Leveraging the value of all your data

Customer experience success today relies on the use of technology. Whether personalized interactions or fast resolution, predictive engagement or informed, optimized decision-making, it all requires the right tools, automation and data.

Investments in recent years in Big Data and analytics highlight how critical this information has become to organizations, while the rise in demand for data literacy shows how vital the ability to interpret and use data has become for non-specialist employees.

See how Carrefour developed an omnichannel customer vision to deliver exceptional in-store experiences and ensure customers kept coming back.

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Dealing with the data explosion

What is a challenge is the amount of data companies are having to deal with.

Having access to data is not the issue; finding the right information is. Companies' digital foundations are not always set up to support the real-time experiences customers are looking for, while increasingly disparate and scattered data sources make it harder to build that critical picture of the customer and be proactive. How do companies bring their data under control? Moreover, how do you reduce time to market on your CX initiatives? How do you improve consistency across your channels? How do you improve business performance?

“The amount of digital data created over the next five years will be greater than twice the amount of data created since the advent of digital storage”

Source: IDC

The rise of customer data platforms and journey analytics platforms

This is exactly where modern customer data platforms (CDPs) kick in. These platforms are designed to put the business user in the driving seat and leverage the value of rich customer profile data.

CDPs achieve this by accommodating growing data volumes and sources and complement existing big data solutions. In addition, they collect real-time data and process it into unified customer profiles. These are then used to segment customers and activate relevant experiences across destination channels, including contact centers, web and mobile apps, chat and partner channels.

CDPs support the delivery of proactive, personalized and consistent experiences as real-time customer data is shared across channels. In addition, they also help apply data protection policies to comply with regulations and respect customer consent preferences.

While CDPs focus on the operational use of data, journey analytics platforms (CJA) help companies identify the right activation strategy. CJAs typically reveal what actual customer paths contribute best to performance metrics, including satisfaction, loyalty, cost-to-serve, conversion or sales. Equally, they help to identify suboptimal customer paths so that businesses can precisely trigger optimization tactics.

The maturing and operationalization of AI and ML

Are you able to predict which customers are more likely to need escalating help? Can you pre-empt when calls might be abandoned? Or identify who is most likely to buy, or indeed stop using your business? Do you know what determines sentiment during a conversation or, ultimately, satisfaction?

Artificial intelligence and machine learning have become key enablers and differentiators in virtually all activity sectors. They allow a wide variety of organizations to optimize business processes, reduce costs, boost sales, and improve customer experience, among others.

As an organization’s data and analytics maturity grows, it will get to a point where it needs to move beyond vendors' built-in AI solutions. The ability to develop tailor-made, proprietary algorithms can be the driver to keep on growing and maintaining competitive advantage. However, this comes with new challenges that are hard to overcome if the required foundation is lacking.

Maturing in AI/ML means thinking in terms of building blocks. Companies need to establish an infrastructure that enables the democratic use of data across all business domains. In addition, they need to provide ML services that support monitoring, automated retraining and the deployment of new ML models. This is then wrapped into an AI layer service layer that provides users and applications with additional intelligence from analyzing text, speech, images or complicated behavioral and predictive challenges.

Learning to prioritize the right use case

This is all good in theory, but we find that many companies struggle to know what to prioritize when developing these use cases. It can help to have an external pair of eyes to look at where the business value is. At Orange we have dedicated consultants who specialize in helping major corporations make sense of their data, and ultimately improve their delivery of the customer experience. Want to know more? Read Yves Van Den Brande's blog on data in the customer experience.

Alternatively, you can download a brochure for further details on how our consultants help companies on their journey, or get in touch to arrange a free workshop.