AI is fundamentally reshaping customer engagement and personalisation by enabling businesses to deliver tailored experiences and responses to individual customer preferences on a large scale.

During our conversation, we discussed how Twilio is at the forefront of this revolution as well as addressing the stark contrast between companies’ claims of personalisation and customers’ actual experiences.

AI News: How are you using AI to deliver more personalised and satisfactory customer experiences? 

Hema Thanki: According to Twilio’s recent State of Customer Engagement report, 91 percent of companies say they always or often personalise engagement with consumers. But, consumers don’t agree. Only 56 percent of consumers report that their interactions with brands are always or often personalised. 

Instead of becoming “customer-centric,” most companies have become “system-centric.” Exploding tech stacks and patchwork solutions lead to fragmented data, an incomplete view of the customer and, ultimately, disjointed experiences.

The reality is that every consumer is a complex individual with unique wants and needs that change from moment to moment. In order to truly put customers at the heart of your business, you need to be able to know who your customers are, understand how to best meet their needs and exceed their expectations, and then activate those insights to engage them how, when, and where is most relevant and meaningful to them. 

These elements make up the engagement flywheel that is key to powering dynamic customer engagement that adapts to every individual customer at scale.

We recently announced Twilio CustomerAI to unlock the power of AI for hundreds of thousands of businesses and supercharge the engagement flywheel. With CustomerAI, brands can expand their perception of customer data, activate it more extensively, and be better informed by a deeper understanding of their customers. 

AN: What are some examples of how businesses can use Twilio CustomerAI? 

HT: Today’s marketers need to not only understand past customer behaviour but must be able to anticipate and act on customers’ future wants and needs. AI and machine learning (ML) models are incredibly effective at doing this but are complex to build and require data science expertise.

With CustomerAI Predictions now generally available, Twilio Segment is putting the power of predictive AI at marketers’ fingertips. Without having to tap technical teams, marketers can now create precisely targeted audiences out-of-the-box, trigger customer journeys, and personalise multichannel experiences based on a customer’s predicted lifetime value (LTV), likelihood to purchase or churn, or propensity to perform any other event tracked in Segment.

Brands like Box are using Predictions to save time, optimise campaign performance, and discover revenue opportunities:

“As marketers, the holy grail is to reach your customers and prospects in a way that is meaningful, relevant and additive to them. CustomerAI Predictions has equipped Box’s marketing team with the ability to forecast customer behaviour to a degree that was simply unavailable to us before.

We’ve been able to explore segmenting our audience based on predictive traits like who is most likely to join us at in-person events or who is more likely to purchase, and this allows us to meet those people where they are in their customer journey. Tools like Predictions put marketers at the centre of this new era of AI which is transforming how companies engage and retain their customers.” – Chris Koehler, CMO at Box.

AN: What other emerging AI trends should people be keeping an eye on? 

HT: When companies rely on managing data in a customer data platform (CDP) in tandem with AI, they can create strong, personalised campaigns that reach and inspire their customers. Here are four trends in AI personalisation.

1. Personalised product recommendations: Using AI to serve personalised product recommendations is a way to ensure your customers are being served optimized content. It can also build trust with your brand, leading to repeat purchases. Take Norrøna, an outdoor clothing brand in Scandinavia. They built a complete recommendation platform – from data collection to machine learning predictions, in just six months. Norrøna relied on Segment for the collection and management of client–side and server–side customer data. Segment assigned an ID to each customer and ensured the data collected on them was clean, thanks to the schema.
2. Behaviour–based email campaigns: AI is getting us as close as possible to identifying patterns in user interactions and helping with creating behaviour–based email campaigns. If a customer frequently clicks on one type of email content, an AI–powered system could trigger an email to that customer containing content related to what they are clicking. Using Twilio Engage, you can send emails right from the Segment app, relying on your first-party customer data to lead the way.
3. Dynamic website content: The days of static landing pages are in the past, thanks to tools that collect user behaviour and churn out personalised website content in real-time. In fact, Segment’s website displays dynamic content to customers based on their own interests—thanks to integration with Mutiny. Whenever a visitor lands on the Segment website from a particular IP address, they will be met with personalised landing pages based on their unique behaviour. This means personalised content for every single visitor.
4. Predictive customer segmentation: Predictive Audiences let businesses target users who have an increased likelihood of performing an event. It works with out-of-the-box audience templates that are pre-built with Predictions. They include templates like “ready to buy” or “potential VIPs.” AI can then analyse these profiles to create predictive segments. For instance, AI could identify a group of customers who are likely to churn based on their behaviour. This allows you to proactively engage with these customers through tailored retention campaigns.

