Tag: Amperity

Australia – Amperity, an AI-powered Lakehouse customer data platform (CDP), and Microsoft Azure collaborate to help businesses change customer interactions via data-driven insights and personalisation. 

Retailers may use first-party data to develop loyalty, reduce turnover, and maximise lifetime value, while also shaping future omnichannel experiences and fostering pre- and post-purchase interaction and brand advocacy.

The innovative cooperation recently received recognition at the Microsoft Retail & Consumer Goods 2024 Microsoft Partner of the Year Award, where Amperity was named a finalist. The organisation was named to a global list of notable Microsoft partners for its achievements in designing and executing customer solutions using Microsoft technology.

The Microsoft Partner of the Year Awards honour Microsoft partners who, during the course of the previous year, have excelled in the creation and delivery of Microsoft Cloud apps, services, hardware, and artificial intelligence advancements. These are multi-category awards, and the winners are chosen from more than 4,700 nominations that come from more than 100 different countries.

Speaking about the partnership, Curt Lockton, SVP of strategic partnerships at Amperity, said, “The synergy between Amperity and Microsoft is pushing the boundaries of AI-driven customer experiences, and empowering brands to harness the full potential of their data to understand their customers, deliver personalised experiences and drive revenue. We are incredibly honoured and grateful to be named a finalist for the Retail & Consumer Goods Partner of the Year by Microsoft. This award is a reflection of the transformative impact our partnership has had on our mutual customers.” 

Meanwhile, Nicole Dezen, chief partner officer and corporate vice president at Microsoft, said, “Congratulations to the winners and finalists of the 2024 Microsoft Partner of the Year Awards! The momentum generated by numerous AI & Copilot announcements this year fueled innovation from our partners, enabling groundbreaking services and solutions to customers. I am inspired by the capability and creativity in our partner ecosystem and this year’s winners beautifully demonstrate the best of what’s possible with AI and the Microsoft Cloud.” 

Sydney, Australia – Amperity has announced that it has been awarded the ‘Communications, Media and Entertainment Partner of the Year’ Award by Databricks. With Databricks and Amperity, brands such as Paramount and Vail Resorts, have maximized the value of their customer data, lowered costs, and increased data democratization to generate and share insights to their downstream systems and business users.

The award was presented this week at Databricks’ Data + AI Summit 2024 and underscores the impact Amperity has made in developing Databricks competency and helping to solve customer data challenges and break into new revenue streams.

Amperity’s Lakehouse CDP is leading the shift towards composability in the marketing technology landscape. Amperity provides automated cleansing, enriching, and harmonizing of customer data and shares it with Databricks Data Intelligence Platform through Delta Sharing, its open, industry-standard protocol. This allows data to be easily accessible across the tech stack through a shared catalogue. 

Together, Amperity and Databricks enable brands to take a more sophisticated and strategic approach to customer data management, paving the way for a new era of data-driven marketing where insights can be easily translated into actionable strategies for boosting engagement, loyalty, and revenue.

Derek Slager, co-founder and CTO at Amperity, said, “We are thrilled and deeply honoured to receive the Communications, Media and Entertainment Partner of the Year award from Databricks. This recognition is a testament to the incredible value our collaboration has delivered to our shared customers.”

He added, “By combining Amperity’s unified customer data foundation with Databricks’ powerful data intelligence platform, we’ve empowered brands to unlock transformative insights and personalize customer experiences like never before. As we look ahead, we’re excited to further strengthen our partnership by redefining what’s possible in customer engagement and to help our clients turn complex data into business value.”

Meanwhile, Roger Murff, vice president of technology partners at Databricks, commented, “In an era where data and AI are pivotal to innovation, Amperity’s Lakehouse CDP plays an important role in delivering data intelligence. Together, we enable brands to seamlessly share live data sets without the need for maintaining ETLs or copying data. Through this composable and secure data flow, Amperity and Databricks empower brands to fuel the data-intensive demands of Generative AI and deliver highly personalized experiences with exceptional data quality.”

Australia – With consumer spending and marketing budgets shrinking, privacy rules tightening, and AI unleashing a new wave of disruption, CMOs and digital professionals are ill-prepared, a new report from Amperity and Arktic Fox reveals.

Marketers feel unprepared for looming Privacy Act reforms. Even more worryingly, they believe those in leadership positions are similarly unready – only 38% of those surveyed believe their executive group understands the importance of adapting to privacy changes and sees it as a key strategic priority to address.

