How to Overcome Challenges in Marketing Analytics

Today, I would like to share a common problem of *Broken Data Pipelines* that have encountered in the past in my career. This disrupts critical decision-making processes, leading to inaccurate insights, delays, and lost business opportunities. According to me, major reasons for these failures are: 1) Data Delays or Loss Incomplete data due to network failures, API downtime, or storage issues leading to reports and dashboards showing incorrect insights. 2) Data Quality Issues Inconsistent data formats, duplicates, or missing values leading to compromised analysis. 3) Version Mismatches Surprise updates to APIs, schema changes, or outdated code leading to mismatched or incompatible data structures in data lake or database. 4) Lack of Monitoring No real-time monitoring or alerts leading to delayed detection of the issue. 5) Scalability Challenges Pipelines not being able to handle increasing data volumes or complexity leading to slower processing times and potential crashes. Over the period, I and Team Quilytics has identified and implemented strategies to overcome this problem by following simple yet effective techniques: 1) Implement Robust Monitoring and Alerting We leverage tools like Apache Airflow, AWS CloudWatch, or Datadog to monitor pipeline health and set up automated alerts for anomalies or failures. 2) Ensure Data Quality at Every Step We have implemented data validation rules to check data consistency and completeness. Use tools like Great Expectations works wonders to automate data quality checks. 3) Adopt Schema Management Practices We use schema evolution tools or version control for databases. Regularly testing pipelines against new APIs or schema changes in a staging environment helps in staying ahead in the game 😊 4) Scale with Cloud-Native Solutions Leveraging cloud services like Amazon Web Services (AWS) Glue, Google Dataflow, or Microsoft Azure Datafactory to handle scaling is very worthwhile. We also use distributed processing frameworks like Apache Spark for handling large datasets. Key Takeaways Streamlining data pipelines involves proactive monitoring, robust data quality checks, and scalable designs. By implementing these strategies, businesses can minimize downtime, maintain reliable data flow, and ensure high-quality analytics for informed decision-making. Would you like to dive deeper into these techniques and examples we have implemented? If so, reach out to me on shikha.shah@quilytics.com

Relative to my book which is in progress, I’ve been getting a good number of requests for posts about specific “lessons learned” from using analytics. Here’s a big one: The #1 reason why #B2B #CMOs have such short tenures versus #B2C CMOs is that most B2B marketing teams have not done the work to understand exactly what’s needed to show their impact and optimize it in the face of a constantly changing marketplace. Many B2C teams did this work a long time ago. Specifically, unidentified #TimeLag is the B2B CMO’s greatest challenge. When CEOs can’t “see” things working, they assume they’re not. The value creation of B2B marketing and sales are heavily offset in time and space, often several quarters. If that’s not identified and forecasted, things will appear to not be working. Several years ago, we did a project with a large #CMO organization that examined 5 companies who had recently fired their CMOs. Using a common “basket” of questions and data types, we analyzed the efficacy of the CMOs’ investments, net of Time Lag, to see if their terminations were justified. 4 out of the 5 not only were not justified, they were completely unjustified. But unidentified Time Lag had made it seem like nothing was happening. (One CMO of the four used the report to challenge their termination, winning a very greatly enhanced settlement, which was predictably sealed.) Bottom line? Without causal analytics you are flying blind against headwinds you haven’t quantified, with unknown Time Lag separating your actions from your impact. You are naked and defenseless against Business leaders who challenge whether you’re having any impact. Whether you use ProofAnalytics.ai or one of our competitors, I urge you to move fast on this. In more and more companies, Finance is scrutinizing GTM investments for evidence of impact. New investments will require a coherent, calculated business case, including forecasted time to value.

One challenging cross-functional aspect of marketing analytics is balancing the opportunity presented by profitable, timeline-specific cohort acquisition against the practical reality of cash constraints. Predicted ROAS curves don't account for budgetary limits: marketing expenses are incurred and then paid back over time, and even profitable spend can put a company in an untenable cash position. One approach to reconciling this is to build a P&L that dictates marketing spend not just as a function of ROAS sensitivity -- ROAS and budgets are naturally inversely correlated -- but by cash needs. I've published an overview of such a P&L project in the past. Another approach is simply to calculate what amount of money can be spent and recovered in the same month and to scale spend from that starting point. This is a Blended Same-Month Return metric. Link in comments.