What is a Recommendation Engine?


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 customer behaviour 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.

Our services portfolio is broad and comprehensive. You may be unsure exactly what service is right for the outcomes you are looking for and your own situational needs. We’d be delighted to talk more about your business and how we may be able to help.

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