They also worry they are falling behind their peers in martech utilisation, partly because they lack appropriately skilled staff. On top of all that, many are now fundamentally questioning their martech investment strategy and moving towards combining ‘best of breed’ solutions, rather than relying on a single vendor. 

Marketers’ aspirations

Australian marketers’ focus remains business growth – 77% of respondents said it was a key strategic priority. Growth is tied to customer acquisition, which came in second (48%) on the list of priorities.

So far, so unsurprising. But subsequent priorities reveal marketers hope to fatten the bottom line by leveraging technology. The third most common priority (42%) was “Building our customer data strategy and better utilising our first-party data”, and the equal fourth (36%) was “Digital Transformation”.

The study also revealed marketers and digital leaders remain focused on achieving goals (over the next 12-18 months) that are only feasible with martech tools. Personalisation was classified as “important” or “very important” by 72% of respondents, who were also strongly committed to CX management (87%), online sales and lead generation (77%) and martech utilisation (76%). To put it bluntly, without sophisticated technology and skilled staff, most marketers and digital leaders won’t be able to implement their planned marketing and digital strategy over the next 12-18 months.

“Australian marketers want to take advantage of the available tools,” notes Billy Loizou, Asia Pacific area vice president at Amperity. “The problem – as they are usually the first to point out – is Australian marketers are struggling to execute. That’s hardly a new situation, but when you add in factors such as the rise of Gen AI, imminent reforms to the Privacy Act, flat marketing budgets and Google deprecating third-party cookies, it’s not surprising so many CMOs are nervous.”

Marketers’ reality

Marketers want to – and increasingly need to – leverage technology effectively. Nonetheless, many fear they are falling behind. This was particularly apparent when leaders were asked about their organisation’s data maturity:

  • Only 29% of respondents agreed with the statement, “We are very effective at activating data to deliver great customer experiences.”
  • Only 22% of respondents agreed with the statement, “Our data is well managed and maintained, providing us with high-quality data.”
  • Only 19% of respondents agreed with the statement, “We have developed a unified view of the customer.”

Teresa Sperti, director of Arktic Fox, is worried but unsurprised by these findings.

“When we undertake digital training sessions or partner with clients on strategy, it’s not uncommon for us to have to explain to an organisation’s staff, including its senior staff, where the organisation’s data resides and help them connect the dots around their martech ecosystem. 

“Brands have been trying to develop a unified view of the customer for at least two decades. Yet in 2024, less than one in five of those surveyed could say their organisation had developed a unified view of the customer that could underpin a data-driven marketing approach. This is why there is a growing gap between the haves and the have-nots in spaces like personalisation, experience delivery and more. Brands that have built strong internal capabilities and robust foundations in data and tech are thriving whilst others are finding it difficult to shift gears.”

Sperti also warns that a casual approach to managing data and, in particular, privacy might result in more than suboptimal marketing outcomes. “Businesses could soon be suffering even more dire financial and reputational consequences for failing to appropriately safeguard their customers’ privacy,” she says. “A privacy or spam breach impacts reputation and trust, which is linked to brand performance and preference. So, I’m amazed there isn’t much more focus on improving compliance and ethics by marketers and digital leaders.”

Is it the machines or the humans?

There’s a consensus that Australian marketers and digital professionals aren’t making the most of martech solutions, but there’s debate about why that’s the case.

Those who question the tools point out marketers in many countries have failed to adopt martech solutions with the enthusiasm that was expected. Many CMOs appear to believe they overspent on technology and that investment has failed to meet their expectations and deliver the desired outcomes.

That’s partly due to the shortage of Australians with martech skills. But Loizou points out that the much-publicised skill gap doesn’t explain everything.

“To grossly oversimplify, the approach in the past was to buy the equivalent of a turnkey, off-the-shelf, full-stack solution from a big-name tech company. Given that 80% of respondents in the 2024 study reported their utilisation of martech was ‘average’, ‘low’ or ‘very low’, that doesn’t seem to have worked out well. The understandable but ill-advised reaction is to devote fewer resources to martech and martech staff training. That’s happening to some extent, with only 12% of respondents reporting they plan to significantly increase their martech budget over the next 12 months. But the noteworthy development is the declining popularity of single-vendor solutions. When asked about their plans for current and future martech investments, 14% said they were leaning towards a single vendor, 29% claimed they were open-minded, and a whopping 57% stated they were leaning towards ‘best-of-breed’ solutions.” 

