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Anticipating customer needs with data and AI

30-second summary:

  • Now more than ever, brands need to make sure they are reaching their customers with the right content in the right channels at the moments that matter on an individual level.
  • To do this, marketers should first define their ambition and develop a strategy with aligning priorities for the business and the customer. For instance, the goal could involve upselling or increasing loyalty – the list can go on.
  • It’s recommended to establish specific use cases and the roadmaps to achieve them. Also, use AI and machine learning to run agile targeting and dynamic creative to meet the current needs.
  • Brands will need to identify the specific data and insights needed to support their use cases, as each use case is unique.
  • While AI can benefit the customer’s experience, it can also help CMOs and marketers with non-CX use cases. For example, AI can help to optimize marketing ROI, improve marketing performance and acquire new customers.
  • Marketers should trust their data and entrust AI to do more of the work by properly designing and deploying algorithms. Humans are still involved, but with more decisions happening autonomously and in real-time, they can focus on other strategic decisions and creative efforts for the customer.

People are aligning themselves with brands that understand their customer needs, recognize them and connect with them on a human level.

In today’s COVID-19 environment, customer expectations are evolving at an accelerated pace – challenging brands to deliver and maintain a trusted relationship.

Now more than ever, brands need to make sure they are reaching their customers with the right content in the right channels at the moments that matter on an individual level.

This is crucial to drive and preserve customer loyalty, with Deloitte’s research showing that it can directly impact a business. Results revealed that thirty-nine percent of people surveyed switched brands after a bad experience, and 62 percent feel they are in a relationship with their favorite brands.

Connecting in this way may seem like no easy feat, but artificial intelligence (AI) and machine learning can help make it possible. These technologies help brands better anticipate customer needs and reach them in the moments that matter through real-time marketing.

Below are steps CMOs and marketers can follow to provide relevant customer experiences:

Establish an attainable plan

The needs of customers can change in an instant, impacting their customer journey. To offer value, brands need to be agile enough to engage with customers and provide them with a more personalized experience based on their real-time needs.

To do this, marketers should first define their ambition and develop a strategy with aligning priorities for the business and the customer. For instance, the goal could involve upselling or increasing loyalty – the list can go on.

Instead of trying to achieve it all at once, it’s recommended to establish specific use cases and the roadmaps to achieve them. Also, use AI and machine learning to run agile targeting and dynamic creative to meet the current needs.

It’s important to recognize that as customer needs, data sources, and the outside environment are in flux, the program execution and results could be as well. CMOs and marketers should follow a test-and-tune discipline and be prepared for an iterative process.

The people component is also key. Having the right talent in place, stakeholders and operating model is critical for real-time marketing success.

Tap into data and technology

Brands will need to identify the specific data and insights needed to support their use cases, as each use case is unique. They should consider the following:

Data

Brands have a lot of data within their own walls, but to meet and anticipate customer needs at the right moments, they need external data to provide a complete view of their customers and fill in the gaps. This can be environmental data such as location and season-based information, trend data, or contextual data.

For example, external social media data could tell marketers what a customer set is responding to – and the type of content they want to see on the platform. Also, given the changing data regulations and third-party cookies going away, brands need to lean into first-party data.

Technology foundation

A customer data platform is valuable as it helps to create a single view of the customer that can be leveraged for the marketer’s needs. It combines a company’s internal data across all owned, paid, earned sources along with the external data.

With the right data and a better understanding of individual customers, marketers will have an important fundamental step completed to help create key personalized experiences.

Decisioning

AI and machine learning are vital for brands to better anticipate customer needs, while also accelerating speed to market experiences. When using complex data sets, machine learning helps with intelligent audience modeling, and AI helps to update a targeting strategy based on real-time insights.

With the ability to analyze more information and gain a deeper understanding, marketers are empowered to make informed and quick decisions to meet the changing needs of their customers across channels, messaging and experiences.

For example, when put into practice for a bank, AI could identify the individual customers who are currently in the market for a house using a set of data signals outside of standard demographic insights and target the individuals with the appropriate mortgage offer in the most optimal channel and moment.

Being able to orchestrate a personalized message deepens the connection and trust within the brand and customer relationship.

Turn process into practice

While these actions can support a certain use case or scenario, it’s difficult for brands to do this at scale and in real-time. One of the main hurdles many companies face is that customer experience is often thought of as a function of marketing or viewed in a silo.

Customer experience should be a true operational discipline with emotionally intelligent capabilities embedded into every area of a company’s operations. A key collaboration should be between the CMO and CIO.

This way, customer expectations and human insights, established through AI and machine learning can be used to influence the strategy and actions of brands in real-time that ultimately drive business results.

While AI can benefit the customer’s experience, it can also help CMOs and marketers with non-CX use cases. For example, AI can help to optimize marketing ROI, improve marketing performance and acquire new customers.

In a contact center, for instance, AI can work alongside customer service agents by informing optimal messaging for the caller.  While CX may not be the core objective, personalized customer experiences can certainly help achieve the use case goals.

Marketers should trust their data and entrust AI to do more of the work by properly designing and deploying algorithms. Humans are still involved, but with more decisions happening autonomously and in real-time, they can focus on other strategic decisions and creative efforts for the customer.

By focusing on customers’ needs and providing the personalized experience they crave, brands can create resilient emotional bonds that lead to loyalty.

Kate Erickson is Managing Director at Deloitte Consulting LLP and Hux by Deloitte Digital.

The post Anticipating customer needs with data and AI appeared first on ClickZ.

Reblogged 1 month ago from www.clickz.com

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