{"id":13990,"date":"2026-02-06T08:56:48","date_gmt":"2026-02-06T07:56:48","guid":{"rendered":"https:\/\/www.optimalways.com\/2026\/02\/the-right-combination-of-data-the-one-that-leads-to-a-decision\/"},"modified":"2026-02-06T16:37:46","modified_gmt":"2026-02-06T15:37:46","slug":"the-right-combination-of-data-the-one-that-leads-to-a-decision","status":"publish","type":"post","link":"https:\/\/www.optimalways.com\/en\/2026\/02\/the-right-combination-of-data-the-one-that-leads-to-a-decision\/","title":{"rendered":"The right combination of data: the one that leads to a decision"},"content":{"rendered":"

100 rows. 6 columns. 3 hours of analysis. <\/strong>And in the end, no decision<\/strong>. It’s a pattern we see on a regular basis.<\/p>\n

\ud83e\udde0 The findings<\/h3>\n

In digital analytics, we’re encouraged to cross-reference everything: more dimensions, more metrics, more depth.<\/p>\n

Result:<\/p>\n

    \n
  • dense relationships<\/li>\n
  • complex tables<\/li>\n
  • teams that spend more time exploring<\/strong> than deciding<\/strong><\/li>\n<\/ul>\n

    \ud83d\udc49 The problem isn ‘t the data<\/strong>. \ud83d\udc49 The problem is the lack of a clear question before opening it<\/strong>.<\/p>\n

    \ud83c\udfaf The filter that changes everything<\/h3>\n

    Before any crossbreeding, just one question:<\/p>\n

    “If we find a significant discrepancy, what decision do we make on Monday morning?”<\/strong><\/p>\n

      \n
    • No clear answer?<\/li>\n
    • No obvious action?<\/li>\n<\/ul>\n

      \ud83d\udc49 Then this crossing is probably not a priority<\/strong>.<\/p>\n

      \ud83e\uddea Concrete example<\/h3>\n

      \u274c Crossroads difficult to exploit Device \u00d7 Viewed pages<\/strong><\/p>\n

        \n
      • Cell phones generate fewer page views<\/li>\n
      • Without conversion rate or business impact, it’s impossible to close \u27a1\ufe0f Interesting but non-actionable observation<\/li>\n<\/ul>\n

        \u2705 Actionable intersection Product category \u00d7 Traffic source<\/strong><\/p>\n

          \n
        • Observation: office furnishings perform well via paid search \u27a1\ufe0f Clear basis for reallocating budgets by range<\/strong><\/li>\n<\/ul>\n

          \ud83d\udd01 Three often useful crosses<\/h3>\n

          1. Source of traffic \u00d7 Tunnel stage <\/strong>Allows you to identify where things get stuck, channel by channel<\/strong>. Example: paid generates traffic, but cart abandonment is high \u2192 possible mismatch between advertising promise and on-site experience.<\/p>\n

          2. User segment \u00d7 Cart abandonment <\/strong>Highlights differentiated behaviors. Example: new visitors give up more at checkout \u2192 reassurance at stake (delivery, returns, payment methods).<\/p>\n

          3. Product category \u00d7 Acquisition channel <\/strong>Each range has its own performing channels. \u27a1\ufe0f Helps to prioritize investments<\/strong> more accurately.<\/p>\n

          \ud83e\udded Three questions to frame any analysis<\/h3>\n

          1. What business problem are we trying to understand? <\/strong>Not “what does the data say”, but \ud83d\udc49 “Why are we losing customers at checkout?”<\/p>\n

          2. If a discrepancy is found, what action can be taken? <\/strong>Modify a campaign, adjust a page, alert a team. \ud83d\udc49 If the answer is vague, the cross-reference is no less so.<\/p>\n

          3. Do we have the necessary (and reliable) data? <\/strong>It’s best to check before<\/strong> investing time in analysis.<\/p>\n

          \ud83d\udca1 To remember<\/h3>\n

          The good analyst isn’t the one who cross-references the most data. It’s often the one who knows which ones to leave out<\/strong>.<\/p>\n

          \ud83d\udc49 And what crossroads have you already used to unlock a concrete decision?<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

          100 rows. 6 columns. 3 hours of analysis. And in the end, no decision. It’s a pattern we see on a regular basis. \ud83e\udde0 The findings In digital analytics, we’re encouraged to cross-reference everything: more dimensions, more metrics, more depth. Result: dense relationships complex tables teams that spend more time exploring than deciding \ud83d\udc49 The […]<\/p>\n","protected":false},"author":7,"featured_media":13991,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[42,41],"tags":[],"class_list":["post-13990","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-things","category-news"],"yoast_head":"\nThe right combination of data: the one that leads to a decision - Optimal Ways, l'agence de digital analytics et d'optimisation pour l'ecommerce (Paris, Lille)<\/title>\n<meta name=\"description\" content=\"100 rows. 6 columns. 3 hours of analysis. And in the end, no decision. 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