How to make behavioral customer data actionable?

Customer Data Platform 11 min read

According to research cited by McKinsey, organizations that leverage customer behavior data to generate behavioral insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin.

Look no further than Amazon, Netflix and Google—all of whom have built their entire respective empires around a core of customer behavior data and -analytics.

In a digital world where customer-centricity, personalization, and customer experience separate the winners from the losers, it’s no coincidence that these companies thrive. Before long, it will grow increasingly difficult to compete in any industry for those who are too slow to follow in their customer-centric, behavioral data-driven footsteps. And managers are aware of that.

And yet, it’s amazing how many organizations still only take advantage of a mere fraction of the behavioral data at their fingertips, only due to lacking proper tracking and monitoring software.

There’s a good chance that this includes yours. And you may think, because you are running a B2B environment, this wouldn’t and couldn’t possibly apply to you. Well, you’re wrong and here’s the good news…

You don’t need to be an internet B2C giant in order to leverage the power of behavioral customer data and -analytics to take your business to the next level.

In fact, with the right knowledge and tools, any organization, B2C, traditional B2B, multi-account-multi-user SaaS to even B2B2Gov can benefit from true behavioral customer data, without the need to invest in data scientists and dev wizards. At least, this should be the task at hand.

Why is customer behavior data so important?

Before the digital age, back when traditional one-way mass marketing communication was not only still a thing, but the only thing, personalization and customer experience didn’t really matter so much. Back then, companies could get away with segmenting customers using primarily demographic and firmographic traits such as age, gender, income, ethnicity, occupation, industry, company size, geographic location etc.

In other words, focusing exclusively on the who.

Of course, there’s no arguing the importance of understanding who your customers are. Today, developing customer personas and ideal customer profiles are undoubtedly vital practices for management to understand target customers and for effective customer journey mapping.

But in the customer-centric world we live in today, just understanding “who” your customers are doesn’t cut it.

As Netflix’s VP of Product Innovation, Todd Yellin, pointed out in an interview with Mashable a few years ago:

“It really doesn’t matter if you are a 60-year-old woman or a 20-year-old man because a 20-year-old man can watch Say Yes To The Dress and a 60-year-old woman could watch Hellboy.”

You can’t possibly make assumptions on individual customers’ interests, needs, wants or values based purely upon who they are, or any ‘persona’ you’ve assigned to them based only on demographic or qualitative characteristics.

Instead, companies like Netflix, Amazon and Google know that the key to understanding their customers can lie more in what they do; that quantitative insights revealed through their behaviors can paint a much more accurate picture of what their customers want and need, and how and when to best deliver it to them.

Use customer behavior data to deliver personalized customer experiences

When you think about the Netflix and Amazon customer experience, the first thing that probably comes to mind is their prolific use of personal recommendations.

But also typical B2B organizations such as IBM, Salesforce.com and Oracle make use of user tracking to link user profiles together into company profiles.

Fuelled almost entirely by customer behavior data, their advanced personalization engines are designed to serve customers dynamically personalized content and product suggestions based upon what their employees have been looking for.

Just how effective are these customer behavior-based recommendation engines?

Here are a couple quick stats to get an idea:

  • 75% of Netflix viewer activity is driven by recommendation (Source)
  • 35% of Oracle’s sales are generated through personalization engine
  • Salesforce’s personalization system gains the company an estimated $1Billion ARR through activations and reduced churn

But as insanely effective as it is, personalization is just one (incredibly lucrative) example for how digital age management is using behavioral customer data and -analytics.

Today, leading companies are increasingly thinking in terms of end-to-end customer journeys. They’re leveraging data from CRM and other sources to understand and segment customers by their behavior to improve customer experience and deliver real business value. This is simply a necessary task.

Drive business impact through behavioral software

Using a CRM and other customer management platforms, you get familiar with the different personalization aspects. Here’s 3 client cases:

1. Use client behavior data to improve acquisition

To understand why behavioral data is such an effective asset for customer acquisition, it makes sense to first take a quick look at some of the top challenges marketers face today.

The modern customer journey is incredibly complex. The path to purchase typically involves many different touch points across multiple channels, over an extended period of time that can often span weeks, months or in some cases even years.

Salesforce’s 2017 State of Marketing Report found that 67% percent of marketing management leaders say creating a connected customer journey across all touch points and channels is critical to the success of their overall marketing strategy.

However, the report also shed light on the fact that this is much easier said than done. For most marketers, 51% of campaign messages are still identical broadcasts from one channel to the next. The majority of marketers also struggle to effectively align teams and strategies with the customer journey, citing difficulties such as; lacking a single view of the client, poor CRM systems, fragmented data sources, and budgetary constraints on needed tasks as top obstacles getting in the way.

This complex, multi-channel, multi-touchpoint, time-spanning journey is also driven entirely by the individual customer, whose motivations, goals, values and requirements can differ completely from those of the customer to their right and to their left traveling along the same path – all of whom demand personalization.

A couple statistics from the same report underscore the extent of customers’ demand for personalization:

  • 52% of consumers are likely to switch brands if a company doesn’t personalise communications
  • 65% of business buyers are likely to switch brands if a vendor doesn’t personalise communications to their company

Between the complexity of aligning with the customer journey and clients’ increasingly high expectations for personalization, it is quite a daunting challenge for today’s CX or marketing management, to say the least.

Many CX and marketing management are turning to journey-driven approaches like journey analytics and customer journey orchestration because it was specifically designed to solve these exact problems, unlike traditional methods and tools.

