Personalisation is a very broad topic. It's not a single experience - ti's a collection of practices, activities, outputs and a reframing of general activity to think about collections of people instead of binary outputs of success/failure.
In this document, we explore the topic and what's possible around Personalisation in Webtrends Optimize.
Areas to consider
Personalisation is a very broad topic, and so there are many areas to consider. This is loosely how we think about the topic.
Data pool
This is about getting your data in order. Collecting, flowing, aggregating, being queryable. We consider this a first-step for people who want to personalise with intention, as opposed to just trialling a single concept.
Together, this data builds a picture of before (imported audiences), now (landing pages, UTMs) and real-time (affinity, behaviour)
WTO data
There is a large amount of data you can collect and store in/with Webtrends Optimize. These include:
Affinity-based scoring
Browsing behaviour (views, purchases)
Survey responses
UTM params
Social Proofing
Price Intelligence
Your data
You can augment this data with your own customer data for a more rounded picture of the customer. In partiular, this is helpful for seeing their long-term history
Imported audiences
Data layers
Actions
There are a large amount of things you can do that fall under the Personalisation umbrella. Some examples are:
Run segmented experiments
Analyse existing experiments by subsegments.
Personalised landing pages
Placeholder experiences
Serve Recommendations
Dynamic merchandising
Social Proofing
Dynamic Pricing
Upsell strategies
Exit intent studies
Etc.
Outputs
What are outputs of this piece of work:
AB Tests that “win” more often, and knowledge of why/who they don’t work for.
Lists of popular products, categories, etc.
Relevant exit triggers
Large-scale personalisation frameworks
Pre-selection of filters etc.
Etc.
To summarise
When we look at Personalisation in Webtrends Optimize, we think about all the data we can get in order, all of the things we could do, the use-cases we could unlock, and then compare these to any given website to understand what makes sense for your website, your products, your customers, etc.