Marketing Cloud Personalisation Recommendation Engines

Use Marketing Cloud Personalisation Recommendation Engines to Improve Your Customer Relationships and Boost Sales

Customer service is the key to success in a business as a client will make their decisions based on how they feel like they’re treated. It is important to not just be friendly, but to also show and suggest solutions. This pays off when predicted well, but may not necessarily be easy.

This is where product recommendation engines come in, to enable you to give your clients that well-predicted personalised journey.

What are personalisation recommendation engines, exactly?

Product Recommendation Engines engines are essentially cloud-based algorithms which use the customer’s data, analyse it, and show the client specific content that they’d like to see and when.

Benefits

A superior product recommendation will personalise the customer’s experience as it uses the client’s behaviour to make the right suggestions. And so, as one can expect, personalisation is shown to increase the ROI and increase sales.

Using engines will also free up sales teams to focus on other aspects of the customer journey, making it more cost effective whilst improving a company.

How to choose the right software

There are many factors to consider when choosing a product recommendation engine, and different business models will have different issues, but the key factors to think about are: ease of integration, usability, effective automation, machine learning and real-time processing.

Personalisation Toolkits in Salesforce Marketing Cloud

In Salesforce Marketing Cloud’s Journey Builder there are many blocks you can use to personalise your customer experience, and facilitate automated content personalisation.

Over time, Salesforce Marketing Cloud Personal advancement progressed from Image Carousels, A/B Tests, Live Images, External Content, Dynamic Content, Reference Content, and now Einstein Content. Solutions on how to make Salesforce Marketing Cloud communications more intelligent and responsive, and achieve smart personalisation have come a long way.

While Salesforce Einstein’s artificial intelligence allows you to get predictive, in general, in Marketing Cloud you can go beyond the Einstein’s AI recommendations and become more granular and specific in your approach. All this, to create more personalised engagement with customers online.

Marketing Cloud User Group Talk

Host Jimson from CloudAnalysts (left), and Sponsor Rachel from Computer Futures (right)

Marketing Cloud Personalisation Recommendation Engine, the topic of a recent Salesforce Marketing Cloud Meetup in London, was met with great anticipation. The popular talks sparked interesting discussions afterwards on approaches to personalisation.

Many thanks to our guest speaker Will McMahon from Gravitai as well as to Rachel Rickard who sponsored the event, set up the venue and arranged great pizza*, wine & beer. Thank you too to our own Jimson Lee, go-to Marketing Cloud expert from CloudAnalysts, who organised and hosted the event. Many thanks to all for attending, contributing and being part of our lovely Salesforce Marketing Cloud Community!

On Personalisation with Salesforce Marketing Cloud

William’s talk on “Getting personal with Salesforce Marketing Cloud” was peppered with information. Included in his talk was an overview of possible solutions on how to make Salesforce Marketing Cloud communications more intelligent and responsive, and achieve smart personalisation. He took us through a Real Estate example on how the engine helped identify 3 groups of keywords, and terms’ relevance in a buyer journey:

A. Informative: e.g. fastest selling penthouses
B. Thought-provoking: Best neighbourhoods for families
C. Decision Making: ‘City penthouse or downstairs loft: What suits you best?‘ And: ‘2018 Real Estate Trends vs 2019 Market: is it a Good Time to Buy?’

Marketing Cloud Personalisation Recommendation Engine Solutions

Applications / Goals when Using Recommendation Engine Solutions

Audience Measurement: find ways to better understand audiences
Media Content Strategy: use of big data to shape media content strategy
Data Trust Principles: Inform marketers of the legal framework of data protection and privacy, and give concrete recommendations on how to implement it and develop common GDPR principles
Recommendation Systems: answer the need to develop recommendation systems for customised online offers.