SYNTASA's Personalization Maturity Index by Kirk Borne, Advisor to SYNTASA TM July 2014

Transcription

1 SYNTASA's Personalization Maturity Index by Kirk Borne, Advisor to SYNTASA TM July 2014 INTRODUCTION: A BRIEF HISTORY OF ONE-TO-ONE MARKETING Marketing is a relatively young discipline, tracing its first significant developments to the early 20 th century. The primary focus of early marketing scholars was to explore and understand the connections between motivations and behaviors in the customer-seller relationship 1. This understanding led to the creation of strategies and tactics for selling more products and services, with little regard for the customer s actual needs and wants. By the 1950s, there was a major shift competition was stiffer, and customers demanded more attention to their needs. Consequently, the marketing philosophy of know thy customer first began to emerge. This perspective had a back-propagation effect on the business in the design of products, pricing, and promotions. We are now seeing another titanic shift in marketing, particularly the insertion of big data and analytics within a digital marketing framework, which is data-driven and customer-centric. For example, it has been estimated that the use of big data analytics could unlock up to $200 billion in new marketing value globally 2. Furthermore, the application of automation to the marketing pipeline can provide even greater benefit e.g., CMOs indicate higher revenue, higher quality leads, and 53% higher marketing qualified lead conversion rates after implementing marketing automation 3. The three principal features of such marketing success stories are digitalization, automation, and personalization. In this increasingly digital world, data collection and data-driven processes are ubiquitous. Automation is key to managing these massive data assets effectively, analyzing them efficiently, and deriving value (and actionable intelligence) from them in a timely manner. But, the real future (and value) of big data is personalization, also called one-to-one marketing or a segment of one. 4 Some have referred to the use of big data in marketing as "the end of demographics". 5 In other words, it no longer makes sense to market or cater to groups of people en masse, but instead each person expects to be treated in a personalized manner (though not necessarily personal) 6. In many aspects of life (including healthcare, education, finance, insurance, and retail), the collected data from an individual can (and will) be used more and more to personalize that person's encounter, experience, and engagement with providers of content, products, and services

2 CAPABILITY MATURITY SYNTASA is pioneering a measurement process that analyzes an organization's unique capabilities and readiness for personalization using the data, technologies, and talent that they have available. The SYNTASA Personalization Maturity Index is a 2-dimensional indicator of an organization s corporate vision (strategic readiness) and availability of technological infrastructure (tactical capability) for "segmentation of one" in their customers' journeys. In the world of big data analytics, there are several emerging standards for measuring Analytics Capability Maturity within organizations. One of these has been presented by TIBCO 7 their six steps toward analytics maturity are: Measure, Diagnose, Predict and Optimize, Operationalize, Automate, and Transform. Another example is presented through the SAS Analytics Assessment 8, which evaluates business analytics readiness and capabilities in several areas. The B-eye Network analytics maturity model 9 mimics software engineering's CMM (Capability Maturity Model) their 6 levels of maturity are: Level 0 = Incomplete; Level 1 = Performed; Level 2 = Managed; Level 3 = Defined, Level 4 = Quantitatively Managed; and Level 5 = Optimizing. The most "mature" standard in the field is probably the IDC Big Data and Analytics (BDA) MaturityScape Framework 10. This BDA framework (measured across the five core dimensions of intent, data, technology, process, and people) consists of five stages of maturity, which essentially parallel the others mentioned above: Ad hoc, Opportunistic, Repeatable, Managed, and Optimized. SYNTASA s PERSONALIZATION MATURITY INDEX Personalization Capability Maturity parallels the Analytics Capability Maturity frameworks within the specific context of data-driven customer-centric one-to-one marketing and segmentation of one. As shown in Figure 1 (the SYNTASA TM Marketing Analytics Stack), personalization is one of several specific use cases (applications) of big data analytics in Marketing. Personalization is certainly related to Targeting, Segmentation, and SEO it is the precision application of small data from a specific customer that is extracted from the big data collection. In the best case, "big data" refers to "all data", so that even the small data from a single customer can provide a "360 view" of that customer, thus enabling personalized services, products, and content to be delivered to the right person, at the right place, in the right context, at the right time. This is Cognitive Analytics 11, based upon the cognitive computing paradigm established by IBM's

