5 min.
AI and CX: powerful allies for more effective marketing
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AI and CX: powerful allies for more effective marketing

Business Strategy Data Science & AI Marketing Client experience & UX

Simpler than you think, artificial intelligence is used to automate analyzes and decision-making that we could not humanly do. What if it could help you improve the experience you offer your customers?


A study by Forrester Research  has shown that companies that excel in customer experience (CX) are able to grow their revenue six times faster than those that place little or no importance. So, in light of this study, it's hardly surprising that CX is poised to become one of the biggest strategic priorities in the marketing world over the next few years.  

Marketers will need to be able to use all available data for a deeper understanding of consumer needs and wants if they want to stay competitive and increase their chance of winning the battle. A study by  eMarketer  also corroborates this assertion. Indeed, it demonstrates that for most executives in the retail sector, the main utility of artificial intelligence for marketing (AI Marketing) is the ability to understand the customer and their consumption habits at a level greater depth and precision.

In today's business environment, consumer needs and preferences are constantly changing. To adapt quickly to these changes and remain attentive to the consumer, brands must improve their time-to-insight metric . In short, the faster a company optimizes its CX according to the changes observed, the more it will maintain a good level of competitiveness. 

The best way to quickly gain insights into the ocean of data that digital marketing now constitutes is to use data science and artificial intelligence. Data scientists and CX leaders both play essential and complementary roles in a company's analytical culture. Aaron Burciaga discusses this topic in an article published on Datasciencecentral.com  : 

“  Data scientists have skills that are totally different from those in customer experience. However, these divergent skills complement each other very well for more effective marketing.

By organizing and synthesizing complex information from different sources, data scientists focus on identifying specific elements and drawing conclusions that are useful for an organization's strategy.

However, even if the job profile calls for a high IQ, data scientists aren't always so good at emotional intelligence. On the other hand, corporate CX implementers need to use empathy to develop a deep understanding of customer and business processes via customer journey mapping. However, they lack the highly technical skills that data scientists have. »


As I mentioned in a previous article , Big Data-based market research is a new field in development that has come from the union of skills in CX, analytics and of data science. But how to use AI to improve your CX? Which approach to adopt? In fact, contrary to what you might think, you don't have to wait for everything in your company's marketing analytics ecosystem to be ready to move forward. Indeed, it's even better to take a step-by-step approach, looking to make some small CX improvements by working with samples of relevant data. 

Take, for example, the agile and piecemeal approach that has been developed by our team. We call it Practical AI . Concretely, rather than embarking on large-scale projects involving long cycles of risky and costly exploratory work, we test certain simple ideas within much shorter cycles. This is what we  call hackathons . 

In order to develop new algorithm ideas, our data science experts work together with our agency's clients and experts in various fields, such as digital media, search engine optimization (SEO), social media, conversion rate optimization, etc.

When our experts submit a concrete problem to us, we follow an approach based on the search for models, techniques and algorithms that already exist in fields such as engineering, medicine or finance. We then analyze and determine if an identified algorithm is deemed satisfactory to draw inspiration from and adapt it to solving a marketing problem.

If so, we work on refining a prototype algorithm until it's ready to go into production. Subsequently, following its evolution and with the help of data engineers and analytical developers, we see to automate the process on the application programming interfaces (APIs) and the  necessary data pipelines  , to then rationalize and optimize model results.

A similar approach can be offered to your company in the context of a collaboration with a person or an agency expert in data science. To get started, arrange meetings between your customer experience experts and data science experts. These meetings should be managed by a person with a good mix of technical and marketing skills to ensure a good understanding of the marketing issues to be addressed on both sides.

The person in question must also be comfortable with data and the business world, as is the case for a specialist in BI (business intelligence) and data analysis. McKinsey calls this kind of resource an “Analytics Translator”. In other words: someone who can bridge the gap between data analytics, AI and marketing experts. The goal here is to identify and understand the pain points your business is having within your customer experience program so you can then address them using these resources and new tools, such as AI. In the current era, traditional techniques are no longer helpful in developing an appropriate solution.

Initially, here are some key questions to ask yourself to identify how to improve your customer experience: 

  1. Is our marketing team addressing the right personas? If so, how well do we understand these segments and their needs?
  2. Does our website or mobile application provide an optimal user experience? What aspects of  user flow  should we prioritize and optimize first? What data could we examine further to answer this question?
  3. What is the main cause of lost customers? Do we understand which variables and factors have the most impact?
  4. And the final $100 question: how can we create a personalized omnichannel customer experience across all of our platforms?

By optimizing your CX skills using data science and AI, you are likely to discover new questions specific to your business environment to ask yourself in order to improve your marketing performance. Thanks to data analysis and AI, you will be able to better understand the important CX aspects to focus on.

The more you can focus on what really matters to your customers, the faster your brand can evolve and stand out.