The digital transformation landscape

Both Sperti and Loizou remain concerned about what they see as an overly relaxed approach to digital transformation. Noting that almost all organisations now talk the digital transformation talk, Sperti wonders how serious they are about walking the walk. “Only about one in five respondents said their organisation had been transforming for a “long time”, with long time defined as three or more years,” she says. “And about one in two respondents reported their organisation was just starting, or had only recently started, their digital transformation journey.

The study also found that only 53% of leaders believe their executive group are aligned on digital transformation priorities. 

“When brands aren’t aligned around digital transformation priorities, teams are set up to compete for resources and funding. That drives siloed thinking and that means it takes twice as long to deliver on ambitions. However, when executives lean into challenging discussions and make strategic choices, it enables the organisation to focus on the digital strategies that will deliver the most impact for the business and customers alike.” Sperti says

“With martech, the two big investment priorities for marketers remain CRMs (43%) and marketing automation (41%),” Loizou adds. “It’s good that CDPs [Consumer Data Platforms] are now the number three priority (35%), but I suspect many marketers still don’t fully comprehend how central CDPs are. The elevator pitch is that they allow marketers to improve the quality of their data, therefore an accelerator to fuel smart growth, retention, and foster a data-first corporate culture.”

Loizou doesn’t claim CDPs are a magic bullet. But he does insist that, unlike more popular solutions, they can address some of the pressing issues marketers now face. 

“Just spending more on a marketing automation platform won’t solve messy customer data problems,” he says. “It’s CDPs that do that, as well as provide an enterprise-unified view, which then solves many of the other business-wide challenges organisations face. We live by the mantra better data = better results!”

Singapore – AI-powered enterprise customer data platform Amperity has announced a new composable approach for customer data management known as the ‘Lakehouse’ CDP.

Through this initiative from Amperity, brands can seamlessly share live data sets between a CDP and a lakehouse without maintaining ETLs or copying data.

IT teams can optimise how data is stored and processed with any platform that uses lakehouses’ open table formats to save time and lower costs. This composable and more secure flow of data ensures brands can fuel the data-intensive demands of Generative AI and 1:1 personalization with high-quality data.

To enable the Lakehouse CDP’s core benefits, Amperity is adding a key new feature: Bridge. Amperity Bridge allows users to point and share data to and from a lakehouse rather than using the slower, less secure method of reverse ETL.

This uses each lakehouse’s open, industry-standard data formats so that data is available across the tech stack through a shared catalogue. This provides the benefits of zero-copy for greater control and compliance without unnecessary network calls and processing.

Going into more detail, the Lakehouse CDP’s features allows brands to utilise AI-powered ID resolution, quick shaping of data for activation, a tool that gives quick access and activate high-quality data from a lakehouse, and a secure platform for sharing data.

Talking about this approach, Barry Padgett, CEO of Amperity, said, “In today’s data-driven landscape, brands are struggling to unlock the true potential of their customer data due to the siloed nature of traditional data management tools. Amperity’s Lakehouse CDP rides the wave of open data sharing, enabling brands to build cross-platform data workflows.”

“Our goal is to ensure high-quality customer data is available across all platforms that use lakehouse architecture without replication. With Amperity, businesses can meet the data demands of Generative AI and personalization at scale with unparalleled data governance,” he added.

In the ever-evolving marketing landscape, one technology emerges as the potential linchpin – Gen AI. Revered for helping businesses move further, faster and more efficiently, does it also hold the key to a new way of marketing?

I recently discussed this topic and more with Rio Longacre, Managing Director at Slalom, and Jon Williams, Global Head of Agency Business Development at AWS.

Gen AI: Making sense of the mania

During our conversation, Longacre describes a paradigm shift in the Gen AI landscape. Moving beyond experimentation, companies are now forging strategic pathways, identifying areas where it can genuinely make a transformative difference.

“When ChatGPT launched, it quickly became the thing every client we worked with was suddenly dedicating significant resources to. They were building out Gen AI demos, investigating it. People were asking us to help them create a Gen AI strategy. And there was a lot of experimentation. There was a lot of wheel spinning too. It was like this Gen AI-mania – everyone just went all in,” he says.

