Let’s assume after graduating from BIM we have just begun working at a newly formed Online Marketing Department for an online retailer and our boss wants to use digital analysis to better understand our customers. Secondly, we also want to give back to our customers by correctly interpreting the valuable data they produce for us. Together with their data and our insights, we want to co-create a better shopping experience for our customers.
What we know
For most of our customers their decisions are made in a multistage process referred to as the customer journey- meaning they typically need multiple online sessions before they purchase an item. We want to understand how close they are to their purchase decision. How a potential buyer starts every session is thus the first categorisation necessary in understanding their buying intentions.
On a basic level a session can be customer-initiated or firm-initiated. As seen below firms use four major online marketing channels. On the other hand, a customer-initiated session can be branded or generic, depending on if they use the company name to reach the site.
We know that in isolation these sessions provide limited insights, where the real value lies is in studying how the session-initiation type progresses and the influence this has on purchase. Being good former academics we found the research of Anderl et al. (2015) and investigate their key findings in order to present to our boss how we can ‘nail’ our online marketing strategy.
Past purchases are the strongest predictor of future purchases.
While this is of no major surprise to us, it reconfirms the necessity to pay close attention to our most loyal customers.
When the previous session was firm-initiated and the current is customer-initiated this is a progression towards the purchase decision.
In the past we struggled with identifying which marketing channels worked best in helping customers progress in their journey. Now with this finding we can observe progression and retroactively identify which firm-initiated channels or ads worked best to return a customer-initiated session.
If the previous session was branded customer-initiated and the current one is generic, the customer is in the processes of evaluating alternatives.
Now we are aware what this subtle progression if they use our company name or not means. Therefore, when we observe this behavior we have identified an opportunity to address our customer’s decrease in interest. This should increase our retention and purchase rates.
Staying in the same channel signals stagnation- meaning no increase or decrease in purchasing probability
We knew that stagnation of the purchasing decision is common, now we have a metric for measuring this-which in turn allows us to identify and subsequently prompt these customers differently in order to increase purchasing probability.
Using all these new insights in parallel, we are confident that we can gain a deeper insight into the journey of our customers and react with greater precision. Thus together with our customers co-create a better online experience for them.
Anderl, E., Schumann J. H., Kunz, W., 2015. Helping Firms Reduce Complexity in Multichannel Online Data: A New Taxonomy-Based Approach for Customer Journeys. Journal of Retailing.