3 Watson computer that easily won the game of Jeopardy against the best of the best human competitors Watson used the complete set of "all data" to isolate the best "small data" in order to deliver the correct response in a specific situation. FIGURE 1: The SYNTASA TM Marketing Analytics Stack To assess an organization's Personalization Maturity, SYNTASA TM has developed a survey instrument, consisting of a questionnaire that probes the readiness and capabilities of a business organization and its data assets for segmentation of one. On a scale 1 to 10, the Personalization Maturity Index is measured in two dimensions: (1) How important is personalization in the organization's strategic vision and marketing plans, is there a culture of analytics "within the building", and what is the corporate buy-in? (2) How many of the key technology components does the organization have readily available (including technology, tools, and talent = skilled workers to deploy and employ those technologies)? The SYNTASA Personalization Maturity Index is based on an organization s survey responses to inquiries about its corporate capabilities and readiness across essentially the same five core dimensions that are included in the IDC MaturityScape Framework: intent, data, technology, process, and people. The survey responses are then scored according to their indication of strategic readiness and tactical capability (= the two dimensions of Personalization Maturity). EXAMPLES OF PERSONALIZATION MATURITY Here is an example of a Personalization Maturity survey question:

4 When a visitor comes to your online site, what level of data-informed content delivery is executed? (1) None. Every visitor is shown the same content. (2) Only one feature from the user profile data is used to determine what content to display. (3) Two features from the user profile data are used to determine what content to display. (4) Three or more features from the user profile data are used to determine what content to display. In questions like this, features from the user profile data may include web analytics features (e.g., did they arrive at your site through an AdSense campaign, or did they arrive via natural search?, or which search words did they use to arrive at this page? ), or the features may be contextual (e.g., time of day, geolocation, or are they using a mobile device? ), or the features may be historical (e.g., number of prior visits, number of prior purchases, time elapsed since last purchase). Here is an example of Personalization Maturity: In online data analytics, it is very common for organizations to provide a recommendation service to its site visitors. This recommender engine will analyze prior purchase/visit/search patterns of other visitors {V} against the patterns of current visitor C. Consequently, when visitor C views a particular page or product P1 (or places P1 in their shopping cart ), then the engine will identify another product/page P2 to recommend to C based on the histories of prior visitors {V}. Specifically, the engine discovers unusually frequent co-occurrence of products or pages viewed in the histories of visitors {V} who also viewed/bought P1, and so current visitor C is presented with P2 (since that is something that they may also be interested in viewing and/or purchasing). If a business responds to a survey question about their recommendation services and the survey response indicates that they are not doing this at all, then this particular survey question would be scored at the lowest level 0 of personalization maturity. If the business is doing this but is using only the simplest implementation of the algorithm described above, then they may be scored at level 1 of personalization maturity. Depending on the number of dimensions of product categories and features that the business is using in the recommender algorithm, their personalization maturity score would go higher for this type of question. The highest score (e.g., 10 out of 10) could be achieved if the business is using the latest in recommender algorithm science diversity! In other words, the business offers recommendations that are uncommon, surprising, unexpected, diverse, and in the long tail of interests of prior visitors {V}. By making a truly interesting offer to current customer C, the business is likely to achieve conversion on this visit, as well as retain and capture repeat business from that customer, and make that customer into a loyal fan of the business!

5 These different levels of maturity in the analytics being applied (as in the recommender engine) also correspond to the different levels of analytics maturity: Descriptive, Predictive, Prescriptive, and Cognitive 12. PLATFORM OF CHOICE FOR PERSONALIZATION MATURITY Extracting actionable intelligence and greater marketing value from a business big data collections (whether it is based primarily on web analytics or on other channels) can drive increased revenues through increased customer awareness, acquisition, engagement, conversion, retention, and loyalty. This delivers significant ROI (Return On Innovation and Investment) from one-to-one marketing campaigns in the big data-rich world. SYNTASA offers a platform for Digital Market Automation (Marketing Analytics-as-a-Service) from its data science team that can inform and assist any organization in achieving greater levels of Personalization Maturity with its customers. NEXT STEPS For more information, or to schedule a demo of SYNTASA s Personalization Maturity Index for one-to-one marketing capabilities, please contact: Grant Wagner Vice President of Sales and Marketing Grant.wagner@syntasa.com