“Within the last few months, there’s been a big shift, which is very positive. Instead of ‘Let’s just try different things’, it’s now, ‘let’s have a Gen AI strategy’. They are looking to identify areas where Generative AI could make a big difference and move the needle. They want to invest in those, whether it’s eCommerce, operations or creative; they want to come up with ideas that could work and test them. If they work, great. They’ll look to start to commercialise them. If they don’t, that’s OK too, then they can pivot and try something else.”

AI as a marketing assistant

Williams shares where Gen AI is shining as a marketing assistant, of sorts. “Amazon Q is a new type of generative AI-powered assistant that can be used specifically for work to be tailored to your business to have conversations, solve problems or generate content. It uses the data and expertise found in your company’s information repositories, such as codebases and enterprise systems.

“You could use Q, for example, to:

● Learn a brand style guide, then
● Use that information to turn a press release into a blog post that adheres to those standards, then
● Analyse how a brand has shown up on social media, then
● Create new posts around those releases that will make sense to followers, then
● Analyse the results of those posts, and finally
● Summarise them for review for teams

“It’s almost like this self-fulfilling circle of incremental productivity that’s happening as a result of leveraging some of the generative AI capabilities that come to use as a result of a bot but are plugged into the systems and data that your enterprise organisation owns. We’re only in the very early stages of that, which is pretty exciting.”

At Amperity, we work with many customers that want to be more data driven and customer-centric. We know that comes down to an understanding of the customer – even figuring out what the key segments are that you should be going after and what types of campaigns you should be launching.

If you think about the analysis you have to do to answer those questions today, there are so many barriers you must understand, including:

● Your underlying data and the schema
● SQL
● How to build visualisations

There are many barriers to a user from the customer data to actually figuring out what strategy should be launched. We’re seeing a lot of folks throw Amperity into the mix here too, developing tools where that data can be more democratised. You can ask questions of the data with natural language as opposed to needing to write SQL.

This will lead to a lot more data-driven decision making as folks are able to more easily access their data relative to the mini barriers that were in place previously.

3 Ways marketers are leveraging Gen AI for greater efficiencies and cost- savings, according to Longacre:

1. eCommerce company: This company has written descriptions for 10,000 product SKUs using Gen AI in a couple of weeks, saving them months of time and about a million dollars.
2. Paid media: As it relates to paid media tools, such as those designed for Amazon Marketing Cloud, there’s a background image generator specifically tailored for crafting lifestyle images. Findings indicate a remarkable 20 to 25% increase in conversion rates for products showcased with lifestyle images compared to those with a plain white background. Swiftly deploying these features, testing their efficacy, obtaining results and subsequently, optimising based on these insights is a gamechanger.
3. Banks and finance: The bank’s creative briefs are now being generated by artificial intelligence, reducing the time spent on back-and-forth communication with agencies by approximately one week. Even with segmented strategies, brands often face resource challenges.

Accelerating the creation of creative briefs, creative imagery and product descriptions allows for a faster customisation of on-site experiences. This progression toward personalisation doesn’t require them to go in a ‘hands-off’ mode where Gen AI is really running the show. Instead, it’s truly like a genuine one-to-one chatbot interaction or conversational AI.

Keeping the human in the loop with AI

Longacre points out that every use case he shared has a human in the loop. Since we’re in the early days of AI, that’s not surprising as most brands are starting with ‘human in the loop’ use cases. This is where AI generates outputs that a person then approves and potentially refines. ‘Human in the loop’ use cases enable productivity gains while minimising risks arising from hallucinations or unexpected outputs.

“Maybe the copy is being written by Gen AI, but a human reviews it,” Longacre says.
“The image might be generated, but it’s not being pushed out into the wild.

“We’re starting to see a little bit of that, but generally, there’s human oversight. Even with chatbots. I mean chatbots have been around forever. Most of them were machine learning based. You need that knowing of, ‘OK, when do you have the escalation? Where do you pass from the chatbot to a live person for certain use cases?’ Identifying that is still super critical.”

