Using shared insights to increase customer engagement

Post-covid data collaboration: How retail and brands can increase customer engagement with shared insights.

Adam Lower, Product Lifecycle Management expert at Centric Software, highlights missed opportunities for improving customer engagement – and ultimately revenue – in the cosmetics and personal care industry

Covid-19 forced seismic changes in many industries, affecting few more than the beauty industry. One of the earliest commercial impacts of the pandemic was that lockdowns caused a sharp drop in cosmetics product sales, with UK department store designer sales down by 40% (equivalent to almost £500 million) in 2020, according to the market researcher NPD.

To stimulate a recovery, it is vital that both brands and retailers use all their resources to accurately measure and react to changing consumer behaviour. Enhanced collaboration between them can often bring extra revenue in, according to a recent report by Coresight.

The report found that 52% of retailers had improved their collaboration with brands over the past two years, resulting in 4% plus revenue increases from better stock management. 55% experienced uplifts in sales of 4% or more from increased shopper engagement. 

Similarly, 40% of brands identified increased collaboration and said improved stock management had contributed more than 4% to revenues. For 51%, better shopper engagement had produced a sales boost of 4% or more.

However, one brand in the same Coresight report revealed that 25% of their retail partners didn’t share enough data with them to drive actions on a regular basis. So what are the barriers that mean one in four retail partners aren’t able to share sufficient information?

Common barriers to sharing data – and why they must be overcome

Common barriers

• Identifying key data: parties need to determine which data is relevant and important, and what should be shared with the other party. 
• Trust: sharing data requires trust and is still somewhat frowned upon as pushes for transparency are still recent. 
• Legal barriers: there might also be contractual issues with sharing data.

Factors pushing data sharing

• Consumer trends: with consumer sustainability concerns growing, sharing data in this area will be vital.
• Law: further regulatory requirements, globally, will also drive data sharing. 
• Medical considerations: the desire to reduce medical and sanitary risks related to cosmetics ingredients and allergen exposure.

Adam Lower, Cosmetics and Personal Care PLM expert at Centric Software, provides insight into how shared data can help brands pivot and retailers gain a competitive advantage:

Pass key data onto consumers for increased product engagement 

First of all, we must be mindful of the type of data, as it’s not all fit for sharing. Brand owners have legitimate reasons to protect intellectual property.

However, we need to learn to shift the paradigm and make these internal / external relationships more transformational, more collaborative and less transactional.

Centric Software customers who have fully embraced internal and external collaboration in PLM thanks to our supplier portal witness increased productivity, creativity and ultimately, better performance.

It’s important to share relevant data regarding brands and products. The more information a brand can share, the more retailers can be transparent with their customers. Ultimately, this should lead to a better relationship between the consumer and the brand. Many consumers are craving product data anyway – whether it be for environmental, health or ethical reasons.

The sharing of data is also important for guaranteeing quality assurance and global compliance – and to help reduce and hopefully eliminate any legal risks, as cosmetics is a heavily regulated industry.

Stay ahead of competition and react quicker to changing market trends

One of the most discussed benefits of PLM is accelerating the time-to-market process – and that’s because it really works! This ability to react quickly is amplified when there’s a strong relationship between brand and manufacturer, retailer and brand or retailer and manufacturer.

Changing trends and sales performance levels are first detected through data analysis. This analysis is obviously much easier if sales data is well managed, by connecting Centric PLM to business intelligence systems, for instance. 

By sharing data in real-time, brand owners, product developers and category managers can use it to make corrective actions to existing products. They can also identify opportunities for new shades or scents, or niches like organic or fairtrade products. Sharing sell-in and sell-out data can also be useful in detecting consumption trends, so the supply chain and production teams can react accordingly. 

Think about the first lockdowns we experienced in 2020: less make-up was used and consequently less was purchased. Brands that had access to trustworthy data (regardless of its source) were able to react quickly to manage stocks, change production schedules and ultimately sales results. 

Become more targeted with range production and planning

The more information retailers can share about sell-out of products, product ranges and types of products – as well as geographic and demographic data – the more insights both brand and retailer can use to develop relevant products for consumers.

Retailers should be asking brands for similar data to help them optimise their range planning, seasonal offerings and promotions. 

Using data to prove sustainability credentials 

Corporate social responsibility is on everyone’s mind lately, but sustainability is an easy buzzword to throw around. If brands, retailers and manufacturers really care about meeting growing consumer expectations in regard to the environment and other ethical concerns, they should be prepared to prove it. 

Achieving true sustainability requires real effort from all parties in the supply chain, and it needs a lot of data. A PLM solution like Centric 8 can make managing information much easier, maintaining and monitoring complex data over the course of the data lifecycle.