Several recent happenstances are transforming marketing. For one, the unprecedented explosion of data being created by consumers, businesses, and governments offers immeasurable value for organizations that use data to understand customer behavior, current trends and coming events.
At the same time, online technology is empowering consumers to easily find the lowest-cost vendors, avoid ads that don’t interest them and influence purchase behavior of tens of millions of people via online reviews, tweets and blogs.
Marketers need analytics to turn these mountains of data into insights and opportunities which will allow them to successfully connect with customers. An effective analytical framework helps marketers make smarter decisions as they execute strategies and plans, and enables analytically driven customer segmentation.
Analytically-Driven Segmentation
Customer segmentation is the process of dividing a buyer base into groups of individuals who are similar in specific ways relevant to marketing. There are two basic types of segmentation: foundation segmentation and targeting segmentation.
Foundation segmentation creates core, top-level segments that enable a company to deliver consistent treatment, and to focus on long-term strategy. Key attributes of foundation segments could include demographics, attrition risk, profitability, etc. All customers are included in such a segmenting process, and each belongs to only one segment. These segments can be subdivided into natural clusters, such as geography or level of profitability.
Foundation segmentation can be used for situations where no targeting segmentation exists, such as the introduction of a new product.
Targeting segmentation identifies customers with specific needs and preferences. Not all customers are necessarily included in targeting segments. Additionally, a given individual may fall into many different segments.
This type of segmentation is useful for specific marketing programs and campaigns. For example, targeting segmentation could identify customers most likely to respond positively to a one-off campaign, or those most likely to leave for a competitor. Attributes might include behavior, time periods, account status or product usage. The focus is short-term marketing activities, not long-term relationship building.
Analytics can be helpful in foundation segmentation, but they are imperative for targeting segmentation. In fact, analytics are the key to evolving beyond foundation segmentation to targeting segmentation.
Consider how a typical telecommunications company might use analytically-driven targeting. Say marketing defines as a primary target group the top 20 percent of the most profitable customers. Assume this group has the additional characteristics of high usage and high churn rate. Marketing might subdivide the group into three distinctive clusters based on two different revenue dimensions: usage revenue, which is generated from per-minute charges, and access revenue, which is gained from rate plans.
Marketing now has three distinct customer groups to further analyze and target with unique offers. Let’s say that, of the three clusters, the high-usage revenue cluster shows the greatest profit per user, the lowest number of subscribers per account, and the middle churn rate. Marketers would use this information to develop an offer that this cluster of customers would desire. For example:
Individuals within the high-usage revenue cluster could receive an offer for a new phone if they renew their contract.
Those in the high-access revenue cluster could receive an offer for a 10 percent discount on their first month’s fee if they renew their contract.
The cluster in the middle could receive an offer for 100 free minutes if they renew their contract.
Because these offers target the specific usage pattern for each cluster, they should be more attractive and generate higher response and revenue rates than those based on non-analytical segmentation strategies.
Expedia.com shows the way
Expedia.com provides a real-world example of applying analytics for targeting segmentation. Expedia.com leverages analytics to isolate customer segment groups based entirely on customer purchasing behavior. One surprising finding was the large number of customers who turned out to be business travelers. This group’s frequency of travel and the days and times they traveled better matched the established “business traveler group.”
Further analysis showed this customer segment is much more interested in seeing a full range of options specific to their time needs than are leisure travelers. So Expedia created offers tailored to appeal to its newly discovered customer profile.
When built correctly, analytically driven targeting segmentation delivers significant benefits, including the ability to channel efforts toward customers most likely to buy specific products or services. It also helps identify an organization’s most and least profitable customers. Marketers can also and avoid markets that won’t be profitable, as well charge higher prices in markets that will bear it.
Larry Mosiman is global product manager for SAS Customer Intelligence
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