Setting your brand up for success with Gen AI

In the journey of crafting a Generative AI strategy, Williams points out five key elements that play a pivotal role in ensuring success. They are:

1. Tech stack: Your tech stack is vital. You should have the ability to explore models, test use cases and choose the right ones.
2. A solid, mature first-party data foundation: Generative AI relies on the data to function properly, which means you must have robust data ingestion storage and management capabilities to make sure that the first-party data is accurate and as close to real time as possible to provide accuracy in the model outputs.
3. Human oversight: You still need that human in the loop intervention to make sure that what you thought was going to happen is happening and there are no anomalies.
4. AI-specific analysis skills: Leveraging AI requires the ability to interpret and accurately apply AI model outputs. You must ensure that your teams have the expertise to understand how the tools work and how best to put the data to work.
5. Process redesign: Consider existing processes or workflows that need to be resigned to take advantage of General AI.

Start small with AI for big results

My advice to brands and organisations when rolling out AI: start small. I would start with a small use case that’s highly measurable and one that doesn’t require major change. One place where clients we work with have seen a lot of success is just with subject line optimisation or optimising the body of emails or paid media ads. Since you can have a human in the loop here, it’s a great opportunity to experiment with creating different segmentation strategies and different messages.

This article is written by Joyce Gordon, Head of Generative AI, Amperity.

A recent survey reveals that CMOs around the world are optimistic and confident about Gen AI’s future ability to enhance productivity and create competitive advantage. In fact, seventy per cent are already using Gen AI and 19 per cent are testing it. 

However, for many consumer brands, the divide between expectations and reality looms large. Marketers envisioning a seamless, magical customer experience must recognise that AI’s effectiveness depends on high-quality underlying data. Without that, the AI falls flat, leaving marketers grappling with a less-than-magical reality.

What AI-powered marketing with poor data quality looks like

Let’s take a closer look at what AI-powered marketing with poor data quality could look like. Say I’m a customer of a general sports apparel and outdoor store, and I’m planning for my upcoming annual winter ski trip. I’m excited to use the personal shopper AI to give me an experience that’s easy and customised to me.

I need to fill in some gaps in my ski wardrobe, so I ask the personal shopper AI to suggest some items to purchase. But the AI is creating its responses based on data about me that’s been scattered across the brand’s multiple systems. Without a clear picture of who I am, it asks me for some basic information that it should already know. Slightly annoying… I’m used to entering my info when I shop online, but I was hoping the AI upgrade to the experience would make things easier for me. 

Because my data is so disconnected, the AI concierge only has an order associated with my name from two years ago, which was actually a gift. Without a full picture of me, this personal shopper AI is unable to generate accurate insights and ends up sharing recommendations that aren’t helpful.

Ultimately this subpar experience makes me less excited about purchasing from this brand, and I decide to go elsewhere. 

The culprit behind a disconnected and impersonal generative AI experience is data quality — poor data quality = poor customer experience. 

What AI-powered marketing with clean data looks like

Now, let’s revisit this outdoor sports retailer scenario, but imagine that the personal shopper AI is powered by accurate, unified data that has a complete history of my interactions with the brand from first purchase to last return. 

I enter my first question, and I get a super-personalised and friendly response, already starting to create the experience of a one-on-one connection with a helpful sales associate. It automatically references my shopping history and connects my past purchases to my current shopping needs. 

Based on my prompts and responses, the concierge provides a tailored set of recommendations to fill in my ski wardrobe along with direct links to purchase. The AI is then able to generate sophisticated insights about me as a customer and even make predictions about the types of products I might want to buy based on my past purchases, driving up the likelihood of me purchasing and potentially even expanding my basket to buy additional items. 

Within the experience, I am able to actually use the concierge to order without having to navigate elsewhere. I also know my returns or any future purchases will be incorporated into my profile. 

Because it knew my history and preferences, Generative AI was able to create a buying experience for me that was super personalised and convenient. This is a brand I will keep returning to for future purchases.

In other words, when it comes to AI for marketing, better data = better results.

So how do you actually address the data quality challenge? And what could that look like in this new world of AI?

Solving the data quality problem

The critical first element to powering an effective AI strategy is a unified customer data foundation. The tricky part is that accurately unifying customer data is hard due to its scale and complexity — most consumers have at least two email addresses, have moved over eleven times in their lifetimes and use an average of five channels (or if they are millennials or Gen Z, it’s actually twelve channels).

Many familiar approaches to unifying customer data are rules-based and use deterministic/fuzzy matching, but these methods are rigid and break down when data doesn’t match perfectly. This, in turn, creates an inaccurate customer profile that can actually miss a huge portion of a customer’s lifetime history with the brand and not account for recent purchases or changes of contact information. 

