Marketing is no longer an art – it’s a science. Measuring performance against key metrics has become an essential aspect of every marketer’s life. But, in order to drive results for their business, ecommerce directors and marketing managers need new types of hard numbers – they need data insights that can truly influence strategy.
We’ve compiled metrics to help you to define a clear plan of action. We’ve called them Smart Metrics, and these performance indicators play a fundamental role in our customer intelligence platform.
In short, Smart Metrics are built with predictive modeling algorithms and are typically categorized into three groups. Watch the video below for a sneak peek of what to expect, and we’ll explain each group in greater detail below.
1. Predictive Revenue
This category is calculated on revenue related metrics such as the future lifetime value of contacts in each lifecycle stage. This is particularly beneficial as you can evaluate how much you need to spend in order to convert/retain/win back each customer.
It also provides the predictive gain and loss for the last 30 days, combining the revenue generated by conversions and the future changes to customer lifetime value, so you know how the conversions, or lack thereof, within each group will affect your bottom line in the future. This metric is instrumental when deciding your next best move.
Predictive modeling allows you to forecast the financial implications of specific strategies and campaigns. For example:
You could see a defecting buyer has a 15% likelihood of converting, and an inactive buyer has a 2% likelihood. This makes it clear that what you are currently doing for inactive customers (maybe sending them the same weekly newsletter) is not working.
Although these are only predictions, they are accurate enough to use as guidelines, especially when comparing different strategies for different segments. They give you a wide-ranging view of the revenue impact of your actions (or lack of actions) presented in a single dashboard.
2. Predictive Analytics
These metrics measure purchase-related behaviors, such as which products and categories each buyer tends to browse when they ultimately end up making a purchase or the amount of time between purchases. This knowledge allows you to engage with each contact more accurately and effectively. Here is an example:
Predictive analytics could say that if your first-time buyers do not make a second purchase within 47 days, they are more likely to defect than to buy a second time. You had previously guessed 90 days. Thanks to real-time data you now know where the watershed lies, so you bring your second purchase incentive program forward from 85 to 42 days.
All of these metrics are based on sophisticated learning algorithms, meaning they are constantly evolving. Who can say if the watershed will move over time, or shift during the year? With Smart Metrics you can keep tracking the results against your business goals and make appropriate changes to your program schedule whenever necessary.
3. Customer Engagement Patterns
These metrics quantify engagement-related metrics, showing which of your customer engagement strategies are the most successful in terms of responses and conversions. They also track the time between online engagement such as opening an email or visiting the website. This allows you to determine the best times and channels to interact with each segment.
A quality marketing automation platform offers the customer data and reporting that you need, and lets you measure impact of retention marketing for your business. More importantly, it shows you whether or not you are improving over time.
Learn how our real-time marketing automation platform allows you to run all aspects of your marketing strategy with ease.