Marketing Analytics for a New Era

Use Cases & Projects Lynn Heidmann

Today, the effectiveness of traditional marketing segmentation is limited by its reliance on fixed methodologies. Marketing teams that move to model-based segmentation, on the other hand, can create dynamic segments of users based on interactions between a huge diversity of data points. 

What are the advantages of switching from a traditional approach to model-based segmentation?

Drawbacks of Traditional Segmentation

With traditional segmentation, customer knowledge is molded upon rigid, and often outdated, categories, an approach that often leads marketers astray when attempting to optimize campaigns to specific targets. A few key limitations include:

Lack of Behavioral Scope

Traditional segmentation often relies on data points from consumer self-reporting, which is limited. This approach makes it difficult to understand and group customers on a deeper level and impossible to learn more about a customer over time.

Small Sample Sizes

Data used for traditional segmentation methodologies typically involves surveys, focus groups, and sales data. The problem with this approach is size - smaller sample sizes do not bode well for discovering trends. WIth big data, it is not in the individual tracking of a person that brings about the value, but rather the trends that are discovered by analysing this plethora of data together into one complete picture.

Siloed Data Sources

Traditional customer segmentation lacks the flexibility to gather and evaluate data from multiple sources, such as an organization’s CRM, e-mail, and social media data.

Data Lifespan... And its Limits

Traditional segments rely on fixed data often updated only on an annual basis, which is far too restrictive when trying to understand the complexities of your customer base. A customer may buy a blue t-shirt in January 2015 but has begun to buy pink t-shirts in June 2015. If my segmentation is done based on old data (i.e., the blue t-shirt she bought in January 2015), I’ll continue pushing “blue t-shirt” campaigns her way and therefore miss out on selling her the pink t-shirts or related products.

Marketing Segmentation in 2017

We are now in a new data space where both the quantity and quality of customer data is increasing in type, complexity, diversity, velocity, and interdependence. These new sources of highly specific information provide a more detailed and ever evolving opportunity to intelligently and dynamically segment customer databases. But how?

Take Advantage of All the Data

Broadly-speaking, there are three types of data (transaction, interaction, and external) that, when combined, provide a holistic view of user data:

ALL the dataTransactional Data 

Transactional data is one of the oldest data types and reflects a wide variety of customer-centric data, such as time, location, price, payment methods, discount values, quantity purchased, etc. All of this data can be combined to convey a precise picture of customer shopping habits and interests.

Interaction Data 

The digital era now enables companies to follow customers and prospects on all channels, whether it’s website interactions, social media, email, phone conversations, or text messages. You can create an almost one-to-one relationship and, when combined with other points of interaction, a global customer view.

External Data 

Defined as all data outside of an organization’s internal operating systems. Historically, this type of data was hampered by the traditional segmentation approach: external data was limited and, when available, only data that fit within the confines of segmentation rules was considered (e.g., average age group, interests filtered by location, etc.). The overall approach was not broad and the results were limited.

Thanks to advances in analytical processing, coupled with expanded data availability (e.g., open data initiatives), organizations can now tap into a variety of dimensional data in order to add layers of meaning to customer behavior. For example, geographic and socio-demographic datasets can be used to provide deep customer insights: how will traffic congestion in a specific area affect retail outlet visits? How will the weather affect foot traffic for outdoor locations?

Leverage Machine Learning

Machine learning is a combination of mathematics, statistics, and computer science that aims to make predictions based on patterns discovered in data. Machine learning can help marketing teams predict what customers are likely to do. This is possible due to a granular segmentation approach — using detailed datasets to determine what specific actions customers will likely perform.

We’re no longer reaching conclusions based on broad standardized queries (e.g., customer income and age); we are using machine learning to reach predictive conclusions based on specific behavioral queries (e.g., when will a customer visit a store given her current social media engagement on her cellphone?).

You’ve Got the Power!

ive_got_the_power.gifMarketing in today’s competitive landscape is a much different environment than it was a mere 10 years ago. In this age of the customer, technology has finally advanced to the point where customer input actually makes a difference… whether it’s requested or not!

Traditionally, marketing decisions are influenced by customer feedback. Companies cross their fingers and hope that customers will let them know about their experience and that this feedback will be useful in improving operations.

By leveraging the huge amount of available data with advanced analytics, marketers do not need to wait solely on customer feedback anymore. If they want to make informed decisions to optimize strategy, they need to collect their sources of User Data (i.e., transaction, interaction, and external) and harness the power of machine learning.

Want to learn more about big data and the new age of marketing analytics? Read about how you can go beyond Google Analytics or check out our white paper:


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