What are Recommendation Engines?
Recommendation engines are an application of machine learning algorithms which aim to provide the most relevant items or content to a user. They can incorporate a variety of data including user purchasing behaviour, browsing behaviour, demographics and real-time triggers.
Today’s customers are increasingly demanding a personalised experience in all interactions with brands. Personalisation is no longer a nice to have but a must have. As such, recommendation engines are an effective way to differentiate yourself from the rest of the market by offering tailored content and product suggestions to your users while driving conversion rates, average order value and customer lifetime value.
Why use Recommendation Engines?
Our clients typically adopt our recommendation engine services for one of the following reasons:
Growth & revenue
By using machine learning to understand and predict what customers are most likely to be interested in you can both improve the customer experience and increase sales at the same time. We custom fit algorithms that are optimised for your business model, goals and for your customer’s motivations.
Data-driven decisions
Decisions on what to show different types of customer at each opportunity involve vast numbers of permutations and variables. We design a bespoke recommendation engine that can either make those decisions for you in real-time or assist your decision making. It’s optimised for your customer types and is developed exclusively from your data.
Consistent personalisation
By creating an engine that can drive the customer experience across channels and devices, consistency is ensured. A consistent experience delivering. It can also learn as it goes, taking advantage of AI algorithms to teach itself from each new interaction and continuously improve.
How does Lynchpin do it?
Our approach is truly bespoke, with no off-the-shelf software. We base our approach on six key tenets:
Models to fit your needs
From self serve dashboards to help drive decision making to advanced methods such as SPADE or Collaborative Filtering we have a solution to fit your needs with the data that you have today.
User Stitching
Applying machine learning techniques to user stitching we are able to deliver the full representation of the customer journey, linking previously anonymous interactions together giving a single customer view.
Transparent analysis
A deep-dive analysis on customer behaviour and purchasing patterns will allow you to understand how your customers are interacting with your brand and the behaviours that drive them.
Scalable Applications
Based on your data, we can build a scalable, bespoke recommendation engine to provide the most relevant products given user behaviour with transparency being at the heart of the model.
Working in partnership
We will work with you to successfully implement the live model and support in the ongoing measurement of the recommendation engines performance.
Ongoing Improvement
Once deployed, we will continue to improve and develop the model as personalisation should be a process, not a one-off-solution.
Other Services
Contact us
If you’d like to explore how we can help, please get in touch with our data and analytics consultants. We don’t have salespeople and your first point of contact will be a subject matter expert from our leadership team.