The AI way to a superior personalized customer experience

Prashanth Allampalli
Published: October 10, 2018

Personalization and Timing to Improve Customer Relationships

Businesses are always looking for ingenious ways to improve customer service. According to a recent Forbes survey, 70% of buying decisions are influenced by how clients feel they are being treated; therefore, a superior personalized customer experience goes a long way toward getting the numbers up.

Exceptional service is not just about being friendly. It requires the right product or service to be offered to the customer at the right moment and at the right price. With a plethora of solutions available, many customers defer to the expertise of the business itself to recommend the right solution for them. Businesses that can predict customer needs and supply effective solutions can ensure that their client base is always satisfied. The adage "the early bird gets the worm" has never been so true.

Unfortunately, customers are not always easy to read, particularly when there are a lot of them. For these situations, successful businesses rely on product recommendation engines (PRE).

What are machine learning (ML)-driven product recommendation engines?

With vast amounts of data about existing and prospective customers, it's impossible to analyze and predict present and future needs. This is where automated procedures driven by ML algorithms come into play.

These sophisticated algorithms take into account massive amounts of customer data, including purchase history, preferences, and direct feedback. The algorithms employ set processes that refine customer data into accurate recommendations. The system can then automatically deliver the right solutions to individual clients.

Simply put, the product recommendation software gives businesses the power to analyze vast amounts of customer data points and use them intelligently to predict which solutions and offerings a specific customer might be interested in.

Some of the important features to be factored when designing a recommendation engine include the following:

  1. Ease of integration: Product recommendation engines need to easily integrate with existing systems or data warehouses.
  2. Usability: Easy-to-use controls, clear reporting functions, and multichannel support options help ensure that users get the most out of the solution.
  3. Effective automation: Effective recommendation engines automate nearly the entire process, collecting data, analyzing it, and then delivering recommendations directly to customers, all automatically.
  4. The right combination of ML algorithms: A vast array of ML algorithms can be leveraged for customer prospecting and recommendation. There is no right or wrong algorithm for the job, but the choice depends on the kind of data, the available computing power, and the final use case. For example, the data points and history for new customers would be vastly different from the data available for old customers. Therefore, the approach for the recommendation of products or services for new customers would be different from that of existing customers.
  5. Real-time processing: The recommendation tool should constantly update and refine the output to stay in sync with the customer and transaction dynamics. If it doesn't, it risks missing out on the right time to suggest the right offering to a client who is ready to be upsold or cross-sold.

Here are some of the important considerations when selecting an ML algorithm for a recommendation engine:

  • Not every ML algorithm works with every kind of data.
  • Run-time complexity may grow with the number of data points.
  • Small amounts of data bring poor results.

To summarize, careful consideration needs to be given when selecting ML algorithms. There is no "one size fits all" algorithm. There are lots of different algorithms that work with training data sets of different types, volume, and accuracy.

What can superior product recommendation do for your business?

The right product recommendation tool empowers businesses to achieve unprecedented levels of personalization in their sales and marketing efforts. According to research by McKinsey, personalization can deliver five to eight times the ROI on marketing spend and can improve sales by 10% or more. Intelligent PRE tools also pave the way for natural and logical cross-selling and upselling opportunities which otherwise gets missed out by traditional sales & marketing strategies.

All in all, ML algorithms help not just the balance sheet numbers but also critical customer relationships and act as a catalyst for sales success.

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