Ecommerce retailers are facing a critical challenge: strike the perfect balance between delivering an exceptional customer search experience and drive business growth.

Shoppers expect the best possible results when they search. More often than not, the “best” results are defined by a range of criteria unique to the brand, product category, and business. Even the word choices used in the search query have an impact.

Online businesses have amassed extensive data on their audiences’ and users’ preferences. While 64% of organizations manage one petabyte of data, 41% surpass that with at least 500 petabytes. Yet, they struggle to leverage this valuable resource to create search experiences that positively impact bottom-line results.

B2C and B2B ecommerce players need a more powerful and nuanced tool to help them weigh different combinations across multiple metrics, integrate rich data, and generate search results beyond basic relevance and popularity rankings. This is where Algolia’s Multi-Signal Ranking comes into the picture.

Multi-Signal Ranking

Algolia’s Multi-Signal Ranking (MSR) lets ecommerce businesses easily incorporate a more comprehensive blend of signals to shape their end user’s search experience.

It helps them create more impactful search and serves as a powerful tool to meet customer expectations, address user needs, and empower ecommerce leaders and merchandisers to generate dynamic outcomes and business growth.

Based on a strategy that relies on a blended ranking approach, MSR allows businesses to create the best search result outcomes by leveraging a variety of data signals—from audience behavior to per-item popularity—to create a final ranking that is explainable, configurable, relevant to the shopper, and optimized for conversion.

With 75% of users not scrolling past the first page of search results, MSR helps keep end users engaged. It gives your team the tools to test and optimize search result rankings for each query and discover what order works best for your conversion needs. As a goal-driven AI-supported ranking technology, MSR delivers the best results to meet the outcomes you expect from the world’s most popular AI search solution.

As business goals evolve, MSR helps ecommerce teams create impactful search experiences.

Standard search ranking: the pain points

When it comes to ranking search results and driving conversions, ecommerce players often have more than one fixed goal. To realize their business strategy, they may need to quickly modify search rankings across parts of their site or in relation to specific product categories.

They may find themselves in a situation where customers disengage if margins are prioritized over popularity. Similarly, focusing on popularity over availability could lead to customer dissatisfaction when stocks are low. At the same time, prioritizing popularity over margins could hurt revenue.

Being able to adjust to these shifting priorities is critically important. Finding the right balance in this complex equation is the key to delivering the one-two combo of bottom-line results and a great customer experience.

Consequently, businesses struggle with the limitations imposed by their existing search technology and the unwieldy task of manually adjusting across too many custom ranking criteria. B2B and B2C enterprises relying on rankings and search face a list of common pain points and limitations that include:

• Forced default to popularity ranking: Though it is a common approach, popularity isn’t always the search ranking businesses want to prioritize. A brand-new product, for example, won’t be popular, but it could still drive conversions because it is fresh and new.
• Inability to integrate complex data: Businesses often find it difficult to incorporate various data signals such as conversion metrics, ratings, user data, and query intent in a way that is optimized for their unique goals.
• Developer dependency: Modifying custom ranking features typically requires specialized tech and engineering know-how. As a consequence, most nontechnical business users struggle to create and implement blended ranking systems internally.
• Difficulty adapting to multiple sales goals: A company with more than one area of specialization, like a department store, may want to implement different search ranking strategies. Different departments may own different pages of the site and want to generate conversions based on different approaches, such as maximizing revenue through high-margin items or increasing sales for specific SKUs.
• Lack of explainability: A system that doesn’t clarify how and which data points drive search outcomes means users can’t be confident search results will align with their business goals. They need to know which data signals drive positive or negative results.

Ecommerce retailers require more control to fine-tune their ranking strategies, specifically based on the various business metrics and user behavior data they already have available.

As business goals evolve, MSR helps ecommerce teams create impactful search experiences that meet consumer expectations without compromising on business growth.

What MSR can do for your site

Fine tuning search ranking according to specific business outcomes can be challenging. Manual control and rule setting is typically a cumbersome process without the help of developers and engineers. MSR enables users to create the best search result outcomes by leveraging multiple data points and harnessing Algolia’s AI-powered technology.

