We have detected 6 maturity levels when it comes to doing marketing. Every level requires a more advanced step of analytics. So the evolution from one level to the next is directly linked to the maturity of the insights you can create. For example event driven campaigns on churn may prove much less effective if you don’t score the people that have a high probability to churn.
The goal of omni-channel marketing is to deliver the right message, at the right time, through the right channel, taking into account your commercial strategy. Because in the endMarketing is not all about customer centricity. You do have commercial objectives you need to reach as an organization; they need to be taken into account when setting your communication goals or defining your marketing strategy.
In this blog entry, I will explain the first step: from mass marketing to customer segmentation.
Mass marketing means that you are sending the same message/product to all of your customers. Imagine that, as a retailer, you hold a campaign for dog food. You would send all your contacts an email for dog food, ignoring the fact that maybe 50 % of the selected population doesn’t have pets, let alone a dog.
Customer segmentation is a marketing strategy which involves dividing all your customers into subsets with a common interest. This means that when you have two segments and you would send them the same product or communication, they would respond in a different way. Let’s take our dog food example. Based on rules we could make a segment of people who bought dog food in the last 6 months and people who have never bought dog food. You obviously have a higher chance that people who bought dog food in the past will react positively compared to people who never bought dog food before. Sending the same message to the people who haven’t bought dog food results in an irrelevant message for that specific segment and a loss in marketing spend as you have no responders.
A more complicated rule-based approach is RFM segmentation. Here you score the customer behavior based on Recency (How row recent was the customer’s last purchase), Frequency (how many times did he/she visit our company), and Monetary (how much has a customer spent). The idea here is that this allows you to identify your most loyal and valuable customers. For more information on RFM you can read this blogpost by Chris Hemedinger.
The best form of segmentation is the analytics-driven approach which involves a descriptive analytical technique, such as a cluster analysis. The benefit of using analytical techniques is that you can use multiple variables to detect which segments you can find in your data. Often an RFM score is part of your cluster analysis.
Until a few years ago, using these techniques was often a bridge too far for marketers. But the rise of visual analysis tools that provide out of the box analytics have changed that. In the picture you can find an example of a cluster analysis in SAS® Visual Statistics.
In the above graph you can see 4 segments indicated by 4 colored lines. The lines go up and down based on the value they have for a specific variable. When you look at the red line, which is segment 2, you can see that people in this segment have a very low amount of visits, are rather young and have high cart abandonment rate. This could indicate that this is for example a price seeker segment that come to our website to look for the best deals.
Interested to read more?
Stay tuned for the next step: from customer segmentation to 1-to-1 campaigns.
Blog series: From mass to real-time omni-channel marketing