Earlier this month, we wrote a post in response to the Digital Excellence Report, more specifically on how 100% of Membership Organisations think that AI will have an impact on membership within 3 years.
This time, we wanted to combine these thoughts to address another key trend from the report, namely the need for personalisation becoming increasingly important for member engagement.
Engaging effectively and efficiently with members remains a key priority for all Membership Organisations, and the top priority for Small and Large Bodies. As MemberWise commented in their report;
“…we need to remember that effective member retention and engagement needs to underpin [increasing new member acquisition] for sustainable and medium-long term growth”
In this blog, we’ll address some of the critical success factors for member engagement, and highlight how AI & Machine Learning can be used to establish these best practices, fostering greater personalisation for member engagement.
Content & Frequency: Critical Success Factors for Member Engagement
The formula for increasing and improving member engagement can be boiled down to two key components; content and frequency.
Content relates to selecting the right message for the right members, all whilst balancing the goals on the membership body with core brand messages and engagement driving topics.
Frequency of engagement relates to selectively increasing (or decreasing where appropriate) the amount of communication.
One of the critical success factors in effective content and frequency is high quality response data. Response data, categorised into clear topics and themes is critical in being able to profile, segment and predict member preferences, thus informing the best content. If done correctly, and tagged data helps in the profiling of customer segments and the creation of engaging content, then this provides communication teams with the confidence to move forward and engage members more frequently.
How AI & Machine Learning can Improve Content & Frequency
One of the great “fears” member engagement and communication teams often have is triggering unsubscribes and fostering reduced engagement through not matching content with customer profiles – and a lack of personalisation. Response rates to email marketing campaigns only ever serve a minority of members (for example, even the best email campaign will have less than 10% click through rate).
What’s the answer? How is it possible to understand preferences more widely if only small groups generate the data points to tell us? The answer: Predictions.
Predictive Machine Learning can score the entire member base to extrapolate the nuggets found in historical data, and stack the odds back in your favour, so you can better meet members’ needs and provide the level of personalisation needed to increase engagement.
The presence of good quality labelled member data is something of a prerequisite to achieving this – and the right approach can be achieved by working with data experts or platforms that provide this categorisation capability.
Enabling Personalisation with AI & Machine Learning
A key challenge that membership bodies face is that their members are often advocates or paid subscribers to the organisation. This only heightens expectations on organisations to demonstrate that they recognise and value each member throughout their membership. This is why an increased number of membership bodies are looking to measure member engagement (60%, up 6% YoY) and personalise online experiences (42%, up 4% YoY).
Where data, AI and Machine Learning can help here is by balancing the organisation’s objectives within that of an individual product / service, so that each member receives a mix that is relevant to them.
AI can be used to predict members’ responses to different messages. For example, if a member doesn’t fit the profile of a potential event attendee, then they would be down-weighted and receive alternative messages.
There is an old mantra from direct marketing experts: “there is no such thing as too many communications – only ‘relevant or irrelevant’ ones”.
Summary
AI will certainly help Membership Bodies deliver the “last mile” impact of incremental engagement. The Bodies that win in this area will use a combination of light touch, advanced toolsets, clear-use-cases and data infrastructure (i.e. inputs and feedback loops) to execute and measure the process.
Regardless of the size of your Membership Body, it’s crucial to ensure you have the expertise and guidance required in order to ensure your AI strategy can be utilised with the tools and the data you have, to deliver the personalisation and incremental engagement increases you need.
At iota-ML, we’re here to help you workshop through AI and Machine Learning, how it can benefit your organisation, and how it can align with your objectives. Please contact me or our Chief Sales Officer, Matt, for more information.
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