Smart integration of data can now help identify and predict customer location and movement along the customer journey continuum. Mapping the customer journey is a vital planning tool.
Mapping out customer journeys is a well-established phase of communications planning. At Ogilvy, this represents the third step in the well-regarded planning platform: FUSION. The customer journey identifies the different phases customers migrate through toward a desired behavioral change destination. The journey phase will differ in each planning effort as the preferred consumer action and marketing objective are all project specific.
Improved understanding of the different stages customers should pass through en route to the ultimate desired location helps planners marshal the right channels, messages, and content to aid the customers along their journey.
The construct around the journey-based plan addresses key questions such as:
• What is our ideal behavioral perception for audiences in a specific stage?
• What are the perceptual challenges that may hinder getting our audience to think in a particular way?
• What are the positive levers that can enhance the likelihood of our audience to respond in a desired manner?
• How do we then move our consumers to the journey’s next phase?
• What channels do we deploy, and at what times, to get our key messages across our audience?
When these are well identified, the output helps make the ubiquitous, overused, but still aspirational goal of “right message, right channel, at the right time, to the right audience,” a possibility.
The customer journey can be complex: recent studies such as McKinsey’s The Consumer Decision Journey have shown that the customer journey has grown more complex. As I have alluded to in my previous article, The Marketing Funnel is Not Dead: A Website Analogy, customers may take several complex detours, but they still have to pass through well-defined phases to proceed with their conversion. The typical phases of this journey start with brand or message awareness and proceed to stimulating interest, trial, usage, commitment, and advocacy. Customers may get caught up in a phase, or proceed rapidly through phases, or even recede at times. However, you generally need to be aware of a product before you can consider using it.
The journey currently produces robust and well laid-out plans to engage and usher consumers on the behavioral change voyage. The next step is to map real customers to each phase and deliver plans against these customers to improve the journey. Before consumer-mapping knowledge, marketers have applied satisfactory approaches including contextual marketing, which aligns messages to media content as a proxy for consumer awareness and the journey phase. Sequential messaging is another approach without mapping knowledge. This approach starts with early-phase messaging and shifts to later-stage messaging based on average phrase duration. Lastly, one could always deliver broad messages, with the hope and expectation that the audience will self-select, and engage with the messages most applicable to their journey. However, the utilization of consumer-mapping information and understanding which individuals are in each phase are preferred.
The availability of customer-level data and the ease of pooling previously unconnected data are making customer mapping a reality. Now we can identify when a customer traverses a specific phase of the journey so that we can execute the well laid-out communication plan against these customers. Data can now help us to answer questions such as who are these customers. What is the likelihood that they will try the product? How quickly will they progress along the journey? How likely are they to become a highly valuable customer? Once the customer journey has been identified, planners and analysts can identify the attributes and traceable behavioral markers that correspond to each phase. Analysts then pool together vast available customer-level data, create new variables as needed, recommend new proxy measures, and categorize customers into their corresponding phase. This is the essence of marketing smart: integrating consumer mapping (segmentation) and targeting with planning from the start.
We recently categorized healthcare professionals (HCPs) into key journey phases using combined data including scripting volume (current value), category share (opportunity), and trajectory of prescription change over time (momentum), as well as other behavioral and attitudinal markers (attributes). We identified “the trialists” as customers who have a low volume of recent activities, or have remained static in their usage patterns. “Adopters” are users on an upward momentum who overindex on usage, while “the passionate advocates” have a large volume of usage and are still increasing their volume. The passionate advocates typically index well in terms of the category’s brand share. Since we can put a face to every target HCP within the customer journey’s important stages, allows us to map the communication plan, as well as behavioral change targets, to specific customers.
A journey infused with data makes evaluating and optimizing marketing effectiveness easier. Goals and targets should be set with behavioral outcome objectives for each customer segment, which makes tracking, assessment, and adjustment more feasible. When customers traverse into the journey’s next phase, the speed and momentum can be quantified, and the effect of channels and messages can be realized. A/B testing experiments are also beneficial to identify and amplify drivers (eg, tactics, content, execution) that have proven effective in engaging and moving customers into the next phase.
In conclusion, data, and the attendant analysis, can enhance our understanding of audiences along the customer journey, thereby enhancing more relevant communication, engagement, and desired responses from our customers. Marketers who put the customer-mapping capability to better use will reap the results of increased customer velocity along the journey, better customer experience with the brand, and higher value per customer.
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