Using data science and behavioral profiles for accurate content matching

Customers today demand personalized experiences and interactions with the brands they love. They expect brands to know the history of their engagement, as well as their preferences and likes. And they want brands to have an accessible history of interactions, from sales to customer service.

Brands are responding. Today, they are delivering with personalized marketing strategies that go beyond demographic personalization. They are creating messaging and experiences that meet the growing expectations of today’s consumer. And for those brands that aren’t doing so, yet there is huge room for improvement.

The rise of behavioral customer profiles

To do all this, marketers are increasingly using behavioral customer profiles. These synopses go a crucial step beyond the customer profiles and personas that are increasingly common in marketing.

Customer profiles give you a broad brush of a customer portrait. They focus primarily on the demographics of those who are already buying or likely to buy your goods and services. But behavioral customer profiles delve more deeply into customer’s shopping behaviors, how they decide what to purchase and how they feel about a company or brand. By segmenting customers by their activity, you gain more insights into whether customers are buying because of loyalty — or due to less impactful factors like price or value.

Building behavioral customer profiles means collecting and using more data. More customer data means more insights that can be gleaned and a better understanding of their motivations, needs, desires and uses of your products or services.

Behavioral customer profiles let you identify your most loyal customers and to understand why they are so loyal. Loyal customers are the most valuable to your business, and the insights you gain let you appeal more to them and address their most urgent needs.

The importance of data science in marketing

Today, through the internet of things, artificial intelligence and machine learning, the opportunity to leverage data is massive. However, with the volume of data available also comes questions brands need to address and solve.

• Which data is most valuable?
• How should we track, store and report on data?
• How can be optimize the data we have and decide on which new data to collect?

Data science, often known as Big Data, is the way to get at these questions and collect, report on and use data for building behavioral customer profiles.

Data science lets you identify the traits that are necessary to track. Data points including means of purchase, time, device used, location, payment method, size of purchase, item purchased, price points, pages visited, time on site and myriad other markets that can help develop the behavioral customer profiles.

Once the customer profiles are built, data science helps you segment customers and target ads and promotions to meet their specific personas and needs.

Data science also lets you develop predictive analytics to project how customer types are likely to respond to ads or campaigns. These precision tools can help you hone in on the customers most likely to respond favorably.

With more customer data available, your brand will be able to price better and create social media, email and traditional campaigns that are most cost-efficient and deliver better results.

How Lytics uses data science for better customer experiences

At Lytics, we’ve developed Lytics Scores that evaluate consumer behavior using data science, providing values on nine criteria for each customer. Each score assesses a behavioral quality that can be used to construct behavioral profiles, including measurements such as:

• Frequency: Measures how consistently a user is interacting with your brand over time. The more frequent the interactions, the higher the score.
• Quantity: Measures a user’s cumulative activity over their lifetime of brand engagement. The more activity the user registers, the higher the score.
• Intensity: Measures the depth of a user’s typical interaction with your brand. More sustained or intense usage means a higher score.
• Recency: Measures how recently the user’s last general interaction was. More recent activity means a higher score.
• Propensity: Predicts how likely a user is to return with subsequent activity. Users exhibiting positive interaction patterns are more likely to return and therefore have higher scores.
• Momentum: Measures the rate at which users are interacting with your brand. Users who are interacting more than average with your brand will have a higher score.
• Consistency: Measures the regularity of a user’s engagement pattern.
• Maturity: Measures how long a user has registered interactions with your brand.
• Volatility: Measures how sporadic a user’s behavior is while interacting with your brand.

Lytics scores provide a powerful way to develop more precise profiles. In turn, those profiles can be used to build personalized, targeted advertising that draws new and return customers to your website.

To learn more about how behavioral customer profiles can help you gain more data insights, contact us today.