How to crack Starbucks user behaviour related to promotional offers
The analysis of purchasing behaviour is of central value for every offline and online retailer. Especially in the digital sector, users can be tracked and their consumer behaviour can be analysed in detail. For companies like Starbucks, it is of great interest to understand how users engage with the app and what purchasing decisions they make. In terms of promotional offers, it is important to know which offers perform how. This project was conducted as part of the Data Science Nanodegree on Udacity and used data provided by Starbucks for analysis. The aim of the project is to analyse data from Starbucks about their customer behaviour in relation to certain promotional strategies. It will be analysed which offer strategies are best received by which customer group and whether there is a significant difference between the groups. In this way, Starbucks could gain insights into how best to promote and offer coupons or other promotional offers. In the end the revealed insights indicate how to increase the effectiveness of promotional offers. In the following I will focus on answering these questions:
- Do user characteristica influence the effectiveness of promotional offers?
- Are there distribution channels that are more effective than others or can be related to a certain group of users?
- Does the offer type play a crucial role in how users react on a promotion?
User characteristica & the effectiveness of promotional offers
A logical approach to analysing promotional strategies is to evaluate whether certain characteristics of users are associated with different behaviour. This can help determine whether strategies should be adjusted depending on gender, age or other demographic characteristics. The challenge in the present data set was to consolidate individual event tracking parameters of user behaviour and prepare them for analysis. The core of the analysis was the assumption of a user journey in which a user receives an offer, views it and can complete it. By completing the individual steps, the effectiveness of the offers can be determined and interesting parameters analyzed.
First, looking at the gender provides some insightful information. With male users having the biggest share in the data it can be expected that they also receive and view offers compared to female and other genders in a relative way. It is all the more surprising that in the step of offer completion, the relative proportion of men and women is increasingly equal. This indicates that women are more likely to complete an offer.
Following on from this, another statistic shows that women have a higher average transaction value than men. This suggests that women spend more money on average than men and are therefore the potentially interesting user group when measured by average transaction value.
Another interesting variable of users is their income. It was observed that, on average, users who complete an offer have a higher income than the groups of users who receive and view an offer. This implies that user groups with higher incomes more often accept offers or per se more often complete a purchase transaction. Interestingly, it is mainly men whose average income increases by 8–9% when comparing the step offer viewed and offer completed. Relatively speaking, this is a higher increase than for women, which would mean that within the male user group, especially those with a higher income are interesting for offer strategies.
Effectivenes of distribution channels
Offers get send to users by mail, through social channels, via the web or mobile. The first question that is raised is, are channels more effective than others when it comes to completion rates of offers? Simply put, yes. The web turns out to be the channel that has the highest rate.
Interestingly, no further dependence on income, gender or age can be found beyond this. This leads to the conclusion that the web itself is somewhat more suitable than, for example, the email channel for placing offers.
The role of the offer type
Another relevant factor in the analysis is the type of offer. This can be a discount, a buy one — get one or purely informative. A look at the absolute (graph) and relative figures shows that discount offers lead to more completions of the offer. Although bogo’s are viewed more often, discounts achieve a higher effectiveness in leading users to a purchase decision. As a result, discounts are the most appropriate type of offer to influence purchase decisions.
Conclusion
To see the big picture, it was necessary to dive deep into the data and into single user steps. For me, the starting point of the analysis was to investigate if the user’s journey from receiving an offer to viewing and completing can be connected to particular characteristics that have the potential to inform further decision about how and whom to present promotional offers.
At the user level, it appears that women tend to be a more lucrative consumer for Starbucks, as they have a higher rate of completing offers and a higher average transaction value. In general, it can be said that the web channel has the best performance in terms of completion rate, followed by social channels. Finally, looking at offer types, discounts have a better performance than Bogo, which could lead Starbucks to conclude this in further strategies.
In summary, to increase the effectiveness of promotional offers, Starbucks could focus on female users, distribute their offers via the web channel and offer more discounts.