A better way to build a unified data foundation actually involves using AI models (a different flavour of AI than generative AI for marketing) to find the connections between data points to tell if they belong to the same person with the same nuance and flexibility of a human but at massive scale. 

When your customer data tools can use AI to unify every touchpoint in the customer journey from first interaction to last purchase and beyond (loyalty, email, website data, etc…), the result is a comprehensive customer profile that tells you who your customers are and how they interact with your brand. 

How data quality in generative AI drives growth

For the most part, marketers have access to the same set of generative AI tools, therefore, the fuel you input will become your differentiator. 

Data quality to power AI provides benefits in three areas: 

  • Customer experiences that stand out — more personalised, creative offers, better customer service interactions, a smoother end-to-end experience, etc.
  • Operational efficiency gains for your teams — faster time to market, less manual intervention, better ROI on campaigns, etc.
  • Reduced compute costs — better-informed AI doesn’t need to go back and forth with the user, which saves on racking up API calls that quickly get expensive

As generative AI tools for marketing continue to evolve, they bring the promise of getting back to the level of one-to-one personalisation that customers would expect in their favourite stores, but now at a massive scale. That won’t happen on its own, though — brands need to provide AI tools with accurate customer data to bring the AI magic to life. 

The dos and don’ts of AI in marketing

AI is a helpful sidekick to many industries, especially marketing — as long as it’s leveraged appropriately. Here’s a quick ‘cheat-sheet’ to help marketers on their Gen AI journey:

Do:

  • Be explicit about the specific use cases where you plan to use data and AI and specify the expected outcomes. What results do you expect to achieve?

  • Carefully evaluate if Gen AI is the most appropriate tool for your specific use case.

  • Prioritise data quality and comprehensiveness — establishing a unified customer data foundation is essential for an effective AI strategy.

Don’t:

  • Rush to implement Gen AI across all areas. Start with a manageable, human-in-the-loop use case, such as generating subject lines.

This thought leadership piece is written by Joyce Gordon, Head of Generative AI, Amperity.

Singapore – AI enterprise customer data platform Amperity has announced two new generative AI capabilities, Explore and Assist, to accompany existing AI-powered capabilities, Stitch and Predict, forming a new comprehensive suite known as ‘AmpAi’.

Through AmpAi, Amperity focuses on fixing data quality and access challenges many brands face with traditional CDPs, promising brands the confidence to make decisions based on a trusted data foundation.

Going into detail on AmpAi’s capabilities, Assist supports marketers, analysts, and data operators with creating marketing workflows more quickly. The first product within Assist is ‘Ai Assistant’, which removes the barriers to creating SQL queries and fixing potential errors within those queries.

On the other hand, Explore is all about enabling business users across the organisation to access and use customer data. ‘AmpGPT’, the first product in Explore, empowers marketers to interact with their data using natural language.

Amongst the new additions, Stitch unifies all data sources to create the highest data quality foundation to make business decisions, whilst Predict helps marketers understand what they need to do to keep the customers they have and make them even more profitable.

The company will introduce more products under Assist and Explore in the coming months to help further democratise customer data and make it accessible to all users in a privacy safe way.

Talking about this, Barry Padgett, CEO at Amperity, said, “We’re on the cusp of a transformative shift in how brands interact with their customer data. For too long, the complexity of data queries and segment creation has been a barrier, consuming valuable time that could be spent on strategic initiatives.”

“With Generative AI, we are empowering all users to be data scientists by democratising data usage and making customer insights accessible across the organisation. We’re not just helping brands save time; we’re empowering every team member to drive value and make informed decisions based on a trusted data foundation,” he added.

The loss of third-party cookies will completely disrupt the digital media ecosystem. While most advertisers think about third-party cookies in the context of targeting, they are used across a variety of tools that span data collection, audience segmentation, data onboarding, and, most importantly, measurement.  

How Google’s deprecation of third-party cookies impacts consumer privacy online

The deprecation of third-party cookies by Google will change the way consumer data is used online. With fewer third-party tracking cookies, there’s less opportunity for widespread tracking of user behaviour across different websites. This shift means consumers have more control over their personal data by allowing users to opt-in vs opt-out of data sharing. This will force companies to explore new consent incentives and test alternative tracking methods. 

