Explaining predictive personalisation for ecommerce — SwiftERM
Explaining predictive personalisation for ecommerce. While many retailers are talking about consumer experiences and personalisation in their digital marketing efforts, there are few brands that are doing this effectively and at scale. The topic of predictive personalisation can be confusing and might seem intimidating for marketers, but regardless of where your brand is on the personalisation journey, it’s important to get started.
What does Ecommerce personalisation mean?
Ecommerce personalisation refers to tailoring consumer experiences to the preferences of each individual consumer. Curating experiences for individual consumers relies heavily on personalisation AI and data science — the goal is to use the data you’ve collected on your audience to curate relevant and meaningful experiences across touchpoints and email marketing. The more personalised the experience the more likely that consumer to making more purchases, and remain loyal to you.
So, what’s an example of personalisation?
Imagine a hair and beauty brand’s historical data shows that Julianne visits the website to view products designed for curly hair, but their data on Maria shows that she adds lots of hair coloring products to her online shopping cart. Knowing this, the AI that enables predictive personalisation can automatically curate website experiences and email product selection content that are tailored to Maria and Julianne’s preferences respectively.
With the right personalisation algorithm, they’ll will each see personalised product based on the data that the brand has. Avoiding segmentation’s this is now accepted as being a mile shy of a viable solution, SaaS solutions, such as the 100% automatic SwiftERM, can tailor perpetually updated product selections for your email marketing that deliver 20x the return of standard ESP marketing, not only based on prior purchase history, but every nuance of each site visit of each consumer you have. i.e. if Julianne only actually buys when she checks an item 3 times, or indicates her intent by the minimum time she spends reading the product description etc .
How does predictive personalisation work?
Personalisation engines can take the data that you’re already collecting about your consumers and make the analysis process easier, allowing you to pull actionable insights about your audience and improve the consumer experience.
Predictive personalisation is somewhat self-explanatory — you’re predicting what products an individual consumer wants to see. This relies on the combination of historical data with real-time contextual information about the consumer. By it’s very definition a machine learning algorithm can take elements previously perceived as subjective and perpetually incorporate them into product selections, for each individual. This might include the relationships between products to one another, cloth, but, material, colour, all elements a hard-working marketing executive couldn’t hope to include to narrow down the selections for each consumer. imagine preparing content manually only to reach a consumer who that item the night before you pressed send, good that you got the sale but actually annoying for the missed opportunity for supporting one by your email,
If an alcohol retailer’s historical data shows that Michael purchases whiskey from their website every two weeks, it would be a safe assumption that the pattern would continue and he might purchase whiskey two weeks from his most recent purchase. This is where it’s important to combine historical data with real-time contextual data. For example, if it’s a bank holiday weekend and the weather is warm and sunny, Michael might be more likely to purchase beer, or gin instead of whiskey. While the historical data may show that Michael almost always purchases whiskey, the ability to provide personalised product recommendations can improve his consumer experience and may lead to more purchases in the future.
What’s lost from this explanation is that Michael is actually a Speyside connoisseur, so finite and accurate can the process be, that it would refine a voice of how old aged-casks Michael prefers too. Sending him anything less finite than this could be quite insulting, if you are trying to win hearts and minds about caring for your customer on a personal level.
The most powerful tool in the toolbox
If you’re trying to improve your brand’s consumer experience, enabling predictive personalisation is a good place to start. Not only can it keep your consumers loyal, it can yield big returns for your business in terms of engagement, loyalty, average order value, lifetime customer value, volume of returned goods, CRM registrations, and sales.
Now, think about curating an experience like the ones mentioned above for every one of your consumers at the same exact time. With the right personalisation algorithms and tools, you can curate a different experience for each individual consumer — if you’ve got 100 consumers, your solution should be able to produce 100 variations of the consumer experience. If you’ve got one million consumers, you should be able to produce a million variations, each changing perpetually, and updating at the nano-second each consumer is about to receive their communication.
Of course, predictive personalisation is impossible to do manually. With thousands or millions of consumers, curating experiences for every single individual isn’t feasible — that’s where the right personalisation tools come in. It takes time to implement a predictive personalisation engine, and without a robust personalisation strategy, it’s hard to get those efforts off the ground. SwiftERM is offered for free for a month, installed as a SaaS on all Shopify, Magento, WooCommerce and Opencart platforms, and bespoke ones too. It is offered for free for 30 days to establish viability on your site for yourself, and you are not tied into a contract thereafter and may come and go as your please.
With the right vendor or software solution, you can enable truly relevant and personalised digital consumer experience today.
Originally published at https://www.swifterm.com on May 28, 2022.