Untangling the spaghetti of Customer Journeys in the era of Digital Transformation

Could the key to fit for purpose customer experience (CX) be lying before our eyes?

Brands, no matter their size, need to be more proactive. Anticipating their customers’ demands, before they start shopping around. Dealing with customer demand is harder than ever, but the rewards for getting it right have become more profitable.

Luckily, almost every brand has a treasure trove of data-sets of customer information available to them – transactions, marketing communications, on-board telemetry, voice and text support communications. The list is endless. Of course, many brands may struggle with this influx of data, online and offline, structured and unstructured, and require scalable technology to help them manage their data, to drive meaningful insights and derive real value from their hoard.

The latest customer journey analytics platforms can analyze this data, building comprehensive customer journeys, one per customer, that cut across the traditional customer silos – sales, marketing, support. Gartner calls this Customer Journey Analytics, others say personalization.

Whatever the name, Artificial Intelligence (AI) matters and enables us to understand our customer better. Employing the latest AI and reinforcement learning, marketers are now able to value and measure customer engagement – the customer’s view of their commitment to a brand and products. Replacing the need for Net Promoter Scores (NPS), but more importantly, bringing customer engagement (CE) to the forefront of a business’s drive for customer success and financial results.

It is now possible to take all this customer data and analyze the billions of patterns inherent in a diverse customer dataset to see what marketing will work for each individual customer. If a customer buys a product, would it be likely that others would do the same? Multiply this across all your customers and you soon have billions of possibilities, requiring trillions of calculations. The key to doing this at scale is to automate this process, from data in-gestation, to building a customer journey and processing actionable insights.

State-of-the-art Customer Journeys

A true customer journey needs to incorporate all manner of customer data based on availability and relevancy. Some of this data is buried deep in the ERP systems, some come from marketing campaigns, sentiment studies, web traffic and external sources -such as macro-economic reports, social media, blogs, etc. Only by combining these sources, can a marketer gain a true, composite view of a brand’s customers.

At Cerebri AI, my team’s ongoing efforts to develop a new valuation system for Customer Engagement (CE) has led to 3 critical insights:

1. Customer engagement metrics are not systematically used to decide customer offers driving both engagement and financial results at the same time. So why do marketers engage customers and ignore these factors for driving financial growth?
2. The process of turning data into actionable insights is complex and requires a multiple-stage orchestration pipeline (from data collection & input to UX).
3. ‘Rules-based’ and ‘AI/lite’ based single Next Best Actions do not generate results needed by large-scale enterprises to drive financial results.

Actively engaged customers spend more money, participate in branding initiatives, and often become your company’s best brand advocates. Therefore, marketers need to measure, track and devise engagement strategies that keep their existing customers engaged and ensure they remain the best advocates of their brands.

Find out more with our solution demo. Write to james@cerebriai.com