The only businesses that might look back at 2020 and 2021 through rose-tinted glasses are those that successfully pivoted or embraced digital-first strategies as lockdowns continued and the high street closed.
Take Nike, the traditional brick-and-mortar brand that saw an 82% increase in digital sales thanks to accelerating its e-comm strategy, or digitally native brands like UK athleisure brand Pangaia, which turned over $75 million in revenue while remaining profitable.
But now that customers have returned to their post-pandemic lifestyles, have these success stories lost their e-comm shine?
At Charlie Oscar, we believe that shifting towards (or back to) an omnichannel business model post-pandemic shouldn’t mean you have to lose the glimmer that comes with e-comm growth.
Let me explain:
Channel diversification provides huge benefits in brand presence and diverse revenue streams, but it’s important to remember that many of these brands were grown primarily using e-commerce growth tactics and data-driven marketing on digital platforms. They also benefited massively from the instant tracking data provided by these marketing platforms (even though some of this data from marketing platforms can be misleading, as it only measures direct impact and ignores incrementality…anyway that is a different topic, one I talk about here).
So when making the jump to omnichannel, brands will often run into challenges when they shift their growth tactics from purely e-commerce to omnichannel objectives. Without the attributed sales reporting, the default response is to instead shift all their focus to engagement metrics, which are less tied to business growth than the revenue metrics they used in the past.
While engagement metrics can be important, and long-term brand growth is something all brands should pay a lot of attention to, there is also a huge benefit to keeping the link to revenue approach that has driven past growth for these brands.
Many people think it isn’t possible to run non-digital sales marketing with this revenue link, but it really just needs a different method of measuring and assessing the marketing.
Plug into our data science lab to get deep insights into what drives revenue.
We believe that everything brands do should be linked back to impact on bottom line revenue, and that applies whether the marketing is online or offline, and whether the revenue is through e-commerce or trade.
This is one of several reasons why we deploy data science-driven marketing attribution models for our clients. These models show us both the direct and indirect impacts of marketing, as well as letting us understand how customers move between digital and non-digital sales for omnichannel brands.
These models run on granular (but aggregate), non-user specific data to give us statistically significant relationships between digital and non-digital marketing channels and revenues.
Running these models gives us a few benefits against competitors:
Firstly these models let us attribute non-digital sales back to digital (and non-digital) marketing channels, so we can apply a non-digital CAC to marketing channels in the same way we would attribute an e-commerce CAC. Getting back the revenue link that digital brands need to optimise marketing campaigns effectively.
This also lets us understand the retail sales drivers. There are several drivers of retail sales, in the same way as there are several drivers of e-commerce sales. Understanding the relative importance of each sales driver lets us understand and predict future sales volumes, as well as helps to better position the brand to retailers. If we know how to help retailers sell our products, we can become better long-term partners.
The example analysis below shows how e-commerce levers become a core driver of non-digital / trade revenue. These range from new e-commerce customers making additional trade purchases, to growth driven from website engagement and digital marketing reach.
Attribution models also help us understand the true omnichannel LTV of new customers. Charlie Oscar analysis has shown that omnichannel revenue can add up to 55% incremental LTV to new e-commerce customers.
When a pure e-commerce brand moves to omnichannel revenue streams then the lifetime value measures that they are used to become out of date. Often you may see the typical e-commerce LTV fall (the blue section below), as some of the purchases for loyal customers will now be made in trade rather than e-commerce (the middle green section). When we understand the true impact of different sales channels on each other we can model omnichannel LTV, and the non-digital uplifts for e-commerce customers are likely to outweigh the slight fall in pure e-commerce LTV. Ultimately more sales channels will help create higher-value customers when measured on omnichannel LTV.
Most e-commerce businesses will have a relatively high proportion of single-purchase customers. Reactivating these customers through smart targeted CRM campaigns has been a staple e-commerce tactic for several years. Omnichannel brands have the added benefit of being able to model when e-commerce customers will not re-activate on e-commerce and instead send them targeted messaging and offers to reactivate through non-digital channels. Adding an extra layer of reactivation to minimise the proportion of one-and-out customers.
As you can see, it is possible to run non-digital sales marketing with a revenue approach when you blend data science and marketing attribution models. This method of measurement and assessment helps us to understand how customers engage between different sales platforms and the impact of marketing on each of those sales platforms. When we consider each customer as a single entity across different sales platforms, we can then optimise marketing impact to target the best messaging and send the customer to the best sales platform at every stage of their journey. Ultimately it allows us to deploy the same growth mindset to omnichannel businesses that have successfully driven e-commerce businesses.