There’s a fine line between privacy of personal information and the convenience of personalisation online today.

With the popularisation of information about data privacy being highlighted in recent media and documentaries, it has brought what was in the past –  a topic purely for marketers and data experts – to the forefront of the general population’s mind.

Trust in organisations to protect privacy online is declining and the desire to shield personal information is growing stronger for customers, yet a positive personalised experience remains key to creating business success. 

This evolution is rippling across the world wide web as marketers look to implement new technologies to responsibly gather and utilise first-party data to provide the experience users desire with the privacy they deserve. 

According to the Australian government’s recent Australian Community Attitudes to Privacy Survey.

9 in 10 people say they want more choice and contril over their personal information online.

The survey highlighted how desire is driven by experience and the top priority when considering a new digital service now is privacy; ahead of reliability, convenience and price. 


Customer-centricity is not a new concept – it’s the key to creating any successful business.

In the past Google, Apple and Facebook employed technologies such as third-party cookies to provide an all access view of the user. Whilst these techniques were successful in delivering a personalised experience, the news cycle, documentaries and tech companies have exposed the full extent of cross-site cookies in recent years resulting in a rise in consumer awareness and decline in trust. 

“First party data is a business’ biggest asset and represents an opportunity to build a unique customer experience and defensible competitive advantage,” Datisan CEO and Co-Founder Chris Rozic says. 

Google’s Privacy Sandbox project has highlighted the importance of protecting consumer information as it iterates its plan to eliminate third-party cookies from Chrome by the end of 2023. 

“We have seen a number of businesses approach data as part of the digital marketing maturity process. What we have found is that while initially there can be some challenges getting the right people and data together… ” Chris says,  

… once a cross-organisational team is formed and aligned to the digital marketing improvement process, it becomes a really exciting journey to be part of and the outcomes of the projects can make a significant impact.

Now, as Apple introduces ‘Opt In’ and consent notifications to its iOS 14.5 and15 devices, we can expect many users will remain opted out of cross-site tracking.

Many marketing and customer experience teams are wanting to future-proof their businesses by utilising first-party data.

Datisan’s CTO Matt Daniels says, “It’s imperative that they ensure granular consent mechanisms are implemented using clear language about what that consent means. Customers need to read and understand what it is that they’re opting into.”

Consumers deserve to be able to make educated decisions on who they allow to access personal information and a law degree shouldn’t have to be a prerequisite. Verizon’s Dan Richardson told Mumbrella in his recent research that

79% of consumers are actually unaware of the changes to third-party tracking, cookies, data privacy, and ad IDs, but at the same time, 76% of consumers said they’re very concerned about data privacy, which is up 27 points from two years ago. 

It’s possible for brands and digital marketers to acquire the necessary data while protecting privacy and enhancing consumer satisfaction. But marketers also have a responsibility to educate consumers and use simple language around first and third-party data collection to allow customers to feel confident in the choices they’re making.

Datisan is a privacy-centric data partner that focuses on leveraging data to enhance customer experience. Speak to the Datisan team about how we can future-proof your customer experience and personalisation.

The doors to the digital retail world are wide open.

Reduced in-store visits amplified by Covid-19 have increased demand. With this, retailers are realising (if they hadn’t before 2020) that a highly personalised experience is essential to establishing and maintaining their customer loyalty online.

Within several months, the global pandemic not only amplified differences between retail leaders and laggards, but seriously condensed the timeline available to play ‘catch up’ in digital transformation and e-commerce.

Retailers of all sizes need to be one step ahead of their customers’ needs and their competitors’ next innovation, requiring agility, adaptability and a digital transformation mindset. Adoption of AI is the backbone of digital transformation. 

Ecommerce specialists can rest a little easier though, with the ever-evolving opportunities and automation of recommendations engines to drive transformation. 

How do recommendations think they know what I want?

In basic terms, recommendation systems are a set of algorithms that give you recommendations based on your history. Common examples of where you have seen a recommendation system in action would be eBay or Amazon – you would see similar products displayed under the particular product you have chosen to explore.

This isn’t a new discussion or retail strategy though – a study in 2017 by Boston Consulting Ground found,  “Brands that create personalized experiences by integrating advanced digital technologies and proprietary data for customers are seeing revenue increase by 6% to 10% — two to three times faster than those who don’t.”

