With the right system in place, an Australian online store can put AI personalisation to work so shoppers are steered to the products they want, their messages arrive at the proper time and they can get on with making a purchase. Good software will draw on product data, first-party information and customer behaviour to customise the site experience, not make your shop feel like a creepy guessing game.
In this guide I compare the principal tool types available: AI for product recommendations and website personalisation, AI email and SMS marketing, as well as AI shopping and customer service for ecommerce. My name is Connor Birchall and my philosophy on technology is much like a long road trip down under: you do not set off with half a tank and no plan, you check the map and know what conditions you are up against.
What Is AI Personalisation?
Simply put it aligns content or messaging with what a shopper is likely to require. By way of example, it will take in signals from search terms, location, device, past purchases and general browsing to determine what should be put in front of them.
Whereas an old “customers also bought” widget has its place, today’s ecommerce personalisation software is able to tie together multiple channels. One might be presented with a product on Shopify, get an abandoned-cart email via Klaviyo and have a word with an AI assistant that is familiar with the very same catalogue. These capabilities align with Adelaide digital commerce solutions.
Personalisation In Plain English
It is not about putting a different website in front of each and every visitor but in making alterations that are warranted by the evidence. An established customer will be shown items he has been looking at; someone new to the site could be offered popular wares or a deal for new customers along with delivery details.
The point is to aid the customer in his decision, not to stand in the way. If he is perusing some hiking boots, then size guidance, waterproofing spray and a pair of socks would be helpful. To put out random products because an algorithm has to do something is little more than digital confetti.
The Case for Australian Retailers
The online market in Australia is crowded and retailers have to contend with varied delivery conditions and great distances. For a smaller operation, personalised ecommerce marketing is a way of making the catalogue more navigable without the expense of a big merchandising staff.
There is also the matter of consent and data security to be had. An unexplained level of tracking or a recommendation that is too on the nose will have a shopper looking for the digital exit; a good experience engenders trust.
Commercial Realities
You will have clientele in the capitals, in regional towns and remote parts of the country. What is relevant to them is dictated by stock, climate, freight and how long delivery will take. The logic behind a winter product offering in Hobart is not the same as in Perth or Darwin.
Then there are seasonal demands to factor in, be it Christmas trading, the school holidays or end-of-financial-year sales. When your personalisation is driving customers to products that are running low, demand forecasting and inventory management take on added significance.
Use Cases Worth Pursuing
Do not turn on every feature in the book, the most effective results are had from a handful of use cases. Identify a problem with the customer and apply the AI that addresses it. This holds true whether one is in fashion, homewares, electronics or any other retail sector; the commercial value will be in the margins, the quality of traffic and how the tool plays with your current systems.
Merchandising And Recommendations
You will find AI product recommendations in the cart, on category and product pages, in post-purchase emails and search results in the form of “similar products”, “frequently bought together” and so forth. But the engine needs to be mindful of suitability, margin and stock. Promote something that is not in stock and you will ruin the customer experience quicker than a mozzie at camp.
Segments And Behavioural Targeting
By way of segmentation a retailer can target shoppers on the basis of purchase frequency, cart activity or an interest in a particular category and put forward the appropriate offer or message. You may wish to distinguish between a loyal customer and a first-timer, or a high-value buyer and one after a discount. The idea is to be relevant with your segments, not to wring every message you can from them.
Site Content, Email And SMS
For SMS and email marketing, AI will help in choosing the subject line, the send time and which products to put before which customer. HubSpot and Zoho are options for CRM integration and the wider customer journey, while Klaviyo is the go to for many when it comes to automation. With the right tools one can put a more personal spin on offers, content blocks, search results and banners. And while Canva Magic Studio is of some use in putting together campaign assets, there is a difference between creative production and true personalisation; even a well made banner has to be matched with the proper message, timing and audience.
Shopping Assistants And Support
An AI shopping assistant will do more than just field product queries: it can make comparisons, spell out delivery particulars and point customers in the direction of what they need. For ecommerce, AI customer service is capable of dealing with the run of the mill order questions and leaving the complicated ones for a human.
Provided it is set up with the necessary safeguards and good store information, ChatGPT can underpin a conversational experience. The onus is on the assistant not to make up stock figures or product specs or delivery times. Where it does not know an answer, it should state as much and put the customer in touch with support.
