- As marketing professionals try to take advantage of a vast social and digital landscape, new marketing methodologies have emerged that emphasize engagement across social and digital channels.
- Three marketing methodologies have emerged that leverage social and digital channels to create better and more personalized engagement with consumers: multi-channel marketing, omni-channel marketing, and consumer decision journeys.
- Multi-channel marketing places the same message – but in different forms – into many social and digital marketing channels at once. This engages consumers in the channels they choose, rather than those the marketing professional chooses.
- Omni-channel marketing delivers messages via content across channels as well, but one at a time. The channel of choice depends on which channels the customer is paying attention to at the moment. As consumers move from channel to channel, they encounter content that reinforces buying behavior.
- Consumer decision journeys envision buying as a path that individual consumers embark on, in which they make decisions and try to garner information along the way. Marketing professionals use highly personalized content to influence the consumer to purchase their products.
- Two elements make these types of marketing possible: analytics and personalized content. Analytics detects signals that signify changes in behavior or decisions that are being made. Personalized content provides a method of conveying messages to the consumer in a way that is personally relevant.
The goal of marketing is to create an environment where someone, a consumer, wants to pay money for something of value. While usually a product or service, the object of value may be a sense of wellbeing, such as the sense one gets from charitable giving. All marketing strategies are subordinate to this main goal.
Marketing professionals use a number of strategies and techniques to create this environment. Increasingly, these strategies hinge on the use of digital and social media channels. Older marketing channels such as broadcast TV and radio, direct mail, and print advertising are not disappearing. It is emphasis that is changing. More attention is now spent on consumer engagement through social media, websites, and mobile devices. Consumer engagement is fostered by the use of personalized content, including videos, graphics, offers, and coupons; general content such as blogs and websites; and, most importantly, interactions through social media.
Having a social and digital marketing strategy only pays off when an organization can execute on that strategy. This has generated a broad need for tools that aid in the execution of social and digital marketing strategies. This need has, in turn, led to a dizzying array of products and suites that touch on a variety of marketing activities.
Modern marketing strategies
Marketing has evolved considerably from pre-Internet and pre-Facebook days. In the past, marketing professionals could focus on a few key outlets to carry their messages; today’s marketing professional has an array of channels and methods with which to transmit messages and drive consumer engagement.
Multi-channel marketing pushes the same message out on multiple digital channels simultaneously. The content that carries the message may differ dramatically from channel to channel, but the message is the same. For example, a company may create a webpage, a Facebook page, a Tumblr page, a blog, and a YouTube video about a product or brand, and use Twitter posts to direct attention to those channels, as well as to deliver the same message. Multi-channel marketing attempts to blanket a target market segment with a message, so that it is inescapable.
Omni-channel marketing, also called cross-channel marketing, engages customers across multiple channels, but one at a time. It is a customer-focused, rather than segment-oriented, methodology. Omni-channel marketing recognizes interest in a product on one channel, such as a visit to a webpage, and tracks consumers as they switch to other channels. As the consumer moves from channel to channel, there are personalized messages and content for ready for them. No matter which channel the consumer chooses for the next interaction, they encounter messages encoded in relevant content. In addition, there are prompts within each channel’s content that drive consumers closer to a place they can buy the product. For example, a consumer may see a mobile ad for a product and click on it. They are then taken to a mobile-oriented web page with more detailed information. If the consumer later visits the web page through a browser, more content will be waiting for them, as well as links to videos and blogs. At each step of the way, in each channel, an ecommerce solution will provide them an opportunity to buy.
Consumer Decision Journeys
The consumer decision journey is a marketing concept that views the movement toward a purchase as a journey or path that the consumer follows. This concept improves on the traditional sales funnel, in which consumers move in a linear path from awareness to purchase. Consumer decision journeys recognize that a purchase is not a clean, straight path but more like a game of chutes and ladders. There are leaps forward and steps backward as consumers switch channels, rethink previous decisions, and seek out more information. The consumer decision journey strategy attempts to understand where on the path to purchase the consumer is at the moment, predict where they will go next, and provide content and engagement that moves them towards purchase and, ultimately, brand loyalty. Unlike omni-channel marketing, content and messages will continuously change in response to consumer signals, even on the same channel.