AN: Do you have any best practices and tools that you use for testing, monitoring, and debugging your AI models and applications to ensure quality and reliability? 

HT: Customer data unlocks the promise of AI as a unique market advantage, but your AI is only as good as the data you put into it. If your data is siloed, stale, inconsistent, and incomplete, your AI outputs will reflect that. At Twilio Segment, we have a long history of helping companies build trusted data infrastructures with unified, real-time, consistent, and consented data that is critical to your AI strategy. 

Our composable CDP ensures your data is AI-ready, helping you collect, clean, and activate customer data with our open, API-first platform and 450+ pre-built connectors that enable you to start with data anywhere and activate it everywhere.

With Segment, you choose where you start. Whether that’s getting data from SaaS products into your data warehouse, or activating existing data with reverse ETL, Segment gives you the flexibility and extensibility to move fast, scale with ease, and efficiently achieve your business goals as they evolve.

AN: Are you seeing more customers looking for AI solutions to improve operational efficiencies amid global economic uncertainties? 

HT: Marketers spend massive amounts of time writing, designing, and building campaigns and customer journeys. With generative AI soon available (scheduled for public beta in 2024) inside Twilio Engage and Segment CDP, marketers can save precious time, boost productivity, and optimise for stronger results. 

Using the new CustomerAI Generative Email coming to Twilio Engage, marketers will be able to enter simple text prompts that turn ideas into HTML in minutes. This builds on the AI capabilities available in Twilio Engage today, such as our Smart Email Content Editor which suggests conversion-worthy email headlines, images, and calls-to-action to drive better engagement with the click of a button.

Meanwhile, marketers will be able to skip the manual process of architecting customer journeys thanks to CustomerAI Generative Journeys. Soon, they will be able to simply describe campaign type (promotional, win-back, etc.), audience definition, and which channels they want to use, then Twilio Engage will automatically build the journey using generative AI—saving marketers time while accelerating growth.

AN: What will Twilio be sharing with the audience at this year’s AI & Big Data Expo Europe?

HT: Twilio Segment is excited to be taking part in AI & Big Data Expo Europe in 2023! As a proud sponsor, we’ll have a strong exhibiting presence – feel free to drop by stand 242 to meet a member of the team and discuss how Segment can collect, unify and activate your customers’ first-party data.

We’ll be joined by Folkert Mudde from Albert Heijn who’ll be talking through their positive partnership highlighting how the supermarket chain supercharges personalisation for 4+ million recognised users with Twilio Segment. Check out this session on 26 September within the ‘Applied Digital, Data & AI’ track at 14:30 CET.

Arthur Viegers, SVP of Global Engineering at Twilio Segment, will be taking part in a dynamic panel discussion on 27 September at 11:40 CET on the topic of ‘The Future of AI-Enabled Experiences’.

Twilio Segment is a key sponsor of this year’s AI & Big Data Expo Europe on 26-27 September 2023. Swing by the company’s booth at stand #242 to hear more about AI from the company’s experts.

The post Twilio Segment: Transforming customer experiences with AI appeared first on AI News.

Sure. We’re a 30-year-old global IT services and professional services provider. We specialise in using emerging state-of-the-art technologies, such as artificial intelligence, big data and blockchain, to solve real business problems. We’re highly obsessed with our customers, about their problems – not about technologies – although we are technology experts. But we always try to find the best technology that will help our customers get to the point where they want to be. 

So we’ve been in the market for quite a while, having originated in Ukraine. But now we have offices all over the globe – US, Latin America, Singapore, Middle East, all over Europe – and we operate in multiple industries. We have some specialised leadership around specific industries, such as retail, financial services, healthcare, energy, oil and gas, and manufacturing. We also work with a lot of digital natives and independent software vendors, helping them adopt this technology in their products, so that they can better serve their customers.

What are the main trends you’ve noticed developing in AI and machine learning?

One of the biggest trends is that, while people used to question whether AI, machine learning and data science are the technologies of the future; that’s no longer the question. This technology is already everywhere. And the vast majority of the innovation that we see right now wouldn’t have been possible without these technologies. 

One of the main reasons is that this tech allows us to address and solve some of the problems that we used to consider intractable. Think of natural language, image recognition or code generation, which are not only hard to solve, they’re also hard to define. And approaching these types of problems with our traditional engineering mindset – where we essentially use programming languages – is just impossible. Instead, we leverage the knowledge stored in the vast amounts of data we collect, and use it to find solutions to the problems we care about. This approach is now called Machine Learning, and it is the most efficient way to address those types of problems nowadays.

