Improved Customer Segmentation and Targeting:

Understanding your customers is the first step in creating effective marketing campaigns. Data allows brands to segment their audience based on various criteria, leading to more effective and personalised marketing campaigns. 

For instance, segmenting your customers to analyse those who buy more than once (typically approx. 20% of your customers) allows you to use cohort analysis to discover their “intra-purchase latency” - the average time between their first and second purchase. This gives you a distinct window where you can avoid discount offers in post-purchase marketing and a distinct window where you know that customers are more likely to make their second purchase and are ripe for remarketing.

Steps:

1. Data Collection
   Gather data from various touchpoints, such as website interactions, purchase history, and social media engagement.

2. Segmentation
   Use analytics tools to segment customers based on demographics, behaviour, and preferences.

3. Targeting
   Select which of those segments are most appealing to your business. Perhaps based on profitability, the likelihood of customers in the segment to refer your brand and increase virality or the customers which are easiest to serve logistically.

4. Personalisation/Positioning
   Tailor marketing messages and offers to each target segment to increase relevance and effectiveness.

5. Evaluation
   Continuously monitor the performance of segmented campaigns and adjust strategies as needed.

   
   

Example

boohooMAN transformed their SMS marketing strategy using data-driven personalisation. By targeting segmented audiences with tailored messages, they achieved a 5x overall return on investment and significantly boosted engagement and sales.

Enhanced Inventory Management:

Holding too much stock is expensive - that tied up cash could’ve been spent growing the business. Holding too little stock is also expensive!! Missing sales because you didn’t have enough inventory is not only disappointing to your customers but it’s a tragic waste of marketing spend. The answer lies in the data. 

It’s rarely perfect, but you can use historic sales data, expected seasonality and projected advertising spend with ROAS to get a fairly accurate picture of predicted sales. If you’re effectively using this data and combining it with your supplier data then you can also respond to changes much quicker and more easily deal with the variance in these types of predictions.

Conclusion

In short, understanding and utilising data effectively is the cornerstone of modern fashion ecommerce. By collecting and managing the right data, fashion brands can enhance their decision-making processes, improve customer satisfaction, and drive business growth. The insights gained from data empower brands to stay ahead of trends, optimise operations, and deliver personalised experiences that resonate with their customers. Incorporating AI-driven analytics further amplifies these benefits, enabling brands to stay competitive in an ever-evolving market.

I agree that data has transformed the way we shop, and our world more generally. And I think it can be an incredibly powerful tool. However, I do think brands can suffer from data overload. The sheer volume of data and companies that can help them interpret and action that data is overwhelming. So a key piece of advice we give to brands when embarking on any sort of data play is to follow the steps outlined above but also be incredibly intentional with the reasons behind that data strategy. Is it simply to enable better decision-making? Is it to drive specific commercial outcomes? By being intentional, they will find the right partners and create value in their business quicker.