Having a flexible analytics platform is key to continuously evolve your offer to changing customer needs and dynamics. In this article, we discuss product recommendation engines and how to implement them to make use of a higher level of advanced sales analytics. 

For service providers to understand which factors influence sales outcomes and use this information to develop ROI-focused growth programs, it’s important to have a flexible analytics platform in place that can facilitate the correlation of sales metrics with a range of contextual data sources. Once this level of analytics maturity is achieved, more advanced data-driven strategies leveraging predictive analytics and machine learning can be developed. Product recommendation engines are a prime example of this, and can be used to increase conversion and reduce churn.

Predictive Analytics: Understanding Product Recommendation Engines

So what is a product recommendation engine? At its most basic level, it’s a predictive analytics algorithm that synthesizes all available data about a prospective customer and predicts the most appropriate products and services to be sold. By the time a prospective customer is engaging in a sales discussion, a wealth of data will have already been collected on them that can be leveraged in this type of algorithm.

For consumers, this information may include: search terms, website browsing patterns, and a range of demographic, psychographic, and customer segmentation information that can be inferred from their address (e.g. age, household size, discretionary income, and even the preferences of other customers living nearby). For businesses and enterprises, examples may include: industry vertical, revenue, employee numbers, IT spend, data storage strategy (e.g. cloud vs. on- site), and office locations/sizes.

By analyzing patterns, sales organizations can determine which of these data elements collected at the point of sale can predict which products and services are most likely to have positive sales outcomes, such as: high conversion rates, low pre-activation cancel rates, and low early-tenure churn rates. Getting this right requires robust statistical analysis.

Starting Out With Product Recommendation Engines 

The process begins with hypothesis testing to determine which data elements have predictive power. It then progresses to model development to build an algorithm that has the highest success rates at leveraging these data elements to predict sales outcomes. Finally, extensive testing is required to validate the model’s effectiveness and refine it accordingly.

Once built, a well-developed product recommendation engine evaluates available customer data and then scores various combinations of products and services based on how likely they are to result in a high level of customer satisfaction. The products and services with the highest scores should be positioned as the lead offer to the prospective customer.

Creating Higher Quality Customer Experiences With Product Recommendation Engines 

While this process is not a substitute for needs qualification, it can help to ensure that the conversation begins with products that have a high likelihood of being relevant, creating a more positive experience, and reducing the average amount of discussion time. These predictions also raise the base level of information in the hands of sales agents, which gives them more confidence throughout the sales process.

For consumers with a shorter sales cycle, these models can be incorporated real-time into the sales process, so that websites recommend most relevant products, and sales agents have the product recommendations available to guide their discussions with prospective customers. For businesses and enterprises which typically have longer sales cycles and may involve more information gathering, these models can serve as a tool that is leveraged throughout the sales process, continuously updated to refine recommendations as more information is available, which can guide proposals and inform outbound marketing campaigns.

Using Product Recommendation Engines For Competitive Success

A product recommendation engine should be a living, breathing model that is refined, improved, and updated as new information becomes available. For example, machine learning techniques can be used to iterate and refine the weights of different factors driving the prediction based on how well the model is working. A/B testing can be used to evaluate a range of model variations, and update the algorithm with the variations that demonstrate the most power to predict positive outcomes. And finally, as new data elements become available, they should be considered and tested for inclusion if they improve the model’s predictive power.

Service providers that build and maintain product recommendation engines to inform the sales process have an edge over their competition. They leverage the valuable data they collect to guide customer interactions, ensuring that the right products and services are always the focus. From the outset, this provides a positive customer experience and improves right sizing, helping to boost sales and increase customer lifetime value.