mass precision segmentation

Basic segmentation exercises like demographic segmentation have their usefulness in understanding large groups while psychographic segmentation helps in linking groups of people based on their mind-set and motivations. 

But as we move deeper into building relevance and realising the potential of mass precision targeting, value exchange and enabling desired action segmenting based on common behaviours becomes a step towards building  higher relevance and actionable marketing plans. Behavioral segmentation will help marketers ensure that their digital plans target the right consumers, engage with the right content that aids conversion – behavior change at scale. Behavioral segmentation promises ‘Mass Precision’.
 
To achieve the mass precision promise of behavioral segmentation we need to do the following:

1. Move from Attention to Action : Desired behavior change (actions) are the end output that digital investments will be measured against. Actions that lead to conversion or that are probable proxies for conversion in the future. Buying an airline ticket might be an immediate conversion action but searching for holiday locations, visiting comparison sites for travel bookings, subscribing to an alert for travel promotions etc indicate the propensity for future behavior change and are good proxy actions.
2. Create Action based Segmentation : Using the behavior change/ action proxies as a filter derive segments that are similar in action but differentiated in the volume, value, context of the action. Within the defined set of consumers who have performed the action/ proxy actions how do they break out into clusters that are different from each other ? Segment clusters will naturally break out on volume/ value parameters but other likely clusters based on destination/ time of year/ type of travel/ adjacent bookings (hotel + airline + type of tour + car rental) will reveal segments that are opportunities for further investment. 
3. Chart the Consumer Journeys : The answer to ‘who does what action and why ?’ is what we are trying to ascertain. The order and cascading prioritization of actions will vary across segments. Internal data (current consumers, visitors to website, subscriptions), 3rd party data (search, social) needs to be blended with a qualitative understanding of the consumer to chart the different segment journeys. Its less important to be precise than to have answers to the key tipping points and barriers in the consumer journey with an in-depth insight into the ‘why’ behind the key actions. Behavior change is the objective and Consumer Journeys are rough guidelines that help interrogate the data and refine the investment choices. In most cases, it will inform organisations about changes that need to be made to improve user experience and remove friction in conversions.
4. Small Data Analysis : Having isolated segments its time to use small data focused on certain key actions (Discovery, Intent, Conversion, Abandonment) to hone in on specific solutions for behavior change by segment. Prioritization based on segment size, actionability and urgency will be an output of this analysis.
5. Pilot-Optimise: Time to action the investment choices as pilots, observe if the hypotheses in the analysis is verified in changing behavior within the segments.

 
Behavioral segmentation is a dynamic exercise as consumer preferences change, actions change in response to context. In order to keep ahead of these changes the next step in behavioral segmentation becomes a serious investment in machine learning/ predictive analytics. Using big data modeling to discover new opportunity - new segments, new actions. 

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