution models that map interactions over months across webinars, emails, whitepapers, and sales outreach.

North America leads in adoption, driven by mature digital advertising ecosystems, analytics infrastructure, and data privacy frameworks. Europe follows closely, although GDPR compliance has pushed marketers toward consent-based and privacy-conscious modeling. Asia-Pacific markets like India, China, and Southeast Asia are catching up, with mobile-first behaviors and performance marketing fueling interest in scalable attribution models.

Barriers to adoption include data fragmentation, organizational silos, integration challenges, and attribution model complexity. Many enterprises lack the cross-functional collaboration or data hygiene required to implement MTA effectively. Additionally, declining third-party cookie support and device identifier deprecation are complicating user-level tracking, pushing firms to invest in clean rooms, hashed identifiers, and secure multi-party computation.

What Is Fueling Growth in the Global Multi-Touch Attribution Market?

The growth in the global multi-touch attribution market is driven by several factors, including the explosion of marketing channels, growing pressure to optimize digital ad spends, and the rising demand for data-driven decision-making. As marketing budgets increasingly shift toward digital, executives are demanding precise, transparent measurement frameworks that reveal the actual contribution of each channel and campaign to revenue growth.

The decline of deterministic tracking due to privacy regulations is encouraging the shift to probabilistic and modeled attribution, further elevating the relevance of MTA. Advances in AI, cloud computing, and identity resolution are enabling the development of scalable, privacy-compliant attribution solutions that can work across platforms and geographies. Marketing leaders are also recognizing the need for continuous attribution measurement, not just periodic reporting, to drive agile media planning.

Strategic partnerships between martech vendors, cloud platforms, and data providers are improving interoperability and accelerating MTA adoption across mid-sized and large enterprises. At the same time, education and awareness campaigns are equipping marketers with the knowledge to interpret attribution insights and translate them into performance gains. As the digital landscape continues to fragment and personalize, the role of MTA as a core capability in performance marketing stacks is becoming non-negotiable, driving robust market expansion over the next decade.

SCOPE OF STUDY:

The report analyzes the Multi-Touch Attribution market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:

End-User (Retail & E-Commerce End-User, BFSI End-User, IT & Telecom End-User, Consumer Electronics End-User, Travel & Tourism End-User, Other End-Users)

Geographic Regions/Countries:

World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; Spain; Russia; and Rest of Europe); Asia-Pacific (Australia; India; South Korea; and Rest of Asia-Pacific); Latin America (Argentina; Brazil; Mexico; and Rest of Latin America); Middle East (Iran; Israel; Saudi Arabia; United Arab Emirates; and Rest of Middle East); and Africa.

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TARIFF IMPACT FACTOR

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TABLE OF CONTENTS

I. METHODOLOGY

II. EXECUTIVE SUMMARY

III. MARKET ANALYSIS

IV. COMPETITION

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