Customers today are more than just customers. Using social media networking, they influence others and reveal their buying interests in products and services. Organizations know that to compete effectively, they must capture as much information as they can about customers and analyze it effectively to discover patterns, trends, and other vital clues. Social media networking activity is generating big data—and these growing sources are the new frontier for customer intelligence.
Read this whitepaper to learn best practices for understanding customer analytics in the age of social media.
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Becoming “customer centric” is a top priority today, and for good reason: as if it weren’t important enough that customers buy products and contract for services, they now do much more than simply buy. Customers participate in social media networks and chat rooms; they write blogs and contribute to comment sites; and they share information through sites such as YouTube and Flickr. Their activities and expressions not only reveal personal buying behavior and interests, but they also bring into focus their influence on purchasing by others in their social networks.
Social media networks have given organizations exciting new channels for marketing and customer engagement. Just as important, however, is that by creating data trails in social media networks, participants generate new types of data that hold great potential for customer insight. Advanced analytics technologies, modern data management and integration systems, and growing “big data” sources are enabling organizations to gain far greater depth and breadth of knowledge about customers, influencers, prospects, and the competitive marketplace. Social media data analysis can expand customer analytics by providing an unfettered, outside-looking-in view of an organization’s brands, products, services, and competitors.
This TDWI Best Practices Report examines organizations’ current practices and future plans for customer analytics technology implementations, with a special focus on how organizations are adapting to the knowledge potential as well as challenges of social media networks. The report offers recommendations for achieving greater return on investment (ROI) from customer analytics processes. This higher return is important because marketing functions are being held more accountable for the effectiveness of campaigns in delivering on financial objectives and their measurable impact on overall business growth. Business functions need customer insights not just for marketing campaigns, but also for informing the organization’s sales, service, support, product development, and other key functions about customer feedback and trends.
Customer analytics, seasoned with insight from social media data, can enable organizations to make faster strides in predicting retention, attrition, and return rates, with the goal of reducing customer churn. Analytics can improve how organizations decide on characteristics for customer segmentation; social media can provide clues to emerging characteristics for definition of new segments. Firms can employ predictive modeling to test and learn from campaigns so that they are able to select the most persuasive offers to put in front of the right customers at the right time.
Speed is a competitive advantage in marketing. If an organization can analyze data faster and feed insights more frequently to its customer relationship management (CRM), marketing optimization, and campaign management applications, it will realize advantages over firms that are locked into seasonal campaigns, do not analyze customer behavior data, or are too slow in analyzing it. This report explores how organizations can use analytics to discover much sooner which message, interaction, or campaign actually had the most influence on triggering a customer purchase rather than attributing it blindly to the last thing that a customer did or saw.
Organizations are thus focused on evaluating and deploying a new breed of analytics and data management technologies to increase the speed of analysis and reduce latency in applying knowledge to marketing actions. These technologies include analytic databases, columnar databases, Hadoop and MapReduce, customer master data management (MDM), and predictive analytics tools. Nontechnical users in marketing and other functions are implementing social media analytics, business intelligence, data discovery, and visual analysis products to allow them to consume insights more easily and explore data on their own.
Customer Analytics and the Social Media Frontier
John Wanamaker, the nineteenth-century U.S. department store merchandiser often called the father of advertising, once famously said, “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.” Today, organizations are not dealing in just “halves”; with multiple channels to choose from for advertising, marketing, and customer engagement, they have complicated decisions to make about where to devote fractions of their resources to achieve the greatest impact. Organizations that are blind to how customers prefer to be engaged in each channel will be less efficient and effective with their marketing and fall short of goals for ROI. It is critical for organizations to build customer knowledge so that they know which actions in which channels are truly the most influential and relevant to a customer’s purchase. They need to integrate information views effectively to support multichannel strategies that require coordinated actions across channels.
With multiple channels to choose from, customers today can make choices and dictate how they want to consume information about products and services. Customers are empowered; gone are the days when a market maker and business leader such as automaker Henry Ford could declare that “any customer can have a car painted any color that he wants so long as it is black.” With customers exhibiting less loyalty and more selectivity about products, organizations need to be smarter. They need timely data and analytics to avoid losing customers to competitors who may be just a click away with products and services that are more in tune with customer preferences. Better customer intelligence is thus vital to more than just the marketing function; product development, services, and other functions in the organization need it.
Customer analytics, the focus of this TDWI Best Practices Report, is about implementing technologies and methods for knowing more about customers’ behavior, their paths to purchasing goods and services, and what actions will engender greater loyalty among those who are most valuable. The goal is to derive accurate information and insights from traditional transaction and service data as well as different types of behavioral data sources so that organizations can better identify, attract, interact with, and retain customers.
Single Views of All Information
Many organizations seek the most comprehensive, 360-degree view of a customer rather than limit their understanding to one source or a diffuse and incomplete picture drawn from disconnected silos of information. They wish to integrate insights from behavioral data sources with those drawn from more traditional data sources such as transactions and service records. They also need to integrate different sources of behavioral data. Some of this data is generated by customers responding and reacting to an organization’s activity, including product introductions and services, sales, and fulfillment processes through its own channels. Increasingly important, however, are other data sources generated by customers’ independent activity in online social media networks, or offline as they visit physical stores and view messages on media such as billboards.
For years, leading organizations in retail, telecommunications, hospitality, gaming, financial services, and other industries have been implementing data mining tools and methods to discover buying patterns, affinities, and other indicators so they can be predictive about—and proactive with—customers. Along with data mining tools, additional technologies that have played a big role include enterprise data warehousing, business intelligence, and customer data integration (often called “customer MDM”). Organizations are now adding newer technologies, such as Hadoop and MapReduce, text analytics, and specialized tools for social media listening and activity analysis to discover insights from unstructured and semi-structured data.
Customer analytics has risen in importance as businesses have grown in size and diversity and struggle to understand and anticipate customers’ desires and concerns. Bigness has come at the cost of intimacy; although organizations may not be able to replicate the mom-and-pop store where customers are neighbors and known by name and reputation, they can restore some of that intimacy if they use information effectively. Strategic initiatives for one-to-one marketing, micro-marketing, finer segmentation, mass customization, and more depend on data insights from customer analytics.
Stepping into the Age of Social Media
The new frontier for customer analytics is social media. The advent of social media networks and related commenting and information sharing zones is a revolutionary change, full of both potential and challenges. In the social media sphere, customers are influencers, not just generators of sales transactions as seen though point-of-sale and e-commerce systems. Using social media networks, customers can influence each other by commenting on brands, reviewing products, reacting to marketing campaigns and product or service introductions, and revealing shared buying interests. Unlike casual conversations, the commentary and social network connections are recorded and can therefore be analyzed and measured. The result is a data tsunami: the actions and content generated by participants in social media create “big data” sources that are full of potential for tracking and understanding behavior, trends, and sentiments.
The biggest difficulty is filtering out the noise, but not so much that the trends, patterns, and other insights hidden in the raw data are lost through aggregation and filtering. The need to analyze raw, detailed data is a major driver behind the implementation of Hadoop. Organizations need an unstructured place such as Hadoop files to put all kinds of big data in its pure form, rather than in a more structured data warehousing environment. The reason is that depending on the intent of the analysis, what might be considered just “noise” in the raw data from one perspective could be full of important “signals” from a different perspective. Discovery, including what-if analysis, is an important part of customer analytics because users in marketing and other functions do not always know what they are looking for in the data and must try different types of analysis to produce the insight needed. They need to filter out noise, yet not be limited to standard, expected types of information such as what they might receive in a BI report.
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