For instance, there will be a significant move towards the collection and management of first-party data because of the numerous benefits it offers, such as improved data accuracy, enhanced customer relationships, greater data control and security, and more effective personalisation strategies.

The elimination of third-party cookies will also significantly alter the data compliance landscape. Companies will need to focus more on obtaining explicit consent for data collection while aligning with a mishmash of regulations across geographic regions, with  GDPR and CCPA. This shift could also prompt new regulations specifically addressing alternative tracking technologies and first-party data collection practices to ensure the data company is consensually provided.

Challenges and opportunities that brands may face as they shift from third-party to first-party data collection

Brands face fundamental challenges such as the need to invest in new technologies for first-party data collection and the potential reduction in the amount of available consumer data. This new reality will present opportunities, including building more direct and meaningful relationships with customers, gaining accurate and relevant data that can inform customer interactions, and enhancing brand trust and credibility. 

The key is to align on a strategic vision and select the right data management solutions to achieve it. Ones that are scalable, user-friendly with out-of-the-box analytics features, compliant, and capable of integrating seamlessly with existing martech and other systems.

Companies should revisit their data governance policies to ensure compliance with privacy regulations and ethical standards. This includes implementing robust consent management systems, ensuring compliance, and being transparent about data collection and usage practices. Investing in management and security to protect first-party data is also crucial. 

Finally, it’s important to remember that while many CDPs offer tools and features that can support compliance, transparency, and security, they are not a complete solution in themselves. Companies must actively manage these aspects in line with their specific needs and regulatory requirements.

How the Privacy Sandbox and other similar initiatives balance the need for user privacy with the commercial need for targeted advertising

Initiatives like Google’s Privacy Sandbox aim to create technologies that allow for user privacy while still enabling targeted advertising. These initiatives may involve using aggregated, anonymized data or machine learning algorithms that process data on the user’s device without transmitting sensitive information. 

But while Google’s Privacy Sandbox is beneficial from a privacy standpoint, it limits the depth of data brands are used to working with. They should not rely on Google to provide the data or tools they need to power personalisation capabilities compared to what first-party data used with a CDP offers.

CDPs consolidate diverse data sources to offer a comprehensive view of customer behaviour and preferences, excelling in personalisation through detailed segmentation and targeted campaigns. This contrasts with the Privacy Sandbox’s limited use of personal data.

CDPs stand out with their real-time data processing, enabling immediate responses to customer behaviours, a feature less emphasised in the Privacy Sandbox. They offer businesses direct control and ownership over customer data, allowing more flexibility in data management and use. This control is crucial, especially compared to dependence on third-party platforms like the Privacy Sandbox.

Additionally, CDPs offer customisation to meet specific business needs and integrate with other tools, creating a tailored tech stack that may not be as achievable with the Privacy Sandbox. They also support compliance with various privacy regulations, such as GDPR or CCPA, enabling responsible and ethical data management while still deriving valuable insights.

The role of artificial intelligence and machine learning in the cookieless era 

The alternatives to replace third-party cookies for tracking and data analytics purposes we’re already seeing include first-party data activation tools, the return of contextual advertising, data clean rooms for measurement and collaboration, and AI. By using AI and machine learning for predictive analytics based on first-party data or using blockchain for transparent and secure data transactions.

As reliance on third-party cookies decreases, AI and machine learning will play a more significant role in marketing. As we move forward, it’s becoming increasingly clear that brands must embrace new AI-driven methodologies to effectively acquire and retain customers. This shift isn’t just about keeping up with technological advancements; it’s a response to intensifying business pressures and evolving consumer demands.

From a business perspective, the pressure is mounting for efficiency, scalability, and the effective use of data to drive greater returns. In this landscape, AI’s role is crucial. For CMOs, delivering a greater return on investment has emerged as the primary objective for 2023, underscoring the need for a more sophisticated, data-driven approach.

However, this shift isn’t solely about the numbers. Consumer demands are equally influential in shaping this new paradigm. There’s a growing concern among consumers about how AI and their personal data are used. They expect brands to be not only conscious but also responsible in their data utilization. Furthermore, as consumers become more willing to share their data, they anticipate a higher degree of personalisation and relevancy in their interactions with brands. They want experiences that are tailored to their preferences, delivered at the right time, and through the right channels.

The intersection of business efficiency and consumer-centricity is defining a new era in marketing. Brands that successfully navigate this landscape by leveraging AI in a responsible, consumer-focused manner are poised to thrive in this dynamic environment.

