Six Ways to Create Better Customer Behavior Analytics

Customer Behavior Analytics

Customer behavior analytics is about understanding how your customers act across each channel and interaction point — digital or non-digital – and what influences their actions. It gives you a way to implement what I like to call, the “four rights” – talk to the right audience, through the right channel, with the right message, at the right time.

Understanding customer behavior can help your organization in more ways than you think. The entire customer lifecycle can be optimized using behavior analytics:

• Customer acquisition: Marketing will target high-value customer segments identified by behavior analytics and study behavior patterns to determine the best potential offers.
• Customer engagement: Behavior patterns will be used to generate personalized next-best, cross-sell and up-sell offers, while behavioral customer segmentation will be used for more general customer marketing offers.
• Customer retention: Behavior patterns will be used to detect possible customer churn and generate next-best retention offers.

The value of customer behavior analytics can be measured in a number of key metrics:

• Increased customer acquisition and conversion rates
• Lower cost of acquisition
• Larger average sale on initial purchases
• Increased number of purchases per customer
• Larger order sizes on repeat purchases
• Lower cost per sale
• Increased lifetime value of customers
• Higher customer retention/Reduced churn
• Lower cost of service

These are all metrics every organization strives to improve and can be dramatically impacted by customer behavior analytics.

There’s a New Sheriff in Town

Until now, the Recency, Frequency and Monetary value (RFM) model was the most commonly used approach to modeling customer behavior. RFM assumes the following:

• Customers who have spent at a business recently are more likely than others to spend again
• Customers who spend more frequently at a business are more likely than others to spend again
• Customers who have spent a higher amount at a business are more likely than others to spend again

Historically companies only had purchase data at their disposal. This made RFM the only way they could model customer behavior.

However, the onslaught of data from the digital age and the variety of new interaction channels have made RFM obsolete.

A holistic customer behavior model that analyzes all interactions over time along with outcomes is not only more accurate and predictable but is also a necessity to compete in today’s digital world. Let’s examine six ways your organization can create better customer behavior analytics that understand the digital age.

1. Use More Data

You have so much more data at your disposal today. The sources are too numerous to list here. Almost every interaction with a customer, in any form, is tracked with data. In addition, there is much more demographic data available.

Take advantage of this data. After all, we are also living in the age of big data, so build behavior analytics that incorporates all the data you have on how you interact with customers, and how they react to those interactions. This will provide you with the most complete view of your customers, individually and in groups.

2. Get More Detailed

Until now, customer behavior analytics would aggregate data based on certain characteristics. Often, this was due to the limitations of analytic technology, which could not handle the volume of data and examine a large number of potential attributes. This would hide the important aspects of individuals.

Big data analytic platforms eliminate the volume and granularity barriers, allowing you to deliver analytics based on detailed actual behavior data. This allows you to understand each customer as an individual and create personalized treatments.

3. Look at Customers Over Time

Like anything relationship, customers are not static and one-dimensional. They will go through different phases and change characteristics over their lifecycle.

Therefore, you need behavior analytics that can see how customers change over time, not just a point in time such as the last transaction. Use time-series, path, and graph analytics to plot the journey your customers are taking and anticipate where they will go.

4. Project Lifetime Value

Hopefully, you will have a long and prosperous journey with your customers. The old model of trying to project and maximize the next transaction is extremely short-sighted, especially in the digital age.

A modern approach to behavior analytics is to model the potential Life-Time Value (LTV) of each customer. Then track path of the customer journey and identify specific actions that will maximize the LTV throughout the journey.

5. Derive Next-Best Actions

Earlier we discussed getting granular – down to each individual – and modeling LTV for each customer. This allows us to see how the journey is unfolding and if we are achieving the maximum LTV for each customer.

It is not sufficient to simply model and track customer behavior. The objective is to map behavior to actions – in other words, make your insights actionable. Each customer-facing part of the organization should use behavior analytics to generate specific next-best actions for each customer, making decisions based on the long-term value of that customer.

6. Continuously Learn

In the digital age, customer behavior can change quickly. Consumers get an onslaught of information that can create swift course changes in their various journeys. Social media is a prime source of information that can drive new behavior.

It is essential to constantly revisit your behavior analytics, testing your assumptions, and re-training the models based on new data. Also, use machine learning to continuously improve customer approaches in ways analysts may not identify during data discovery.

Your Data Transformation is Key

Effective customer behavior analytics starts and ends with clean, organized, and processed data.  How you transform your data is critical to this, in terms of both process and how.  Customer behavior analytics are assembled by analyzing complex and diverse datasets that need to be cleansed, blended, and shaped into final form.  Often times this involves high degrees of collaboration between data engineering and analytics teams.  It also requires rich data documentation to back up compliance processes.

Data transformation can be a difficult and complex part of the process when building customer behavior analytics.  You may have solved the simple EL part of the process through data loader tools that can extract data from sources such as Marketo, Hubspot, Google Analytics, and Adobe Analytics and load it into your Snowflake data cloud.  But that’s the simple part.  Transforming this large, diverse, and complex set of data into something consumable in your dashboard is the difficult part.  At least until now!!!

Datameer is a powerful SaaS data transformation platform that runs in Snowflake – your modern, scalable cloud data warehouse – that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics.  With Datameer, you can:

• Allow your non-technical analytics team members to work with your complex data without the need to write code using Datameer’s no-code and low-code data transformation interfaces,
• Collaborate amongst technical and non-technical team members to build data models and the data transformation flows to fulfill these models, each using their skills and knowledge
• Fully enrich analytics datasets to add even more flavor to your analysis using the diverse array of graphical formulas and functions,
• Generate rich documentation and add user-supplied attributes, comments, tags, and more to share searchable knowledge about your data across the entire analytics community,
• Use the catalog-like documentation features to crowd-source your data governance processes for greater data democratization and data literacy,
• Maintain full audit trails of how data is transformed and used by the community to further enable your governance and compliance processes,
• Deploy and execute data transformation models directly in Snowflake to gain the scalability your need over your large volumes of data while keeping compute and storage costs low.

Learn more about our innovative SaaS data transformation solution by scheduling a personalized demo today!