Collecting Data for Hyper-Personalized Marketing - Fleximize

Collecting Data for Hyper-Personalized Marketing

Jason Lark of Celerity shares his tips on how to collect the right types of data to inform your SME's hyper-personalized marketing campaigns

By Jason Lark

In an age of increasing security consciousness, customer data has become something of a currency. Generally, customers are more likely to share their data if there is a clear ‘value exchange’. For example, many brands offer discounts, special offers, and loyalty points in exchange for signing up to a newsletter or becoming a member.

For companies handing out such offers in exchange for data, it’s important that they’re collecting data that they can put to good use. And nowadays, what constitutes ‘useful data’ has changed. Hyper-personalized marketing communications require more than just basic identity data (e.g. name, location).

Before we look at specific data types, here are three simple objectives to bear in mind when collecting customer data:

1. Make it personal

Personalization isn’t exactly a new technique in marketing, but the way in which it’s carried out has changed. Overall, there’s a bigger need for transparency in terms of what data you’re collecting.

A good example of this in action is Spotify’s annual ‘Wrapped’ campaign, where it reports back to consumers on what they’ve been listening to over the past year. It’s highly personalized, with sharable infographics on what type of genre you listen to the most, your favourite artist and favourite songs of the year. The main takeaway for marketers is the transparency of it – there is no effort to hide what information they’re collecting and instead, they present it back to the customer and make it sharable and interesting, too. It’s a win-win.

It is worth noting that other campaigns from Spotify have fallen flat with consumers. Personalization is hard – it’s really all about getting the balance right and providing insight that you know your customers will value.

2. Remove obstacles

Customers need to have autonomy over the buying journey. It needs to be their decision to make a purchase and they need to be able to do so with minimal fuss. In this sense, brands are responsible for maximising convenience and removing obstacles to reaching the checkout – that way, they add more value to their service and have a greater justification for accessing customer data.

Some do this by providing membership services (which help to build long term relationships with customers), and others provide free delivery on items. Another common process to overhaul is the login process. Some brands are introducing social sign-ins on their apps, so that customers can like, share, and interact with the business without having to set up a bespoke log-in. This also allows the brand to collect more data and develop sophisticated insights into user behaviour.

3. Create preference centres

Preference centres are the logical extension of personalization in marketing. These centres collect more specific data to provide customers with recommendations that are very specific and personal, rather than generic. An example of this in action is online clothing brand Thread. It asks customers about their favourite kinds of clothing, their favourite colours, and their favourite fabrics, and then acts as an online stylist by emailing them with styling recommendations.

Data types

In order to execute on these strategies, it’s necessary to look at the data types that brands need access to:

Quantitative data
Quantitative data enables you to understand how a customer usually behaves, transacts and reacts with the business. This can include any information from the activity which has taken place between the customer and the business, including:

Qualitative data
Qualitative data is usually collected via questionnaire-style scenarios so that the customer’s attitudes, motivations and opinions are collected. Some of the information that can be collected includes:

Descriptive data
More descriptive data can provide more context than numerical data, which can help when making business and marketing decisions. Some of the relevant information to gather includes:

Different types of data come from different sources – but which is the most valuable? Here's a quick look at data sources:

First party data
First party data is the relevant information that your company accumulates from its customers and prospects. It’s important because it enables you to clearly identify individual customers and deliver highly relevant experiences. This data comes from:

Second party data
Second party data is similar to first party data, but it comes from a source other than your own. It’s important because it’s usually much greater in scale, which allows for more detailed data analytics on customer behaviours, new audiences and relationship building exercises. This data usually comes from:

Third party data
Similar to second party data, third party data is bought from outside sources, but the difference is that the sources aren’t the original owners of that data. These sources collect data and put them together to sell to a third party. This information is important because it helps specifically with new business and increases the precision of your targeting.

First party data is the most important, reliable and valuable data that you have. Second and third party data can fill the gaps, but it isn’t always relevant or useful. At its most basic level, your first party data is all about your customers – the exact group you’re looking to appeal to.

Keep it clean, make sure its accessible to all who need it and set your objectives of what you want to do with it, and your first party data might be all you need to enhance your marketing campaigns.

There are plenty of insights to gain from the right kind of data. The challenge is identifying which data is most relevant to your marketing goals and how you can use it wisely. Make your offering relevant, seamless and transparent and you’ll reap the rewards of your efforts.

About the Author

Jason Lark is the Managing Director of Celerity, a data, marketing, and technology consultancy. He co-founded Celerity in 2002, as he envisaged how data and marketing communications would lead businesses of the future. After taking on their very first client, Nectar, Celerity quickly became a multi-award winning agency and system integrator with offices around the world including in the UK, USA and Spain. Jason continues to lead the company’s vision, strategy and operational performance.