Digital Transformation Brand

Is digital maturity the solution to your e-commerce problems?

By Christopher Attewell |

Search Laboratory

|

Opinion article

September 23, 2022 | 7 min read

What do we mean when we talk about digital maturity? For The Drum’s e-commerce deep dive, Christopher Attewell of Search Laboratory clears up the concept and argues that it’s time for brands to use their own data to better understand their audiences.

A green leaf in a pile of brown leaves

The key to unlocking the customer journey is in businesses’ first-party data / Clay Banks via Unsplash

Given the increasing hurdles around cookies and privacy regulations, it’s becoming even more difficult to track customers’ journeys. Digital maturity is the data-driven solution to e-commerce problems around attribution and user tracking.

Adapting to the ever-changing digital landscape and improving the customer experience starts with using your own data to make informed decisions that will help you to reach your audience.

Harnessing the power of first-party data

Developing true, end-to-end measurement across channels and implementing an agile test-and-learn approach will allow you to shape your marketing strategy around your audience and data you already own.

For example, what is your audience interested in? What messaging resonates best among specific customers? Are there any moments of friction across the site? By digging into audience metrics, you can start to deliver better and more seamless digital experiences.

Leveraging customer data is a two-way street. Brands can deliver more effective marketing, while customers receive a better experience in the form of useful information, incentives and offers.

Unlock your true return-on-ad-spend

Identifying the main challenges in terms of low margins, high return rates and customer lifetime value will begin to uncover a true picture of your profits.

For example, how is each product performing? Is there a significant drop in September? Developing focused KPIs with immediate actions is key to reacting to rapid market changes and adapting goals in real-time.

Margins, returns and customer lifetime value are three critical factors in e-commerce. Analyzing these factors will allow you to understand varying profits and losses between products.

Margins

By viewing margins on an item-by-item basis, you’re able to see which items are bringing the most profit and where is best to invest.

For example, one item could be receiving lots of revenue, although its small margin and high return rate mean that it brings in less profit than an item selling significantly less volume but with a higher margin.

This detailed view of your products and categories allows you to see which item is going to benefit from which channel, allowing you to optimize your ads and streamline your strategy.

Returns

UK fashion retailers saw more than twice the number of returns in January of this year compared with 2021. The impact of returns is one of the main challenges in e-commerce.

The cost of processing returns, environmental impact and the risk that they can’t be resold all have a significant influence on your business. Optoro estimates that just 50% of returns make it back into inventory.

By analyzing individual product performance, you’re able to make informed and profitable changes to your strategy, saving both financial and economic resources.

Customer lifetime value (CLTV)

Adding customer lifetime value to your analysis allows you to target future growth through new customer acquisition.

Each customer journey is driven by different desires. Understanding the likelihood of future purchases through existing user behavior can inform strategic, data-driven decisions that increase revenue and sustainability.

For example, a customer returning an item isn’t going to boost their lifetime value, whereas returning to browse a new range via an email promotion will. This will be influenced again by which product and price range they engage with.

Start small and scale in complexity

It’s important to remember the value of starting small. There is significant potential to evolve this approach with automation and machine learning over time.

Eventually, it’s possible to instruct machine learning to dynamically value each individual user’s behavior and pass that value back to Google Analytics for optimization. However, the first step is to centralize and automate your first-party data and pivot your strategy toward unlocking the entire customer journey.

Implementing these new values into your channel mix is simple. More information can be found in Search Laboratory’s retail white paper.

For more dispatches from the frontiers of selling online, head over to our e-commerce deep dive hub.

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