Making A Comparison In Australia
You will not find one AI tool that is best for all online stores in Australia. What is right for you is a function of your platform, the size of your catalogue, the kind of data and support you have in house, and so forth.
The table below is offered as a way to begin any commercial due diligence. It is a comparison of what the various categories of tools might do, no more. One would be hard pressed to say they are all alike in price or feature set.
| Tool Or Platform | Potential Role | Best Fit | Check Before Buying |
|---|---|---|---|
| Shopify | Store platform, apps and the like for platform-based personalisation | Those after a commerce foundation that is connected | Monthly outlay, app compatibility, theme performance and data access |
| Prime AI | Product recommendation and discovery via AI | Where catalogue navigation could be improved | Stock synchronisation, feed requirements and support from within Australia |
| Klaviyo | Customer journeys, SMS and email automation | Active marketing of your own audience | How pricing is done on contacts, event tracking and consent |
| Personyze | Behavioural targeting and website personalisation | For rules, segments and an on-site experience | The limits of integration, analytics and how much work to implement |
| HubSpot / Zoho | To automate marketing and segment with CRM | Sales, service and marketing under one roof | User permissions, workflow and commerce connectors |
| Birdeye | Workflows for communication, reviews and engagement | Retailers who want to meld reputation with messaging | Ecommerce data links and Australian channel support |
| Amperity | Unifying customer data and identity | Bigger retailers with records in fragments | Enterprise costs, governance and the resources for implementation |
A smaller store may find Shopify’s AI personalisation a sensible way to get started with native integrations and apps. Amperity type of customer data platform is something a larger retailer will require if customer records are to be found in marketplaces, service systems, websites and stores.
Then there is Prime AI, Klaviyo, Personyze, HubSpot, Zoho and Birdeye. These ought to be measured against the customer journey as it is. A tool can be very impressive in a demo yet flounder when faced with your variant structure, product feed or consent model.
Software Comparison Done Right
Do not go by the length of a feature list but by the commercial result. The costliest platform is not necessarily the wiser buy and a cheap app from Shopify can prove expensive in the manual labour it engenders.
Put vendors to the test with a real scenario. Hand them a typical campaign brief, a sample of your catalogue and some stock constraints and see how they handle common questions from customers. You will learn more from that than from a neat slide deck.
What To Put To Vendors
- Can we exercise control over the data we share and what sources does the tool draw on?
- Is our CRM, analytics, email provider and customer service system supported as well as Shopify?
- Will recommendations be filtered for anything restricted, low margin or out of stock?
- In terms of reporting, do we see incremental revenue or merely the attributed kind?
- How are opt-outs, deletion and access requests put in order?
- And what sort of work is left for us to do once it is launched?
When it comes to pricing, ask for it in AUD with GST included, along with any fees for usage, contacts, messages or modules. If a vendor puts forward a wide range of prices, note the assumptions rather than take the bottom line at face value.
Privacy And Data
None of the personalisation is dependable without the data to back it up. There has to be consistency in the naming and timestamps of behavioural events, order history, product feeds and the rest.
Before any new data is put to use, an Australian retailer would be wise to check on privacy compliance. The guidance from the Office of the Australian Information Commissioner is a good place to start on one’s obligations with respect to personal information.
First-Party Data Is Paramount
This is the data of your relationship with the customer, their account, purchases and preferences. As it is based on what a customer has actually done and not some third-party surmise, it tends to be of greater utility for service and retention.
Put on record the data you are collecting, your reasons for doing so and the retention period, as well as the suppliers to whom it is given. You will want to see if your obligations are in any way impacted by AI providers, sub-processors or hosting overseas. And make sure your privacy notice is a true reflection of the technology at work, not some earlier draft from before the plug-ins were put in place.
The Data Route
Make it easy to follow the path from the storefront through to support, analytics, CRM, SMS, email and the recommendation engine. Consider it the digital version of having a look at the Stuart Highway prior to a long drive; one wants to be aware of where the road gets rough and where the fuel stops are.
Enforce deletion procedures, audit logs and access controls. See for yourself what transpires should a customer put in a request for their information to be expunged or withdraw consent. There is no point in putting a personalisation system into production if it is unable to accommodate such events.
