Imagine you walk into a bookstore looking to discover your next favorite read. There are shelves upon shelves stacked high with books – fiction, non-fiction, classics, new releases and everything in between. The choice is overwhelming. Where would you even start? The friendly store employee sees you looking lost and comes over. "I noticed you picked up that sci-fi book – if you liked that one, here are three others I think you‘ll love." She hands you a personalized recommendation list based on the clues she observed about your tastes.
This is the power of a recommendation system. Much like that helpful bookseller, recommendation engines leverage data and analytics to suggest relevant items to customers. The more insight into each user‘s preferences and past behavior, the better they are at matching people with products likely to delight them.
In our digital world overflowing with choice, recommendation systems have become essential to connecting consumers with what they want faster. Top companies like Amazon and Netflix owe 35-75% of their sales to product suggestions fueled by their recommendation engines.
As personalization grows ever more important to customer experience, recommendations will only increase in strategic value. This definitive guide covers everything brands need to know, from what recommendation systems are to how to implement one that supercharges sales.
Contents
- What Are Recommendation Systems & How Do They Work?
- The Business Value and Benefits of Recommendations
- Use Cases and Applications Across Industries
- Examples and Impact Metrics From Top Companies
- How To Implement a Recommendation System
- Emerging Innovations and Trends
- Key Takeaways and Getting Started Tips
What Are Recommendation Systems & How Do They Work?
Recommendation systems, also called recommender systems or recommendation engines, are software tools that predict which products or content a customer is most likely to be interested in. They work by analyzing data about:
- A user‘s past purchases, browsing history and interactions
- Similarities with other customers who made comparable choices
Sophisticated machine learning algorithms examine large datasets to identify patterns that connect customers with relevant suggestions. The underlying models continuously improve their accuracy the more data fed into them.
Approaches to Develop Recommendations
While there are many complex technical nuances, most engines use two fundamental approaches to match users and items:
Collaborative filtering looks at similarities between customers to make suggestions. For example, Netflix determines users who watch all the same kinds of horror movies are likely to enjoy other scary films one user rated highly.
Content-based filtering recommends items with comparable attributes to what a specific user previously liked. If you purchase an action-packed science fiction book, Amazon suggests books tagged with similar genres and keywords.
Hybrid systems combine both collaborative and content-based filtering for more accurate and personalized results.
The Business Value and Benefits of Recommendations
So why do recommendation engines deserve a starring role in any company‘s customer experience strategy? The numbers speak for themselves:
Recommendations can increase revenue by 10-30% by driving more conversions and higher order values. Forrester reports companies see an average 14% sales lift from product recommendations alone.
75% of what people watch on Netflix comes from recommended content. The streaming giant awarded a $1 million prize for the best movie suggestion algorithm, underscoring how critical recommendations are to their business.
Amazon‘s recommendation engine generates 35% of their revenue, running on advanced machine learning models built on 20 years of customer purchase data history.
Beyond the impressive sales impact, recommendations boost other key metrics:
- Higher user engagement and satisfaction – Connecting people with relevant products leaves them happier and more likely to return
- Improved loyalty and brand perception – Personalization reinforces positive associations with a brand‘s ability to understand each customer’s needs
- Richer data sets – More user interactions provides fuel for machine learning algorithms to continually refine suggestions
For online businesses competing for customer mindshare, recommendation engines are no longer “nice-to-have” features but essential components customers actively expect and demand.
Use Cases and Applications Across Industries
Virtually any consumer-facing industry can benefit from recommendation systems:
Verticals perfectly suited for recommendations include:
Ecommerce
Recommendations are table stakes for modern online retailers. Analyzing historical purchase data to suggest relevant products is a classic application.
Amazon masters using recommendations throughout the shopping journey – from curated sections like “Inspired by your Browsing History” to email campaigns with personalized product picks.
Over 35% of Amazon purchases happen through their recommendation engine, translating to billions in extra revenue.
Media & Entertainment
In the battle for viewers’ attention, media platforms leverage suggestions to keep people engaged. Netflix and Spotify both attribute over 70% of streaming to their recommendation algorithms.
Beyond movies and music, news and magazine subscriptions like Buzzfeed and The New York Times also rely recommendations fueled by reading history to point readers to more click-worthy content.
Financial Services
Banks use transaction data and customer details to identify timely offers – for example, suggesting a particular credit card to a customer with a major purchase upcoming.
Wealth management companies can recommend investment opportunities fitting a client‘s risk tolerance and goals. Recommendations here build crucial trust and satisfaction.
Telecom & Utilities
Service providers can harness customer usage patterns to recommend optimal plans. By analyzing mobile data and calling habits, cell providers can steer customers towards appropriately priced options before users overpay or churn from frustration.
Examples and Impact Metrics From Top Companies
Here are some stellar examples of enterprises generating major value using recommendations:
Amazon
The ecommerce pioneer powers recommendations through advanced machine learning algorithms trained on rich customer behavior data built up over 20+ years.
Results:
- Over 35% revenue attributed to recommendations
- Millions in incremental profit through suggestive selling
- 14% higher conversion rates for suggested products
- Shoppers who buy recommended items have 60% higher lifetime value
Netflix
The streaming giant refined recommendations into an art and science, leveraging various algorithms that filter titles based on watch history, content semantics analysis and more.