More touch points can mean more challenges, but —with the right software— it also means more opportunities to uncover actionable insights.

As client take various journeys to achieve their unique goals, you can monitor performance and measure journey success or failure. Over time, patterns in behavioral data emerge that may explain those outcomes. Using journey analytics, CX or marketing management teams can easily identify what caused some to succeed and blocked others from reaching their goal.

Armed with these valuable insights, you can leverage journey orchestration software to optimise personalization decisions across touch points. These solutions help you ensure that every offer or message reflects every prospect’s or customer’s overall experience with your organisation, is relevant to their current journey and helps them achieve their next goal.

2. Use customer behavior data to increase customer retention

According to research by Esteban Kolsky, 67% of customers report bad experiences as a reason for churn, but only 1 out of 26 unhappy customers complain. 91% of those unhappy non-complainers simply leave.

Of the few that do actually complain, by the time they get around to it, it’s often already too late.

The lesson here is that you can’t rely on your customers to raise a red flag in order to accurately gauge customer experience, satisfaction, or to predict churn and retention.

You can, however, often spot the red flags through a customer’s behaviors and, with proper analytics, make sure signs of trouble are picked up on your radar early, so there is still time to act. As they say, actions speak louder than words (especially when unhappy customers often don’t even say anything.)

Not receiving enough value from a product or service can be another leading cause of churn that clients often won’t complain about, and that can be difficult to detect. Without customer behavior analytics and segmentation, that is.

Let’s look at an example of how Netflix has been able to integrate this incredibly effectively.

Leveraging behavioral customer data and analytics, Netflix is able to pinpoint the amount of usage activity an individual customer needs each month in order to receive enough value to continue subscribing. If a customer’s monthly content consumption falls below that threshold, the likelihood of them churning skyrockets.

By creating a behavioral segment for all customers that fall below the minimum product usage value threshold, Netflix is able to easily identify at-risk customers, discover insights that can lead to low usage, and monitor this over time.

With this insight, Netflix management knew they needed to find more ways to keep users engaged and consuming more content each month.

They launched a series of initiatives to improve their recommendation algorithms and find new opportunities and channels to provide personalised content recommendations based on user interest behavior. These recommendations would be delivered to users inside the product, as well as through targeted email campaigns and push notifications.

Netflix also uses customer behavior software to make decisions on what content to produce and license, which also helps them to prevent churn, as well as improve customer acquisition.

As a result of these efforts, Netflix has significantly reduced their churn rate to a point substantially below that of many of their top competitors.

In addition to increasing the lifetime value of their typical client, this also allows Netflix to spend more on customer acquisition, while having to acquire less new customers in order to backfill those that do churn.

Netflix executives estimate that this saves the company $1Billion a year.

3. Use customer behavior data to drive growth

Individual customers may have a higher likelihood of converting on particular cross-sell, up-sell or repeat purchase offers at certain times, but they might not know that you have something they want, and often don’t even know exactly what it is they want themselves.

The answer to knowing which offers to show to which customers (and when) can be found in tracking customer behavior data. When it comes to up-sell and cross-sell, few companies do this better than Amazon.

Regardless of your company or industry, the same high level concept behind Amazon’s infamous “customers who (blank) also (blank)” recommendation call-to-actions can be applied to identify cross-sell and up-sell opportunities through machine learning and predictive analytics.

For it’s product suggestions, Amazon’s recommendation algorithm (the one responsible for driving 35% of their revenue) uses customer behavior data such as:

  • A user’s purchase history
  • Items in their shopping cart
  • Items they’ve rated and liked
  • What other customers have viewed and purchased

But, depending on your business, there are many different possible behavioral data points and sources that can be leveraged for this. Most of these are probably already hiding in isolated corners of your existing technology stack (e.g. CRM).

When building behavioral customer segments for cross-sell, up-sell and repeat purchase opportunities, customer satisfaction is another critical factor to consider.

If a customer has recently had negative experiences with your company or hasn’t been getting enough value from the products they’ve already purchased, this probably wouldn’t be an ideal time to reach out with a cross-sell or up-sell offer.

For a customer who may have exhibited past behavior that’s predictive of a cross-sell, up-sell or repeat purchase opportunity, not only can the likelihood of conversion dissipate after a negative experience, but extending an offer at this point in time can actually do even more damage.

Leveraging customer behavior data and analytics, a customer who has encountered negative experiences recently can be identified and moved to a low-satisfaction customer segment which is temporarily suppressed from receiving certain promotional offers. This way, they can be targeted with other retention-focused initiatives to win back their favour and increase their satisfaction.

If those retention initiatives are successful, the customer might re-qualify for a cross-sell offer, at a much more opportune moment.

How to make customer intelligence actionable?

Today marketers need to take steps to close the gap between raw data, meaningful insights and real-time action, including:

  • Collect and reconcile customer data about identities, profiles, purchase history, preferences and transactions
  • Transform and augment this data into a 360° view of the customer with context, intentions, relationships and interactions
  • Turn data into insights with segments, scores, forecasts and recommendations
  • Connect in real time to the customer touchpoints and turn those insights into increased conversion rates and customer loyalty.
  • Integrate the insights with the software you already use to take customer actions in.

With your 1st and 3rd party data, you can build custom audience segments and glean insights about each audience segment to inform your data-driven creative and overall marketing strategy. By matching creatives to the audience segments provided by DSPs and DMPs, marketers can target their desired audiences more precisely, achieve message relevance and personalise creatives that resonate more with each audience segment. journy.io is a customer intelligence platform that will do just that!