Machine Learning Will Revolutionize Market Segmentation Practices

Transcription

1 Topline Machine Learning Will Revolutionize Market Segmentation Practices Sponsored by: Qubit Gerry Brown January 2017 Philip Carnelley MACHINE LEARNING OFFERS SIGNIFICANT ADVANTAGES OVER TRADITIONAL METHODS OF MARKET SEGMENTATION The application of machine learning to online consumer market segmentation provides an opportunity for business to consumer (B2C) enterprises to achieve a step change in the accuracy and relevance of their customer communications and customer engagement. Machine learning is a form of artificial intelligence (AI) that enables computers to learn without explicit programming, and is rapidly entering the strategic plans of businesses worldwide as its power becomes recognized. Machine learning uses algorithms that learn from data, such as continuously improving the prediction of future consumer behavior, with increasing levels of forecast accuracy as the volumes of data increase. IDC sees AI as a critical Innovation Accelerator for businesses to drive digitization, which is an essential factor in responding to the threat posed by disruptive innovators that seek to emulate the successes of Uber and Airbnb. IDC predicts that spending on AI software for marketing and related function businesses will grow at an exceptionally fast cumulative average growth rate (CAGR) of 54% worldwide, from around $360 million in 2016 to over $2 billion in 2020, due to the attractiveness of this technology to both sell-side suppliers and buy-side end-user customers. A key marketing use case for machine learning is its ability to measure scientifically and empirically what customers are doing at any point in time. One of the strengths of machine learning is that it automates the market segmentation discovery process, identifying priority customer groups for targeting, highlighting the monetary opportunities they represent, and creating new clusters of interesting potential customers for attention. Machine learning answers the marketer questions: "Tell me what I need to know about the behaviors of our target segments, and what I don't know about customer groups outside our defined segments and where I should act?" Hence machine learning can help B2C marketers to not only optimize the performance of existing target market segments, but also identify new untapped market segments that offer revenue and profit potential. Machine learning frees up marketers to spend more time on the high-value activities such as strategic segment evaluation and activation of segment opportunities, rather than labor-intensive customer data collection, data integration, and anomaly detection all of which machine learning can automate in a real-time and human-error-free fashion. January 2017, IDC #EMEA

2 FIGURE 1 How Machine Learning Changes Everything Source: IDC, 2017 Traditional B2C web site marketing methods are starting to "flat line" their performance is already optimized with little room for further improvement. A/B testing in particular is no longer producing the required performance level for many B2C users. This "glass ceiling effect" is also evident for traditional customer analytic tools used for analysis and reporting of historical data. In particular, predictive customer analytics has shown precious little impact on marketing performance for those wanting to go beyond traditional data mining. The reason is that marketers have been unable to convert analytic insights into monetizable marketing actions and a "marketing performance gap" has opened up between customer analytics and concrete marketing actions. For marketers, customer data collection is often a highly challenging manual process plagued by the complexities of combining multiple spreadsheets and many different data sources. Often such work is completed in an ad hoc manner using many systems and different databases, and may contain misleading statistical false-positives around anomaly detection as well as human error. Such reporting inaccuracies produce flawed decision making and have a negative effect on business performance. In the first instance, the volume, variety, and veracity of real-time behavioral, quantitative, and qualitative customer data, from across perhaps many hundreds of different data sources, makes customer data collection a most daunting task. Machine learning helps particularly where customer data collection is predictable and systematic and creates efficiencies by eradicating the digital marketer's less productive work. Secondly, machine learning can also automate collected data analysis by separating the tangible signals of consumer interest and demand from the omni-present marketplace data clutter and noise. The latter often derails manual analysis work completed by digital marketers. Machine learning excels at handling uncertain and contradictory customer data and places results in a visual, easily consumable format IDC #EMEA

