Introduction to Marketing Analytics

Similar to other departments of a company such as products and finance, the marketing team too needs analytics to make informed decisions. The process that helps in this is called marketing analytics.

Marketing analytics is the study of data collected through various means, including marketing campaigns, that helps the marketing team assess their initiatives. It also helps them make improvements if needed.

For instance, the analytics can help the team determine the Return on Investement, or ROI, for initiatives such as marketing campaigns, blogs, call-to-action as well as product performance and advertisements.

Why is marketing analytics important?

Customers today have a lot of options to choose from. With so many alternatives, they have also become wary of new products or services. While a quality item is necessary, the marketing teams also need to engage customers to try out the company’s products or services in offer.

For this, analytics tools help in targeting customers properly based on their interests rather than relying on demographic cohorts.

Marketing analytics helps the team serve the right customer at the right time and in the right manner.

Marketing analytics tools provide deep insight into the collected data that is not possible to ascertain manually. The data can be contextualised and can show a customer’s journey from the very beginning. The right application of analytics tools can help a company improve their product and even sell them better.

 

 

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Podcast suggestion: Using Data Science to solve challenges faced by marketers today with Piyanka Jain, Founder & CEO Aryng

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How do marketing analytics tools help companies?

Marketing analytics tools can help companies make data-driven decisions in various segments, such as for updating products, campaigning, or targeting customers.

• Product trends: The tools can offer insight into what the customers want. This can help businesses develop better products or modify them to suit the customers’ needs.
• Unify data: It can help organise the data properly and offer insights that would be difficult and time-consuming to get manually from spreadsheets.
• Modifying messages: The tools can help companies tweak messages that are shown to customers based on their preferences. This can improve the engagement and also make them interested in the company’s products ad services/
• Improving outcomes: Marketing analytics can show the details of a campaign in the most granular form possible, allowing companies to optimize the information to improve their initiatives.

 

 

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If you want to learn more about marketing analytics, you can read this excerpt from the book: Behind Every Good Decision – How Anyone Can Use Business Analytics to Turn Data into Profitable Insight.

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Consider a breadth of industries: financial services, consumer goods, e-commerce, automobile, technology, media, and so on – a CMO broadly expects three critical outcomes for his business initiatives:

• Bring more future customers to the door in the most cost-effective manner.
• Convert more of those who come to the door into customers
• Keep the current customers buying, thereby reducing churn

In essence, the CMO seeks a broad and targeted top of the funnel and higher conversion at every stage. This will achieve maximum revenue at an optimal ROI. Data can support optimized funnel through questions like who and where to market; how much to spend on each channel; what drives response and conversion; who best responds to what message, offer, and Product; and what drives churn. Gable wines discussed earlier represent these questions. 

While this may seem like a compelling case for predictive analytics, we think a CMO can realize a better ROI using simple business analytics techniques to arrive at insightful and informed decisions. ROI can be improved using marketing analytics. Here’s a sample of how it can be done with straightforward analytics methodologies (the specific analytic methods are in bold):

Methodologies

1. Bring more future customers to the door in the most cost-effective manner by:
Increasing the marketable universe by identifying new channels based on the existing customer profile.	Aggregate Analysis  Sizing and Estimation
Better targeting of messages and offers based on past marketing campaigns to increase response.	Testing Correlation Analysis
Optimizing channels to increase ROI and decrease cost of customer acquisition	Correlation Analysis
2. Convert more of those who come to the door into customers by:
Identifying conversion drivers. Do certain fulfillment options, user experience, review options, cart options, payment options, offers and promotions drive incremental conversion?	Testing  Correlation Analysis
3. Keep the current customers buying by:
Segmenting the base to drive engagement	Simple Segmentation- RFM
Launching an engagement campaign, customized by segments, to stimulate buying.
Understand engagement drivers (like certain offers, discounts, bundling, loyalty memberships and such) for each of the customer segments.	Correlation Analysis

 

Understand engagement drivers (like certain offers, discounts, bundling, loyalty memberships and such) for each of the customer segments.	Correlation Analysis
Campaign analysis – what resonates with customers and what doesn’t	Testing Aggregate Analysis Correlation Analysis
Understanding drivers of churn – identify factors that make customers leave your business	Correlation Analysis

If you are a marketer, hope this helps you get started on your data science journey.