Data analytics may seem nerdy, but they’re used frequently in a B2B sales incentive context. They help understand and plan product margins, service costs, loyalty, or market share. Knowing your customer base and spending levels is the most valuable information when planning sales strategies and B2B engagement programs.
If not, that is the starting point for using analytics to engage with your customers. The aim is to understand how much each one contributes to your bottom line. What is their value?
If one side of the ‘value coin’ is customers, the other is potential value. By this, we mean: Do you know how much of your customer’s share of wallet you’re capturing? Looking at your own numbers can understate how much a customer spends in your category. There are ways to estimate the share of wallet, and it’s a precious and insightful exercise.
So, the first recommendation is to understand your customer base’s current and potential value pools.
A good place to start is segmenting your customer base into similar groups. Although hyper-personalisation and 1:1 marketing currently leave segmentation as a somewhat primitive approach, for an organisation just starting out on its Analytics journey, it can present a quantum leap forward in understanding your customers and how best to engage them.
A popular approach originating in retail is called RFM Segmentation. This methodology looks at transactional data and groups individual customers into clusters based on three dimensions:
How much time has elapsed since a customer’s last interaction with the brand? The more recently a customer has interacted with a brand, the more likely that customer will be responsive to communications.
How often has a customer transacted or interacted with the brand over a specific time? Customers with frequent activities are more engaged and probably more loyal than customers who rarely do so.
This reflects how much a customer has spent with the brand over a particular period. Big spenders should be treated differently than customers who spend very little – reinforce this behaviour!
By taking the dollar value of all a customer’s orders and dividing them by frequency gives you the average amount, an essential secondary factor when segmenting customers.
While it may sound complex, this exercise is straightforward and contains the correct data. Brands can use these groupings to target communications better and achieve higher response rates and engagement. It also has the benefit of informing where not to spend your money by trying to engage customers who are unlikely to respond.
A second recommendation is to implement a segmentation model, which helps you find customer groups of similar entities. With data analytics, you can avoid viewing customers as an amorphous collection of individuals.
A final recommendation sits at the very heart of data analytics: measure outcomes. Analytics, as a data-based discipline, makes it possible to track changes in customer behaviour with a high degree of accuracy when implemented correctly.
Tracking customer behaviour closely and accurately means you can understand which engagement activities resonate with your customers and achieve the desired outcomes. The ideal environment for conducting this analysis is an incentives and rewards program.
The nature of these programs means you have a direct line of communication with individual members and are notified when they make a purchase, which gives you a closed-loop system to track behaviours.
The final recommendation for those wanting to leverage the potential of data is to find a partner who can help you. You are an expert in your business and the challenges you face, so team up with an expert and use data to find solutions to these problems.
With 212F Labs, our internal data team are able to work with your brand to optimise your program and ensure it correctly engages with your customer base. We ensure that your program clicks with your audience and monitor all the data so that we can provide up-to-the-minute reporting. We handle the data analytics so you don’t have to!
