What e-commerce tech can put you in a winning position for peak trading?
Beginning on Black Friday-Cyber Monday (BFCM) weekend on November 25-28 and stretching over winter to January 2023, peak season will see ecommerce sites receive a huge spike in traffic from new and existing customers shopping for gifts.
You’ve already put in the hard work researching trends, curating a stellar inventory and designing tempting offers—but is your ecommerce tech ready?
Let’s take a look at some of the ways e-commerce brands can leverage tech to improve product discovery, create compelling personalised journeys and beat this season’s KPIs.
Product searches—a peak season challenge
Whatever the season and regardless of intent, your site’s search bar is almost universally shoppers’ first port of call.
If the search isn’t optimised to give shoppers what they want, figures show they’ll leave in droves. Our research suggests that over half (52%) of customers will abandon a full cart to shop elsewhere if they can’t find at least one item to complete their haul.
In the worst case, poor search isn’t just a cause of journey abandonment but could cause shoppers to fall out of love with your brand altogether. Almost three-quarters (74%) of shoppers said they would avoid returning to a site that offers a poor search experience, and 85% said an unsuccessful search would impact their view of the brand.
The figures were collated during ordinary trading, but you can expect shoppers to be particularly sensitive to zero-result searches at peak season when they don’t always have specific buying goals in mind and are especially time-pressed.
The power of AI search
Where conventional on-site search matches queries to the most relevant inventory products by keyword, AI-powered search leverages additional data about the user’s search and browser behaviour and history to return relevant results.
Unlike conventional search, AI-powered search self-learns and improves as it collates more data. As a result, it can make real-time data-driven decisions about which products most closely match the user’s intent (e.g. gift-buying Vs bargain-hunting).
Natural Language Processing (NLP)—another feature of AI search—offers a further benefit to goalless peak-season shoppers. It allows the search to respond more naturally to human search terms. NLP-driven search understands, for example, the difference between a ‘black dress’ and a ‘little black dress’ and can also serve up relevant, personalised results for vague search terms like ‘makeup’ or ‘gifts’.
Leveraging user data to contextualise queries and applying NLP to return more relevant results effectively eliminates zero-results searches. Improved functionality can result in a 98% boost in conversions from on-site searches.
Personalisation & recommendations
To personalise shopper journeys with targeted product recommendations, e-commerce brands must constantly collect, collate and process users’ data to build an accurate picture of preferences, likes and dislikes.
This is especially challenging for ecommerce brands over peak season for a few reasons:
- Tempted by the prospect of bargains, peak-season shoppers often arrive online without specific buying goals.
- They shop for gifts for recipients who do not conform to the user’s everyday buyer profile.
- Goals can change radically from one session to the next: They could find themselves shopping for Hallowe’en decorations in October, browsing for bargains over BFCM, and purchasing varied gifts over the remaining months to Christmas.
- Sites may see an influx of new visitors about whom they hold little or no data.
The first of these challenges is solved by the AI algorithms we’ve already seen at work optimising on-site search, which can reliably serve up strategically placed product recommendations in line with shoppers’ favoured colours, style brands and more.
But solving the final trio of challenges requires more data. In addition to understanding the buyer’s persona, they must also know their intent. Perhaps most crucially, they must also know when goals change from one session to the next and be able to serve up new recommendations without missing a beat.
React with Crownpeak
Crownpeak seamlessly collates and analyses data to build up an accurate user profile that merchandising teams can use to strategically place highly personalised product recommendations based on past purchases, preferences, favourite items and much more.
The dataset becomes more accurate and insightful with every user interaction and can be matched against similar profiles to offer genuinely unique recommendations.
But where Crownpeak really excels is understanding shopper intent and the speed with which it keeps up with changing goals to present new ‘flavours’ of recommendations in real time across all channels as goals change.
Crownpeak clients report that as much as 80% of site turnover is generated from the 20% of products with personalised recommendations applied, and implementing site-wide personalisation results in an uptick of 21.1% in adds-to-basket.
Merchandising—the data challenge
Peak season gives already stretched merchandising teams a whole host of extra factors to consider when displaying products in a way that maximises engagement and conversions to meet challenging KPIs.
Meeting merchandising demands to match high-volume traffic to extensive inventories across multiple channels isn’t easy at the best of times. At peak season, however, teams have additional factors to contend with in ensuring their efforts are seasonally relevant, on point with market trends, and ready to meet heightened demand.
Teams often face one of two opposing problems: Either they have more user data than can be quickly and efficiently leveraged, or they can’t collect sufficient data to make the relevant, personalised recommendations that aid product discovery and ultimately meet merchandising KPIs.
For teams without the data science know-how to work with large user datasets, Attraqt offers pre-trained algorithms that allow them to optimise omnichannel merchandising strategies straight out of the box.
And where teams face the opposite problem—a scarcity of data about new users chasing offers over peak season—Attraqt provides pre-trained, self-learning algorithms that merchandisers can deploy alongside to begin beating KPIs and generating ROI from the get-go.
Attraqt lets teams leverage data without needing to take a deep dive into complex science, freeing up to 60% of merchandisers’ time.