The future of recommendations 

So that’s basic AI – advanced AI recommendation algorithms are much further along than that and can look into demographic data, social media impressions and digital footprints of consumers to decode their interests. 

In January 2021, Google announced the launch of a whole suite of solutions designed to support retailers enhance their ecommerce capabilities and deliver personalised consumer experiences. One of these is Google’s Recommendations AI, which is now out of beta.

Google’s Recommendations AI (or Recs AI) is a sophisticated analysis tool created specifically to inform users in delivering personalised recommendations to customers.

This technology shifts the emphasis from specific product recommendations to the individual and how their viewing history informs their purchasing decisions, says Google Product Manager Pallav Mehta.

Its context-hungry deep learning models use item and user metadata to draw insights across millions of items at scale and constantly iterate on those insights in real time in a way that is impossible for manually curated rules to keep up with.

Recommendations AI draws on years of experience in delivering user-specific content across Google Ads, Google Search and YouTube.  Data is drawn from the retailer’s catalog and Google Cloud services such as Google Tag Manager, Google Analytics 360, Cloud Storage, Big Query and Merchant Centre. With a capacity to support catalogues of tens of millions of items, models are based on the objective; engagement, revenue or conversions. 

New models can be developed within two to five days depending upon the complexity and are previewed prior to publishing. Existing models can be re-trained daily to capture changing catalogues, items with sparse data and user insights.  Since Google began trialling the technology, users have reported an increase in online revenue. 

So there is value and reward for both the brand and their customer?

Absolutely. Personalised product recommendations using AI/ML can improve customer omni-channel experiences by providing individualised product suggestions and other communications (e.g. online and in-store messaging) – not only to a given customer but also to specific moments within a customer/shopping journey.

Highly relevant recommendations can be powerful drivers of basket expansion and increased order value for a brand – and they can also lead to an enhanced customer experience as well. Shopper loyalty increases when trust grows that recommendations reflect personal taste and enable discovery of new products. 

Tom Sowerby, Datisan’s Head of Cloud and Martech, says,

Providing timely recommendations to customers is a big step in the right direction, but the real value comes when you train these models based on a full set of customer data from both online and offline behaviour. Leveraging all the data you hold in a way that’s useful to your customers is the difference between an okay customer experience and a great one.

Value: Recognised or Blocked?

What makes a retailer more likely to succeed in capturing value from AI / ML?

Based on Google Cloud’s research, there are 5 key factors that retailers have identified as the top enablers for success. Together, these factors lead to approximately 60% of the value capture.

Top 5 enablers of value for specialty retailers

And if there is a top five enablers of value, there has to be be a top five about what makes a retailer less likely to succeed in capturing value from the use cases around AI and ML. 

Google’s research found that there were 5 key barriers that retailers, who have captured less than the expected value from the implementation of these use cases, identified as the drivers for potential failure. 

Grouped, these barriers are cited on average 60% of the time when retailers surveyed for the research looked back at initiatives that failed to deliver the full potential they targeted.

Success with Recommendations AI?

The customer loyalty of beauty brand Sephora, who has thousands of stores globally, has circulated online with a 50 per cent increase in click through rate (CTR) on product pages since implementing Recommendations AI. 

“We wanted to deliver the same highly personalised shopping experience to our clients on our digital platforms that they receive in our physical stores”

Says Jaclyn Luft, Manager of Site Personalisation and Testing at Sephora. “We started working with Google Cloud to explore how we could leverage its innovative machine learning technology to provide enhanced personalisation to our online customers through product recommendations.”

A 2% increase in overall conversion rates, relative to the previous machine learning recommendations, has convinced Sephora to expand their application of Recommendations AI to “power recommendations on other areas of our ecosystem, such as within the checkout flow and in our emails.”

This highly personalised touch allows retailers to create an experience that fosters a sense of loyalty from its customers, crucial to their online success. 

The accelerated adoption of AI/ML will have wide-ranging effects. For retailers who move fast, the outcome will be more resilience in operation despite the uncertainties in the world – and the opportunity to focus on serving their consumers in the emerging new ‘normal’ environment.

