How the AI Revolution Can Impact Your Users’ Digital Experience

How the AI Revolution Can Impact Your Users’ Digital Experience

By Kevin Daly, Head of First-Party Data at Making Science

Marketers can no longer rely solely on rigid best practices, as the world of digital marketing changes with the emergence of new technology and the seemingly infinite ways customers now interact online. Brands must march in line and at the same pace as their customers.

In today's digital age, where customers have become increasingly sophisticated in their online interaction, all marketers need to be performance marketers. Customers are more likely to engage with brands that offer relevant and personalised experiences, and they expect brands to demonstrate their value and their differentiators.

However, many businesses struggle to achieve true personalisation due to challenges in integrating customer data or leveraging the right technology, stemming from their restricted digital capabilities. Artificial intelligence (AI) has also now become a cornerstone of the digital conversation.

With the latest research showing that 15% of all UK businesses have adopted at least one AI technology, and with that figure looking like it is only going to increase, it has never been more critical for marketers to fully understand the effect that AI can have on their digital customer’s journey.

However, it is not as simple as buying the newest AI tool as a quick fix. To truly leverage the technology, long-term, businesses must strategically work towards digital maturity.

Data, data, data

Data is the lifeblood of AI and the foundation of any strategy to incorporate the technology and improve customer experience. Both the quality and volume of data are key, so businesses must ensure they are correctly collecting, storing, and activating their first-party data, which also helps to avoid the long-lasting effects of not doing so efficiently.

Inaccurate data can become a major headache for a business, particularly when it is used to train AI models that are intended to improve efficiency. Low-quality data produces low-quality outputs, which in turn affects decision-making. Therefore, it is crucial to clean up existing data and ensure that it is used to inform logical decisions. Without data maintenance, duplicated and incorrect data can result in irrelevant or even unprofessional communication with target audiences.

One challenge marketers face when cleansing and making their data usable is silos. Data silos occur when different parts of the business — such as the sales or customer service departments — keep hold of their own customer information. These department data sets must be combined to gain a comprehensive understanding of customer behaviour and preferences. Without a unified view of customer data, marketing campaigns may lack relevance and fail to meet customers' needs and expectations.

Silos may also occur with external data, such as the information provided across different platforms, including publisher sites or social media. However, companies need to have the technology and processes in place to integrate this data with their own, and deduplicate it for accurate attribution.

Marketers should ask themselves, are we accessing all the information about where our customers exist? If they are not, they face a significant disadvantage in today's data-driven marketing landscape; without unification of data and taking steps to ensure its quality, it becomes difficult to understand the full customer journey, and to accurately target them.

Unleashing optimal AI content

Once the technological foundations are in place, marketers can feel the full benefits of their digital maturity, including a new world of AI-powered tools.

One way in which businesses can put AI to good use is to help with personalised messaging. Customers can quickly feel digital fatigue from brands bombarding them with irrelevant ads. Personalisation not only counters this negative effect, but offers marketers wider benefits, such as the ability to predict the value of web customers in real-time. This enables marketers to align their marketing priorities with business objectives.

AI assists with messaging through brand language optimisation, to create a consistent, relatable, and objective-driven brand identity. AI plays a critical role in optimising language by utilising techniques such as natural language processing (NLP) and machine learning (ML) to analyse data and deliver personalised experiences.

You only need to look at the current economic climate for a good example of what different messaging can look like; with many customers struggling financially, it becomes more prudent to show a level of understanding in the messaging that is put out — less of the ‘you need this’ and more of the ‘this will help you’ could be a better approach.

AI can also help marketers tackle issues caused by insufficient first-party data — such as data biases, a lack of context, and limitations in personalisation — by utilising additional proprietary data from other sources.

This level of hyper-personalisation allows marketers to take control of the narrative for each individual customer, which leads to better engagement and increased loyalty with all customers.

While having accurate data to input into AI tools is essential, maintaining human control is a crucial part of the process. In a shifting landscape, where customer demands and motivations are constantly changing, a test and learn approach leads to agility. Marketers need to be prepared to adjust their strategy at any given moment to maintain control of messaging and customer experience, afterall, what worked last year, or even last month, might be ineffective now. It comes down to a marketer having the bravery to adapt an approach when they see its effectiveness declining, and knowing their decisions are based on reliable information will give them the confidence to do this.

Predict need, not just behaviour

If a marketer knows their audiences well enough for personalised messaging, then they may also hold the information to enable them to predict customer behaviour — AI can take the use of this data to the next level to improve digital experience.

AI-powered technology today allows brands to understand what the customer needs before they may even be conscious of it themselves. By detecting patterns of behaviour, marketers can personalise a message to meet a need before even being told by the customer, giving them the optimal lead time to target that customer for the best chance of success. Once again, hyper-personalisation is coming to the fore and being driven by AI.

AI is also playing its part in helping marketers get their attribution right. Rounding back to how data feeds AI, marketers must also capture offline data and integrate it with their online data. In doing so, marketers can let AI enrich their customer data platforms (CDP) with algorithms to optimise media bids.

The upshot of this approach is that marketers have control of a CDP that details the whole customer journey. By creating a platform that can be utilised to its full potential, marketers can direct algorithms to be more efficient with bidding and avoid bombarding the customer with ads, messaging, and products that are not relevant or effective. Combining both data sets (online and offline) offer a greater understanding of those customers that use a brand’s website or online store to browse, but who prefer to make the final purchase in-store.

Digital maturity underpins the effective use of AI, which should be embraced to deliver the best possible customer experience. To keep pace with consumers in the digital age, businesses must take a fresh look at their marketing strategies and wider data practices to effectively incorporate AI tools and benefit from touch points along the customer journey.

Marketers should not shy away from the AI-driven revolution, they should be taking a step ahead of the competition and navigate into a digital-first future along with consumers.