Retention is the key purpose for a business to build its customer personas or buyer persona. Understanding how your customers tend to act and react, segment them, fine-tune your marketing campaigns and offer personalised services accordingly – the ultimate way to guarantee your customer experience.
How to build the customer personas? The core is: Understanding your customers. And obviously, you are not able to understand our customers by talking to them, visiting them, hanging out with them just like you do with our friends and neighbours. The number of people you need to deal with can be much larger and that’s where big data and automation come to help.
Creating Customer/Buyer Personas
1. Define Your Objectives, Customer Groups, Data Required…
- What do you want to know?
- Who is your target customer?
- What data are you looking for?
- Where to find this data?
The first question can be asked in a different way: What do you want to achieve with the customer personas? Retention can be a persuasive reason. However, you can have your own goal that serves the best your current business situation, for example:
- Improve the conversion rate from awareness to action, or
- Increase the average Price Per Customer
Your goal can be more specific than these. Secondly, pinpoint the group of customers you want to draw personas from. Current customers are always the best resources: the data is definitely real and hence reliable. Again, which group of people you should be targeting depends on your goal.
What do you need to know about them? Businesses vary a lot. The reason why people are buying a cup of coffee can be extremely different from paying for car insurance. You should figure out the decisive factors that can sway people’s buying decisions and the behavioural indicators that differentiate these customers from others. Finally, list out places where you can reach them and where you can find the data related to them. When you have these questions clearly answered, you are having a good start. And the next step would be data collection.
2. Collect Customer Data
If you look into what data is used to build customer personas or buyer personas in eCommerce, there can be categorised into a few types:
- Behavioural data – How they view, click, add to cart, pay and review, etc.
- Demographic data – Age, gender, country or city, profession, etc.
- Business data – Prices, inventory, sales, ratings, reviews data, both on the front listing pages and in the backend reports.
To get Customer data, what you need to do is to aggregate this data and integrate the data into a structured, ready-for-analysis format. Say I am in the niche of handmade gifts and want to collect business data from Amazon. I would love to have data like this in my database:
Get Bulk Product Data
Without a good automation solution, data collection can be tedious and time-consuming. The data displayed above is what I scraped with Octoparse (there a few different platforms that do the same). I just took a shortcut: the software offers Amazon scrapers to get product detail data in bulk and there are more scrapers applicable to other eCommerce platforms. This is really helpful for gathering business data from multiple eCommerce marketplaces, and when the data is well organised in a spreadsheet, it will save you a lot of time from data cleaning in later steps.
Gather Prospective customer data
Product review pages are the best place to learn who are your current customers, what they are talking about, what they care about and complain about. More discussions can be found in social media and online forums as well. While product review pages are well-targeted at a specific product. This makes product-oriented analysis much easier.
If you have ever paid attention to the product review page on Amazon like this page, you will know how valuable data surrounds you in an unexpected way. We are not getting into big data analysis yet. However, you can see already that the product is welcomed by people in a romantic relationship. And if you want to know more about your customers, their profile will tell you more. This information will be extremely helpful for your marketing strategic decisions.
Review data is what we know as qualitative data. Some companies may gather customer data by distributing questionnaires and the answer to an open question is also qualitative. There are various ways we can look into it – this is what we are going to discuss in the next step. Since you may be scraping personal data in this case, the data collector should make sure the data scraping process and the use of such data is GDPR compliant.
3. Dig into the data
The purpose of data analytics is not about generating sophisticated, fancy charts and graphs. It’s about finding correlations among data and this will give you an opportunity to make the right decision.
Take this graph as an example:
This is a sample data showing how the number of deals distributes in 24 hours of a day. The benefit of visualisation is: you will be able to recognise at the first sight there are two peaks. And that would be the best pick to launch your Paid Ads.
Well, maybe we can have a second thought. Let’s see another set of data:
This graph shows the average value of the deal made in different hours. Obviously, the number of deals should not be the only decisive factor of your profits. How to analyse your data strongly relies on what business or industry you are in and the kind of problems you are solving. And this is also a challenge to the business runners: do they really know their businesses? For qualitative data analysis, you may resort to data mining or machine learning techniques.
4. Show the Team Evidence-Based Conclusions
We have dived into details in the above steps. However, when you finally complete the data analytics process and come to the conclusions of how your consumer personas look like, it should be a basic guideline that leads every small move of your business. Walk your team through how you did the research and how you came to the conclusions. Keep every member on the same page so the ‘guideline’ can be taken into real action.
Summary + Common Mistakes (to Avoid)
In this age of digitalisation, data should not be undervalued. Building up the customer personas is just a small fraction of big data applications. While, how to perceive data, in what way can we efficiently collect the data, and how should it be analysed are topics we should always endeavour to explore. Before you get started, below are some of the common pitfalls when building personas to be aware of.
- Not Building Convincing / Evidence-Based Profiles – It’s very likely that people would start off working on the customer personas with great excitement and curiosities, while end up with indifference to it when taking marketing actions. Why would this happen? One of the reasons can be: You do not believe in the customer personas you created. You may just gather random data or irrelevant data for casual analysis and it is not persuasive enough even to yourself.
- Quantitative Overriding Qualitative Data – What do you think of when you think of the word “data”? The first impression is more likely to be numbers. This is fair. The most simplified and basic form of information is represented by numbers in computing science. However, human information is more complex and always sentimental. Without qualitative validation, quantitative conclusions can be misleading. This is why academic researchers always supplement quantitative research with one-on-one interviews or case studies.
- Keeping Pace With a Changing Market – People are subject to changes and inevitably their behaviours change – just look at how the Covid-19 pandemic transformed people’s lives. Hence, building up the customer personas is not a once-in-a-lifetime job. Renewal and updates are required as changes happen. The challenge lies in the updates of all existing data. Even just going back to all sources may cost you enough time. In fact, this should be done by a data collection tool that can update the data as scheduled.