Another brand which has had success utilising Recs AI is Hanes Australasia. You can read more about Hanes Australasia’s experience here.

When we A/B tested the recommendations from Recommendations AI against our previous manual system, we identified a double-digit uplift in revenue per session.

Peter Luu, Online Analytics Manager, Hanes Australasia

How can this apply to my business?

Capturing value through recommendations requires only a small cross-functional team and relatively little change management, but some business process changes may still be required to deliver the full potential over time. 

Many businesses have found that working with partners helps fill the vacuum of talent limitations and often acts as a bridge to support true cross-functional teaming and organisational collaboration to achieve the best value from AI in this space.


Datisan is a trusted Google Cloud Partner in Australia and New Zealand – certified in multiple expertise areas, including retail. Drop the Datisan team an email to find a time to discuss how Recommendations AI could be incorporated into your customer strategy.

Source

If it hasn’t already, artificial intelligence (AI) is coming to a contact centre near you – and not just in the form of the ubiquitous chatbots that pop up with their friendly greetings on sites you visit. 

Industries are always looking to up their game when it comes to their marketing, customer service and experience, and one way many are doing this is by integrating contact centre AI, with 20% of all customer service requests expected to be handled by AI by 2022.

Contact Centre AI can help to provide a more effective and efficient customer experience, while saving businesses time and money by simplifying and easily integrating into their current workflow systems.

This is one of the most important ways businesses respond to and meet customers needs

It does this by providing human-like conversations via virtual agents. This increases time and cost efficiency, by minimising the time live agents are online and providing answers to simple, frequently-asked questions, to which solutions can easily be applied. 

So how are real businesses utilising Contact Centre AI and what are the benefits of its implementation? 

Marks and Spencer is one company that successfully integrated Google Cloud’s Contact Center AI (CCAI) with their workflow, enabling them to report an improvement of more than 10 seconds in its average handling time. Live agents are more satisfied as they no longer have to redirect calls and can work on more complex customer inquiries. Customers are also happier and in turn, brand loyalty has increased significantly. 

GoDaddy has had a similar success rate by using Virtual Agent, powered by Dialogflow CX, a chatbot component of Google’s CCAI, which enhances the customer experience by allowing the business to create virtual agents that are able to handle all enquiries and offer simple solutions to frequently asked questions, meaning that more complex issues are passed on to live agents. 

Tell me more about Dialogflow

Popular US streaming service Hulu has also integrated this technology, by using Contact Centre AI to respond quickly and efficiently to customer enquiries. With quick responses to frequently asked questions, and automatic responses that help customers get the best experience possible.

With AI becoming more progressive and the future of marketing leaning into new technologies, is now the time to invest in Contact Centre AI?

Marketsandmarkets predicts that the market for AI technology in contact centres will increase from $800 million in 2019 to $2.8 billion by 2024. From increased customer satisfaction rates, to reducing live agent chat time, to automating business workflow, the proof is in the pudding.

When asked to share his thoughts for future trends in Datisan’s latest Digital Marketing Maturity Growth Report, Xpon Technologies Founder and Group Managing Director, Matt Forman says,

2021 will be one of the most exciting years yet for marketers that have invested in getting their data in order and ready to take advantage of the change and automation that modern cloud based AI and ML will deliver.

What else did our 2020 Digital Marketing Maturity Growth Report find? Download your free copy here.

Contact Centre AI will not only have a positive impact on customers and clients, but also on the cost effectiveness of business and on contact centre staff, with reduced live agent time and the ability of Contact Centre AI to handle smaller enquiries and complaints.

Watch Google’s video below on Contact Center AI:

To find out how you can drive call centre efficiencies with Google Cloud, get in touch with Datisan today.


What is Dialogflow?

Part of Google Cloud Platform, Dialogflow is a lifelike conversational AI with state-of-the-art virtual agents. It is available in two editions: Dialogflow CX (advanced), Dialogflow ES (standard). 

Powered by Google’s leading AI, it supports rich, intuitive customer conversations in one comprehensive development platform for chatbots and voicebots. It’s goal is to improve the customer experience while increasing operational efficiency. Some of the key benefits of using Dialogflow include:

A Dialogflow agent is similar to a human call center agent. You train them both to handle expected conversation scenarios, and your training does not need to be overly explicit.