Generating zero and first party data in DTC: A framework
It’s absolutely essential today for brands to build up a full picture of their customers to realise the full potential of their DTC e-commerce channel. The most successful e-commerce businesses, whether they’re native DTC or established brands pushing into the space, are building agile customer insights setups that enable them to personalise their products and messaging, and target campaigns much more effectively. While those relying on more static insights or just their intuition will struggle to compete effectively and get the best return on their marketing investment.
Personalisation isn’t optional anymore
The main reason for this is the intense competition in the consumer products markets, especially in e-commerce, and on digital ad channels. Firstly, most categories are well covered by great e-commerce businesses, the only opportunity left to stand out is to cater for a very specific target audience. Which requires detailed personalisation: relevant content, familiar tone of voice, and meaningful product development.
Secondly, the monopoly position of Facebook and Google in targeted advertising, combined with stricter privacy rules, has been pushing up costs and will continue to do so across all channels. The only way to get value from ad channels is to generate data on customers to improve targeting and personalise ad creative, messaging and customer journeys. It’s simply too expensive and inefficient to rely on campaign testing without doing the upfront work of understanding the customer better.
The main uses of customer insights for DTC growth are to improve customer journeys, fine-tune ad creative, open up better audience targeting options, and improve the growth channel mix. Also, they will inform new product development and the choice of partnerships or locations for retail stores,
Democratisation of customer insights generation
Thankfully, it’s much more realistic for brands of any size to build meaningful customer insights without the need for huge investments. The list of digital tools that are free or charge a low monthly subscription cost is growing every month, and the implementation requires much less technical understanding than in the past, thanks to easy integration with e-commerce platforms. So the main challenge left for brands is to identify what data and insights are actually most meaningful, and what conclusions to draw from them.
There are three categories of insights that let brands build up a holistic picture of who their customers are, what they’re looking for and how they’re behaving: understanding the context of the customer and their decision-making process, their intentions, and their actions. Building or improving an insights strategy should always start by going through each of these categories, identify where there are gaps in knowledge, and decide on the insights methods to use to fill those gaps.
1) Context
Most purchasing decisions are highly influenced by the context of the customer and the product. The context is split into two areas: The market or category, and the customers’ personal environment.
Understanding the market or category in detail seems obvious, but it’s crucial to identify the most meaningful differentiators of the product that give it an edge in the market. In DTC today, this most likely doesn’t come from the brand alone but from the focus on a clear audience and/or a specific customer need. Secondly, customers’ personal environment shapes most of their decision-making, so it’s crucial to identify their cultural context, place of work, family situation, interests and brand affiliations.
The main methods to analyse context are:
Secondary research of market and category trends
Analysing trends in competitors’ customer reviews and their search advertising activity to understand their USP
In-depth interviews and focus groups with existing and potential customers
2) Intention
Marketers have to understand customer needs in detail, what products they’re actively in the market for, and what content they’d like to get from brands they follow. Asking people directly for what they want is sometimes unreliable - we are all shaped and influenced by our environments and assumptions which we’re often not aware of. Which is why it is crucial to avoid leading questions that are too focused on the product or brand as opposed to the set of customer needs. And, brands need to get insights across all three categories to build up a reliable picture.
There are very effective ways to gather data on customer intentions which will powerfully shape direct response campaigns and content marketing.
The main methods to analyse intention are:
Keyword search data analysis
Data collection with email signup, quiz or product finder (to understand needs and interests)
Quantitative surveys of potential customers to understand purchasing intentions
Quantitative surveys of existing customers based on lifecycle stage
Social listening tools to understand problem areas
3) Action
On a more tactical level, measuring customer actions directly makes it possible to streamline the purchase funnel. Ultimately, those actions are the only reliable proof that our assumptions and insights about the customer are valid. They become a measure of whether we’ve built up the right kind of customer insights and built the right personalisation into the funnel.
The main methods to analyse action are:
Website analytics platforms (including heat maps)
Digital ad testing
Content & email marketing engagement
The task of finding the right setup
There is an almost endless number of platforms and tools in the market, to help DTC e-commerce marketers build more rigorous and comprehensive customer profiles. For more established online businesses, it’s crucial to create a unified source of customer data by implementing a full Customer Data Platform (CDP), and continuously feed it not just with more obvious Action data but also gather data on Context and Intention on a regular basis.
Who is doing this well? Some great examples of insights generation
For some inspiration, here are some fast-growing brands that have created mechanisms to continuously improve their understanding of their customers. Some of them fit smoothly into their customer journeys, meaning instead of a conversion drop-off they speak more relevantly to customers and keep them onboard longer. This enables them to reduce acquisition costs and increase lifetime value.
Homeware brand Brooklinen used a combination of search data analysis and customer surveys to identify a positioning for its new pillow brand, Marlow. There was increasing interest in memory foam pillows which led them to launch with that focus. It’s a very interesting strategy to launch a new product line under a separate brand, to focus on a very clear value proposition and message. The growth trend so far seems to prove them right.
Flower delivery brand Bloom & Wild wanted to improve their Valentine’s Day offering, and found through surveying customers that red roses are the least favourite gift. Taking them out of their offering and pushing that messaging helped them become the most talked about brand in their space, and increase Valentine’s sales by 400%. They might have had a hunch before, but the data gave them the confidence to fully commit to the campaign.
Gainful sell high-quality sports nutrition products, tailored exactly to customers’ workout habits and personal goals. Every new customer has to go through their product finder questionnaire to be able to sign up. They don’t see drop-off because customers understand the value of the personalised offering. This data can be used to target more relevant audiences and solve their most common challenges.
Bleach London make it easy to find the right hair colouring product by taking prospective customers through a product finder quiz. They’ll recommend a product based on their hair colour and type, desired look and confidence in dying their own hair. This is optional so even if a small share of site visitors go through this it’ll generate meaningful insights.
What to do?
Brands should regularly review their insights generation setup as there might have been new tools released that make their lives easier. Or they might have grown to face completely different growth challenges which require more in-depth insights. It’s important to draw up a comprehensive picture of the customer across Context, Intent and Action, identify where you’re relying on assumptions instead of data, and fill the gaps. Ideally as much of this process is automated or built into the existing funnel. More qualitative research can be done on a set schedule, say twice a year or based on certain milestones like new channel marketing or country expansion. Getting this right can make the single biggest difference for the business in the coming months and years.