A Question Of Commercial Return
There has to be a link between the value you get out of a business and its personalisation efforts. That means tracking everything from the conversion rate, profit and revenue to average order value, return and unsubscribe rates and the demands placed on customer service.
Wherever you can, employ A/B testing or a control group. An AI recommendation on every page does not make for an easy case that it was responsible for the sale. Put similar sessions or customers side by side with and without the experience, making allowances for any promotions or seasonality.
An ROI in numbers
Take 10,000 sessions yielding 200 orders; 200/10,000 gives a 2% conversion. Introduce a personalisation experience and you might have 300 orders from the same sessions, up to 3%, or 50% more than the 2% to begin with. Yet do not assume the tool is the whole story, stock changes and traffic quality will have a part to play too. Do the sums on incremental gross profit and take out the costs of staff, software, creative and implementation. It is a fine problem to have when a revenue lift is in fact losing you money.
Be judicious in what you implement
Rather than go off and run ten experiments, pick a single high-value journey. You will find better evidence from a guided product finder, product recommendations on the cart or an abandoned-cart email.
Before you do, have the owner, the metric for success, the data fields, a fallback and the approval process in order. Should the AI service go down, customers must still be able to search, buy and get in touch with support.
For a launch you might follow this:
- Define the baseline metric for a particular customer problem.
- Do an audit of event tracking, consent records, stock and product data.
- Pick a tool your team and platform can handle.
- Have a pilot under control, with sensitive products excluded.
- Look at the results in terms of location, device, product category and customer type.
- See if there have been any escalations to support, returns or complaints.
- Write up what was successful, what was not and the plan going forward.
AI personalisation is not something you buy and put on a shelf to be forgotten about. Behaviour and product data will change, campaigns will introduce new patterns and someone has to be on top of things to review edge cases and keep the system in line.
Who it is for
If a store has the repeat purchasing, variety or volume to make sense of customer behaviour then AI personalisation is well suited. Specialist retailers can also make use of it to offer some direction to the buyer.
Not so for a new store with scant traffic, an unclean set of product data or a very small catalogue. In such instances an AI layer is less valuable than good photography, accurate delivery details and clear copy.
Retailers will ask
One hears these questions because the talk surrounding AI shopping tech has more of a sheen to it than is warranted. The answer is a matter of budget, data, expectations and the platform in question.
What makes the best Ecommerce Platform in Australia?
Many an Australian online store will do well with Shopify for the integrations, payment options and app ecosystem it puts at hand. But it is not the right fit for all. Make a decision after weighing up transaction costs, reporting, local support, fulfilment and how complex the catalogue is.
In what way does AI figure in online shopping?
You will find it in demand forecasting, fraud detection, segmentation, search, customer service, behavioural targeting and product recommendations. What matters is whether the feature brings about an outcome you can measure for the business or the customer.
Which is the best AI for shopping?
Depends on the job. For the retailer the best system is one that is integrated into operations, can account for its decisions and is working from sound stock and catalogue data. The shopper may want an assistant to put options head to head or an engine to put products in front of them.
Will AI build an online store for you?
It can be of assistance in coming up with marketing ideas, images, layouts and scripts for the service team. Decisions on pricing, suppliers, accessibility and privacy are another matter. Use it as an assistant and vet the output before it goes live.
Is it safe for the customer?
As long as there is human oversight and sensible security, and the retailer has valid consent and is forthright in its notices. One should steer clear of inferences that are sensitive or targeting that cannot be explained. Let the customer have some say over their data and marketing preferences.
Let personalisation justify itself
Tools for an Australian online store should be good at the basics: to help a purchase come to fruition, put an end to superfluous messages and field a question.
Select a use case and put it to the test against a control. Measure the profit, not just the revenue, and have your privacy compliance in plain sight. It does not matter if you are with Prime AI, Klaviyo, Personyze, Amperity, Zoho, HubSpot, Birdeye or Shopify, the provider is only half of it.
You need people to spot when the algorithm is pointing to something sold out, and merchandising and data to back it up. That is the kind of discipline Adelaide digital commerce solutions bring to the table for retailers who would like to make personalisation of some use rather than just a word of the moment.