Georgi Grogan , Co-founder , Mancave

IT ’ S BEEN VERY INFORMATIVE FOR US FROM TWEAKING AND FINE TUNING TO

REAFFIRMING OUR STRATEGIC

THINKING AND TESTING HYPOTHESES . and Boots , and of course , Amazon . As a growing multichannel brand , customer data is key to a sustainable growth strategy , and the Mancave DTC Shopify store is at the centre of understanding customer behaviour and buying habits .

The Mancave team knew they didn ’ t want to create a high volume , high churn situation , but instead cultivate a cohort of loyal brand advocates with a high lifetime value ( LTV ) and repeat purchase potential . The COVID trading years had caused a big shift in popular products , location of sales and marketing performance . So , the challenge was to find its own ‘ new normal ’ and understand who its new customer cohort really are and it matches its ideal customer profile .

How Distil delivers detailed customer insights

A rich vein of customer data collected from orders , interactions on the Shopify store and engagement on the Mancave marketing channels were all brought together into Distil ’ s AI-powered Single Customer View , ready for investigation and analysis . Duplicate customer profiles were unified through identity resolution , and customer datasets that were previously disconnected became a holistic single source of truth .

AI-driven customer segmentation

Mancave knew its core customer was likely male , but what about gift purchasers , or family purchases ? Customers to the DTC store were segmented using Distil ’ s AI generated tags , which instantly picked out cohorts of customers who were high value vs low value , those who are loyal to the brand vs those who were at risk of churn .

Geographic location and proximity to store

The team was keen to uncover more about the role of the in-store retail locations in the customer lifecycle and buying journey . With a list of postcodes of in-store retailers in the UK , Distil was able to overlay that onto the location of purchases made through the DTC store to see if there was a correlation between the distance from a retail location and DTC online purchases .

Georgi Grogan , Co-founder of Mancave , said : “ Every piece of data is so important to us . As a small team , we need to maximise the return on our time and resources .”

Challenge # 2 – Is our marketing hitting the mark ?

No brand is immune from default attribution models that favour the platform they are based in . However , relying on these creates a skewed picture of how well marketing channels are performing . Last click attribution has been the simple and easy perspective of attributing sales to marketing channels for some time . Similarly , some platforms even claim sales generated from first click engagements as entirely their own . But neither give the most holistic version of marketing channel performance , and there is a better option .

Opening the black box of marketing attribution

The team was grabbing manual reports from each marketing platform and trying to create a consistent view of performance using this data , but they knew there had to be a better way . How could they see a true reflection of performance by channel ? The answer – use an attribution model that makes more commercial sense .

Linear attribution model

Distil ’ s 30-day linear attribution model meant the team could go beyond last click or first click analysis and overcome platform bias in their reporting . They saw the effectiveness of every single channel in this complex customer journey .

Distil ’ s tracking tag

Making use of behavioural signals picked up by Distil ’ s proprietary tracking tag , the Mancave team was able to capture users moving from their marketing to their store and all the on-site interactions after that . Having a consistent unique identifier meant the team was able to get a view of its new and returning customers from a marketing perspective , not just an order perspective for the first time .

Grogan said : “ I would definitely recommend Distil . It ’ s been very informative for us from

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