How brands can ensure transparency and maintain trust with their customers during this transition

Brands will maintain consumer trust during this transition by being transparent about their data collection and usage practices. Regular communication with customers about how their data is being used and providing them with greater control over their data will foster trust. Additionally, keeping pace with and adhering to growing privacy regulations and ethical standards is key.

There is no silver bullet to replace the third-party cookie, and advertisers have the opportunity to reimagine their digital media tech stack. In fact, we’re seeing that brands that leave the cookie environment now are experiencing a competitive advantage and will continue to do so since most of the industry has not shifted their reliance on 3P cookies.

This article is written by Peter Ibarra, head of adtech solutions, Amperity

The insight is published as part of MARKETECH APAC’s thought leadership series under What’s NEXT 2023-2024. What’s NEXT 2023-2024 is a multi-platform industry initiative which features marketing and industry leaders in APAC sharing their marketing insights and predictions for the upcoming year.

Melbourne, Australia – AI-powered enterprise customer data platform Amperity has recently announced its appointment by omnichannel retailer Accent Group, to accelerate its first-party customer data strategy and deliver highly personalised interactions for a better customer experience.

In partnership with Amperity, Accent Group will look to unify, manage and activate its customers from multiple online and offline touchpoints to deliver personalisation at scale.

This collaboration comes into fruition with Accent Group managing over 34 brands under its roster, and needing a comprehensive solution to bring together and manage multiple data sources of its various brands to advance its marketing goals.

With Amperity’s patented, AI-powered technology, Accent Group will utilise enterprise-scale identity resolution to build unified customer profiles to deliver audience segmentation and insights for retargeting and creating lookalike and suppression campaigns.

Talking about the partnership, Deena Colman, group general manager digital & marketing at Accent Group, said, “We strive to provide exceptional customer experiences across all of our brands, which requires a CDP that delivers on the promise of unifying all online and offline customer data and making it actionable. With Amperity, we can unify and activate all of our customer data with the goal of creating a seamless, personalised omnichannel journey for our customers.”

Meanwhile, Billy Loizou, area vice president at Amperity, commented, “Accent Group is solely focused on putting its customers at the centre of the experience—that starts with a clean and accurate data foundation. We’re honoured Accent Group has chosen our AI-driven platform to help them scale their personalisation efforts and optimise marketing spend.”

Washington, USA – Customer data platform Amperity has recently announced that more than 50% of its customer base has adopted Amperity for Paid Media. The rapid adoption of this new application of Amperity demonstrates the important role first-party data will play in informing paid media strategies.

Since its launch in May 2023, Amperity for Paid Media has used industry-leading ad connectors and first-party data to deliver more than 11 billion unified customer profiles each day. 

These are delivered to the ad platforms of Amperity customers, across a range of industries, including retail, quick-serve restaurants (QSR), consumer packaged goods (CPG), travel and hospitality, sports teams and leagues, and financial services.

Brands using Amperity for Paid Media also report experiencing a better conversion rate using unified customer profile lookalike audiences over third party audiences, an 85%+ match rate across major ad platforms, 30% onboarding savings, 5x increase in ROAS (Return on Ad Spend), 94% savings in data management and stitch processing, as well as a 70%+ reduction in marketing timelines. 

To quantify the impact Amperity is having on paid media, the company commissioned Forrester Consulting to examine the potential ROI enterprises may realise by deploying its CDP. According to the study, the composite organisation not only experienced a 505% ROI but also experienced other benefits over three years. 

Other benefits include a $3.4 million incremental increase in net operating revenue from effective messaging, a $3.8 million incremental increase in net operating revenue due to targeted paid media spend, a 25% increase in productivity impact of more efficient campaign preparation and execution, and $4.5 million worth of savings in paid media spend from deduplicating customer records. 

Barry Padgett, CEO at Amperity, said, “Today, we find ourselves at the epicentre of a marketing revolution. The tides have shifted and the old ways of acquiring and retaining customers are giving way to a new era of data privacy and consumer-centricity.”

“In Q1 of next year, Google is going to disable 1% of third party cookies and fully remove them by Q3. This poses a massive challenge for brands across the board. But within this challenge lies immense opportunity. At Amperity, we’ve taken it upon ourselves to lead the charge and help brands and agencies navigate this shift,” he added.