Recap on the Importance of Customer Decisioning

In our previous article ('Customer Decisioning: Why it’s Crucial to an Unbound Experience'), we highlighted the significance of customer decisioning in enhancing the customer experience. Best practice in customer decisioning involves using data, analytics, and real-time decisioning to engage with each customer individually, treating them as a unique ‘segment of one’. It personalises customer interactions and communications based on a customer's preferences, such as preferred communication channels, and content presentation. Drawing parallels with personal relationships, where we adapt interactions to friends' interests, this approach aims to treat each customer uniquely. However, implementing such practices can be challenging due to complexity and uncertainty about the technology. In this article, we delve deeper into customer decisioning to address these challenges and expand on our previous insights.

Customer Decisioning – A Worked Example

Now that we grasp the power of customer decisioning, let's explore its technical intricacies, delving into how it operates beneath the surface. Keeping in mind the core principles of customer decisioning – personalised engagement, relevance, and leveraging comprehensive customer information – let's walk through an example to see how it works.

Step 1: Customer Arrival

An individual engages with your brand, whether through the mobile app, website, inbound call, or in-store visit. This example focuses on inbound channels, but the framework applies to outbound scenarios as well where decisioning is triggered by an event identified in the data or as part of a decisioning batch run.

Step 2: Centralised Decisioning Request

A real-time call is made to the decisioning brain, seeking personalised content for the identified customer. The request includes contextual details, and the decisioning engine can provide a specified number of next best actions (NBAs).

Step 3: NBA Eligibility Rules

Efficient NBA filtering occurs in real-time, applying rules (like credit hygiene rules to suppress product lending NBAs) to reduce the library of NBAs for the individual. This ensures relevance and maintains application performance, which in turn, results in efficient interaction with the consumer.

Step 4: Identify Relevant NBAs

After applying eligibility rules, the decisioning brain determines which NBAs are relevant for the individual customer. Previous interactions shape this, allowing suppression of irrelevant NBAs if for example, the customer has flagged they are not interested in a certain product.

Step 5: Apply Interaction Constraints

Decisioning applies contact policy rules, preventing overexposure of NBAs to avoid customer fatigue. This ensures a balanced and engaging experience. If a customer has had the chance to see an NBA several times, but has not engaged with the content, the brain will learn and will not re-present the NBA subject to the specified contact policies and constraints.

Step 6: Manage NBA Conflicts

In cases where multiple NBAs are requested (for example, in the case of the website having space for three NBAs covering a banner, sidebar, and footer), the decisioning brain checks for conflicts, preventing contradictory messages on the same channel.

Step 7: NBA Prioritisation Calculation

A final prioritisation is established based on propensity models, business values, channel context, and business levers, aligning customer interest with commercial priorities. Propensity models and business values balance customer interests and brand priorities, identifying the most suitable actions and messages. Incorporating commercial values in prioritisation ensures decisioning aligns with long-term business profitability, while channel context and business levers enhance NBA scores based on customer insights and strategic factors.

Step 8: Instruction Sent to the Channel

After prioritisation, relevant NBAs are sent to the channel for hyper-personalised presentation, considering the customer's context and channel interaction.

While these steps might seem complex, they occur in real-time, typically within a fraction of a second, ensuring minimal impact on channel performance. Best-in-class technology facilitates this dynamic decisioning process, enabling brands to deliver timely and personalised customer experiences.

Completing the Cycle and Implementing Self-Learning to Drive Success

NBA determination and presentation are just two components of the process; the decisioning engine also records sent NBAs, tracks customer responses, and captures nuanced feedback, refining the process for subsequent interactions. Best-in-class decisioning utilises this process multiple times within a single interaction, updating NBAs as customers engage with digital content or provide information in colleague-facing channels. Additionally, the decisioning brain continuously updates models and calculations using adaptive techniques, ensuring AI-driven propensity scores reflect current customer engagement, sales performance, and industry trends, ensuring the overarching decisioning platform works as effectively as possible to create the very best customer experience. These techniques create updated propensity model scores that can reflect up-to-date customer engagement and the latest product sales performance. This automated approach reduces the reliance on data scientists for model rebuilding, allowing them to focus on strategic oversight and driving business value.

Merkle's Contribution to Customer-Centric Excellence

Hopefully this article has gone some way to explain the core customer decisioning principles and technical capabilities within industry best-in-class platforms.  Just like modern car engines, you don’t need to know how every single element works, instead you can get started by understanding the core principles that have been developed and built into the decisioning engine – armed with this information you can trust the platform to perform, just like you would with a modern car engine.

Decisioning is key in delivering exceptional customer experiences. By embracing a 'segment of one' approach and data-driven insights, Merkle empowers precise, timely, and personalised interactions that build meaningful moments and experiences. With Merkle's help, businesses can deliver exceptional customer engagement in this competitive landscape, fostering enduring relationships. Merkle's commitment to customer decisioning marks a vital step in achieving customer-centric excellence.

Want to read more? If you missed our Decisioning Congress in October, you can access all the valuable content on-demand from across the two days. Explore the five stages of the Decisioning Life Cycle and gain actionable insights on how you can become a world-class decisioning business.

For more information on our decisioning practice or to hear about the results we’ve achieved for clients, contact Neil.Faulkes@merkle.com, Vice President of Decisioning – EMEA.

If you’re considering a complete transformation of your customer experience, get in touch with Merkle today to drive real, valuable results.