Today’s marketing teams need to be tactical and precise to efficiently use their budget and gain the highest return on investment. This means finding the right customer to target at the right time, and in turn saving resources on wasted outreach and increasing the likelihood that a given campaign will translate into revenue. Customers in a database can be divided into segments which help to define the audience. There are tons of ways to achieve this. Below are just a few of the ways consumers can be classified.
Life Stage Segmentation
Where are your consumers in their lives at this moment? Are they students? New parents? New pet owners? Customers’ needs change as their lives take them in different directions. A college student, such as myself, might be inclined to order bulk packages of Ramen, whereas a new parent might be more inclined to spend money on baby food. There are certain unique attributes, spending habits, and needs that individuals develop as they move from life stage to life stage. Because life stages tend not to change too often and they tend to be fairly predictable, targeted advertisements and promotions can easily be placed to an appropriate audience at an appropriate time.
You can also examine the buying patterns of customers to group them into categories. Some customers have brand loyalty, and no matter how many advertisements and promotions you throw their way for Samsung phones, they’ll only ever buy from Apple. Other customers tend only to buy things when they’re on sale, and a well-timed promotional offer might trigger an instant conversion. You can use this information to best figure out which customers will buy what products, and at what prices.
This is a segmentation that groups together three related metrics, all having to do with the customer’s prior purchase behavior.
- The R stands for “recency,” which is a measure of when a customer’s last purchase occurred.
- The F stands for “frequency,” which is a measure of how frequently the customer buys.
- And the M stands for “monetary value,” which is a measure of how much they tend to spend.
RFM Segmentation is the most popular form of segmentation, but it can be a little complicated to derive. Each of the three letters can act as a measurement that is fed into the overall score. If we give each measure a score of 1 to 5, there are 125 positions a customer can sit on the overall RFM scale, from lowest value (lowest recency, frequency, monetary value scores) to highest value (highest recency, frequency, monetary value score). This is a measurable way to determine whom to target in a database containing past shopping habits. For example, if customer A last came to your shop nearly a year ago and spent $1500 in one purchase, and customer B last came to your shop nearly a month ago, and spent $200 across two purchases, which customer should you target? Depending on weighting behind each metric, RFM scores can give you a standardized answer to this question.
These are just a few ways customers can be organized inside of databases. Segmentation helps marketers understand whom to target, when, and what the reasoning behind those selections are. All of these are important in crafting a message that resonates with an individual customer, and can help turn prospective buyers into revenue for your company.
Josh Fayer manages Marketing Communications at Zylotech, where he brings an extensive background in public relations and computer science. Josh Loves music! He sings with an a cappella group, Orange Appeal, (whose recent album Unpeeled he shamelessly plugs at work), and at home he loves to mess around with a variety of instruments. Josh enjoys spending quality time with his adorable dog, and exploring around his new home near the South Shore of Boston.
If you liked this post, check out our other blog post on how hyper-personalization can help drive your marketing campaigns.