A recommendation engine uses data filtering algorithms to suggest content, offers and products based on individual or audience profiles. It does this by using collaborative, content-based or personality-based rules to surface recommendations.
If you’re looking to provide better user experiences and higher returns, you’ll face multiple challenges. To succeed, you must lean on data and manage content efficiently.
Poorly integrated tools.
Manual processes.
Lack of marketer control.
Poor data quality.
Recommendation engines do more than make it easy for users to find music, films or news they enjoy — they help strengthen your relationship with your customers.
Self-learning algorithms find relationships between products quickly then connect user behaviour to determine how likely a consumer will convert. This leads to faster product discovery.
Recommendations can increase content consumption, shorten the path to relevant content and boost time engaged with your brand.
As users engage with recommended content, more granular customer profiles and personas are automatically built, which helps with targeting look-alike audiences. Historical and real-time data are combined to provide continual updates.
Recommended products increase purchase size and lift customer lifetime value through improved personalised experiences.
Adobe Target gives you unprecedented control with its recommendation capabilities, including automatic content optimisation and customisable algorithm settings. Target helps you to drive users to the most relevant content and products.
See how Adobe helps today's brands build complete personalised experiences.
“It’s important that we first understand how customer journeys are performing and secondly…look to optimise those journeys.”
- Will Harmer,
Senior Manager of Insights and Optimisation, EE
“It's critical for us to give a good experience prior to [a] trip, making sure we give them information on places they're interested in."
- Marlies Roberts,
VP Marketing Operations, Overseas Adventure Travel
How does a recommendation engine work?
Most recommendation algorithms cycle through three phases: feedback collection, learning and prediction. Datasets collected during feedback collection can be memory based, model based or observation based.
Does a recommendation engine use real-time data?
Yes. Systems can be set up to analyse real-time data. However, some engines perform batch processing, which updates recommendations periodically.
How do recommendation engines select products to display?
Systems use filtering algorithms to provide product selections. Filters include collaborative, content-based and hybrid recommendations that look for similarities in items or user behaviours.
What are explicit and implicit feedback?
Explicit feedback is collected as users interact with the recommendations. Implicit feedback infers user preferences by analysing actions like purchase history, navigation history and time spent on web pages.
Are there recommendation engines for mobile environments?
Yes. Automated mobile recommendations offer personalised, context-sensitive recommendations, which can be based on location, season, daypart and more.
How do you measure a recommendation engine’s performance?
A/B testing can reveal a system’s performance. Marketers use several metrics, including customer lifetime value, click-through rate, conversion rate and ROI to measure results.