The winter retail period is a critical time for sales and an opportunity to harvest a goldmine of valuable data. Each year seems to become more significant of an opportunity than the last.
As research from Deloitte points out, a key factor in the forecasted uptick in retail sales growth during this past holiday season can be attributed to online sales.
- Ecommerce generated close to $290 billion in retail sales this past holiday season.
- Online sales now represent close to 16% of overall retail buy in the US with year over year of growth of almost 9%.
- For younger millennial shoppers, ecommerce represents over 80% of retail sales volume.
The season is also a key resource for the rich data that can guide merchandising and customer journey decisions moving forward into the new year.
Now a critical component of the B2C ecommerce mix, the data generated by AI-powered search means that retailers have easy access to insights that help drive focused data-based decisions and important KPIs.
So what are some of the best ways to harness all that customer data as you move forward, post-holiday, at the start of 2025? How do you meet customer expectations and continue to grow your ecommerce platform's relevance, reach, and appeal?
Let's take a look.
What do you do with the customer data you’ve collected?
It's time to take stock of your ecommerce search strategy — look at the data, identify gaps, and assess the effectiveness of customer profiles and the impact on the customer experience.
It’s not enough to set up your search strategy and forget about it. Ensuring online operations are generating desired bottom-line results and can scale and pivot to deal with shifts in customer numbers and inventory is a continuous process.
The power of AI search
AI search is now integral to the customer journey, and the search box is an important piece of real estate on any retail website, delivering a bounty of valuable data about customers and product.
The post-holiday period is the time to take a deep dive into search analytics and refine KPIs to understand your customer and get the most value out of your search strategy.
The AI search toolbox
The data generated by AI search enables merchandising and technology experts on your team to take a careful look at what worked and what didn't.
It is an opportunity to test different blends of manual curation and AI to better leverage search and merchandising decisions — taking the time to do the analysis, experimentation, and A/B testing that helps improve conversions.
By taking advantage of the tools and resources available on an AI search platform like Algolia AI Search, enterprises can use the technology to show online shoppers what they need, and better evaluate customer intent and generate the search results that positively impact ROI.
Are you asking the right questions? What needs to improve and the search audit
In the dynamic and competitive world of retail, shoppers expect a superlative customer experience, each and every time. Knowing your customer is critical to making the kind of choices that help generate conversion and a positive impact on new and returning shoppers. Testing and optimization is an ongoing process and a critical part of maximizing your search strategy and the capabilities of your AI-powered search and discovery technology. The post-holiday reset is an ideal period to review data and experiment and test new and existing implementations. It is also an opportunity to ask some simple but important questions and generate valuable insights that can improve ROI and the customer experience:
- How did my search strategy and implementation perform over the busy holiday season?
- What can I do to improve it?
The search analytics audit
The first place to get the insights and hard numbers for traffic, conversions, and revenue data is by using the analytics your search or ecommerce platform provides.
A typical analytics audit will provide a summary of key benchmarks such as conversion rates, Average Order Value (AOV), and bounce rates. This data should also be segmented into two categories:
- Shoppers who use the search bar
- People who browse through catalog, category, or product listing pages (PLP)
This helps you and your teams identify some common search issues like slow load times (latency) and the relevance of search results.
The data you retrieve in your audit will help answer questions about search performance against six key criteria.
1. Maintaining consistency
Merchandising needs to be consistent across channels to maximize sale opportunities. Did omnichannel functionality work as it should with no critical discrepancies between web, mobile web, and mobile applications?
2. Manual curation alongside AI
AI augments the work your team is doing, but there are times when it is best to manually direct search results:
- Are you using your merchandising solution to manually curate specific parts of your catalog and applying AI-based optimizations to other areas?
AI can boost results based on real time data, such as pushing best-selling or high margin items to the top. But curated rules are great when it’s time to create an immersive brand experience and control sponsored items.
