Respectful personalization turns engagement into delight

Imagine discovering a boutique clothing store that completely understands you. The staff knows what you have in your closet, what you are missing, and what you're willing to spend. They understand your style - the colors, patterns, and cuts you like but also what looks good on you and what you're comfortable wearing. They go beyond selling you a shirt or a jacket to curating your wardrobe, making recommendations from socks to suits that they know complement the clothing you already have and that you will find delightful. And they understand how to communicate with you about those recommendations - in a way that ensures you are actually excited to receive that email, text message, phone call, or invitation to a trunk sale.

Now imagine there was a way to deliver this level of service and sales recommendations at scale - enabling a multinational brand to provide that boutique level of personalized experience to its customers.

That's what respectful personalization is all about. It leverages data and AI (sML, and other advanced data-analytics technologies) to enable brands to engage with each customer - with the right content, at the right time, on the right channel, and at the right frequency - in order to build satisfaction and loyalty. It does this while complying with all data-privacy laws and other regulatory requirements, and in a manner that doesn't feel invasive, unsettling, or untrustworthy to the customer.

In helping Capgemini's clients leverage data and AI (advanced analytics) to improve how they engage with their customers, I've identified some common attributes that characterize the most successful deployments.

First, many companies struggle with how to use Data Science (advanced analytics technology). Teams that aren't well versed in understanding how AI works often impose rules that choke the AI's recommendations. The most successful deployments occur when people define business goals - for example, to (drive EBITDA) increase loyalty in a certain segment, or boost viewership, or minimize inventory - and then allow the AI to optimize for that. This comes more naturally to organizations that have fostered a culture of experimentation - one in which the enterprise tests engagements with customers, collects explicit and implicit feedback, learns from the experience, and modifies its strategies accordingly.

Equally, once AI makes recommendations, it's important that teams share them across the organization. For example, if a company's marketing team learns its customers are more concerned about sustainability, there are implications for the product design team - but also for the supply chain and sourcing teams. Insights must be embraced, enterprise-wide, to ensure they're acted on effectively.

Successful implementations also recognize that context is key. Customers demand different things at different times of the day or at different stages of their lives. Their preferences may even change depending on the device they're using. For example:

• A person visiting a website via a laptop may be open to exploration.
• If that same person connects via an app on a phone, they're likely more interested in quickly completing a transaction.
• If they're using a company computer, they may wish to receive communications about work-related products and services but not personal products and services.

Respectful personalization has become a crucial component of any customer-engagement strategy - so much so that my team and I ensure it's front and center when we work with clients to deploy the Capgemini Data-driven Customer Experience solution. If you have questions or comments about this, I would be delighted to hear from you.

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