Analytics in Product & Marketing: Metrics that Matter

In today's data-driven world, understanding and interpreting the vast amount of information we collect is crucial for making informed decisions.?

Analytics is the scientific process of discovering and communicating meaningful patterns found in data, and it is increasingly being used in various industries to gain insights and make better decisions.?

In this article, we will take a closer look at analytics, particularly in the context of marketing and product development. We'll explore what marketing analytics is and how it can be used to gain insight into marketing campaigns. Additionally, we'll delve into product analytics, the process of tracking and analyzing user engagement and behavior data to improve and optimize digital products.?

Whether you're a marketer or a product manager, understanding the power of analytics is essential for success in today's digital landscape.

What is Analytics?

Analytics is the scientific process of discovering and communicating the meaningful patterns which can be found in data. (Techopedia, 2021).

It is especially useful in areas which record a lot of data or information.

In computing, data is information that has been translated into a form that is efficient for movement or processing.?

Marketing analytics is analytics applied to marketing data.?

Marketing analytics is connecting data from multiple channels – social media, web, e-mail, mobile, and more – to give marketers comprehensive insight into how their marketing campaigns are performing.

Product analytics is analytics applied to product data.?

Product analytics is the process of tracking and analyzing user engagement and behavior data within a product (digital), to improve and optimize the product.

Digital products are programming code-based assets that deliver a particular interactive value proposition to the final user. These are mostly web, mobile, desktop apps, digital dashboards, controller apps, and many more.

The Key Distinction: Marketing & Product Analytics?

The key difference between marketing analytics and product analytics is?

1.The channel within which the data is derived.

2. The goal of analytics.?

The table below gives us more context:

The functions of marketing and product are not very dissimilar when it comes to improving the customer experience and driving loyalty. For many applications (in product marketing), marketing teams focus on acquiring users via acquisition channels, and product teams focus on retaining and growing customers by providing a great product (digital) experience.

Ultimately, all product and marketing initiatives should add to the bottom line - driving revenue. In some companies this makes up the Product Marketing Team in collaboration with Sales, and Customer Success.

But, how do we make sense of all the data that we have at our disposal? We focus on specific metrics.

What are Metrics?

Metrics are numbers and statistics that are used to measure or track performance.

Metrics indicate the health of your marketing & product development efforts. We use business metrics to encourage improvement. They also tell us where to focus our time, effort, and money.

What makes a Good Metric?

A good metric is comparative.?

Good — “We recorded a 20% increase in landing page conversions from last week”.

Not as useful — “We recorded 200 landing page conversions”

A good metric is understandable.?

If people can’t remember it and discuss it, it’s much harder to turn a change in the data into a change in the culture.

A good metric is a ratio or rate.?

Good — “We recorded a DAU/MAU of 30% on our product this month” — 300 daily users, from 2,000 monthly users.

Not as useful — “We had 300 daily active users last month” or “We had 2,000 monthly active users last month”?

What metrics should you track?

Track metrics that have an impact on the bottom line — directly or indirectly driving business growth.

Businesses grow in two ways:?

1.increasing profits (i.e. make more sales, increase revenue, lower costs),?

2. or increasing volume (i.e. acquire more paying users/customers, increase product usage, reduce churn).

The AARRR Pirate Metrics Framework

The AARRR framework was devised by investor and entrepreneur Dave McClure (founder of 500 Startups) out of necessity for a simple, universal solution that any startup can use to:

1. Develop a model of customer behavior that leads to business growth.

2. Improve marketing and development efforts by focusing on metrics that really matter.

What are the Metrics that Matter?

There are a variety of metrics that can be used to track performance, but it's important to focus on the ones that truly matter. By understanding these metrics and how to measure them, you can make data-driven decisions to improve your product and drive growth.

Recurring Revenue

• The amount of revenue generated per annum (ARR) or per month (MRR) for subscription-based products.
• E.g. Our MRR is $15,000

Net Promoter Score (NPS)

• The NPS is generally seen as an overall indicator of how happy your customers are with your product.?
• “On a scale of 0 - 10, how likely are you to recommend the product to a friend?”. Promoters (9-10) and detractors (0-6).
• NPS = % promoters - % detractors.
• E.g. “Our NPS score for this month is 56 vs 45 last month”.

Customer Retention Rate

• Customer retention is a company's ability to retain its customers over time.?
• Retention% = No. of (Customers at End of Period — Customers Acquired During Period) / Customers at Start of Period)) x 100
• E.g: You start the year with 20 customers, gain 5 new customers in the first quarter, and have 1 customer churn: Customers at end of period = 20 + 5 - 1 = 24, Customers acquired during period = 5, Customers at start of period = 20, Retention = (24 - 5) / 20 = 95% retention.

Customer Acquisition Costs (CAC & CPA)

• Customer acquisition is how much it costs to gain a new customer or deal per $1.?
• CAC = total marketing spend / no. of new customers
• Cost Per Acquisition (CPA) = total campaign spend / no. of campaign conversions, where conversions = downloads, consultations, email signups etc.

Customer Lifetime Value (LTV)

• The value of a customer during their lifetime with your product.
• Customer Lifetime Value = Customer Value x Average Customer Lifespan (where Customer Value = Average Purchase Value * Average Number of Purchases)
• E.g Our LTV is $500.

Active Users

• Number of users who are using your product at a given period — daily (DAU), weekly (WAU), monthly (MAU).
• E.g. 200 daily active users, 1,500 weekly active users, 4,000 monthly active users.

Conclusion

Analytics is an essential tool for understanding and interpreting large amounts of data in order to make informed decisions. In the context of product and marketing, analytics can be used to gain insight into marketing campaigns and user engagement and behavior data to improve and optimize digital products.

Both marketing and product teams should focus on metrics that drive business growth, such as recurring revenue, customer acquisition cost, and product engagement.

In my next article, I delve into the various tools and platforms available for extracting insights from data.

If you need to see a more detailed implementation of these, you could connect with me and pick my brain. Cheers ??