But with the amount of data we can now collect, the compute power available in the cloud, the efficiency of training and the algorithms that we’ve developed, we are able to get to the stage where we can get superhuman performance with many tasks that we used to think only humans could perform. We must admit that human intelligence is limited in capacity and ability to process information. And machines can augment our intelligence and help us more efficiently solve problems that our brains were not designed for.

The overall trend that we see now is that machine learning and AI are essentially becoming the industry standard for solving complex problems that require knowledge, computation, perception, reasoning and decision-making. And we see that in many industries, including healthcare, finance and retail.

There are some more specific emerging trends. The topic of my TechEx North America keynote will be about generative AI, which many folk might think is something just recently invented, something new, or they may think of it as just ChatGPT. But these technologies have been evolving for a while. And we, as hands-on practitioners in the industry, have been working with this technology for quite a while. 

What has changed now is that, based on the knowledge and experience we’ve collected, we were able to get this tech to a stage where GenAI models are useful. We can use it to solve some real problems across different industries, from concise document summaries to advanced user experiences, logical reasoning and even the generation of unique knowledge. That said, there are still some challenges with reliability, and understanding the actual potential of these technologies.

How important are AI and machine learning with regards to product innovation?

AI and Machine Learning essentially allow us to address the set of problems that we can’t solve with traditional technology. If you want to innovate, if you want to get the most out of tech, you have to use them. There’s no other choice. It’s a powerful tool for product development, to introduce new features, for improving customer user experiences, for deriving some really deep actionable insights from the data. 

But, at the same time, it’s quite complex technology. There’s quite a lot of expertise involved in applying this tech, training these types of models, evaluating them, deciding what model architecture to use, etc. And, moreover, they’re highly experiment driven, meaning that in traditional software development we often know in advance what to achieve. So we set some specific requirements, and then we write a source code to meet those requirements. 

And that’s primarily because, in traditional engineering, it’s the source code that defines the behaviour of our system. With machine learning and artificial intelligence the behaviour is defined by the data, which means that we hardly ever know in advance what the quality of our data is. What’s the predictive power of our data? What kind of data do we need to use? Whether the data that we collected is enough, or whether we need to collect more data. That’s why we always need to experiment first. 

But I think, in some way, we got used to the uncertainty in the process and the outcomes of AI initiatives. The AI industry gave up on the idea that machine learning will be predictable at some point. Instead, we learned how to experiment efficiently, turning our ideas into hypotheses that we can quickly validate via experimentation and rapid prototyping, and evolving the most successful experiments into full-fledged products. That’s essentially what the modern lifecycle of AI/ML products looks like.

It also requires the product teams to adopt a different mindset of constant ideation and experimentation, though. It starts with selecting those ideas and use cases that have the highest potential, the most feasible ones that may have the biggest impact on the business and the product. From there, the team can ideate around potential solutions, quickly prototyping and selecting those that are most successful. That requires experience in identifying the problems that can benefit from AI/ML the most, and agile, iterative processes of validating and scaling the ideas.

How can businesses use that type of technology to improve personalisation?

That’s a good question because, again, there are some problems that are really hard to define. Personalisation is one of them. What makes me or you a person? What contributes to that? Whether it’s our preferences. How do we define our preferences? They might be stochastic, they might be contextual. It’s a highly multi dimensional problem. 

And, although you can try to approach it with a more traditional tech, you’ll still be limited in that capacity – depths of personalisation that you may get. The most efficient way is to learn those personal signals, preferences from the data, and use those insights to deliver personalised experiences, personalised marketing, and so on. 

Essentially, AI/ML acts as a sort of black box between the signal and the user and specific preferences, specific content that would resonate with that specific user. As of right now, that’s the most efficient way to achieve personalisation. 

One other benefit of modern AI/ML is that you can use various different types of data. You can combine clickstream data from your website, collecting information about how users behave on your website. You can collect text data from Twitter or any other sources. You can collect imagery data, and you can use all that information to derive the insights you care about. So the ability to analyse that heterogeneous set of data is another benefit that AI/ML brings into this game.

How do you think machine learning is impacting the metaverse and how are businesses benefiting from that?