Results:
- 75% of all shows watched happen through suggested content
- 10% increase in streaming satisfaction score from improving recommendations
- Won 1st Netflix Prize for the best collaborative filtering algorithm (CVR score improved 10%)
- 20% lower cancellation rates for engaged recommendation users
Spotify
The music app creates personalized playlists like Discover Weekly for each user showcasing songs that align with their taste profile and listening habits. This powers over 30% of listening.
Results:
- Over 30% of streaming happens through personalized recommendations
- Hours streamed per user grew 20% after launching suggested playlists like Discover Weekly
- Increased projections for subscriber growth attributed largely to investments in better recommendations
The above examples demonstrate the solid revenue potential of recommendations when executed thoughtfully. While results will vary by vertical, properly implemented recommendation systems commonly see over 20% sales lift.
How To Implement a Recommendation System
Now that the business case is clear, how can brands actually deploy recommendation capabilities? There are a few routes ranging from leveraging an out-of-the-box solution to building fully custom:
Route 1: Plug & Play Recommendation Service
Turn-key recommendation services handle the heavy lifting of data science and machine learning so companies don’t have to build expertise from scratch.
Leading options like Momentum Sage or Adtrib simply integrate with existing tech stacks through APIs or code snippets. Merchandising and digital teams can manage recommendations through easy interfaces without IT help.
Benefits
- Fast time-to-value – getting up and running in under 1 month
- Ongoing optimization – vendors continuously enhance underlying algorithms
- Latest innovations – leverage cutting-edge techniques like reinforcement learning
This route works for most use cases. For best results, integrate recommendations across web, mobile, on-site and email channels.
Route 2: Custom Build
For advanced companies with specialized needs or enormous scale, custom building in-house recommendation engines allows tight control.
Benefits
- Competitive differentiation by tightly tuning to unique business needs
- Leverage existing data science team and tailor to internal tech stack
Challenges
- High complexity & investment to develop and maintain complex machine learning
- Scarce internal talent – data scientists are scarce and expensive
Companies like Netflix take this route since recommendations are utterly strategic to their business. For most other companies, buying is better than building.
Route 3: Hybrid Approach
Rather than totally custom internal builds, brands can take a hybrid approach by partnering with recommendation specialists who create customized engines.
Consultants like AIMultiple work closely with clients to tune solutions to their specific users and inventory. This balances internal control with external expertise.
Compared to off-the-shelf SaaS recommendations, custom projects better adapt to each brand‘s business logic, data feeds and flows. The joint effort combines industry-leading thinking with internal perspective.
Emerging Innovations and Trends
While maturity varies across verticals, recommendations are an essential component of future personalization capabilities. Exciting innovations to leverage include:
Reinforcement Learning
Moves beyond static rules to have algorithms actively choose actions that maximize a desired outcome based on environmental feedback. Recommenders can adapt in real-time to serve the most relevant suggestions based on user response.
For example, a user clicking but not purchasing an item provides negative feedback that informs future recommended products.
Contextual Recommendations
Incorporate more dimensions like time, location and traffic information to dynamically tailor suggestions to each user‘s immediate contextual situation. This takes personalization to the next level.
If a commuter frequents coffee shops, a contextual recommendation would surface different items depending on whether they are at home or work location.
Recommendation Explanations
To build trust and help customers evaluate suggestions, some engines attach “why I am seeing this” explanations. Transparency allows users to give tighter feedback for improving relevance.
Explanations strengthen user connections with personalized picks and improve opt-in rates to receive future recommendations.
Hybrid Recommendation Systems
Hybrid recommendation systems combine both collaborative filtering, content-based filtering, and even contextual signals into a single model for maximum relevance.
Mixing approaches allow recommendation engines to adapt fluidly based on the amount and quality of user data – serving highly individualized picks for established users while offering more generalized popular or trending options for new visitors with minimal history.
Companies like YouTube leverage myriad hybrid signals to connect viewers with their next irresistible video click.
Getting strategic with recommendations now will pay significant customer experience dividends as personalization expectations continue rising.
Key Takeaways and Getting Started Tips
Recommendation engines leverage data science to drive measurable revenue gains by connecting customers with relevant products in hyper-personalized, contextual ways.
Brands aiming to supercharge personalization and loyalty through recommendations can take the following steps:
Audit your data – surface behavioral insights that could fuel recommendation models
Map objectives – which key metrics like conversion rate or average order value should improve?
Explore plug-and-play recommendation services – identify leading solutions fitting your technical environment
Run A/B tests – validate recommendation efficacy by measuring impact on KPIs
Iterate and optimize – continuously tweak models and interfaces to improve relevance, click rates and business value
This guide should provide brands a 360 degree perspective on recommendation systems – illuminating what they are, why they create value, real-world implementations, emerging trends and advice to get started.
The future of extraordinary customer experiences and sustainable competitive differentiation lies with creative applications of recommendation engines. Now is the time for forward-thinking companies to build competencies and leverage recommendations as a core component of modern digital experiences.
