Today’s marketing landscape is riddled with more challenges than ever before. The evolution of technology like social media and automatic opt-out preferences has put an increasing amount of power in the hands of consumers, allowing them to control not only who can contact them, but when and how they can be contacted. At the same time, marketers are faced with increasing competition, greater ad fatigue from consumers, higher customer expectations and a proliferation of new media and sales channels.

To succeed in this ever-competitive and challenging landscape, marketers need to focus on engaging customers and prospects with information that is relevant and useful to them as individuals. To effectively do this, marketers must be equipped with a 360 degree view of individual customers and the ability to anticipate customer wants and needs, so that they only reach out with relevant marketing messages. The incorporation of predictive analytics into a customer interaction strategy provides the needed insight to ensure that marketers suggest only relevant products and services that benefit the customer, not the department or organization, thus improving the overall effectiveness of their marketing efforts. Basic customer relationship management systems, widely employed by customer-facing organizations, do a great job of capturing a customer’s interaction history with the company, but they fail to provide the additional information that allows marketers to truly understand the individual customer’s needs .

Additionally, even with a CRM system in place, the quality of the customer/employee dialogue still depends on the staff and the extent of their training. Incorporating real-time decisioning software, driven by predictive analytics, into an existing CRM system changes the game. Intelligent analytics provides the ability to determine a customer’s propensity to respond to an up-sell or cross-sell offer and, with real-time decisioning software, helps guide the interactions, advising precisely how to respond to customers and adapting to the dialogue as it develops.

What Exactly is Real-Time Decisioning?

Essentially, real-time decisioning does exactly what the name implies – it helps employees make the best marketing decisions in real time. Many organizations are now turning to real-time decisioning technologies to create relevant and bespoke interactions between their brands and their customers. Forrester Research predicted that it will become the fastest-growing segment of all enterprise marketing segments, increasing by 26 percent annually through 2013. As with any emerging technology, there is still uncertainty as to how best to implement and integrate real-time decisioning tools into existing marketing efforts.

Real-Time Decisioning in Practice

As a general rule, real-time decisioning technologies fall into three categories: rule-based, product-based and customer-based. Each of these three has its unique benefits, limitations and optimal application. The process of determining which methodology represents the most effective solution for a business’s real-world scenarios often causes significant confusion.

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 it is an effective choice for organizations seeking to automate a well-defined best practice.

Rule-based solutions are well-suited to organizations with basic, straightforward decisioning requirements. However, there are a number of limitations that should be kept in mind when requirements get more complex. For example, when administering such systems, marketers will often need to create and experiment with different rule sets in order to determine which best matches their specific requirements, and it becomes extremely difficult to create complex rules or predict results when many rules overlap. In addition, rule-based solutions generally do not take into account any events or activities that may have occurred immediately before the interaction. And, they do not address longer-term issues such as customer satisfaction, retention, or lifetime value. Thus, without the ability for rule-based decisioning to obtain a contextual understanding beyond the initial business-rule trigger, these engines have the potential to harm the long-term client relationship by suggesting inappropriate offers.

As such, before selecting a rule-based engine, marketers must ask themselves this question: “Can I apply simplified best practices without losing sight of longer-term considerations?” Given their functional limitations in this capacity, rule-based solutions are best for situations when the number of potential outcomes is very 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, in acquisition situations where historical customer information is not available.

Product-Based Real-time Decisioning

The second real-time decisioning option to consider is a product-based engine. This type of solution 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 have the ability to sift through a large amount of transactional data and product offerings, which allows them to out-perform rule-based engines, especially when contextual information is abundant and ever-changing. This feature makes them ideal for single-channel, Web-only deployments – for example the ubiquitous “customers who bought this also bought…” message.

The drawback of product-based solutions 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. Like rule-based engines, product-based engines can also prove detrimental to individual customer relationships when short-term gains override long-term satisfaction and profitability goals. In fact, a common pitfall with product-based solutions is that they tend to drive marketers away from making long-term, customer-focused marketing decisions.

Product-based engines work very well to drive promotional sales within specific channels, 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 option for real-time decisioning engines is a customer-based model that leverages individualized marketing strategies to drive customer satisfaction, retention and long-term value. This model helps deliver an integrated, cross-channel experience that is highly relevant for individual customer 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, so they can best coordinate communications across online and offline channels. 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 type of engine leverages mathematical algorithms built offline by statisticians directly into and across operational systems. It expedites implementation and allows for much greater command and control of marketing efforts. This produces a safer deployment, as experienced analysts and marketers can test and validate their models in staging areas prior to going live in production.

Similar to the other two engine options, customer-based engines take full advantage of information gleaned from real-time events. What sets them apart is their ability to also leverage a company’s full understanding of each customer, driving retention and lifetime value. They are more suited to large B2C organizations in the financial services, telcom or retail sectors, where understanding the customer’s personal data, transactions and channel of choice would enhance the customer experience and increase levels of satisfaction. However, customer-based engines rely on experienced statisticians to build and refine models. So if organizations don’t have this resource in-house, they may need to look outside of their organization to help implement. Given the long-term view they provide, they are usually well-worth the investment. Customer-based models are more commonly optimized for deployment across multiple channels and customer touchpoints. They act, in effect, as a central customer intelligence hub, which can connect the customer experience across these channels both dynamically and contextually.

Choosing the Right Real-Time Decisioning Engine

As each of these three technologies has its own set of benefits and challenges, an organization must always consider its long-term strategic goals when deciding which option to implement. No single solution can be seen as a one-size-fits-all answer, but sustainability can be achieved with a sensible approach to people, process and desired outcomes, a practical solution that fits the brand, respects the customer and achieves a balance between short-term performance and long-term business..

In whatever capacity it is utilized, real-time decisioning software, fuelled by intelligent analytics, is a powerful tool for organizations trying to differentiate themselves and their services in an increasingly competitive and challenging marketing landscape. Given today’s marketing obstacles, an added advantage when engaging with customers can yield tremendous results for marketers, improving cross-sell and up-sell opportunities, and creating a personalized and memorable experience for the customer.

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