The Role of Analytics
Multi-channel marketing, omni-channel marketing, and customer journeys rely on two central capabilities. The first is comprehensive knowledge of the consumer. It’s not enough to know which segments a consumer belongs to and the characteristics of those segments. New marketing requires a deeper understanding of the consumer, including where they are, where they may go, what their habits are, how they feel at the moment, and what they are likely to do or not do next. The ability to accurately predict consumer behavior is the single greatest need for modern marketers.
The second capability important to new marketing is extreme personalization. This is especially true when adopting a consumer decision journey model. The point of modern marketing is to deliver relevant messages via content to a consumer at the right place and moment so as to influence the consumer toward a sale. For that to be effective, content needs to be highly customized to the individual consumer at a point in time.
Data and Signals
How does a marketing professional gain a deep understanding of a consumer? How can they know what a consumer wants or if supposedly relevant content is actually right for an individual, or even a market segment? There are two ways: personal interactions and data. The first works well for sales professionals, whether retail sales associates or field sales representatives, who can deal with consumers on a one-to-one basis. This is impractical for marketing at scale. Instead, marketers need data to help them make decisions and interact directly with consumers. Traditional methods, such as focus groups and surveys, are still valid ways of understanding a market or market segment. However, they don’t help as much when trying to market to individual consumers through digital and social channels, especially at scale.
This is where big data and analytics helps. Since data is at the heart of these marketing solutions – data about markets, products, and most of all, consumers – then a data model that explains and predicts consumer behavior empowers the marketer to interact with large numbers of consumers as if they were individuals.
Personas, Predictions, and Signals
At the center of multi-channel, omni-channel, and consumer decision journey marketing is a data-driven representation of the consumer called a profile or persona. Personas present a view of the consumer within the context of a product or brand.
The term “persona” is, unfortunately, used in different ways by software vendors. Traditionally, a persona was a description of a person as a member of a market segment. A persona applied to groups of people, but detailed their common characteristics so that a group could be treated as a single person. It’s just as common now for persona to represent a set of characteristics of an individual that related to their buying behaviors.
A key enabler of effective personas is predictive analytics. Predicative analytics creates statistically based scores that represent the likelihood that a consumer will behave in a certain manner when confronted with a trigger. For example, a consumer may be scored on their propensity to buy a certain product when sent a digital coupon. Using personas with scoring allows the marketing professional to predict the type of content that will generate a desired behavior in a consumer.
Creating accurate scores for personas requires significant amounts of data about consumer interactions and transactions with the product and company. Analysis of data from social media, such as Facebook or Twitter, helps surface additional information about consumers. This information helps to determine if there are life changes or evolving attitudes that will affect their propensity to buy.
This same data – social media, digital interactions, and transactions with a vendor – can reveal signals that indicate a change in thought or behavior. Traditionally, marketing professionals look for signals that indicate a shift in a market or segment. With new analytics platforms, it is becoming increasingly possible to detect individual consumer signals, or at least signals from a small group of consumers.
It’s Not Magic
Marketing analytics is not all-knowing magic, but rather an inexact science. One of the most difficult problems that still persists is how to identify an individual across channels. Most consumers have presences in several different digital and social channels, as well as data from human-to-human transactions such as CRM records. Unfortunately, it is not always possible to determine if all forms of social and digital presence represent the same real person. A certain degree of guesswork is involved in trying to determine if multiple personas actually represent a single distinct person. At present, it is not unusual for a consumer to have multiple personas within the systems of a single vendor.
Subsequently, analytics are best used as a guide to, but not a replacement for, human decision making. Analytics can help drive simple, automated decisions within marketing automation systems. Complex decisions and processes, such as campaign planning, need human interpretation of the data. That interpretation is predicated on a full understanding of the underlying assumptions and model that power the analytics.
Overall, there is a greater need for analytics that can process, in real-time, an array of information from internal systems such as customer relationship management systems, web sites, mobile devices, sensors, point of sale systems (especially in retail stores), and social media. The implementation of new marketing strategies affects the IT and the marketing professional differently, though.
For the IT Professionals
Data management, integration, and normalization is the most important part of enabling the analysis of large amounts of data for marketing. Models, designed by data scientists or using self-service data mining applications, need to have data available that is already cleansed, masked, deduplicated, deidentified, and enriched. It is important to remember that data that supports marketing efforts comes from a wide variety of sources. It must be rationalized before it can be useful to marketing professionals.
