Providing an exceptional customer experience is essential if you want to build customer loyalty and increase revenue. But today’s marketers face a plethora of challenges that can prevent them from connecting with their customers in an effective and meaningful way.
What’s more, the development of technology such as automatic opt-out preferences has put an increasing amount of power in the hands of the consumer. This allows customers to control not only who can contact them, but when and how they can be contacted.
Add to that increasing competition, greater ad-fatigue, higher customer expectations, and a proliferation of new media and sales channels, and you’re faced with an obstacle-ridden marketing landscape.
So how can marketers differentiate themselves from the rest of the crowd and interact with their customers in a meaningful way?
Focus on engaging customers and prospects with information that is relevant to them as individuals. To effectively do this, you need a 360⁰ view of each individual customer, as well as the ability to anticipate their wants and needs, so that you only approach and respond to them with relevant messages.
Taking this type of care with each customer improves the overall effectiveness of marketing efforts. Incorporating predictive analytics into a customer interaction strategy provides the insight to ensure that merchants suggest only relevant products and services that benefit the customer, not the department or organization.
What is real-time decisioning? It’s software that helps employees make the best marketing decisions in “real-time,” throughout conversations with customers. The technology enables merchants to create relevant and bespoke interactions between their brands and their customers.
There are currently three different types of decisioning engines available: rule-based, product-based, and customer-based. Each has its own set of opportunities and challenges, and determining the most effective engine for your business depends on your specific marketing demands.
Rule-based real-time decisioning
A rule-based decisioning engine works on a simple “if/when” principle, deploying predefined business rules in order to apply a best practice to a specific isolated event. It is the easiest and cheapest of the three technologies to deploy, and an effective choice for organizations seeking to automate a well-defined best practice.
This type of engine is best when there are straightforward decisioning requirements, but it lacks the ability to obtain a contextual understanding beyond the initial business-rule trigger. Given their functional limitations in this capacity, rule-based solutions are best for situations when the number of potential outcomes is limited, when historical and contextual information is not available or of little use and when tight guidelines can be created to limit collateral damage.
For example, it’s helpful in acquisition situations where historical customer information is not available.
Product-based real-time decisioning
A product-based engine is well suited to drive promotional sales within specific channels when there are a large number of potential outcomes or when complex business rules exist. With this type of technology, the analytic models are fully embedded within production systems, business rules are applied to limit and guide outcomes, and algorithms are developed and redefined by automated, self-learning applications in real-time.
Product-based engines can sift through a large amount of transactional data and product offerings. This allows them to out-perform rule-based engines–especially when contextual information is abundant and ever changing.
The drawback of this engine is that they analyze anonymous transactional data, which produces anonymous transaction-oriented recommendations. As a result, their performance is poorer within broader, customer-focused, cross-channel deployments.
Product-based engines work well to drive promotional sales within single channel Web-only applications, but they are less effective in environments where historical, multichannel customer data is essential for selecting the best course of action.
Customer-based real-time decisioning
The third engine option is a customer-based model that uses individualized marketing strategies to drive customer satisfaction, retention and long-term value. Customer-based decisioning leverages a longer-term view of the customer, pulling data from all systems across all points of interaction with prospects and customers.
This model helps deliver an integrated, cross-channel experience that is highly relevant for individual customers’ needs. It allows organizations to move beyond simply determining which product or service to offer during a specific interaction and helps them determine how, when, and even if each customer wants to be contacted, allowing them to best coordinate communications across online and offline channels.
The downside of customer-based engines is that they rely on experienced statisticians to build and refine them, as they leverage mathematical algorithms that are built offline. So if resources aren’t available in-house, companies may need external help with implementation. That said, given the long-term view these engines provide, they are usually worth the investment.
The final difference is that this type of model is more commonly optimized for deployment across multiple channels and customer touch points. The system in effect acts as a central customer intelligence hub that can connect the customer experience across these channels, both dynamically and contextually.
No single solution can be seen as a one-size-fits-all answer, but incorporating real-time decisioning into a marketing strategy can help improve communication with customers and improve their overall experience.
Given today’s marketing obstacles, any advantage when engaging with customers can help increase in revenue and customer life-time value.
Mark Smith is executive vice president of sales and marketing at Portrait Software, a provider of customer interaction management software.