Conversational AI For Recommendations: How To Personalize Product Advisory

Updated: Jul 8

Orai's blog on Conversational AI for recommendation
Conversational AI For Recommendation

With organizations looking into the conversational engagements of customers, the market is rapidly deploying conversational AI. Artificial intelligence is driving the entire customer journey by designing a personalized buying experience and improving the way products are offered.

Conversational AI for recommendations

There was a time when quality customer experience was defined by quick query resolution. But today, customers want more! They want brands to hear their needs to take quick yet personalized actions and provide value during every interaction. That’s where conversational AI for recommendation can enter.

You can stand out as a brand with an intelligent AI available to provide product advisory based on every customer’s intent and interests. But even AI needs to be optimized in order to do the job right. You need to be prepared to understand the details of how customers interact with your AI chatbot. The bot itself should collect feedback and improve experiences by adding more valuable information.

The correct use of data can give infinite range to AI in personalizing customer experiences, product and process recommendations.

Data Needed to Enable AI Recommendations

More enterprise data means better quality of recommendations provided by AI to your customers. The information is used for machine learning training, so your virtual assistant can identify customer data and product listings to form accurate correlations. Only then, they can recommend more appropriate product suggestions.

Here are the basic data sets used in order to train conversational AI for recommendations:

  • Customer data: Each customer’s personal interests, name, demographic data, probable interests, and other information.

  • Traffic data: All information regarding a site visitor including names, contact details, browsed web pages, products they seemed interested in, and more

  • Transactional data: Historical data about customers' commercial transactions with a business. The pricing range that usually convinces a customer to buy, product buying habits, and more.

For example, a use case for an e-commerce business:

An e-commerce business would require the following data sets in order to ensure accurate product recommendations-

  • Product details including product names, package types, and relevance based on different demographics.

  • High-demand product details based on seasons, offers, or locations.

  • Price data for all available products.

Benefits of Personalized AI Recommendations

When you have a conversational AI with data intelligence, it resolves a variety of customer experience issues.

1. Responses that customers are looking for

Inaccurate replies are probably the biggest issue in customer communication for companies that use a manual approach or generic chatbots. Both methods fail to capture the intent of a user in real-time. They can’t immediately understand user preferences by looking at the message history or previous purchases.

Thankfully, conversational AI can do all that. It can form accurate responses based on real-time intent data analysis as well as the queries and messages shared by a user previously. This capability allows an AI chatbot to master the art of personalized recommendations.

2. A smart way of collecting insights and feedback

Every conversation between an AI virtual assistant and a customer can be used to collect insights and feedback. Conversational AI can satisfy user demands with valuable recommendations, and then, ask for their feedback.

Similarly, your internal team can look at those conversational insights to find the most common queries among customers and resolve them faster. Such patterns become easy to identify through conversations happening 24*7 with humanlike intelligence.

For instance, if multiple customers complain about the same issue in a product, you can take immediate actions to rectify it before the problem becomes too big.

3. Accurate understanding of customers persona

Very often, manual analysis of customers’ persona gets contaminated by incomplete conclusions, biased hypotheses, and missed out key factors. One excellent advantage of merging conversational AI with data intelligence is the improvement it brings to the customer persona.

While learning about customers’ personal preferences, AI bot builds a persona that can be used by the marketing, sales, and support staff to optimize and modify their respective efforts. AI-enabled customer understanding leads to more data-driven marketing and sales decisions, allowing revenue to become all-time high.

4. Reduced manual labor

Using AI recommendations is the only practical way of reducing manual labor from marketing, sales, and especially customer support without worrying about the quality. It’s the only technology today that can automate business processes to match the scale of digital channels with personalized interactions.

With AI there to recommend solutions, products, and services, internal teams can have more hours to study customer demands, market trends, and make strategic decisions.

5. Finding intents that were hidden before

A lot of times unique customer intents are missed among all repetitive, common questions. These intents require immediate attention of support agents, which doesn’t h