by George Alam, Technical Product Manager at eddress
Wondering how it's done? eddress explains the difference integrating AI features into your customer-facing solutions makes.
AI drives customer engagement
When it comes to driving customer engagement, AI can be a powerful tool. You can use AI to provide personalized recommendations, automate tasks, and engage your customers in conversations, hence boosting customer engagement and retention.
But how exactly is AI used to achieve these goals? Here are two crucial concepts:
- AI-powered recommendations give customers personalized suggestions for products or services they might be interested in. Customers are made aware of products that match their interests without needing to spend time searching.
- Automating common tasks like customer service inquiries or appointment scheduling frees up valuable time for your business, so you can focus on what you do best.
The advancement in AI allows mobile applications to create and deliver personalized and relevant content, ensuring the customer experience can stand out from the "online noise". It goes beyond traditional rigid and static rule-based recommendation systems; AI generates an automated, smart, and trainable algorithm-driven system that grows the more data it receives. Hence, the AI solution can learn customer behaviors, monitor trends, and offer customer-specific product recommendations to improve brand loyalty. According to Statista, 90% of U.S. consumers find marketing personalization very or somewhat appealing. More to the point, SmarterHQ reports that 72% of consumers say they only engage with personalized messaging.
And by doing so in an automated way, AI automation becomes instrumental in overcoming the challenges of traditional personalization efforts and conversion. Indeed, static personalization activities are time-demanding and counterproductive, as 63% of consumers will stop buying from brands that use poor personalization tactics. (Smart Insights).
Maximizing the unique home screen experience
Artificial Intelligence is typically used in mobile applications to customize the user experience. Users can have a personalized home screen displaying relevant products & services based on their profile and behavioral data.
According to the AI research firm Emerj, AI-driven personalized home screens result in a 60% clickthrough rate and 75% total sales conversions. For comparison, rule-based recommendation systems often lack relevance, which could put off 91% of shoppers. Indeed, AI content suggestions not only increase the relevance of the content to the user but also improve the overall customer experience, resulting in increased time spent on the mobile app and reduced dropout rates.
Improving Recommendations and Baskets
An AI-driven recommendation engine is used for improving impulse sales, cross-selling and upselling. Using smart technology, users can easily and quickly find products, promotions, and deals that are relevant to them. Additionally, these recommendations are able to evolve and adapt to changes in users' needs and behaviors.
Additionally, you can send individualized push notifications and product recommendations which will considerably boost cross-selling and upselling for your business.
According to Amazon, using the machine learning-driven personalization engine Amazon Personalize increases the response to product recommendations by five folds compared to static recommendation engines. Needless to say, higher recommendation engagement directly affects your revenue.
At eddress, we are confident that leveraging AI-driven personalization technology for small businesses can more accurately determine the customer's journey and anticipate their next step. That’s why we keep a close eye on AI progress. Indeed, in a hyper-competitive market facing giants such as Amazon, delivering a superior customer experience can take your sales efforts to the next level. More and more companies choose to avoid the hassle of developing, training and implementing a DIY machine-learning solution, and deploy a customized and self-learning personalization recommendation system instead.
