So, this client was running campaigns in 60+ countries. Their data? All over the place.
Finance was in SAP. Marketing had their own tools. Campaign data was somewhere else. They also had a list of many AI ideas but no clue which ones to build first.
The ask was simple: 𝐡𝐞𝐥𝐩 𝐮𝐬 𝐟𝐢𝐠𝐮𝐫𝐞 𝐨𝐮𝐭 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐬𝐩𝐞𝐧𝐝 𝐞𝐟𝐟𝐢𝐜𝐚𝐜𝐲 𝐚𝐧𝐝 𝐰𝐡𝐢𝐜𝐡 𝐀𝐈 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐚𝐫𝐞 𝐰𝐨𝐫𝐭𝐡 𝐝𝐨𝐢𝐧𝐠 - 𝐚𝐥𝐨𝐧𝐠 𝐰𝐢𝐭𝐡 𝐚 𝐬𝐭𝐫𝐨𝐧𝐠 𝐝𝐞𝐬𝐢𝐫𝐞 𝐭𝐨 𝐫𝐞𝐝𝐮𝐜𝐞 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐜𝐨𝐬𝐭𝐬.
Here's what we actually did:
- Sat down with 20+ people across all of their functions to understand the business and 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞𝐲 𝐫𝐞𝐚𝐥𝐥𝐲 𝐧𝐞𝐞𝐝𝐞𝐝 - Applied our advisory framework to narrow 28 possible use-cases down to 𝟓 that made sense - Checked 𝐰𝐡𝐚𝐭 𝐝𝐚𝐭𝐚 𝐭𝐡𝐞𝐲 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐡𝐚𝐝 and what would were the gaps - Built them a 𝐥𝐢𝐠𝐡𝐭𝐰𝐞𝐢𝐠𝐡𝐭 𝐀𝐈 𝐂𝐨𝐄 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 for ongoing governance so this wouldn't be a one-time thing - Unified their data, enriched it with external affiliates data to tie back campaign performance to marketing costs and enabled 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬, 𝐚𝐥𝐞𝐫𝐭𝐬 𝐚𝐧𝐝 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬
𝐖𝐡𝐚𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐢𝐧 𝟗𝟎 𝐝𝐚𝐲𝐬:
- 𝟑% 𝐝𝐫𝐨𝐩 𝐢𝐧 𝐂𝐀𝐂. They could see which campaigns weren't working and move money around. - 𝟏𝟎-𝟏𝟓% 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐠𝐚𝐢𝐧𝐬. Teams stopped working with static BI dashboards and spreadsheets for faster insights.
The system now tells them where to spend, not just what already happened.