How beauty brands can maximise the D2C opportunity

Beauty brands all over the world are leaning on cutting edge tech to create highly engaging, personalised experiences. High profile examples include L’Oreal’s acquisition of AR technology firm modiface, which lets shoppers virtually sample cosmetics, and the emergence of AI-powered personalised skincare brand Proven.

To make the most of tech-enabled personalised shopping experiences, brands should:

• Focus on the needs of your target audience.

Who are you trying to reach? What are their demographics, interests, and pain points? Once you have answered these questions, you can start to narrow down the types of technology that would be most relevant to your audience.

• Consider your technical capabilities

If you have limited technical knowledge, you may want to choose a technology partner that can provide support and training. SaaS (Software as a Service) solutions are also easy to use and don't require extensive technical expertise.

• Assess your budget

It's important to set a budget that you're comfortable with, and to consider what the additional longer-term costs of both adoption and operation are likely to be.

If you aren’t sure where to start, our personalisation and experimentation team can provide impartial advice and hands-on support with implementation. Contact them here.

Rewarding and nurturing loyal customers should always be a key component of any D2C strategy, but it is especially important in an industry as competitive as beauty. Using some basic segmentation approaches on your customer base can be a good starting point for understanding where and how trading strategies can be fine-tuned to grow sales.

We suggest starting with Decile segmentation to quickly identify the top 10% of your customers, along with their spending habits. These customers are usually responsible for a disproportionately high percentage of overall sales. Once you know who they are, you can target them with tailored offers to continue driving overall sales.

Beyond Decile segmentation, RFM analysis is a slightly more nuanced segmentation approach that scores customers based on the recency, frequency and monetary value of purchases made. Based on the scoring, customers are then grouped into different cohorts, such as VIP customers (the most valuable), sleeping giants (not currently active but potentially high value) and prospects (not yet converted customers), with different strategies used to engage each group.

Speak with our Marketing Automation and Precision (MAP) practice for help segmenting your customer data and developing the right targeting strategies.