Journey Feat Image

With Big Data, Learn How the Customer Journey really Works

  • John Morrell
  • February 26, 2018

And just like life is a journey full of many experiences, you want your customers to have long journeys with your organization, filled with many positive experiences. Today, 56 percent of all customer interactions happen during a multi-channel, multi-event journey. And 38 percent of all customer journeys involve more than one channel of interaction.

According to McKinsey, a deeper understanding of the customer journey can lead to insights that are 30 to 40 percent more predictive of customer satisfaction and churn. Therefore, understanding and optimizing the entire journey — not simply individual experiences — can create huge value for your company.

Five Ways To Amp Up Your Customer Journey with Big Data

So what can you do about this?

Previously, companies would just report what was happening. However, with big data analytics you can go beyond seeing what happened on the journey (the individual experiences) and delve into why and how certain behavior occurred along the journey. In this way, you can define actions that encourage success and eliminate failure.

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Let’s go over five ways big data can boost your company’s understanding of customers and their journeys.

1. Tie Together Multiple Experiences Into a Journey

As we discussed earlier, the true customer journey is the sum of the different experiences customers have over different channels and touch-points. Each experience can cause behavior changes that alter the journey in a positive or negative way.

Big data allows you to bring together the entire journey (sometimes over many years and transactions) and dig deep into the data to see what the experience was and the impact on the journey. It’s important to see all three of these items — journey points, experience and impact — if you’re to understand what actions to help create a fantastic journey.

2. Find Hidden Correlations

With all this data, many of your insights might be well hidden. In the case of the customer journey, you need to see the trees from the forest — find the unique correlations that create the big picture.

Leading big data analytics platforms give you advanced analytics and data discovery to find the hidden patterns in the data that make up the journey and more importantly, are guiding the journey. This may lead to correlations and conclusions you never considered before such as:

  • Unknown paths customers are taking
  • Different time sequences in taking actions
  • Sentiment that customers express along their journey
  • The behavioral reactions taken based on certain experiences

3. Delve Into the Minds of Your Customers

We’ve talked a little about behavior so far, but it plays a major factor in determining outcomes. Therefore, it’s an essential component of your customer journey analytics. Behavior analytics shows you the mindset of the customers at each step in the journey and more importantly, what led them to take their next action.

The digital age has given us tremendous amounts of data that help us understand customer behavior. Big data analytics enables you to harness this data to directly correlate experience to next actions, which is behavior. From this data, a company can optimize and personalize the experiences to impact behavior and guide the customer down the journey to right outcomes.

4. Increase the Lifetime Value of Your Customers

In today’s world, a customer’s journey doesn’t end with one purchase. Your goal is to create a long, profitable lifetime relationship with the customer. After all, it is called Customer Relationship Management.

Using big data-driven insights, you can devise advanced strategies to retain customers, and sell add-on or complimentary products. Advanced customer journey analytics mapping means you’ll be able to gain more value from each customer.

5. Increase Your Ability to Experiment.

As I discussed in a recent blog, Five Ways Big Data Delivers Business Agility, big data analytics has the potential to increase the speed at which companies do business, adapt to change, and find what works and what doesn’t. Some people call this “agile.” Others call it “learning to fail fast.”

Experimenting at a big picture view does not give you the granularity or ability to apply actions to your experiments. But with data-driven customer journey analytics mapping, your experiments and testing will produce more insights, be directly applicable to relevant actions and allow you to see the full impact of your efforts.

How Amazon Navigated the Big Data Customer Analytics Jungle

Few companies have been as effective at leveraging big data as Amazon. The company has revolutionized the retail world (Yes, retail, not just online retail) with customer journey analytics that are among the most advanced in the world. Amazon doesn’t just sell goods; it crunches and analyzes the digital customer journeys of millions of customers. Using advanced predictive modeling and other big-data enabled processes, Amazon discovers consumer wants, needs and preferences.

One of Amazon’s most powerful abilities is its recommendation system, which puts relevant products right in front of customers. Amazon analyzes the accounts and preferences of tens of millions of customers by combining clickstream data, purchasing history, past behaviors and correlations between purchases. This allows Amazon to delve deep into customer behavior.

Amazon has also used big data to build a tremendous customer experience in their service department, using the customer’s journey to enter a new phase beyond the purchase. Amazon drives to a long-term view of customers, trying to create a higher lifetime value as they return for repeat purchases.

Every time a customer contacts Amazon’s customer service department, the representative has access to the customer’s past purchases and interactions. Using this data, reps work with and improve the customer’s overall satisfaction. As a result, Amazon is constantly at or near the top in customer service rankings.

Using Journey Analytics to Increase Customer Lifetime Value

Surfdome, a leading European specialty retailer, needed to fuel growth through targeted marketing, customer cross-selling and higher repeat purchases. They had a large volume of data about their products, customers, transactions and purchases in different silos, and needed to bring this data together to gain deeper insights.

The company used Datameer to integrate their data, and then analyze it to identify deeper customer segmentation and customer behavior. Surfdome used the analytic results to drive highly targeted marketing to acquire new customers, cross-sell in the purchase funnel and use customer marketing to spur additional purchases.

As a result, Surfdome boosted their customer acquisition rates by targeting the most valuable segments, raised the average purchase size through improved cross-sell efforts and increased the average lifetime value of customers through greater customer loyalty and repeat purchases.

Conclusion: Big Data is Now a Competitive Must

By using big data to understand customers and their journeys, companies can gain deeper insight into customer psychology. They can also uncover hidden correlations that reveal behavior patterns and the actions they can take to impact the behavior to produce positive outcomes. This leads to a longer relationship, a higher customer lifetime value and increased customer retention.

Organizations that use big data to optimize the customer journey not only create more profitable customer relationships, but also create competitive advantage in highly context consumer markets. Just think how often you purchase from the companies with the most streamlined journey with a superior experience and imagine what that could mean for your company.

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