The infrastructure also needs to be in place to process large amounts of unstructured, semi-structured, and transactional data in real-time or near real-time. New marketing techniques, especially consumer decision journeys, rely on the ability to detect signals and immediately act on those signals. Typically, this means distributed processing environments, such as Hadoop, or in-memory databases, such as SAP Hana, IBM DB2 Blu, or Oracle Database In-memory. Maintaining this infrastructure can be difficult and expensive; it makes sense to do so only if you are planning on using big data and analytics for other purposes across the company. Otherwise, cloud solutions are mature enough and more cost effective.
It’s important to remember that analytics is only one aspect of new marketing. The ability to engage consumers and respond to market signals – to do something with the data – is even more important. Omni-channel marketing and consumer decision journeys require that the analytics feed and drive marketing automation. Not only is there a need for data integration, but also for systems integration. In heterogeneous software environments, achieving the desired degree of integration between disparate marketing automation software systems, especially cloud-based products, will be a challenge. Attention should be paid ahead of time to the ecosystem and developer environment, especially REST APIs. Depending on the number of software elements involved, comprehensive suites of pre-integrated products may prove to be more cost effective than so-called “best of breed” implementations that require extensive integration.
For Marketing and Sales Professionals
Marketing and sales professionals attempting to implement multi-channel and omni-channel marketing and consumer decision journey methodologies may find the sheer number of software products necessary to carry them out overwhelming. The natural reaction might be to start with one marketing automation product, such as email marketing or social listening, and branch out from there. This approach needs to be taken cautiously. Buying piecemeal can lead to problems later if various products don’t interact together well. This does not mean that it is necessary to buy big marketing suites or ignore powerful but independent products. It does mean that how the parts work together matters. Understanding the way software products interact is a key feature to be considered upfront.
Since analytics sits at the heart of new digital and social marketing, it should be the first product that a marketing professional considers. Unfortunately, this is the least likely scenario. Typically, a marketing team will look for a tactical solution to a problem – social media publishing for example – and then implement additional pieces as budget and need arises. Analytics, especially the type of analytics that creates customer personas, are expensive relative to other marketing automation systems. However, analytics can enhance all other digital and social marketing functions, enabling them to be more efficient and effective. Even if the analytics platform is a future purchase, marketing professionals need to consider how they will fit analytics into their marketing efforts from the beginning.
Finally, moving from multi-channel to omni-channel to consumer decision journeys will require additional investments in automation. It is possible to begin with a multi-channel approach and extend functionality slowly, until the organization is capable of supporting a completely personalized approach, such as consumer decision journeys. However, decisions made early on will affect the ability of the marketing team to be able to evolve to consumer decision journeys.
Social and digital marketing has made the marketing landscape more complex, but has opened up new opportunities. Software tools enable marketing professionals to easily take advantage of new channels and create personalized content. The ultimate goal is one-to-one marketing that follows the consumer through the entire process of awareness of a need or want to a purchase – the consumer decision journey.
At the heart of these new social and digital marketing methods is analytics. Understanding the market, products, and especially consumer behavior is the only way to ensure that a marketing team can effectively find, interpret, and act on signals that customers are generated.
Recent Related Neuralytix Research
Neuralytix wants to thanks the following academics and software vendors for providing information for this report.
Amy MacMillin, the L. Lee Stryker Assistant Professor of Business Management at Kalamazoo College. Professor MacMillin provided important insights into the state of the art of marketing, especially consumer marketing.
The following vendors provided briefings for this paper, helping us develop an expansive view of the market.
- Adobe – Adobe Marketing Cloud
- SAP – SAP HANA
- Oracle – Oracle Social Cloud, Oracle Marketing Cloud, Oracle Data Cloud, Oracle Database In-Memory
- Blab – BladPredicts
- GaggleAmp – GaggleAMP Amplify, GaggleAMP Distribute
- TIBCO – TIBCO Engage
- NextPrinciples – Social Marketing Automation
 Throughout this paper, the term “consumer” will be used instead of customer. In this context, a consumer is anyone who potentially may buy something; It is not limited to business-to-consumer or retail situations. The lines are increasingly blurry, especially in the digital marketing world, so thinking of everyone as a consumer is more effective. A customer is a more specific term, and describes someone who has actually purchased something.
 A more detailed explanation of the consumer decision journey concept can be found at http://www.mckinsey.com/insights/marketing_sales/the_consumer_decision_journey