In plain speak, that means having the capability to:
- Capture customer behaviour across all marketing platforms - whether that's engagement with ambassadors on Instagram or opens on email,
- Aggregate and view it holistically, and
- Analyse it to optimise their marketing and product journey's.
The challenge is that digital brands have a growing number of purchase environments (D2C transactions, subscription, Instagram commerce, offline retail) and none of them play nicely together. I.e. Shopify won’t automatically sync with systems outside of Shopify apps, and Amazon is tough to natively link to any other systems. This creates the frequently spoken about problem of data silos. To make matters more challenging, the standard methods used to combine these data silos are becoming harder with Apple's restriction of mobile device ID's and the decay of cookie tracking.
The solution lies in data science. I'll walk you through the kind of growth our in-house data science lab unlocks for our brands by creating a single customer view, and in doing so, hopefully convince you that data science is non-negotiable infrastructure for brands trying to scale up.
Plug into our data science lab to get deep insights into what drives revenue.
OK, let's take brands like Harry's Razors or Naked Wine that offer both subscription and shop. In my experience, brands like this frequently see 30%-50% of customers buying from both purchase environments. But remembering our gold standard, it's not enough to know what's happening, we'll need to dig into why so that we can use that insight to optimise our marketing. The kind of questions I would be asking are:
Do subscription customers also top up with shop purchases or does their subscription replace the shop purchases they used to make?
Does an initial shop purchase make them more or less loyal when they later subscribe?
Discovering the answer to these questions is only possible with a single customer view that blends shop data with subscription data. With this infrastructure in place, the optimisation process might go a little something like this:
1. Brand X discovers that there is a 15-25% increase in subscription loyalty from customers who initially make a shop purchase.
2. They build a 'try before you buy' product journey that funnels customers to buy first, and then upsells them a subscription via email.
3. They see an initial hit on subscriber numbers (as people are funnelled to shop) but after month 4 see a 10% uplift in subscription retention.
4. Further analysing the data, they also discover that with this new product journey customers are more likely to buy top-up purchases via the shop as well.
5. When we blend these two effects together, we see higher incremental revenue over a 12-month period. We have to accept a 2 month decline on customer LTV, but by month 4 the increased loyalty and higher propensity to buy top up purchases leads to consistently higher lifetime revenue. Customers that shop first, then subscribe ('try before they buy') are now worth up to 40% more by the time they reach a 12 month lifespan.
This is just one optimisation. You're likely experimenting with between 3-10 marketing channels at any given time, and so you can only imagine how many more growth opportunities are buried in your data. Think higher lifetime values, lower CPA's, and creative that is rooted in behavioural insights.
Solid data science foundations can ensure that all of the blood, sweat and tears you put into your growth and marketing efforts deliver the best results.
The question of how to build a single customer view, unfortunately, isn't a straightforward one. It'll be specific to your business, and depend on the channels you use for growth and marketing. There are, however, some key fundamentals to keep in mind:
Control: You'll want to make sure that you control as many distribution channels as possible, as well as actual delivery of your products. Outsourcing fulfilment, or relying heavily on 3rd party distribution channels means you will lose a lot of visibility on who's buying what - crucial data for proper optimisation. The same applies for your marketing efforts - use freelancers and agencies where required, but make sure you are plugged into the back-end.
Consistency: You'll need to align data between purchase environments, so that customers and products can be identified consistently between data sources. Make sure that products are named consistently, and make sure that customers can be matched between systems (usually by an email address or similar).
Ownership: Assign someone in the business full ownership over this single customer view, and give them enough control to change the user experience to create consistency if it doesn't currently exist.
These three pillars should give you a good foundation to create a reliable single customer view, from there you can run deeper analytics to understand customers better and run experiments in the customer experience to help both customers and your revenues.