3. Long tail optimizations
When shoppers use longer search terms like “the best socks for my grandpa” — essentially reproducing the way they speak — the search query becomes more complex. Long tail queries are difficult to predict, and it is difficult to write rules and synonyms for all of them. To deal with the dilemma, your search solution needs to be optimized across three variables:
- Query understanding: was your AI search able to parse the query using natural language processing (NLP)?
- Retrieval: did your AI search technology enable a hybrid approach that made use of AI-based vector search capabilities to deliver meaningful results for complex queries?
- Ranking: was AI ranking capable of reinforced learning and able to push results getting the most clicks, conversions, purchases, ratings to the top?
4. Instant indexing for accuracy
Up-to-date indexing ensures that the latest product varieties, prices, new SKUs, and inventory levels are accurately represented in the search results. Ultimately, indexing capabilities are a test of speed at scale:
- How effective was the search technology at reflecting critical updates such as product varieties, prices, new SKUs, and inventory levels?
- Was your search solution able to handle frequent and massive changes, including schema updates, customer reviews, new merchants, etc., without affecting search speed and accuracy?
5. Performance and scalability
IT infrastructure is critical to ensuring that customers are being well served online particularly in periods of peak demand:
- Did the architecture allow you to scale without increasing spending to adjust systems?
- Was the infrastructure able to cope with surges in demand?
- Did systems remain robust, quick, reliable, and secure at all times?
6. Ability to easily manage site search tools
With deep knowledge of your brand and customer, merchandising and marketing teams have a leading role to play in shaping the online experience for customers:
- Did merchandising and digital marketing teams have the tools to optimize search results and relevance quickly, without the need for IT support?
- Were data insights — product preferences, sale volumes, and browsing habits — available in real time?
Reinforcing the search experience: how to use your merchandising data
With 88% of online users not returning to a site after a bad experience and average bounce rates of 41% to 55%, retailers have few second chances. Retail merchandising data encompasses all the information generated by your shoppers’ interactions in your online store or app. It includes details like search queries, product views, cart additions, and purchases. After a post-holiday data audit, focus your merchandising, marketing, and IT teams’ attention on finding innovative ways to improve browsing, navigation, and the overall customer journey. Be on the lookout for opportunities to increase the richness and relevance of the search experience.
Here are some ways to harness the power of AI search and your merchandising data:
Make data-driven merchandising decisions
Merchandising data gives you the scope to make data-driven decisions that can yield insights on which of your sales channels are most effective. It also helps identify the best times to engage with your shoppers, such as specific days or times. Being sensitive to timing can significantly impact the effectiveness of your campaigns.
Improve your website architecture
Your website's functionality can profoundly impact how your shoppers interact with your content. Your merchandising data can offer some key insights. For instance, if the data shows that shoppers often search for but can’t find certain SKUs, that suggests a need for a better organized layout or more-intuitive search functionality. Targeted improvements based on merchandising data can also shorten the path to a purchase, while improving the customer experience and driving sales.
Optimize product placement and inventory management
Merchandising data on product views, cart additions, and purchases help identify which items are most popular among your customers. This information lets you feature best sellers and works for inventory management too, by helping forecast demand with greater accuracy.
Build effective pricing strategies
Your merchandising data can accurately inform your pricing strategy, helping ensure that what you charge is competitive and profitable. It allows team members to adapt pricing to market demands in real time. You can also use your merchandising data to confidently clear out inventory and keep your stock fresh and relevant. This can help you compete in markets where price sensitivity is high and shoppers are looking for the best deals.
Identify and leverage trends
Use merchandising data to identify emerging and seasonal trends that can help you stay ahead in a dynamic market. By spotting where things are headed early, you can then adjust your inventory to include appropriate products and tailor your marketing campaigns to align with your shoppers’ interests.
Reduce cart abandonment
Why are your shoppers leaving your site or app before making a purchase? Analyzing your data can uncover the reasons why your shoppers may purposefully be leaving empty handed — maybe they don’t like your high shipping costs, they see unexpected fees, or the checkout process takes too long.
