Twenty years ago, Campaign Management or Database Marketing capabilities were key cornerstones of any Marketing department. These activities would involve teams utilising various data selection tools to pull customer lists that met a specific profile, these files were dispatched to a mailing house for printing or inclusion in the forthcoming customer statement run – a process that might evoke nostalgia for those who recall the era of statement inserts.
Over time, businesses evolved their campaign management strategies to incorporate weekly or monthly email campaigns, resulting in cost reduction and enhanced speed in executing marketing initiatives. This transformation also gave rise to Customer Relationship Management (CRM) practices, as brands sought to navigate the expanding array of communication channels.
The growing reliance on data analysis became essential for discerning customer channel preferences and understanding distinct purchasing behaviours. Propensity models were leveraged to assess customers for tailored product offers, contributing to a notable enhancement in Return-on-Investment (ROI). Brands became more astute in their campaign targeting strategies, although communications were predominantly approached from a business-centric perspective. For instance, Brand ABC aimed to promote Product Y and therefore sought to identify the most suitable X thousand customers from their database to receive the offer within their allocated budget.
Customer decisioning represents a significant advancement in our approach to customer interactions, as it treats each customer as a unique individual. Our objective is to gain a deep understanding of the most relevant topic for each customer, whether it pertains to a product, service, or operational message. This considers all available customer information and evaluates the likelihood of a positive response, factoring in the content and prior conversations and interactions that the customer has had.
This shift in customer engagement moves away from the traditional segmentation of similar customers into groups and aims to function at the level of an individual, similar to a highly personalised shopping experience. Think of it as automating some of the principles observed in the old-fashioned shopkeeper model. Much like the shopkeeper who recognised each customer, understood their background and personal circumstances and habits, and used that knowledge to anticipate their interests and preferences, customer decisioning endeavours to replicate this level of personalised service, in turn, creating a much more meaningful customer experience.
In today's digital landscape, brands no longer rely solely on physical storefronts. Instead, they leverage a multitude of digital channels as their shopfronts. The challenge lies in translating this promising concept into practical implementation.
To create a seamless omni-channel customer experience, brands strive to foster continuous engagement and dialogue with each customer, regardless of the communication channel they prefer, encompassing social media and digital platforms (such as websites and mobile apps) to more traditional channels such as telephony and stores (e.g. retail outlets, etc.). The ideal scenario blends customer interactions across digital, social, and telephonic channels. Inbound and outbound communications (e.g., phone, email, mobile notifications) influence digital channel content, creating a seamless, unified, and unbound experience.
Segmentation has its limitations. Even with similar high-level profiles (age, life stage, value segmentation), customers may differ in channel usage, behaviour, and content preferences. Customer decisioning aims to treat each customer uniquely, as a 'segment of one,' by gaining comprehensive insights and tailoring interactions to meet their specific needs. This ensures delivering the right message at the right time, through the preferred channel, with optimal personalisation.
This approach is made possible through the establishment of a central decision-making hub and the maintenance of interaction history. It is further enhanced by data science capabilities that harness the most current data to determine the best next action for each individual customer, ensuring a highly personalised and effective customer engagement strategy.
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.
Check out this video from our 2022 Decisioning Congress, where we gathered industry leaders to discuss what steps businesses need to follow in their decisioning strategy to achieve a first-class customer experience.
For more information on our decisioning practice or to hear about the results we’ve achieved for clients, contact firstname.lastname@example.org, Senior Director of Decisioning.
If you’re considering a complete transformation of your customer experience, get in touch with Merkle today to drive real, valuable results.