Five Actionable Lessons Learned from Predictive Customer Journey Analytics

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I’ve been actively researching customer behavior in the financial and retail industries for over 30 years now, and what fascinates me the most is determining the leading indicators of future customer choices and interactions. At Corios we call this predictive customer journey analysis.

journey noun_81349The most important and most elusive part of being responsive to customer journeys is identifying leading indicators. Leading indicators will tell you what each customer is likely to need or want, in advance of the customer making a decision, in a way that gives the firm enough time to prepare and respond with a relevant solution.

Predicting future customer activity is more important than merely observing past activity. If used correctly, history can be a wonderful predictor of future choices. However too often, marketing recommendations are based on customer averages, rather than on trends or events. The customer average is a good predictor if we have no other information, but in this era, we have much more information available to help predict the customer’s next actions. Being aware of, and utilizing this information effectively, is vital.  Often firms have one shot at delivering a relevant solution within a finite window of opportunity. Once that window closes, another opportunity may not present itself for days, months or years.

To underline the importance of effectively tackling the challenge of predictive customer journeys, I’ve researched the actions of our Corios clients, and catalogued the five practices that lead to the biggest differences between successful and less-than-successful efforts.


1) Set predictable goals, and monitor progress towards achieving them

Do you remember the era of web server logs and vendors like WebTrends? Amazingly, they are still in business, a few blocks away from Corios HQ in downtown Portland. When their software first emerged, it was a very solid way of reporting web server statistics: pages, hits and errors (the reports always reminded me of baseball scores.) However, what has always been missing from these reports was any indication of whether the firm using the web server was actually making any money from customers. A problem that is still relevant now. Business owners receive too many reports providing historical perspectives on activity, rather than whether these activities moved the needle by delivering meaningful value to their customers.

Today, these activity reports take the form of interactions across channels, hopefully integrated to the customer level, but the problem still remains that none of these reports inform decision-makers as to whether key objectives are being met, improved, or diluted, as the result of said activity.

Which brings me to my first “difference-making” action when considering customer journey analysis: Can the firm’s decision-makers trace a direct line from interactions to value-producing objectives? The path between interaction and valuable objective should go straight through an applied analytic model that identifies which interactions produce value, such as incremental closed sales or increased value per sale, as opposed to being completely irrelevant.

When this line of sight between activity and outcome is clear, unambiguous, and a leading indicator, decision-makers can redirect resources toward stimulating the most value-producing interactions with customers.

2) Monitor results at the customer-transaction level

Customers are all unique, but some are more unique than others. Some firms measure customer similarities and differences in terms of demographics, others in terms of products owned, and yet others in terms of Recency – Frequency – Monetary metrics. Although these strategies all have their merits, I’ve found the best way to characterize the differences between customers, and ensure their needs are being met, is tied to their behavior, and measured by granular transaction and interfocus noun_66177action trends and sequences.

This makes practical sense if you consider how you might plan to relocate your family to a new city; or buy a new car; or even select a new television show for your family and friends to watch together. When choosing to watch Game of Thrones vs. the Olympics vs. Peppa Pig, your information gathering and decision-making is often only marginally influenced by your demographics or average minutes of television watching. This decision is much more closely influenced by your transactional behavior. (OK, so if you are going to watch Peppa Pig, maybe your family demographics have a little something to do with that decision…)

Therefore, the second “difference-maker” is to divide customers into fine-grained segments tied to behavioral differences, and to measure their change in activity at as granular a level as is possible and practical.


3) Watch for changes in activity, not merely the presence of activity

What we have found again and again is that firms tend to compare actively engaged customers with those who are inactive at a point in time, and try to use this difference as a driver of future customer value. However, we have found that the vastly more effective approach is to measure the change in a customer’s behavior over time, not simply whether the activity is present or not.

At its most basic, customer interactions can be broken into three groups.  Some have a growing relationship with your firm, others are in decline, while the average enjoy a relationship which is steady and stable. Yet even these simplistic terms aren’t enough to characterize the real behavior underlying each customer’s decisions. Even stable relationships can be volatile, cyclical, seasonal, or spiky. At Corios, we see many decision-makers simply using snapshot averages to imply what’s representative about a group of customers’ behavior, but this is a mistake. True customer behavior moves, shifts and trends over time.

It’s this unpredictability which leads to the third “difference-maker”: Monitor changes in activity at the customer level, and not merely for the presence of activity. It’s the changes in customer behavior that hold high-impact, actionable insights.


4) Monitor a combination of financial and interaction signals

In vogue right now are tools that monitor customer interactions across channels. They’re new and cool, but they are missing out on the most important data element, namely, what the customer spends, saves and invests. No matter their interactions across channels, customers ultimately vote with their pocketbooks.

At Corios, we’ve found that neither financial transactions nor channel interactions when used in isolation do a complete job of predicting customer journeys or changes in behavior. In our experience, the best value is gained when you combine these insights together to build a more complete view of the customer. Activity (measured by interactions) is neither a commitment nor a risk for the customer; conversely a financial transaction isn’t a search for information and education. However, when you put both of these dimensions together, further augmented by the customer’s static attributes, you now have a winning combination proven to be effective at predicting customer journeys and optimizing the timing of an offer.

This is why integration of financial transactions with channel interactions (in ways meaningful to developing the most effective leading indicators) is the fourth “difference-maker” between success and near-success.


5) Deliver a targeted offer to each customer rapidly and crisply

several weeks calendar noun_26271Ultimately, the world’s most precise and insightful analytic leading indicators don’t mean much, if you cannot rapidly and crisply execute a relevant offer when the signals tell you that you should. Your analytics and your offer targeting, delivery and measurement need to be in sync and use the same information in order to be business-relevant. The window of opportunity for providing value to a customer will close, sometimes within hours. Often, you won’t be able to enjoy long data refresh intervals in order to provide a customer with a relevant offer.

To gain some perspective on this statement, let’s go back to the example of selecting a television show sighted above. Your network choices are plentiful: HBO, broadcast, Amazon, Netflix, Hulu etc.  Once you’ve selected a provider, you’re eager to choose the best entertainment for your audience. You want something worth watching, that you haven’t seen yet, that is appropriate for your interests, values and audience, and that meets your willingness to pay.  Additionally, the span of your patience to scroll through endless choices is short. Yet, most of us have experienced that overwhelming onslaught of “just too many lame shows to pick from” and end up selecting something that won’t offend anyone in the room, but is probably not the incredible viewing experience we crave.

Clearly we have a long way to go in recommending the right television shows to watchers in the moment of need. Additionally, the other hypothetical situations suggested above, buying a car or relocating to a new city, are fraught with the same perils: not enough of the “right” information presented to the right decision maker at the right time. Yet all the data to make more solid recommendations is available, if only we could monitor the effectiveness of our recommendations, identify when our recommendations resonate with the decision maker’s behavior, and deliver an effective recommendation every time rapidly and crisply.

Using customer offer delivery capabilities, that are tightly integrated with leading indicators, behavioral triggers and predictive customer journey analytics, is our fifth and final “difference-maker.” To be most effective, you need to be able to deliver a relevant, high-impact offer to your customer at the right time in order to maximize acceptance and meet your customer’s needs when the window of opportunity is open.


Robin Way

The Founder and President of Corios, Robin’s professional passion lies in democratizing and demystifying the science of applied analytics. An established thought leader fueled with 30 years’ experience in the design, development, execution and improvement of applied analytics models, Robin welcomes every opportunity to move the analytics conversation forward.

Connect with him on LinkedIn , or reach out to Corios to get in touch.