When we say “retail waste”, you’re thinking of unsold products going in the bin, right? Think again.
Failing to make full use of data is costing your business, big time. In fact, our CCO Thomas Hill estimates it’s costing retailers up to 25% of their marketing budget every year.
Of course, we’re not saying unsold stock isn’t a huge cost. But we are saying retailers need to stay alive to other hidden costs – like automatically rewarding loyal customers; mistargeting marketing ads; and not using data to inform promotions. This form of waste means you’re giving away money unnecessarily – to your competitors, customers and AdTech companies.
Stop automatically rewarding customers.
Convincing your CFO to invest in loyalty is a challenge in and of itself. But how’s that going if you’re trapped in a cycle of automatically rewarding customers?
Before we dive into it; what is ‘auto reward’? We define it as rewarding customers with incentives without actually changing their behaviour.
Of course, retailers need to reward devoted customers – and encourage them to stay loyal. But unfortunately, loyalty programmes are the biggest perpetrators of auto reward – as they often give away revenue to customers who would’ve purchased anyway.
Auto reward frequently happens in stretch offers i.e. customers are encouraged to spend over a specific threshold to earn points or a discount. However, if the stretch calculation is wrong or inaccurate – e.g. spend over £100 for 10% off, but the customer usually spends £80-£100 per shop – it’s a wasted offer as they would’ve hit that threshold anyway.
Loyalty programmes should stretch customer behaviour to create habits and direct customers to spend more; in a way that works for both parties. It’s a fine balance between retaining customers and improving sales.
The best loyalty solutions drive incrementality through achievable goals, such as:
- Audience-level offers, where analytics and insight balances the rewards a customer receives (i.e. genuinely relevant for consumers, and impactful for the retailer).
- Stretching loyal customers in new ways e.g. offers for areas of the store they haven’t shopped previously.
- Inspiring consumers with products that’re loved by lookalike customers.
End ad mistargeting.
Many retailers rely on Google, social media and affiliates to acquire new customers. However, paid media is only rising in cost. And increased competition means ROAS is declining.
Despite this, retailers are mistargeting ads by:
- Treating every customer the same.
- Targeting customers with products they’ve already purchased or items/sizes they’ll never be interested in.
- Spending more on ads than the profit made from new customers acquired.
Retailers need smarter media strategies – and removing wasted ad spend is a great way of achieving that.
So, what’s the solution? Data.
First party data makes media more efficient – and it’s critical for building exclusion audiences.
If a customer has already bought a product (or a similar product), or their size is out of stock, it’s a waste of budget to serve them an ad. First party data helps build a picture of which customers are in the market – and which should be removed from the advert’s audience. And it can be combined with intent signal from media for a truly successful campaign.
Start using data to inform promotions.
Too often, retailers waste money by giving away incentive discounts on top of promotions, when one would have sufficed.
A great example is a fashion retailer allowing shoppers to apply a 15% discount code on clothing that’s already marked down in a seasonal sale. Double discounts like this are great for the consumer, but not so good for a retailer’s bottom line.
Promotional activity should be informed by customer and product price sensitivity. Do shoppers respond better to money off through incentives and offers? Or to product-level sales and discounts? This insight helps retailers target offers effectively, so they avoid offering incentives and promotions at the same time.
Once you understand your customers, it’s time to understand your products from a price elasticity and sensitivity perspective. Coats, for example, are highly elastic and high ticket, so there’s potential for a greater discount. On the flipside, trainers and basics have a lower discount value.
This insight should be balanced against stock. If you have too much stock, a typical reaction is to drop the price by a significant amount. However, if the product is inelastic, this could be the wrong approach. That’s why both stock and price sensitivity should dictate the optimal promotion price – and it should always be data-driven to maximise both margin and volume.
Are you ready to end auto reward, mistargeted ads and double discounting? Speak to our team to find out how people + tech can help.