Spot cross-selling and upselling opportunities
One of the best ways to increase AOV is through cross-selling and upselling by making AI-powered recommendations based on your merchandising data. Merchandising data provides reliable insight on which products are frequently bought together, enabling you to suggest item pairings and complementary items.
Create personalized experiences
AI-powered search allows you to use your merchandising data to tailor unique experiences for each and every visitor based on their preferences and behavior. Shoppers are more likely to purchase, recommend, and repurchase from companies that use the technology.
Reinforcing the search experience: 4 strategies to maximize AI search capabilities
Using an AI search technology that better understands customers’ wants and needs means that shoppers are more likely to purchase, recommend, and repurchase from your ecommerce site. An essential tool shaping the online retail landscape, Algolia AI Search enables a range of options that can generate more detailed and curated search results and online shopping experiences. It helps you connect with your online customers and provide them with meaningful product recommendations and suggestions.
If you implemented AI-powered search capabilities for the first time this past retail season or are looking to enhance AI search capabilities moving forward into the new year, here are four important strategies to keep in mind.
1. Improve the relevance of AI search results
Some of the ways to further enhance the quality and relevance of your customers search results:
- Improve textual relevance with typo tolerance and synonyms, taking into account plural forms of words
- Use your business metrics like click-through rates, conversion rates, and popularity to tune relevance, ensuring that the user sees content or products they are most likely to act on
- Adjust parameters regularly based on up-to-date search analytics. Review the results for your most popular searches with high-click positions and modify for popular searches that generate no results
2. Perfect your UX/UI
Giving online shoppers the ability to find what they are looking for and refine queries in an intuitive way is key to getting the most out of your customers’ online journey:
- Provide a unified search experience that allows users to start by either searching or navigating, then further refining their results by leveraging other strategies like facets
- Display results from the first keystroke in the search bar. Users may be discouraged if search findings only come up following the second or third keystroke
- Highlight and display searchable attributes on a results page. For example if “spandex” appears in the query, highlight “spandex” in the results
- Use federated search to highlight all you have to offer from different content sources on your site like eBooks, blogs, and documentation
3. Take a mobile-first approach
Adapt search capabilities to the demands of mobile devices!
- Ensure that the search bar is prominent and well-placed on mobile
- Enable device capabilities like the GPS to pre-fill address fields, speech-to-text capabilities, or the use of biometric sensors rather than passwords
4. Increase conversions through personalization
- Personalization can take many different forms depending on the amount of individual user data you have available
- Tools like Algolia's AI Personalization use merchandising data to help teams tailor unique experiences for each and every visitor based on their preferences and real-time in-session behavior
Test and iterate! The continuous learning approach
We all know that B2C is a dynamic and fiercely competitive market. For customers, the next best option is only a click away. Taking a careful look at your search and merchandising analytics is an ongoing process. Merchandising, marketing, and IT teams have to continually perform A/B tests and assess the value of new product, price, and user interface combinations.
Are there better ways to structure product data to improve search ranking and relevance? How can we improve AI personalization to provide more meaningful customer journeys?
Testing is an opportunity to review search capabilities and strategies. It allows teams to verify if products are accurately described and represented, and if the correct products show up when your customers use specific search terms.
Alongside tools like the Algolia Merchandising Studio, Algolia AI Search makes it easier to test new implementations and promote products and content. Our technology empowers digital strategists, product managers, merchandisers, marketers, and content editors to manage the search experience for their customers.
This means it can be as easy to optimize and adjust your search strategy for shoppers on Valentine's Day as it is on Black Friday. It also becomes easier to deploy a global strategy and adapt it to local customers and markets.
Having powerful AI-supported search tools to translate what you know about your shoppers’ demographics, preferences, and needs is the key to B2C success.
Review, analyze, test, and iterate! It's time to improve the customer journey, increase ROI, and delight shoppers.
Ask questions and learn more at Algolia.com
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