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There are plenty of uses of analytics in a B2B sales incentive context. Firstly, understanding and planning product margins, cost to serve, loyalty or market share. Most importantly, knowing who spends the most with your business. This is the most valuable answer when we are planning sales strategies and B2B engagement programs.
If not, then that is the starting point to using analytics to engage with your customers. The aim is to understand how much each one of them contributes to your bottom line. What is their value?
If one side of the ‘value coin’ is customers, then the other side is potential value itself. 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 share of wallet and it’s an incredibly valuable and insightful exercise to do.
So, a first recommendation is to understand the current and potential pools of value in your customer base.
A good place to start is by segmenting your customer base by dividing them into similar groups. Although currently hyper-personalisation and 1:1 marketing leave segmentation as a somewhat primitive approach, for an organisation just starting out on their Analytics journey, it can present a quantum leap forward in understanding your customers and how best to engage them.
A popular approach, which originated 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 activity or transaction with the brand? In most cases, the more recently a customer has interacted or transacted 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 certain time? Customers with frequent activities are more engaged, and probably more loyal, than customers who rarely do so.
Also referred to as “monetary value,” this reflects how much a customer has spent with the brand over a particular period. Big spenders should usually be treated differently than customers who spend little. Taking the dollar value of all a customer’s orders and dividing them by frequency gives you the average amount; an important secondary factor when segmenting customers.
While it may sound complex, this exercise is straightforward when you have the right Data. Brands can use these groupings to better target communications and achieve higher response rates and engagement. It also has the benefit of informing you of where not to spend your money by trying to engage customers unlikely to respond.
So, a second recommendation is to implement a segmentation model. It helps you think of customer groups of similar entities. With analytics, you can move away from viewing customers as an amorphous collection of individuals.
The third recommendation sits at the very heart of Analytics: measure outcomes. The beauty of Analytics as a data-based discipline is that when implemented correctly, it makes it possible to track changes in customer behaviour with a high degree of accuracy.
This means you can understand which engagement activities resonate with your customers and achieve the desired outcomes. The ideal environment with which to conduct this analysis is an incentives and rewards program.
The nature of these programs means you have a direct line of communication to individual members and you’re 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.
If you would like to talk to us on how to leverage your Data and plan an engagement strategy such as an incentive or rewards program, we will be more than happy to talk.