Main customer types for the new age
There have also been challenges – most notably in creating omnichannel experiences. H&G shopping has always been a very traditional in-store experience but sales have now moved into a more hybrid online and offline model. As a result, new buyer personas have evolved.
Our Home & Garden domain experts have identified three main customer types for the new age. Later in this article, we are going to share a little more insight into each one, as well as explain how Deep Learning-powered retargeting campaigns are vital in communicating with these individuals.
How do retargeting ad campaigns help H&G stores right now?
Upper- and mid-funnel campaigns: Brand Awareness and Consideration
Loyalty to a brick-and-mortar store doesn’t always transfer directly to the same retailer’s e-commerce offer. This McKinsey report showed that 46% of customers in the US tried new retailers in the first months of the pandemic and customers nowadays are more open to change. Brand awareness campaigns are essential in keeping people aware of and engaged with your business across all channels.
Lower-funnel campaigns: Performance
Retargeting campaigns bring a proven competitive advantage to Home & Garden stores. We’re currently running over 250 campaigns for H&G companies, and the results are compelling. For example, RTB House helped Leroy Merlin, the leading French omnichannel retail brand in DIY and home improvement, to achieve a threefold increase in Return on Ad Spend (ROAS) and a 9x increase in conversion value from retargeting ads.
Who are the new H&G buyer personas?
Heavy Buyer
Renovating, decorating, or doing major work. This buyer makes many high-value purchases in a short space of time. The goal of advertising is to increase average order volumes through recommendations of complementary products, alternative items at the right price points for better margins, and predicting what the user will buy next. The more powerful the algorithm, the more effective the campaign is at delivering these increased revenues.
For example, predicting what a user will buy next is possible if the algorithm is able to cross-match the user’s purchases against those of other cohorts and also understand the context. A user who is undertaking a major project may start with buying tools and hard building materials, then move on to decoration, then furnishing.
Deep Learning algorithms that can learn from similar buyer behaviors will be able to assign the user as a Heavy Buyer earlier in the process than technology like Machine Learning or basic AI. And that earlier identification is crucial when the buyer is only making high-volume purchases for a short time.
Diligent Buyer
Many people are now working on more limited household budgets, so they will spend more time looking for best offers. As the user is visiting your store and those of your competitors, you need to show your products and pricing while highlighting such factors as doorstep delivery, installation, returns policy, and discount pricing.
However, capturing the attention of the diligent buyer doesn’t mean simply displaying more ads than your competitors. It’s about showing more effective ads, following some golden rules, such as: don’t target users directly after they leave the store, don’t show multiple ads on the same webpage. These are the basics, but Deep Learning technology and ad capping allows us to take it further. In order to grab the attention of the diligent buyer, ads should only appear in prominent placements on the page, usually „above the fold”.
Lastly, when going up against competing vendors, design is important. You need to make sure that your ads look better and are fully in line with your brand book.
Spontaneous Buyer
Spending so much time at home, we notice all the things that are not there and those that are not quite right. Spontaneous buyers make a sudden decision that they need another cushion or that a door handle needs replacing. These small purchases add up. Repeat customers are the lifeblood of business.
The aim of campaigns is to get quick conversions before the user loses interest in their latest purchase idea, but also to show them products and categories in your store that they may not have visited – or thought of – before. This way, you can increase purchase frequency, even if average order volumes stay low.
Deep Learning algorithms can make a difference here as they can see what related products or items from other categories were popular with similar users and make more precise recommendations. It’s not a case of just showing the user „other things that might interest you,” it’s about showing the user „things that we are sure will interest you.”
If you would like to find out more about adding Deep Learning campaigns to your marketing efforts in the H&G domain and how to unlock higher revenue streams, please do not hesitate to contact us – we would love to help you.
