Digital marketing experts estimate that the average American is exposed to between 4000 and 10 000 marketing and sales messages per day. In a world where consumers find themselves inundated with options, with various tasks and expectations leeching their time, enterprises find themselves in a position where they need to provide a compelling reason for consumers to invest their time, energy and money into their product.
ecosystem.Ai’s Intelligent Sales Module makes use of experimental strategies and real-time adaptation to move a consumer from a state of indifference towards your product, to gripping their interest and initiating a meaningful interaction that keeps them coming back.
Adding value at scale, in real-time
Intelligent Sales enables your systems to give consumers a good reason to engage with your product. Identifying relevant pain points is a key aspect of this and is done through analyzing past actions and behaviors, such as past searches and interactions, or purchasing patterns.
But focusing on past actions might not always reveal what is important to your customer in the present. A person’s emotions and desires are in constant flux; changing from day to day or hour to hour.
By using tools like geolocation, and taking into account factors like current data balance or recent transactions, Intelligent Sales can make recommendations suited to the consumer in real time.
The dance between hyperpersonalization and intrusiveness
Intelligent Sales also recognises that getting too personal can sometimes be considered intrusive by customers. Consumers don’t like it when you know too much about them, but will pay no attention to you if you don’t know enough. So how do we master this dance between hyperpersonalization and intrusiveness?
Decisions, and subsequently, actions, are driven by emotion. Intelligent Sales therefore ensures that the effort you put in meets the consumer in their prime emotional state, situating them to act – a marriage called sentimental equilibrium.
Knowing what we don’t know
Intelligent Sales caters for the unknown. Cold-start customers have no historic data to work with, but by deploying tools like Dynamic Experimentation, your systems can learn about your customer on the spot. Experimentation includes randomising messaging style, and employing A/B and multivariate testing until something sticks.
Recommendations also form part of this experimentation process and serve a dual purpose – they are not only an output of a process of analysis, but serve as an additional input in a continuous learning process. Adjusting in real-time, dynamic recommenders enable your systems to continuously learn more about your customer.
By employing a combination of explorative and exploitative algorithms, Intelligent Sales further enhances your systems’ ability to adapt. While exploitative algorithms ensure your customers’ preferences are satisfied, explorative algorithms recognise human beings’ need for novelty. By challenging your customer with novel suggestions and at the same time meeting their repetitive needs, Intelligent Sales enables a well-rounded approach to customer satisfaction.
This approach can also be applied to customers that have become dormant, working to reinvigorate interest in your product through nudges and new recommendations.
To sum it up
Intelligent Sales aims not only to acquire new customers, but to retain existing ones. Deploying novel approaches is essential to keep up with human beings’ constantly changing moods and desires. ecosystem.Ai’s Dynamic Experimentation and real-time adaptation form the backbone of giving your systems the flexibility they need to be effective. Intelligent Sales gets you a few steps closer to solving the mystery that is your customer.
