Checkout out our new website on customer journeys: www.journeylytics.com
Customer journeys are hot. Marketing specialists feel that there is a lot to be gained from understanding what are the steps that the customers take before buying a product or a service:
- What brand communication do they see?
- Through which communication channels?
- How often are they exposed to brand communications?
- Do they go to the brand website?
- How often and for how long?
- How long does a typical customer journey take?
By uncovering patterns in customer journeys, we could possibly achieve better segmentation and ad targeting, better website personalization, etc. Currently, we
observe that behaviour is often summarised simple metrics, such as by visiting certain pages or by consumer engagement with certain keywords. For example:
- We’ll target everyone that has been to www.runningshoes.com
- We’ll put a higher bid for everyone that has been to my website
- We perceive those that searched for “buying running shoes” as the most important target
The above is executed with a lot of sophistication but we feel that the time-dimension defining behavior is missing with most analyses, for example:
- Are consumers that first go to website 1 and then to website 2 more likely to convert?
- Should we focus on consumers that visit many of our online assets in a short period?
- Should we increase our bidding a couple of weeks after a consumer starts their journey showing interest in our products?
However, including the time element is very hard. Dealing with time in data is by itself very complex. What’s worse, including the time dimension makes each consumer unique (we can segment those that went to a website but only one consumer went to the website first, stayed there for 12 minutes and went to another website 18 hours later, etc). Compressing the time dimension into a predictive model is certainly an additional mathematical challenge.
At Coders Co, we understand how to deal with time-stamped data. With our own tool, Rax, we are able to extract customer journeys from data in any form. With a number of partners, we conducted a research project which lead to creating a new methodology for comparing and extracting patterns from customer journeys (see here, but note that the applications are closed). Finally, we created a visualisation tool for customer journeys.
If you need to understand the journeys of your customers, but don’t know where to start, contact us, and we can help you.