It’s time to target people, not browsers. Shopper behaviours are changing rapidly as consumers grapple with the effects of skyrocketing inflation. So how do you know how, when and where Aussies are shopping and reach the right consumer for optimal digital impact in a sea of brand switching? Routine (along with loyalty) has changed as households struggle to make ends meet, and change retailers and brands for better value, convenience and availability.
Aussie shoppers are increasing brand-switching habits by always comparing prices (73 per cent); making unplanned purchases after seeing promotions and discounts (68 per cent) and stocking up on sale items (80 per cent) (1). Beyond value, seven in 10 are changing for new and appealing features or benefits (2). Confused? So are marketers. But with 45 per cent of Aussie shoppers making product decisions when in-store (3) as hyper-inflationary supply chain and pricing amplify trading challenges; they’re moving budgets to digital channels. But they’re also trying to reach the right audience in an increasingly fragmented media landscape and face extra pressures on the right media spend in an impending cookie-less digital ecosystem.
Easy access to smarter data that identifies and reaches high-propensity shoppers is key
Past purchase is the best predictor of a future sale, but how can you be broad targeting to retain existing buyers and acquire new ones? If you don’t have purchase data or rely on outdated demographic or contextual targeting tactics, this is a tough mountain to climb. And even if you have first-party data, it’s hard to reach relevant audiences at scale.
However combining granular purchase data with rich demographic data helps predict consumer spending. The biggest determining factor of someone buying a product is if they’ve already bought it – but deterministic modelling targeting known buyers is expensive and limits growth. Targeting audiences most likely to buy a specific brand based on past behaviours can help identify the highest sales opportunities, increase brand penetration and reduce wasted ad spend. This is the power of propensity modelling – accessing precision media activation at scale across households with the highest propensity to purchase.
Use sales-based data to define your high-propensity digital audience
A sales-based digital audience comprises potential customers who’ve already shown some level of interest in your product or service – either through previous brand/product purchase/interactions or modelled propensity to purchase (a statistical technique predicting the likelihood of an individual to engage in a specific behaviour). Using a high-propensity audience is a very effective way to promote your products or services through digital channels. By targeting high propensity to purchase individuals, you can expect:
- Higher conversion rates – because a high propensity audience consists of people most likely to purchase the product or service being promoted, they’re more likely to convert into paying customers. By targeting this audience with tailored marketing messages, you can increase the likelihood of conversion and achieve a higher ROI.
- Increased engagement – a high propensity audience is more likely to engage with marketing messages as they already should have some level of familiarity with your category or segment.
- Cost-effective advertising – targeting a high-propensity audience is a cost-effective way to advertise because you can use data from previous purchases and interactions with the category or segment to create targeted campaigns and lookalike audiences that are more likely to result in conversions. This means that advertising spend is more likely to result in a positive ROI.
- Improved customer retention – by targeting a high-propensity audience with relevant and engaging content, you can improve customer retention. This is because shoppers are more likely to feel valued by the brand and continue to make purchases over time.
- Better insights – using a high-propensity audience can provide you with valuable insights into your customer base. By analysing the behaviour and preferences of this audience, you can gain a deeper understanding of your target market and use this information to refine marketing strategies.
Investment in valuable data is not a cost, but a powerful solution to deliver ROI
With propensity modelling, you can gain unparalleled access to purchase-based behaviour. Pre-optimising campaigns based on past consumer purchase behaviour can lead to significant improvement in campaign performance. And targeting audiences most likely to buy a specific product based on past behaviour can help identify the highest sales opportunities, increase brand penetration and reduce wasted ad spend. For example, we worked with a US confectionery brand wanting to optimise TV advertising. Accessing Circana ProScores audiences allowed the brand to:
- Remove low viewability networks and dayparts and shift spend to those that indexed high with category buyers and significantly reduce ineffective spend by reducing those not providing quality audiences.
- Add ‘long tail’ networks to their media plan for the ability to reach their target at a much lower cost.
- Increase total rating points by $1.8 million and achieve a 29 per cent increase in total rating points.
Purchase-based targeting delivers precision audience figures based on in-store purchases, improves your media spend with effective targeting and uncovers your brand’s next best buyers. It is key to growth and loyalty through this cost-of-living crisis. Your most loyal customer still shops elsewhere, but propensity scores can help you find additional shoppers who will value your product or offer even if they haven’t been a heavy purchaser from you in the past.
(1) IRI, AU Household Weight Jan 2023; base n = 4697.
(2) IRI, AU Household Weight Jan 2023; base n = 4697.
(3) Agree or strongly agree. IRI, AU Household Weight Jan 2023; base n = 5031.
Drawing on the Circana Shopper Panel – Australia’s largest shopper panel of nearly 14,000 households, combined with nationwide demographic, psychographic behavioural data dimensions and linear modelling across categories, sub-categories and brands – Circana ProScores builds discrete scores for product categories enabling businesses to better target shoppers with a propensity to buy. This is based on a broad range of detailed and complex factors identifying those consumers most likely to purchase a brand’s products. These unique audiences are packaged up and available for activation via several publishers and DSPs. Circana has partnered with leading industry providers and is supported by reputable media platforms to reach most consumers across Australia directly in an efficient, rapid and scalable way.