Consumer electronics is a rather crowded space with a bevy of brands trying to make their presence felt across all categories – from wearables to TVs and headphones to laptops. Unless a shopper knows exactly what they are looking to buy, consumers in this space often fall victim to “overchoice,” a term coined by Alvin Toffler in his 1970 book Future Shock.
As the name suggests, the overchoice effect occurs when a buyer is overwhelmed with a large number of available options, often leading the person to give up decision making altogether, or worse, take their business somewhere else. For today’s consumers looking for instant gratification, experiencing this is a nightmare. And for a retailer, that’s bad for business.
In an effort to improve product discovery throughout shoppers’ digital commerce journeys, retailers have invested heavily in personalization. According to a Forrester study, personalization ranked first among technology investments in 2021.
The same goes for consumer electronics retailers. B.TECH is among the top Egyptian retailers in this category, with over 100 stores and a growing online presence. The retailer saw a surge in e-commerce revenue in 2020 as consumers stayed home and relied on electronic devices for their business, social and entertainment needs.
That said, B.TECH realized that product discovery was a problem – it was important to surface products relevant to each shopper and their current context. Doing this consistently is a surefire way to earn (and keep) a customer’s loyalty.
To individualize commerce experiences in real time and at scale, B.TECH has deployed an AI-based personalization engine. Let’s take a look at their customization in action.
- Category page
When a shopper visits a category page, they are likely to be in explore mode and open to suggestions. The image below shows a merchandise placement for “Top 10 Best Sellers” at the top of the electronics category page. This helps a shopper discover popular items that they probably hadn’t considered exploring before. This approach also works well for new or unfamiliar visitors for whom no behavioral and preference data exists.
- Product detail page
When a shopper visits an item’s page, they’ll also see the “Compare with similar products” option. While this may be a common feature, what makes it more convenient is that the buyer can easily compare specs without having to visit each product page.
This placement uses advanced merchandising that allows for relevant upsell and cross-sell recommendations based on the item being viewed, without the need for manual merchandising.
- Add to cart page
When adding an item to the cart, the shopper gets relevant cross-sell recommendations for accessories or products that are compatible with the main product, saving them the hassle of searching for those items separately. For example, wireless AirPods are recommended when an iPhone is added to cart.
- Cart Page
When the shopper goes to the shopping cart page, the engine again reminds them of the add-on items they might want to buy with the main product, without being pushy. But what is unique about this recommendation block is that the shopper can switch between cart items and view recommendations for each item separately.
And when a shopper empties their cart, instead of a simple ‘Oops! “Your cart is empty” engine offers solid alternatives to items that the shopper has deleted. These recommendations make sense because the buyer had a clear purchase intent.
In addition to the above-mentioned efforts, B.TECH provides relevant recommendations on the homepage, as well as based on a shopper’s search queries, previously viewed items, and items in their shopping cart, allowing the buyer to more easily pick up where he left off. stopped.
Product discovery is now child’s play for B.TECH customers. Since customizing its online store, B.TECH has recorded solid commercial results:
- 18.6% of website, mobile site and app sales can be attributed to personalized engine-driven recommendations (up from 11% previously)
- 5% cross-sell revenue
- 10X RPMV on cart page
Verkkokauppa.com is another retailer that has turned to customization. The company is among Finland’s largest online stores, with 65,000 SKUs across multiple categories, including consumer electronics.
Verkkokauppa has moved from traditional search on commerce sites to personalized self-learning search to address pressing issues such as irrelevant search results and instances where a shopper sees a page with no results after performing a query. of research.
To elaborate, when a shopper searches for “Apple”, the search may show all available Apple products. But would that be relevant to the buyer? Probably not. Custom Search helped Verkkakauppa solve this problem using a strategy known as Wisdom of the Crowd (WOC).
WOC typically uses a machine learning algorithm that learns from the collective behavior of shoppers, their search queries, and the product they subsequently view or purchase. It then uses this information to display search results that are likely to match the buyer’s intent. Shoppers using search often have a clear purchase intent, and personalized search has helped the retailer convert these shoppers faster.
In addition to search, Verkkokauppa has also customized other business touchpoints of product recommendations, browse or category pages, and content. Here are the business results they achieved as a result:
- 31% more conversions
- More than 24% increase in basket size
- More than 25% of sales attributable to product recommendations (compared to 6% previously)
- Sessions involving research convert 5 times more than those without research
In conclusion, it’s paramount for retailers to personalize every key touchpoint of the online shopping journey, including search, product recommendations, navigation, and content. This will enable a more holistic experience that customers expect today. Consistently creating relevant, in-context experiences will also help retailers become top brands in an era when customers are spoiled for choice and loyalty is hard to come by.
Sarath Jarugula is Chief Product and Technology Officer at Algonomy. In this role, Jarugula drives the vision, strategy and delivery of enterprise-class MLAI products in customer engagement. A seasoned leader with a strong track record in digital experience innovation, he champions the use of machine learning and data science to create user-friendly products that deliver business value. Prior to that, he was CEO of RichRelevance, where he led growth, mergers and acquisitions and helped found Algonomy. Jarugula has held leadership roles in several growth-stage companies, helping with strategic exits, including Sysomos, LucidWorks, InQuira, eGain, Microsoft and others.