MSR uses a combination of various signals to determine what is relevant and therefore what converts. It provides ecommerce teams with a nuanced tool to generate weighted combinations across multiple metrics and incorporate a more comprehensive blend of inputs.

It also expands the range of traffic that can be re-ranked by enabling the analysis of more unique queries across a broader set of data inputs improving sales and conversions.

The technology allows business, merchandising, and tech teams to continuously test and optimize search result rankings for each query, with a toolkit of easy-to-adjust parameters and analytics to discover what works best for their business’s conversion needs.

Example use cases include:

• Promoting high-margin products while preserving popularity: Use margin data as a signal to boost the visibility of high-margin products. Incorporating margin data into your ranking signals and prioritizing items that balance margin and popularity makes it easier to increase conversions.
• Showcasing inventory surplus: Help move stock faster by identifying products with excess inventory and boosting their ranking in relevant searches, ensuring they are surfaced more prominently while maintaining standard relevancy checks.
• Highlighting fast shipping and reliable delivery: Apply fulfillment metrics like shipping speed and on-time delivery rates to prioritize products with quicker delivery, ensuring they appear more prominently to increase conversions without compromising shopper relevance.
• Accounting for preferred brands or sellers: Encourage repeat purchases by highlighting known, sponsored brands or sellers. Continue to drive higher conversions while improving user satisfaction, long-term brand loyalty, and third-party partnerships.

How MSR works

MSR balances manual control with AI optimization. It gives ecommerce leaders and merchandisers the flexibility to select the data signals they want to prioritize and lets AI automatically adjust the weights of those signals. MSR complements the reach and capabilities of our Dynamic Re-Ranking technology, allowing for the re-ranking of even more unique queries by leveraging a wider range of input data.

The power of popularity

While popularity-based ranking works well for high-traffic queries, MSR introduces new signals like personalization and textual relevance without disrupting the effectiveness of popularity-based ranking if it is already performing well.

It intelligently balances when to use popularity and when to introduce other ranking signals for better outcomes. What makes MSR unique is its ability to incorporate not just standard signals like clicks and conversions, but also any data represented in the search index, such as textual and facet signals.

Blend and fine tune signals

MSR offers several options for users to customize and personalize search, giving teams the ability to:

• Combine and blend different signals, including:
  • Events, clicks, and conversions
  • Business metrics and data stored within individual Algolia records, including margin, offline revenue, availability, shipping, sponsored brands, and promotions
  • Relevance scores based on textual relevance via our tie-breaking algorithm
  • Textual signals such as the number of typos or distance between words
• Fine-tune search ranking rules according to specific business goals
• Independently manage search rankings without needing the help of developers or engineers
• Use multiple ranking signals to drive search performance
• Test and iterate a range of input signals to experiment and find the best search result ‘formula’
• Balance manual control with AI optimization and have the flexibility to prioritize specific data signals, making it easier to find the combination that best addresses business goals

Visualize signal impacts

MSR also produces search results ranked in a way that optimizes for conversion in real-time. It provides an easy-to-understand visualization that displays the impact of various AI ranking factors and signal criteria.

The visualization offers explainability. It’s easy for users to see how specific data inputs and signals positively or negatively influence outcomes.

In the process, businesses can test different sets of criteria to help them achieve a variety of business goals, such as increasing average order value or revenue.

By leveraging multiple data points ranging from audience behavior to per-item popularity, MSR helps create a final ranking that is explainable, configurable, relevant to the shopper, and optimized for conversion.

We give your team the tools to test and optimize search result rankings for each query and discover which inputs work best for your conversion needs. The visualizer also makes it easier to conduct A/B tests and experiment to find the best combination for your needs.

Technical enabler: harnessing the full power of AI Ranking

Ranking is a fundamental lever for improving search effectiveness, second only to retrieval. Customers using search primarily focus on top results, with 30% of clicks going to the first result. Consequently, the order of search results is crucial for driving business growth.

