As a marketer or marketing data scientist, KPIs are the keys to measuring the successes and failures of any marketing efforts. The proper usage and definition of KPIs ahead of embarking on any marketing campaign is crucial for the effective measurement of marketing successes and failures. However, improper usage of KPIs can result in misinformation being gathered and often wasting significant financial and human resources that have a direct negative impact on the business.

For example, if your marketing goal is to raise brand awareness of a targeted group (i.e., Generation Alpha) but you are not properly breaking down the sources of marketing content impressions (i.e., text vs. image vs. video, or YouTube vs. Instagram vs. TikTok, or influencer referrals vs. paid ads), it may tell a completely different story than what is actually happening. We will review some of the frequently used KPIs in this section and move on to the implementation and real-world...

With the foundation of various marketing KPIs that we have discussed so far, let us dive into a potential real-world use case of some of these KPIs, how to compute and visualize them, and how to interpret them for further data-driven decision-making. We will be using an insurance product marketing dataset for our example.

From a financial point of view, the success of marketing is to bring in more revenue with less spending. The most straightforward approach in tracking and measuring this is to look at conversion rate, CLV, and CPA. The conversion rate tells us the percentage of leads converted into paying customers, CLV tells us the estimated revenue being generated per acquired customer, and CPA tells us how much it costs to acquire a paying customer. By combining these three KPIs, we can understand the ROI and contribution to the net income.

Using the above-mentioned example dataset, let’s discuss how these KPIs...

Computing and building KPIs is one key aspect of decoding marketing performances, but staying on top of the progressions and changes in KPIs is another equally important aspect of having a good grasp of your marketing performance. There are numerous ways to visualize KPIs and countless ways of tracking and sharing the KPIs with key stakeholders. In this section, we are going to share some of the common approaches to visualizations and ongoing KPI tracking.

Choosing the right visualization for KPIs

When presenting KPI data, the choice of visualization plays a crucial role in conveying insights effectively. Here are some guidelines to help choose the most appropriate type of visualization:

• Bar charts: Ideal for comparing quantities across distinct categories, such as sales performance across different regions or products.
• Pie charts: For illustrating a composition or proportion of a whole, like market share distribution...

In this chapter, we have laid a concrete foundation for building data and AI/ML-driven marketing models and strategies. We have discussed commonly used marketing KPIs that not only help the business measure the marketing performance but also provoke action items for improvements based on the strengths and weaknesses discovered through multilevel analyses. Using an insurance product marketing dataset as an example, we have seen how these KPIs can be measured and analyzed with Python for further improvements and optimizations in future marketing efforts.

We have also discussed how various dashboarding tools, such as Tableau, Power BI, and Looker, can be used for reusable real-time KPI tracking. As everyone emphasizes, AI/ML starts with data and deep exploratory analysis to decide what to train AI/ML models with and what to optimize for. The items and KPIs covered in this chapter will come in handy when designing and developing advanced AI/ML models for marketing in the future...