Data analysis buyer persona research

The objective of analyzing the available data is to gain a better understanding of our clients’ customers, in order to extract buyer persona profiles for them, where our client wants to use the (6) product categories that yield the most turnover as the first practical segmentation.

Based on the information collected from Google Analytics, Magento and the physical store appointment list we need to identify patterns in the buying behavior(1) of the type of users that generate the most revenue in each category, as well as patterns in terms of their gender(2), age(3) , geographic location(4), interests(5), purchase frequency (6), preferred channels(7) and devices(8).

Once different patterns are identified for the 6 categories, we need to make a comparison(9) between them, in order to determine the similarities and differences between the patterns identified for each category. After identifying and comparing these patterns we will be able to analyze and link the most important patterns to the most profitable buyers in each category which will help us in creating Buyer Personas for the most important categories.

Many questions will arise in this process, so it is important to collect all of these questions and question the customers that fit these criteria.

1- Regarding purchase behavior, to be taken in account:
What is the frequency of purchase?
What product(s) are purchased?
What is the average basket value?
What are the touch points of the customer journey (visit the store and then buy online? visit the online store, then the store and come back online to buy? Visit the store and the online store and pick up your product at the store)?

2- We have to be able to categorize the audience of the brand and of each category by gender, not only the most profitable buyers but also the website users and in-store visitors for example, this will help us determine if more than one person is perhaps involved in the purchase process.

3- We have to be able to categorize the audience of the brand and of each category by age range, not only the most profitable buyers but also the website users and in-store visitors for example.

4- Besides finding the location of our clients’ most profitable buyers is also important to be able to define if their place of origin is within or outside the 50km radius of our client.

5- In terms of interest we would like to know the affinity groups of the most profitable buyers per city, gender, age etc. to see which patterns are found

6- It is essential to be able to match this information with other factors to be able to determine patterns such as, female purchase more frequently, X category generates more revenue because is selling more cheap products repeatedly.

7 - Here it is important to be able to distinguish the channels that buyers use most, and the channels through which they came to make a purchase.

8- Regarding the devices, we are interested in obtaining and purchasing information regarding the type of device (mobile, tablet or desktop) as well as the brand and the use of the devices in order to solve questions such as: there are many mobile users but none of these users generate profits? From which device do they generate more profits?

9 - Throughout this process it is very important to keep in mind that the comparison of different patterns should be possible in all aspects, in order to solve questions like... do male shoppers of a certain age prefer to buy online and pick up their product at the store? or what is the average age of male shoppers coming from X city that have a high purchase frequency?

Questions to you:

Does this briefing contains all the necessary info that you need to;
Provide a brief plan of approach
Point out to be used data analysis techniques
Provide an estimate of the required time to deliver a sound data analysis and describe the results for drawing up first conclusions for defining buying personas.

Furthermore:

Hourly rate
Willingness to sign NDA
Availability