Today’s brands need to be able to use the power of segmentation and personalisation to create highly relevant and optimised experiences. Location specific, targeted services and customised communications that seamlessly guidecustomers from the initial discovery right through to the final transaction. And it doesn’t end there.
In addition to building their understanding of the customer as an individual, brands are also expected to better understand their customers’ networks. This insight can enable them to offer valued recommendations and services, such as offerings around pet insurance or anniversary gift ideas. However, personalised and effective digital engagement is not possible without an effective data-driven capability that underpins it.
Delivering next-level personalisation with AI and ML
High quality, well governed, and accurate data, is the fuel that lights up a growth engine. The ability to use data in the right way, can be the single biggest differentiator with customers in today’s digital economy especially as Artificial intelligence (AI) and machine learning (ML) continue to grow in prominence.
Both AI and ML have the power to analyse massive amounts of data created though customer communication (be that on email, mobile, social media, e-commerce or in-store) and deliver valuable insights into patterns of behaviour.
However, if an organisation feeds its AI and ML system with poor quality data, it’s in danger of making incorrect decisions at an accelerated pace. For example, using historical datasets as the foundation for AI, could meaninformation is inaccurate or out of date. It means customers don’t get relevant and personalised benefits (such as discounts, loyalty points, and so on), and they then disengage or even feel disconnected from the brand.
Another major challenge companies face is in the ‘final mile’ of data delivery. Essentially, data often doesn’t get made available to the right person at the right time or in the right context. Capturing high-quality, accurate data and making it available to the right people is imperative to the democratisation of data. In turn, it allows CX teams to move quickly, capitalise on customer data, and deliver the communications, offers, and updates customers really want.
Steps to success
To remove the risk of poor customer experiences or legal penalties, you’ve got to have great visibility of your own data and where the bias lies. Organisations with the ability to segment that data, measure and test it affectively can then benefit from AI without risking amplifying pre-existing prejudice.
As a result, in the year ahead, many businesses will look to improve the quality and stewardship of their data, building in governance and even monetisation into their data management strategies. In fact, our recent survey of UK Chief Data Officers revealed the effective sharing, democratisation and use of data is a critical priority for data strategies in the year ahead, alongside improving governance over data and data related processes (48%).
To get the best out of AI-based CX, business leaders must be able to answer several key questions. Is the data you’re using to train your AI models coming from the right systems with the right quality? Have you removed personally identifiable information and adhered to all regulations? Are your processes transparent – and can you prove the lineage of the data your model is using?
Two stories of success
Let’s end where we began: with the possibilities that AI unlocks for Customer Experience and Marketing professionals. Brands need to take the time to develop a clear and comprehensive data strategy. One that unlocks the value in data so AI and ML can be used effectively to drive the business forward and enhance customer engagement. If this is done correctly, the results will be worth the effort.
For example, Rent-A-Car, France’s leading car rental company, recently invested in technology to build a 360 view of its customers and better understand their needs. In addition to enabling the delivery of a seamless rental experience, including app-based self-service options, the capability integrates data from multiple sources so the company can better spot patterns of behaviour that might indicate a renter is a risk for fraud, theft, or unsafe driving. End result? More engaging customer experience – and increased loyalty.
Likewise, PUMA, known for its lifestyle shoes and athleticwear, wanted to provide its customers with a hyper-personalised experience including more accurate product recommendations. By adopting a data-centric approach that incorporates machine learning to better match disparate records into a single view, PUMA gained a far more detailedunderstanding of its products and individual customers – and used that understanding to boost sales significantly enough to achieve immediate business value and generate increased sales.
With a strong data foundation, advanced technologies like AI and ML can make a significant, measurable difference to business performance. Now is the time to invest in data-driven strategies in collaboration with the Chief Data Officersand Customer Experience leaders to check for bias – and step into a new era of customer experience.