The personality inventory known as the “Myers-Briggs Type Indicator” is premised on the idea that different people process information in different ways. Some people are verbal processors. Others are visual processors. Some people respond more strongly to images — others to text.
The most effective way to learn is to map the different learning styles to each individual’s characteristic strengths, according to Myers-Briggs. Moreover, when used in combination, the different styles become reinforcing components of the overall learning experience.
By the same token, different consumers have different channel preferences. Some prefer receiving brand-related messages via the mobile channel, for example, while others prefer email or direct mail. Some consumers like to interact with companies via the call center, while others choose to engage primarily through social media. When used in combination, the different channels can become reinforcing components of the overall customer experience.
The key to effective multichannel campaign execution is collaboration. Campaigns should “collaborate” among channels — give consumers a QR code to scan in a print ad to receive information through video and text, for example, or promote a URL through a direct mail piece. The channels should be complementary. Each channel’s unique strengths should work toward the common goal of attracting, retaining and increasing the value of profitable customers.
The ability to centrally manage the design, execution and measurement of marketing campaigns across a multitude of both offline and online channels is a big selling point when it comes to multichannel campaign management platforms. Almost half (47 percent) of companies continue to rely on channel-specific technologies to manage campaigns that straddle two or more channels, based on research from Gleanster.
At the same time, Top Performers are more than twice as likely as Everyone Else (69 percent compared to 34 percent) to leverage a single technology to support their multichannel campaign management activities.
Integrated multichannel platforms generally combine the benefits of multiple best-of-breed components — not only channel-specific technologies, but also self-service business intelligence and data visualization tools for “data discovery” and performance reporting. The platforms provide a single sign-on, and the components are designed to work together seamlessly.
The fact that the platforms are cloud-based is important, too, given that the pace of innovation and the diversity of functionality are evolving so rapidly in several online channels. On-premises software requires manual updating and is also generally difficult to customize, which means that organizations are often forced to adapt their processes to the package’s one-size-fits-all frameworks.
Other obvious benefits include Web-based access to data wherever authorized individuals have Internet access, via desktops, tablets or smartphones, and world-class infrastructure and security without hardware or software to maintain. The reduced investment in IT staff, hardware and software matters — especially to smaller companies in an age when capital budgets have been static or shrinking.
To be fair, many marketing platforms are now largely cloud-based. The real differentiator, then, is the integrated nature of the platform. Compare the implementation of a multichannel campaign management platform (ClickSquared’s Cross-Channel Marketing Hub is a great example) to the need to deploy each of the individual components — an email marketing solution, a mobile marketing solution, a social media marketing solution, an outbound call center solution, etc., on a standalone basis. Each solution has a different user interface. Each solution requires separate training and comes with separate support.
Assessing and securing channel-specific campaign management and reporting capabilities from different solution providers can be a cumbersome process. Trying to cobble together a mix of incompatible marketing solutions — if only for the purpose of cohesive campaign reporting — can be an exercise in futility.
Beyond being able to effectively coordinate channels through a unified campaign management platform, marketing improvement largely hinges on the ability to gain a better understanding of customer data — to generate customer intelligence. Invariably, customer intelligence comes from the application of various analytic skills and techniques.
Some of these techniques are relatively new to the marketing arena. Others have been practiced by traditional direct marketers for decades in their relentless efforts to translate customer information into more effective marketing campaign outcomes.
Top Performers view the need to focus on customer data as a strategic imperative, Gleanster research suggests. Analytics allows marketers to understand the drivers of customer loyalty and attrition, and to determine which levers sit at the forefront of the customer purchase decision. Most large enterprises today rely heavily on analytics in their continuous efforts to understand customer behavior at an individual level — and act upon that understanding to deliver the most relevant marketing messages, offers, and customer service treatments at the so-called “moment of truth.”
Of course, analytics is also the basis for any customer retention program that seeks to segment a customer base in an effort to determine the most profitable category of customer.
Customer intelligence relies not just on data analytics, but also on data integration, hygiene and enhancement. This requires the expertise of an operations team skilled in the art and science of customer data management, including the ongoing process of data integration and cleansing. Customer intelligence also depends on the implementation of a centralized customer data repository.
The repository provides the foundation for creating and housing robust, multidimensional customer profiles that include such marketing mix variables as price sensitivity and channel preferences. The repository also provides the basis for customer value management. Without a centralized repository, a company could never know how much an individual customer spends across all of its channels, product lines and geographies, making it impossible to accurately project the lifetime value of that customer relationship.
Without a holistic view of that customer’s purchase history, stated preferences and other personal profile information, a company can never know which specific products and services to cross-sell and up-sell to that customer.
Many companies still lack the ability to systematically identify unprofitable customers. Beyond the technical challenge of connecting customer data silos, the traditional corporate mindset assumes that all customers are profitable, which is rarely the case. Top Performers reappraise the value of their customers on an ongoing basis, either investing in or divesting those relationships that are on the fence in terms of profitability.
Top Performers also focus on predictive modeling, which involves looking at large quantities of historic data, searching for meaningful patterns, and then creating mathematical equations that represent the underlying relationships within the data to forecast future behaviors. Predictive models are often called “behavioral models,” because they may be used to predict the future behavior of a customer.
By enabling companies to instantly differentiate between desirable, less desirable and undesirable customers — and assign different marketing treatments based on their propensity to behave in a certain manner — predictive models allow companies to take actions to increase profitability. These models can rank customers and prospects from “best” to “worst,” not only in terms of their likelihood to respond to a specific type of message, but also through a specific channel or combination of channels.
Customer intelligence means analyzing large quantities of data, examining numerous combinations of variables, and uncovering previously hidden relationships.
For example, an insurance company might ask, “What are the common attributes of customers who purchase life insurance?” Having identified those attributes, it could then score individual customers based on the extent to which their profile information corresponded with the model of, in this case, “the life insurance buyer.”
Customers with higher scores might be sent a special promotion of one kind through one channel; those with lower scores might be sent a promotion of a different kind through a different channel.
Successful cross-channel marketing campaigns combine the science of database management and customer data analytics with the art of creative and strategic campaign development. The right brain and the left brain are complementary and need to work in tandem, just as the different channels are complementary and need to work in tandem.