E-TAIL DATA EXPERT ADVICE
reveal customer needs? EXPERT ADVICE
Can the online uptick
Daily analysis of e-commerce data is more illuminating than ever, as beauty brands pivot to online amid lockdowns, says Fran Quilty
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ata can shine a spotlight on your customers and what they want from your brand, and acting wisely on data can spell the difference between success and failure. Here, Fran Quilty, CEO and co-founder of Conjura, tells Cosmetics Business about the benefits of good data analysis and why data should be checked daily to help you stay informed.
What are common mistakes when it comes to using data? Selling online demands top-notch data analysis skills and not investing in those skills means you are working blind. There are still too many beauty and personal care brands that aren’t looking at their data to understand customer behaviour and to run their businesses better. For brands that pivoted hard to online when the pandemic hit, now is the time to ensure they build-in daily reporting that covers key metrics like product replenishment rates, customer churn and the lifetime value of different customers, which can be impacted by the device they buy on, or which advertising channel brings them in.
How has Covid-19 changed buying behaviour?
The closure of non-essential retail; changing behaviour where outlets were still open during lockdown; and the devastation of duty free saw a dash to online. Across our beauty brand clients, we saw a trend towards in-home care. With salons closed and people stuck at home, there was a real opportunity and desire to experiment with that product category. We also saw that consumers who tried a beauty brand for the first time during lockdown were more likely to go on to buy from that brand again than pre-pandemic.
cosmeticsbusiness.com
Fran Quilty, CEO and co-founder, Conjura Fran Quilty is CEO and co-founder of Conjura, which was set up in 2016 to help brands collect and act on the data that comes from selling online
www.conjura.com
a new set of behaviours that moves them into a different segment, meaning you need to communicate and engage with them differently.
What patterns can be highlighted via data analysis?
Because people have beauty and personal care routines, you can predict with a good deal of accuracy when they will be looking to re-order based on past behaviour and online sales data. Brands that have sophisticated online operations segment their customers across as many as 70 different groups based on what they buy, when, average purchase value, items they buy together, how responsive they are to different advertising channels, etc. But none of this is set in stone and daily reporting and refreshing of key e-commerce data will tell you when a consumer exhibits
Daily reporting and refreshing of key e-commerce data will tell you when a consumer exhibits a new set of behaviours
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What time frame is necessary to make data worth acting on? The best teams analyse data on a daily basis and review marketing spend frequently – what worked well to bring in new customers this week might not work a week later. Regular reporting will also allow you to identify anomalies that are leading to customer churn. A simple glitch in a payments system, or a broken link can be enough to drive customers away, but can be easily remedied if you stay on top of the data.
What are the benefits of good data management?
Good data management makes for happy customers that will stick with you for the long term. Exciting start-ups such as Carbon Theory go really granular to understand their customers’ buying patterns. They can estimate how long it takes someone to get through a particular product and send them a nudge, and potentially a cross-sell, when the time is right. A joined-up data approach also allows businesses to align their marketing strategies to stock levels.
Where should brands begin? The key is getting internal buy-in: making sure the senior team understands the value of data to drive profitable and strategic growth. There are a variety of options depending on in-house skills and budget available; an in-house data team, an outsourced function or a combination of the two can all work equally well. There is a cost attached to this, and take note that managing and interrogating data is too big a job for just one person
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