The majority of retail marketers are familiar with the concept of the particular store apocalypse . Legacy merchants are increasingly losing ground towards the one-click convenience, price transparency, as well as the endless assortment offered by Amazon plus similar disruptors.
But it’ s not every doom and gloom. Brick-and-mortar suppliers like Sephora, Zara, and Nike pas cher are bucking the trend. So are web commerce superstars like Bonobos, Warby Parker, and Everlane. These brands obsessively prioritize customer insights, and have committed to the technology to deliver a smooth customer experience across channels.
As the most popular season of the year for store approaches, brand new research from customer analytics firm Custora (disclosure: my employer) has revealed some surprising insights about what’ s driving retail growth. The study looked at more than 40 of our store clients to understand which metrics are usually most correlated with positive year-over-year income growth.
Consumer acquisition vs average order regularity
The results? For obvious reasons, high-growth manufacturers invest significantly in acquiring new customers. It’ s a sure-fire way to develop top-line revenue. But it’ s i9000 actually relatively inefficient. In fact , the 1% increase in order frequency can be 3x as impactful from a growth perspective like a similar increase in the number of clients acquired, and driving order regularity is often far less costly.
To put it in a different way: if the average customer buys as soon as every 180 days, shortening that will replenishment window to just 178 times can drive nearly 3% income growth.
That’ h a pretty stunning discovery – and savvy retailers have taken observe. They’ re increasingly investing in strategies like welcome series, hybrid membership models and personalization across stations and devices to drive brand wedding and product discovery — most of in the hopes of shortening inter-purchase time.
The particular impact of increasing average purchase value
An additional powerful finding buried amongst the results of the research will also be helpful to merchants looking to drive growth. Specifically, the 1% increase in average order worth (AOV) is correlated, on average, using a 1 . 3% increase in revenue.
How, then, do merchants increase basket size? Our study suggests that the most successful and data-driven retailers excel at one of two dimensions:
- Maximizing items per order (cross-sell): A fast fashion brand utilizes predicted individual-level product affinity ratings to suggest complementary products that the customer might be interested in.
- Increasing average item price (upsell): A good apparel and footwear retailer ratings customers based on their predicted optimum price point, and shows big-ticket clients premium merchandise and full-price brand new arrivals.
The challenge is that, by and large, expanding basket size is seen as more difficult compared to driving repeat purchases. As one store executive put it: “ There’ t an one-size-fits-all playbook for traveling repeat orders. The promotions plus touchpoints are time-tested. But escalating AOV requires personalization. ”
There’ s i9000 a kernel of truth for this: the most effective AOV growth strategies begin with deep customer insights. But many suppliers don’ t realize how simple the playbook – plus attainable the results – can be.
The most profitable retailers follow a 5-step methodology
- Combination customer data: bring together transactional, CRM, plus customer engagement data to create a comprehensive picture of each customer’ s romantic relationship with the brand.
- Set goals: identify whether your own brand’ s primary opportunity would be to cross-sell or upsell, and set targets accordingly. One rule of thumb: increasing products per order is generally a good beginning approach for retailers of fairly low price-point goods, impulse buys or a wide merchandise assortment. Suppliers of homogeneous or high-consideration products might instead focus on increasing product price through upsell.
- Identify relevant information: based on which AOV lever you choose to concentrate on, analyze your customer data designed for insights about what types of products often get purchased together — plus which customers tend to buy on the premium or budget end of every category.
- Experiment: The most successful retailers adopt the crawl-walk-run approach – starting basic (e. g., in a single channel, or even focusing on a single segment) and gradually iterating as they find what works. For instance , a retailer looking to implement the cross-sell approach might start with just one email tile or a single item recommendation on the checkout page. Calculate impact on the goal in question when you iterate through different tactics.
- Enhance and automate: Once a retail has recognized tactics that work — for example , web site personalization based on a customer’ h predicted price point — it’ h essential to ensure that insights are being rejuvenated continuously and are integrated directly along with marketing execution tools.
Ultimately, improving AOV represents an untapped section of opportunity for brands as they fight not merely to survive — but to grow — in the new era of store.
Opinions expressed in this article are those from the guest author and not necessarily Advertising Land. Staff authors are outlined here .
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