Contact level decisioning drives content relevance
Personal relevance is key to consumer engagement
The creation of engaging and relevant content is something today’s consumer now expects from brands. Research shows 75% of people state lack of relevance as the biggest reason they choose not to engage with communications such as Email.
Therefore, it’s a simple challenge. The more relevant our communications are to an individual’s preferences and circumstances, the more engaged they will be, ultimately delivering higher ROI.
Treating people as individual contacts and not groups
For many years Brands and Email service providers (ESP’s), have planned, executed and measured communications based on performance amongst broad groups of people. Typically this would be metrics such as click through’s, opens rates etc. Performance would then be optimised through various creative and audience split tests.
However, contact level data decisioning and digital technology advances have taken personalisation capabilities to a new level. Brands can now deliver ‘one to one’ real time communications at relatively low cost, and optimise performance accordingly
The basic premise is that people are treated as individuals rather than groups to optimise the communication strategy. For example consumer ABC received Email X and clicked through on Y link. As a result we would now send them email B based on their personal interaction, despite the fact overall when a split test was conducted email A outperformed B.
Contact level decisioning requires a Single Customer View
However, the capability to identify all consumer touchpoints across multiple channels is key. Typically, this entails incorporating data feeds from all sources. This is combined with analytics to provide powerful real time insight to identify key ‘moments of truth’ during the purchase cycle.
For example a High St retailer would potentially integrate the following sources:
Examples of decisoning in action
Many of the best in class Email practitioners are retailers. With thousands of different product lines spanning a wide range of categories, even a small advance in Email communication relevance data driven content decisioning can have quite a dramatic uplift in results.
1. Amazon.co.uk
Amazon uses a variety of transactional, basket abandonment and browsing data to determine the content priority of their Emails.
Their content system recommends various products based on an individuals probability of still being in market, or cross-selling related items. Their model also incorporates user generated data via the on-site ‘Amazon Betteriser’ where an individual can refine their particular interests.
35% of their product purchases come via tailored reccomendations.
2. Argos.co.uk
Argos moved from a 1 size fits-all generic content approach, to multiple personalised dynamic content modules utilising an individual’s previous transactions, comms engagement, and browsing behaviour.
This strategy saw an uplift of 11% in the Click-thru rates, and a 30% uplift in conversion versus generic email
But this is only a glimpse of things to come...
Decisioning engines to deliver true ‘one to one’ communications are clearly being utilised by many brands.
However, the next big advance will be to integrate third party social data sources to leverage the impact of peer recommendation (e.g. Facebook). Research shows that people are more likely to believe consumer opinions about products than directly from brands, and the highest level of trust is from someone they know. The trust level is as high as 90% when our friends recommend us something3.
Therefore, brands need to start combining both real time behaviour data with data from individual’s social graph’s (an example could be Friend X recommended this new camera).
These data sources integrated into a Single Customer View, will be extremely powerful offering brands a massive opportunity to maximise communication impact.