There are two different aspects. ‘Metaverse’ is quite an abstract term, and we used to think of it from two different perspectives. One of them is that you want to replicate your physical assets – part of our physical world in the metaverse. And, of course, you can try to approach it from a traditional engineering standpoint, but many of the processes that we have are just too complex. It’s really hard to replicate them in a digital world. So think of a modern production line in manufacturing. In order for you to have a really precise, let’s call it a digital twin, of some physical assets, you have to be smart and use something that will allow you to get as close as possible in your metaverse to the physical world. And AI/ML is the way to go. It’s one of the most efficient ways to achieve that.

Another aspect of the metaverse is that since it’s digital, it’s unlimited. Thus, we may also want to have some specific types of assets that are purely digital, that don’t have any representation in the real world. And those assets should have similar qualities and behaviour as the real ones, handling a similar level of complexity. In order to program these smart, purely digital processes or assets, you need AI and ML to make them really intelligent.

Are there any examples of companies that you think have been utlising AI and machine learning well?

There are the three giants – Facebook, Google, Amazon. All of them are essentially a key driver behind the industry. And the vast majority of their products are, in some way, powered by AI/ML. Quite a lot has changed since I started my career but, even when I joined SoftServe around 10 years ago, there was a lot of research going on into AI/ML. 

There were some big players using the technology, but the vast majority of the market were just exploring this space. Most of our customers didn’t know anything about it. Some of the first questions they had were ‘can you educate us on this? What is AI/ML? How can we use it?’ 

What has changed now is that almost any company we interact with has already done some AI/ML work, whether they build something internally or they use some AI/ML products. So the perception has changed.

The overall adoption of this technology now is at the scale where you can find some aspects of AI/ML in almost any company.

You may see a company that does a lot of AI/ML on their, let’s say, marketing or distribution, but they have some old school legacy technologies in their production site or in their supply chain. The level of AI/ML adoption may differ across different lines of business. But I think almost everyone is using it now. Even your phone, it’s backed with AI/ML features. So it’s hard to think of a company that doesn’t use any AI/ML right now.

Do you think, in general, companies are using AI and machine learning well? What kind of challenges do they have when they implement it?

That’s a good question. The main challenge of applying these technologies today is not how to be successful with this tech, but rather how to be efficient. With the amount of data that we have now, and data that the companies are collecting, plus the amount of tech that is open source or publicly available – or available as managed services from AWS, from GCP – it’s easy to get some good results.

The question is, how do you decide where to apply this technology? How efficiently can you identify those opportunities, and find the ones that will bring the biggest impact, and can be implemented in the most time-efficient and cost-effective manner? 

Another aspect is how do you quickly turn those ideas into production-grade products? It’s a highly experiment-driven area, and there is a lot of science, but you still need to build reliable software on the research results. 

The key drivers for successful AI adoption are finding the right use cases where you can actually get the desired outcomes in the most efficient way, and turn ideas into full-fledged products. We’ve seen some really innovative companies that had brilliant ideas. They may have built some proof of concepts around their ideas, but they didn’t know how to evolve or how to build reliable products out of it. At the same time, there are some technically savvy and digitally native companies. They have tonnes of smart engineers, but they don’t have the right expertise and experience in AI/ML technologies. They don’t know how to apply this tech to real business problems, or what low-hanging fruits are available to them. They just struggle with finding the best way to leverage this tech.

What do you think the future holds for AI and machine learning?

I generally try to be more optimistic about the future because there are obviously a lot of fears around AI/ML. And I think that’s quite natural. If you look back in history, it was the same with electricity and any other innovative technologies.

One of the fears that I think does have some merit is that this technology may replace some real jobs. I think that’s a bit of a pessimistic view because history also teaches us that whatever technology we get, we still need that human aspect to it. 

Almost all the technology that we use right now augments our intelligence. It does not replace it. And I think that the future of AI will be used in a cooperative way. If you’ve seen products like GitHub Copilot, the purpose of this product is essentially to assist the developer in writing code. We still can’t use AI to write entire programs. We need a human to guide that AI to our desired outcome. What exactly do we want to achieve? What is our objective? What is our user expectation?

Similarly, maybe this technology will be applied to a broader set of use cases where AI will be assisting us, not replacing us. There is a quote that I wish was mine but I still think it’s a very good way of thinking about the role of AI: if you think that AI will replace you or your job, most likely you’re wrong. It’s the people who will be using AI who will replace you at your job. 

So I think one of the most important skills to learn right now is how to leverage this tech to make your work more efficient. And that should help many people get that competitive advantage in the future.

• Iurii Milovanov is the director of AI and data science at SoftServe, a technology company specialising in consultancy services and software development. 

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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