We often hear about and see the different lifestyles the millennial generation leads. This group is a prime example of the influence of the digital age on lifestyle, including how people shop.
The 2016 Deloitte study, “Navigating the New Digital Divide”, found digitally-influenced consumers buy more and spend more. Those who use digital while they shop in-store convert at a 20 percent higher rate compared to those who do not.
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:
The value of customer behavior analytics can be measured in a number of key metrics:
These are all metrics every organization strives to improve and can be dramatically impacted by customer behavior analytics.
Until now, the Recency, Frequency and Monetary value (RFM) model was the mostly commonly used approach to modeling customer behavior. RFM assumes the following:
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.
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 incorporate 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 on your customers, individually and in groups.
Until now, 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 the 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.
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.
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.
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.
In the digital age, customer behavior can change quickly. Consumers get an onslaught of information that can 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.
With the flood of data from the digital age, big data analytics is really the only way to properly understand behavior and map it to actions that will maximize LTV for each customer. Looking at the various metrics we outlined earlier that behavior analytics influences, the potential impact is enormous.
Our new eBook, Using Customer Behavior Analytics to Increase Revenue – A Start to Finish Guide, delves into more detail on how you can use big data to create holistic, digital age customer behavior analytics. Take this big data journey so your organization can reap the benefits of closer customer relationships.