Defining One Customer Journey per Customer is now Business-Critical

Customer Journey, Customer Experience, CX, AI, Artificial Intelligence, Customer Data Analytics, Customer journey analytics (CJA), CRM, Customer Relationship Management, AI-driven data, ERP, Marketing, Management, China, Net promoter scores (NPS), Mar-tech 2.0, Martech, Marketing Technology, Data Privacy, Personalized Marketing, Targeted Marketing, Firewalls, Customer Data Privacy, US, Canada, UK, Qatar, Thailand, Singapore, Data Security, Customer Data Analytics CEO, CMO, Customer Journey, Customer Experience, CX, AI, Artificial Intelligence, Customer Data Analytics, Customer journey analytics (CJA), CRM, Customer Relationship Management
AI Enriching the Customer Journey with Enhanced Customization

There are no drawbacks that are important enough to NOT take full advantage of the power of AI, says Jean Belanger, Co-founder and CEO of Cerebri AI

What is Customer journey analytics (CJA)? Why is it so crucial to the future of CX, and why is it superseding CRM?

Customer journey analytics (CJA) takes everything you know about a customer that is digitally recorded, puts it into a time series, and lets AI-driven data science loose on it. Cerebri AI’s models help our users to gain insight into which deal to offer, to whom, when, and at what price. CJA determines a brand’s next best actions with their customers, in real-time. Our Cerebri Values CXV platform automates this process from data in-gestation to processing the insights.

How crucial is it? Our approach renders every rules-based, AI-lite, customer, or business intelligence tool in use today, obsolete. That is the power of modern AI data science. The excellent news for large-scale enterprises is that they have massive data troves in their siloed applications like ERP, marketing, support, and warranties to get started with Cerebri AI.

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Why is the ability to define “one customer journey per customer” so crucial for today’s enterprise?

Every business wants to become proactive with their customers and to thrive in the mobile/Internet/digital age, so defining “one customer journey per customer” is now business-critical. Nothing brings this home more than my recent trip to China. We met with senior executives of one of the largest digital banks in the world, doing micro-lending, who have 200M+ customers after just five years. Everything that the bank does is as near to real-time as possible — processing millions of transactions a day, all based on the ‘one customer journey’ model. Customers are more demanding than ever. Does nobody want to wait for anything anymore? We even insist on same-day delivery for things that we used to have to go to the store to get.

While AI will undoubtedly add value to CX, what challenges are you prepared for?

We are often asked about access to data. Data drives AI.  Everyone wants the exact right answer all the time, but the reality is that we grow and nurture our data over time. That said, Cerebri’s users only need enough good data to move the needle on the KPI that they are trying to improve. Then they start curating their data better, collecting more data, and expanding the variety and richness of that data. The cost is sustained by the profits they are seeing from using they’re ‘easier to obtain’ data better. Getting access to the correct data is a challenge for many businesses, but it’s not half as tricky as making sure you are using the very best science to analyze it. In CX you need the very, very best science. It is not all the same.

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What does your ‘one customer journey’ approach mean for measurement tools like NPS?

Net promoter scores (NPS) fail because we misuse it. Period. Everyone knows it. At best, NPS is a “point-in-time,” semi-measure of how your customers feel about your brand. I have been driving the same car brand for 20 years — several cars. Any NPS survey done today would not reflect the ups and downs of my 20+ year relationship with my car vendor. It is that simple.

Instead, “one customer journey” gathers all the data you have digitally recorded about a customer, organized in a time series, from the start of the relationship to the present. Powerful AI tools like reinforcement learning can then generate incredible insights by looking at the billions of patterns in the data.

Everyone must understand how this works; once they do, it is easy to see why it is so powerful. Machine learning in all its flavors is used to predict an ‘outcome,’ for instance – what kind of vehicle you will buy in the next 90 days. The model weighs all the data in your customer journey (CJ) and makes a prediction. Either you will buy a vehicle or not in the next 90 days. Let’s assume that you do. The model thought that you would buy a $50,000 cross-over, but you buy a $40,000 minivan. The model then says I did not even come close, I am going to have to change the weights I assign to the event in this CJ, and next time I will get the right answer.

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But wait. We have 5 million customers in the model. How about we look at all the other customers to see if this $40,000 minivan purchase looks like something they might do? If it does, the model changes its weights as well. You can see that even a basic model in this circumstance would analyze billions of patterns and do trillions of calculations. This is the strength of computers. They calculate and learn by example, not by inference – the way humans learn. We make leaps of logic, but computers are not good at that.  But they sure can calculate, at scale. You can see why cloud vendors prefer AI. It’s a perfect fit.

AI seems to have a significant role to play in your NBA(set)s. What role does analytics have here?

AI, and more specifically, reinforcement learning, is the core of our value proposition for NBA(set)s. Once you have established which marketing events (remember we can take time, value, and sequence up to 4 marketing events in a set) drive the best possible outcomes, then this is, in practical terms, one result in our analytics process. Targeting specific products for specific buyers is another such example.  But once all these AI-based assessments are made, our software allows you to slice the customer base, customer journeys, and their events and proposed insights, as required, because CVX v2 features a sophisticated data mining approach to AI.

Hyper customization of customer interactions has its own drawbacks and challenges; how is your platform designed to deal with them?

There are no drawbacks that are important enough to NOT take full advantage of the power of AI. The biggest challenge for us is to break through the noise of mar-tech, where there are literally thousands of companies with obsolete technology. That is how fast AI has made life difficult for vendors. We call this Mar-tech 2.0, and it’s approaching.

How do you plan to handle the security challenges on your platform? Data privacy issues?

Cerebri AI operates behind our customers’ firewalls, and we don’t have any public sites, so we conform to the best practices of all our customers globally. We have customers and pilots in the US, Canada, UK, Qatar, Thailand, with two more pending in China and Singapore. So, we operate globally, and we are incredibly conscious of the requirements of our customers, many of which are the most significant players in their markets globally.

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Jean Belanger is the co-founder / CEO of Cerebri AI. Cerebri AI develops and sells CVX, one of the most sophisticated CX platforms in the world.  CVX measures customer engagement (CE) and uses these CE values to drive financial results. Our CVX platform is a leap forward in applying AI and reinforcement learning to draw insights from customer journeys. The CVX Next Best Action{set}s insights are driven by patent-pending object-oriented AI & reinforcement learning modelling methods that time, value, and sequence up to four events rendering both rules-based and AI-lite technologies obsolete for driving maximum results. To learn more about Cerebri AI and CVX please visit