Improving the customer experience is the main driver of companies’ adoption of artificial intelligence. This means better knowledge of their behaviour through data analysis. A prerequisite, according to Christopher Wiltberger, Managing Director for Southern Europe at Freshworks, a start-up specializing in customer engagement.
“AI is the engine of the car, it’s an essential part to propel it to excellence. But the fuel of this vehicle would be represented by the data collected by the company”: with Christopher Wiltberger, the understanding of the issues related to this complex universe becomes clearer. Customer relations and high tech are no secret for this expert who has spent most of his professional life in American technology companies, always in sales-related roles. He joined Freshworks, a unicorn [start-up valued at more than $1 billion, NDLR] Indian specialized in customer engagement with various solutions in SaaS mode (Software as a service), in early 2019 to accelerate development in France and Southern Europe.
Engineer Techniques: For retailers in particular, AI’s goals seem pragmatic: predicting behavior, segmenting to customize messages to customers and prospects, modeling buying behavior, and managing the customer database. Can we detail each of these objectives?
Christopher Wiltberger: Before detailing each of these objectives, we think it is essential to remember that without data,, l’artificial intelligence will not be able to provide real teaching or improvement to your operations. To set up AI in your business, it’s essential to start by making sure you’re able to capture, store and analyze your business data.
To get to the heart of the matter, you’ve listed the 4 main ai goals for retail:
- Managing the customer database: it is impossible to set up an AI that interacts directly with customers without first implementing it internally to analyze the customer base. A properly implemented Artificial Intelligence will be able to recommend actions to support agents when solving customer problems and will identify patterns and groups among their clients.
- Predicting behavior: Once data capture is in place at all levels of the customer journey, AI is able to cross-reference customer information to predict their behavior, with a probability increasing with the mass of data collected. This is possible by bringing customer behaviour closer to that of a group with similar buying behaviours.
- Segment to personalize the message to customers and prospects: once again, it’s essential to capture enough data to customize messages. Once “groups” of users have been formed, retailers can send them personalized messages. Don’t forget to continually test different messages on similar audiences while measuring each other’s results. This will help refine the groups and distribute the right message to each customer.
- Modeling buying behaviour: this is quite similar to the second point mentioned. It is a question of finding patterns among groups of customers in order to identify a sequence of action that frequently leads to the purchase. Retailers can strive to make these routes more accessible in order to maximize their sales.
In reality, is AI effective?
There is no doubt about the effectiveness of AI. However, companies must give themselves the means to achieve this from both a structural and technological point of view.
Integrating AI into a company requires important preparatory work that is essentially data-based. If it is incorrect or incomplete, all the resulting analysis will be wrong. It is therefore essential that the starting data is complete and structured and the algorithms perform to analyze them. In addition, process automation is essential to integrate AI across the company.
Finally, a team trained in data culture and trades-related skills must be able to use data to interpret results. It is only by aligning all these parameters that AI will be a great success for companies.
What are the challenges for the customer experience to take full advantage of AI?
As I mentioned earlier, good data capture is an essential dimension to the success of AI implementation. You have to imagine the customer experience as a car: the AI is the engine of the car, it is an essential part to propel it towards excellence.
But the fuel (petrol or electricity!) of this vehicle would be represented by the data collected by the company. Without data, the AI is absolutely useless, will run empty, and will even end up damaging the customer experience. Once gasoline is in the engine, however, companies are propelled into a world-class customer experience.
In addition, AI is too often reduced to simple chatbots. Although they represent a very interesting application of this technology, we must not limit ourselves to this. This is a step in the implementation of AI, but not the cornerstone. AI goes far beyond a simple conversational application.
Finally, we must not forget that the improvement of the customer journey is well influenced by decisions taken both internally and externally. It is crucial not to restrict the use of AI to a mere “customer-facing” role, of course, customer contact staff play an essential role in the customer experience; but they are not the only ones in direct contact with customers.
It is necessary to plan to use AI for support agents, especially through the standard response suggestion. With this, they will be able to better advise clients, and avoid an accumulation of repetitive tasks to focus on solving real value-added problems. This will also allow them to find a greater interest in their work, and as we often say at Fresh works: “Happy employees – Happy customers”.
Can all companies, even EPTs, take advantage of AI to better satisfy their customers?
AI is no longer just for large companies. Nevertheless, the most important challenge for small businesses remains data and more specifically data capture that is important enough to implement AI effectively.
At Freshworks, all our products are enriched with an artificial intelligence called Freddy that is accessible to all as an option. Freddy supports sales, customer support and marketing teams of all sizes to solve customer issues more efficiently and efficiently across all channels.