allbeauty is a successful online retailer recognised by Which? as a Recommended Provider Beauty Retailer – amongst other accolades – specialising in providing the best beauty and fragrances from high profile best sellers to prestige, artisan, cult and niche.
With over 13,000 product lines they are a one-stop shop for beauty fanatics to get their favourite products quickly and reliably.
150,000 customers and growing – allbeauty’s main mission is to continue to encourage their customers to replenish their beauty supplies with them and not let them fade away, or fall into competitors’ hands.
The health and beauty industry is set to be worth a huge £27bn by 2022, and with the average cost of acquisition being at least 6x higher than servicing a repeat customer, allbeauty recognised the importance of keeping their existing customers engaged and returning to purchase their favourite beauty products.
To encourage this behaviour allbeauty implemented an automated reactivation campaign to target all customers 15 months after their last purchase. However, not only were they treating every customer as the same, over time allbeauty realised this current method was targeting customers once they had potentially already lapsed, therefore encouraging these customers to re-engage with their brand was proving difficult.
Recognising the loss of potential revenue by not encouraging a repeat purchase sooner, allbeauty looked to RedEye’s predictive model suite to help combat their high churn rate and retain valuable customers.
allbeauty understands that every one of their customers is unique, from the products they purchase and their favourite brands to how much they spend. And, no strangers to personalisation within their lifecycle campaigns, allbeauty knew the next step for their customer retention strategy was to catch each customer at their own individual moment of lapsing and before they churn.
RedEye’s predictive models use Artificial Intelligence and Predictive Analytics to successfully identify key ‘next likely actions’ of prospects and customers within your database; the predictive churn model uses customer transaction data to calculate which individuals are most likely to churn in the next 90 days.
Through applying the predictive churn model onto their customer database, allbeauty have been able identify which customers are likely to lapse based on their individual purchase behaviour, allowing them to optimise their reactivation campaign to target and convert customers who would have previously lapsed.
We helped allbeauty to achieve an impressive 414.6% increase in sales and 518.7% increase in revenue, simply by letting their customer data drive the decision.
By predicting the right time to send and who to send to, allbeauty has been able to re-engage and retain a larger proportion of valuable customers, achieving an impressive 414.6% increase in sales and 518.7% increase in revenue, simply by letting their customer data drive the decision.
Although the predictive model also allowed them to reduce the overall number of emails sent by 27% they actually saw a 11.7% increase in open rates and a 6.2% increase in click-through rates.
These uplifts are even more impressive when you consider allbeauty chose not to update or amend their existing email creative and messaging from their existing reactivation campaign – further proving the power of reaching the right customer at the right time.
increase in revenue from personalised reactivation campaigns.
reduction in emails sent using the predictive model.
Clickthrough rate increase from lapsing customers.