Wouldn’t it be ideal if brands could form a one-to-one relationship with every customer – and cater to their individual preferences whenever they engage. The evidence shows consumers want companies to tailor every interaction to match their needs in this way.
The trouble is delivering personalisation at every point of contact is not easy. It requires brands to always know exactly who they are dealing with and then behave consistently with each customer across every possible engagement channel.
This poses several complex challenges that have previously been difficult to surmount. AI, however, is helping businesses to overcome these.
Creating a unified customer view
The first obstacle brands face is how to create a unified view of the customer. AI can help bring together all the customer information collected within the various systems governing often numerous consumer touchpoints.
Beyond obvious channels such as online and offline point of sales, customers are now interacting with various service agents, loyalty schemes, social media outlets, chatbots, digital assistants and much more.
In this new omnichannel world, the typical number of customer-company interactions in a purchase journey has grown enormously – from an estimated average of nine touchpoints in 2014 to between 20 to 500 today, according to Boston Consulting.
Real-time data integration
The challenge companies face when they are engaging across several channels is that customer profiles need to be updated immediately. Otherwise information will be outdated when they cross over to another touchpoint.
If the brand, for example, is aware that a customer is searching for a particular product, say a summer dress, it may want to promote this product to them across multiple channels. If they then buy that product, it can use predictive analytics to evaluate what they might purchase next – perhaps some shoes – and promote that.
However, if the customer buys the product and it takes too long to coordinate that information across all channels, the brand could end up wasting advertising budgets promoting a summer dress to them for several days after it was bought. For many brands, the reality is that this regularly happens as it can still take weeks to collate all this information manually.
Making sense of complex data
With an AI-enabled data infrastructure, however, it’s now possible to read, analyse and make use of data wherever it sits – with insights being made instantly available within a single customer profile. The deployment of AI can also iron out any inconsistencies within the data, which can often occur when information is shared across different platforms.
It’s absolutely crucial that brands are cleaning data in this way, otherwise the ability to identify when they are speaking to the same customer across different channels becomes almost impossible.
This is because personal data changes all the time. People move house, change names, use alternative email addresses, etc. They may even deliberately use different identifiers, such as a ‘burner’ email, when they sign up to access initial incentive offers.
Resolving identity challenges
With the support of AI, however, brands can spot commonalities and make connections between profiles far faster. A big benefit of this is that it helps to reduce the number of duplicate profiles held in their databases.
This is often a bigger issue than many realise. AI analysis, conducted by Amperity, of data held by hundreds of brands in Europe, North America and Australia, found that almost a quarter (23%) of customers’ profiles are not unique. This means that, more often than not, brands do not have a complete picture of their genuine customer base.
Building for advanced AI applications
When brands do not have a full 360 degree view of each customer, it creates a series of challenges that inhibit the ability to form one-to-one relationships. For instance, if a brand thinks it is talking to two separate people instead of one, there is every chance the experience it serves up will be different, and even conflicting, across different channels.
In addition to inconsistency, misidentification also presents other problems which include:
● An inability to identify high value customers. If a consumer is engaging and buying across several channels, brands will want to pay special attention to them but, without the full picture, they will fail to see their true value to the business.
● Reduced confidence in predictive analytics. Inaccurate data will lessen the capacity to anticipate future purchases, and lower the return on investment from promotional activities.
● An inability to create accurate look-a-like profiles, which can be used to target similar personas through advertising channels. This will increase the cost of acquisition and lead to budget wastage.
Potentially worse than all the above will be failure to meet the requirements of current and future AI deployments. Whether we’re talking about predictive analytics, GenAI, Agentic AI or any other solutions, well organised and accurate data is a prerequisite for brand success.
Brands can start the process of preparing for these more advanced applications of AI today, however, by embracing the AI tools currently available to collate, clean and make sense of the data they possess across all their consumer touchpoints. With this in place, they will be in a much stronger position to provide ever more personalised interactions and form the one-to-one relationships everyone is looking for.
