Defining Meaningful Metrics: 6 Soft KPIs to Measure Customer Preference Collection

Type: Blog
Topic: Customer Insights

Measuring metrics and data can be overwhelming and confusing. Have you ever had trouble deciding where to start?

Do you remember our previous blog series 5 Key and Measurable Reasons to Adopt Preference Management? We discussed some hard KPIs that may be the initial motivator for a company to implement a preference management system. There are plenty of other reasons in addition to those 5, though. We refer to those as “soft” KPIs. They’re generally not the #1 reason a company wants a preference management system. However, they definitely make a difference when looking at whether it’s been effective or is important to continue. Many of these soft KPIs have more to do with customer retention, instead of new customer conversion.

The Harvard Business Review found that it’s between 5-25x more expensive to gain a new client than to keep an old one. 

The range depends on what industry you’re in – but measuring customer retention is valuable.

Companies’ marketing ecosystems are increasingly complex. To help cut through the clutter, you have to be specific about which metrics matter. Performance measurement needs to focus on the lifecycle of the customer and how likely customers are to engage with your brand. “Customer Interaction” metrics focus on retention rather than conversion. Those metrics need to be shared across teams and life-cycle stages.

Because these metrics gauge the customers’ perception, they are more difficult to quantify. That means there needs to be agreement across the organization about the right data to track. Once you know what data is important, you’ll be able to identify the baseline information. And once you know your baseline, you’ll be able to track and measure success.


6 Examples of “Soft KPIs”:


• Customer perception of your brand – do they find you trustworthy? Likeable?
• Perceived customer experience – do they find your company easy to work with? Are processes easy for them (how to pick out a product, how to buy the product, how to track their product after the sale)?
• Customer satisfaction – did the customer get what they wanted? This is important for both the process of the sale and the product itself. Were they happy?
• Customer loyalty – do your customers come back for another sale? Even better, do they recommend you to someone else?
• Customer lifetime value – How much is each sale worth to your company? Sometimes this is hard to measure. If you sell a wide variety of products or services, try to find an average sale value. How many times will your average customer buy from you? Multiply your average sale with the average number of purchases by each customer, and you have a general lifetime value for a customer. Of course, one of your goals will be more purchases by each customer. That will increase the lifetime value of a customer.
• Customer ROI (rather than Campaign ROI) – How much does it cost you to gain/attract a new customer? How much does it cost to keep an existing one interested? Compare this to your average sale value to estimate a return on your investment.

Having established a baseline, companies are then able to examine the data point that really matters: the trend line. For example: does customer loyalty stay stable over time? Does it soften, as the customer forgets about you? Or does it increase, as you show that you’re trustworthy and easy to work with? How about your online reputation? Are your general social media mentions positive, negative, or neutral? Are you present on job boards and company review websites, like Glassdoor or Yelp? How’s your company ranking? Do people look at you favorably?

With preference management tools in place, tracking soft KPIs becomes much easier. 

Customers will select useful or timely communications. They’ll opt out of campaigns and communications they don’t find interesting or relevant. In the short term, the data will let you course-correct. This will reduce churn and improve marketing efficiency. Over the long term, as the amount of data grows, it will become a large sample size. Once you have a large sample to track, making large-scale marketing decisions becomes easier.

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