Given that your end users will be naturally drawn to the search results that take the least effort to understand, it is important to ensure those results are the ones users want to click on. MSR addresses this requirement by offering a more sophisticated, powerful, and adaptable ranking solution.

Taking our search solutions one step further

MSR builds on our existing features like Dynamic Re-Ranking and custom ranking. It leverages multiple business signals that can be applied uniformly across an entire index, enabling more nuanced business goal optimizations.

Our AI-supported ranking capabilities integrate deeply with customers’ internal data in a symbiotic relationship that drives search success.

As AI advancements make sophisticated data models more accessible, the Algolia AI Search platform can accommodate a growing array of predictive signals, from purchase propensity to product return likelihood.

With AI’s increasing accessibility, smaller companies can also leverage MSR’s sophisticated data models to build richer datasets that combine diverse data signals.

Along with lowering costs and making access to AI-powered solutions easier, MSR delivers:

• The best order of search results, maximizing desired metrics for different business goals
• An ability to select different signals to improve relevance and increase conversion
• World-leading re-ranking technology at the individual query level
• Explainable results without having to understand the technological details
• Deep integration with customer data and existing site recommendation systems

5 reasons to choose Multi-Signal Ranking

Reason 1. Explain search outcomes

MSR lets you understand exactly how your data drives search outcomes so you can be confident your results align with your goals. MSR gives you explainability and helps you understand the data behind each search outcome. You have clear visibility into which data points drive positive or negative results and the ability to fine-tune the search experiences precisely.

Reason 2. Balance manual control with AI optimization

MSR allows you to choose signals while using AI to help you find your goldilocks zone. It lets you select the signals you want to prioritize while AI operates in the background, automatically adjusting weights, making it easy to experiment and find the best combination for your needs.

Reason 3. Leverage data without technical expertise

MSR uses the data you already have to turn clicks into conversions. It improves search relevance and ranking without needing to call on the resources of your technical team. And by using your existing data, you can create better search experiences that directly align with your strategic goals while improving the customer experience and bottom-line results.

Reason 4. Optimize for different signals within a single site

MSR lets you adapt your search strategy based on shifting business priorities and the needs of your team. Whether your objectives change seasonally or different internal teams need to focus on different goals, MSR allows you to adjust your ranking strategy in real-time so that every team can optimize search for their specific needs.

Reason 5. Drive growth based on a blend of ranking goals

MSR drives holistic business growth by targeting and increasing more than just conversions. It lets you and your teams balance conversion rates with other crucial metrics including, average order price, revenue, and shipping.

Algolia MSR: helping drive revenue and conversions

Partnering with Algolia gives your B2C or B2B ecommerce site unrivaled traffic advantage and scale. Serving over 1.75 trillion search queries a year and collecting over 200 billion events from user interactions, Algolia offers a huge advantage in being able to help you classify your customer queries into powerful, relevant results that generate bottom-line results and meaningful returns.

• Comprehensive understanding: Algolia’s massive data set allows us to identify and analyze complex user behavior patterns across several industries and use cases
• Global reach: With customers spread across multiple countries, Algolia technology is designed to account for regional search behavior and preference differences
• Scale and reliability: Algolia’s infrastructure is capable of handling trillions of queries. It ensures that your search and discovery capabilities can be deployed at scale without compromising performance

By unlocking your data and enabling our AI solutions, Algolia helps you tailor relevant search results that generate conversions and positive business outcomes.

Algolia constantly collects, analyzes, and updates our AI models based on signals collected from millions of end users, so your business benefits from our class-leading technology.

As part of our suite of powerful AI-powered ranking tools, MSR gives customers the flexibility to meet and surpass their needs. It adds to the capabilities of our existing Dynamic Re-Ranking solution, allowing for the re-ranking of even more unique queries by leveraging a wider range of data as input.

Algolia AI Search and our MSR technology offers what other search platforms simply can’t — striking the perfect balance between delivering an exceptional customer experience and driving business growth.

MSR drives holistic business growth by targeting and increasing more than just conversions.

Discover how to put Algolia AI Search and MSR to work for you

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