When your marketing analytics reveal unexpected results, managing client expectations starts with transparency and effective communication. Begin by clearly explaining the findings and the potential reasons behind them, using data visualization tools to make the information more digestible. Conduct a root cause analysis using methods like KPI Decomposition, segmentation, or regression to pinpoint the source of the unexpected outcomes. Then, assess the impact on the client’s goals and present a revised strategy with actionable steps to address any concerns. This approach not only manages expectations but also positions you as a proactive and reliable partner

Be upfront. Don’t sugarcoat the results—clients appreciate honesty. Start by explaining the data clearly, focusing on what went differently than expected and why. Provide context—was it an industry shift, audience behavior, or something else? Next, show what you’re doing to adjust the strategy. Lay out actionable steps and a revised plan to address the gaps. Managing expectations is about transparency and reassurance. Make sure they know you’re on top of it, adjusting fast, and committed to delivering results. Keep the focus on moving forward.

The first rule is to be transparent. Explain the result to the client. Explain how those metrics affect your business and what the possible solutions may be. The optimal point of productivity is when technical marketing knowledge, analytics and business knowledge come together. At that point is where the best results are usually found.

Actually, this situation might be a blessing in disguise. If you receive unexpected results, it typically means that they are objective and that you didn't let your expectations influence analytical data. Marketing should be driven by insights rather than assumptions. Highlight that to the client before your research in case of unexpected findings.

First thing’s first — be transparent in communication. If the problem is big enough that may cause delays in deliverables, communication is important to manage expectations. Then, everything else can follow suit such as verifying your datasets, validating the syncing processes, deep diving into data discrepancies if any, etc.