AI exploded in 2023 with the coming of age of generative AI. The immediate benefits from this AI type has the world of marketing excited for how it’s being applied to email campaigns.
However, another AI type has quietly gone under the radar for years, despite generating huge returns for those early adopters – we’re talking about machine learning and predictive analytics.
Because machine learning squirrels away behind the scenes analysing customer data sets and tweaking its algorithms, it’s not as immediate as generative AI for capturing the mind and heart. So, let’s address that in this article…
1. Converting more of your prospects into customers
Trying to convert all those ‘never purchased’ email addresses in your customer database is a daily grind that predictive analytics has revolutionised. No longer is it a chore or a complete mystery.
The Prospect Conversion Model predicts the likelihood that a prospect will make their first purchase. Having this knowledge allows you to target this sub-segment of prospects differently than the rest with tailored personalised messaging!
Those identified with a high propensity to purchase can be set on a dedicated multi-channel journey that’s solely geared towards encouraging purchase based on their website behaviour, including which products and categories they visit most.
Those with a lower propensity to purchase can be sent on a nurturing journey that educates and informs. By doing just that, this brand increased conversion by 27.5%.
2. Converting your one-off purchasers into repeat customers
It such a source of frustration to have a healthy, active, and engaged customer database who have only purchased once and not again.
Why are they opening and clicking but not converting from your campaigns? It’s yet another mystery which predictive analytics and machine learning is managing to solve.
The Repeat Customer Model uses a wealth of transactional, behavioural, and multi-channel engagement data to predict the likelihood of customers making a second purchase, splitting the data by zero, low and high propensity to purchase again.
And as you’ve probably guessed with your own predictive mind, the answer is once again a dedicated customer lifecycle journey for those customers the model has identified as high propensity to purchase again.
This tailored campaign would include content and messaging purposely created to inspire a second purchase, with Emails, SMS, Facebook Retargeting Ads and Push Messages aimed at encouraging browsing and incentives. This brand saw a month-on-month increase of 87% in second purchases!
3. Identifying and increasing the number of your VIP customers
Now we’re getting to the business end of the customer lifecycle, and predictive analytics and machine learning are on hand to help here too.
To realise the potential of your customer database and maximise lifetime customer value, email marketers need to identify, nurture, and reward their most loyal customers.
VIP customers typically generate the largest portion of your revenue. But how does a customer become loyal and reach that VIP status?
The Likelihood to Become a VIP Model searches for patterns of behaviour in existing customers that match existing VIPs in your database. This extremely clever algorithm is literally panning for gold!
Once identified, customers who are exhibiting this potential VIP behaviour are given the red carpet treatment in, yet another, personalised customer lifecycle journey.
Tailoring your email content, promotional offers, and product recommendations to suit their specific tastes is the starting point. Making them feel special and valued is the ultimate aim.
Target potential VIPs with exclusive offers, early access to sales, or personalised recommendations will increase their average order value and frequency of purchases, leading to a higher ROI. This brand increased its VIP segment volume by 65.62% in just 8 months.
4. Knowing when your customers are ready to buy again
You may be sending replenishment emails out as static reminder emails every couple of months and hoping it lands at the right time to convert the customer. There is a better way – and yes, you guessed right, it’s another predictive model!
The Predictive Replenishment Model can accurately predict the time when each product you sell is due to be re-purchased for every individual customer by analysing past purchase behaviour and frequency.
The model also learns from overall browsing behaviour, usage patterns, and identifies variables that could influence the replenishment prediction, such as purchase frequency and seasonality.
Triggering a replenishment email that automatically sends based on an individual’s unique purchase cycle is the epitome of the email marketing mantra ‘right message, right channel, right time’.
5. Reducing customer churn and unsubscribes
Losing more and more customers to churn and increased numbers of unsubscribes is what keeps email marketers up at night. Fear not, machine learning is on hand to help you rest easy!
Reducing customer churn is one of the most profitable activities you can employ. It is far more expensive to acquire a new customer than to retain an existing one. This brand managed to reduce it’s lapsed segment by 3.9%.
The Predictive Churn Model identifies customers whose behaviours have changed and are starting to indicate that they are likely to churn but are not yet there, so there’s still hope.
By identifying these customers early, taking them out of the regular tactical campaign sends, and putting them on a dedicated win-back journey by including increased product personalisation, exclusive incentives, time limited discounts, and maintaining an emotional connection through added value services you’ll start seeing a reduction on your lapsed customer segment.
Sometimes the most difficult thing an email marketer can do is to do nothing at all! That’s the beauty of the Predicted Unsubscribe Model. Those customers who are showing signs of unsubscribing need to go on a separate journey outside of normal promotional campaigns.
By automatically excluding all customers who fall into this predicted segment, the chance of unsubscribing is dramatically reduced. In this case by 31.05%! And keeping a customer subscribed they’ll likely purchase again in the future, increasing the overall value of your database.
So, that’s a wrap on transforming your customer lifecycle
Yes, Generative AI is new, very cool and saves so much time on campaign creation, but the real AI hero of your email marketing story is machine learning – the shy quiet one, sat behind the scenes not seeking attention, but generating you huge amounts of increased revenue from your tactical and automated marketing campaigns.
If you’re not using Predictive Analytics with your current ESP or MAP then you’re missing out! Get in touch and we’ll help transform your customer lifecycle marketing.
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