3 Finally, machine learning uncovers customer behavior patterns using automated real-time multidimensional analysis, for example, of web site visitors' browsing behavior, page views, and product purchases. Consolidated personalized profiles are continuously and progressively updated and enriched to provide a true reflection of real-time customer experiences and potential customer needs. SEGMENTS CAN BE AUTOMATICALLY GENERATED OR PREDEFINED FOR MARKETING ACTION Machine learning delivers personal profiles into segmentation "buckets" which can be predefined or automatically machine generated. Dynamic market segments are then sized and prioritized based on the untapped incremental revenue opportunity. However, this capability is not limited to predefined segments, as machine learning can also slice and dice customer data sets to identify potential new segments of customers who are undermonetized relative to their peers. For example, machine learning might identify how the behavior of a region or country varies from the global norm and requires a specific product assortment and pricing mix; or perhaps time-based variations that show that late-night shoppers have a propensity to abandon a cart. Such insights drive marketing action and machine learning can recommend the "next best action," be it an "abandoned cart recovery" communication or the provision of more social media proof points, for example. Segments are created on-the-fly for automated identification of trend patterns. For example, by comparing performance measurements, such as the revenue per visitor (RPV) metric, a new "high conversion" segment might be identified for targeting with a customdesigned marketing communications action. An interesting use case involved an international furniture retailer that spotted that high-value visitors from outside their domestic market were not converting and so placed a "shipping abroad" offer on their web site home page that produced a 25% increase in conversions of foreign visitors. MACHINE LEARNING ENABLES A WIDER CUSTOMER UNIVERSE TO BE ADDRESSED Marketers are obsessed with TAMs total addressable markets but few digital tools provide the level of accuracy required to instill marketers' confidence in the results provided. Hence marketers tend to mine data against a small number of predefined segments (typically 7 to 10 segments), which are often subsets of the real available addressable market. This is due to the high cost of segment discovery and the inability to scale marketing actions across a wider market definition. Machine learning can help to define a statistically valid TAM of digital brand engagement at any specific point in time, adjusting segments based on the continual real-time behavioral feedback of the marketplace, reducing marketing cost to serve and time to market, and improving market reach. The average digital marketer can only hope to scratch the surface of analyzing the breadth and depth of consumer data trails that are left behind by web site visitors. Machine learning can take all this data and make sense of it, uncovering untapped revenue opportunities and enabling the fulfilment of practical action recommendations by digital marketers. Machine learning helps B2C businesses to identify their true market revenue opportunity and drives relevant and differentiated customer segment engagements that seize and optimize incremental revenue opportunities IDC #EMEA

4 FUTURE OUTLOOK: MACHINE LEARNING WILL BECOME EMBEDDED IN CUSTOMER AND MARKET SEGMENTATION It is only recently that the complementary technologies of cloud and Big Data storage and processing have allowed machine learning to be deployed in these types of applications. Furthermore, until recently there has been a dearth of usable tools and applications, thus its use has been restricted to large or specialist organizations that can call on the skills of knowledgeable data scientists to build appropriate models and systems and the business case has therefore been elusive. In the case of market segmentation, this situation looks set to change. Machine learning has the potential to reinvent the art of market segmentation and democratize the use of advanced segmentation tools, as this paper hopefully reveals. What marketers need is a "digital assistant" that releases them from the drudgery of customer data collection, joining up disparate data sets, and finding new segments for monetization within an "ocean" of noisy customer data. Machine learning fulfils this role by automating tasks and by guiding marketers to new segment opportunities and to optimize revenue from existing customer segments. Machine learning offers this promise by smoothing the customer "path to purchase" through the provision of relevant and contextual information and content during different stages of the customer journey. In the future, machine learning will increasingly be embedded in many if not all aspects of digital marketing across customer acquisition, retention, and loyalty management. Customer segmentation is an early market use case for machine learning and could well become the benchmark and role model for future applications. Unquestionably, machine learning's ability to analyze millions of addressable segment possibilities cannot easily be replicated by traditional analytics and BI tools. Enterprises typically serve well their known target customer segments, but machine learning's ability to uncover a plethora of unknown new segment opportunities offers great potential for delivering a step change in new incremental revenue and profit IDC #EMEA

5 About Qubit Qubit is a pioneer in delivering context-driven customer experiences through its digital experience management platform. Enabled by the Qubit Visitor Cloud, the platform allows organizations to act at scale, uncovering and prioritizing untapped opportunities with machine learning and advanced segmentation and personalization technology. Qubit primarily serves the retail, travel, egaming, and finance industries, and powers experiences for 400 million consumers each day for brands including Topshop, John Lewis, Emirates, NET-A-PORTER, Spirit Airlines, Ladbrokes, Thomas Cook, and UNIQLO. Founded in 2010, Qubit has offices in London, New York, Chicago, San Francisco, Paris, and Munich. About IDC International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the information technology, telecommunications and consumer technology markets. IDC helps IT professionals, business executives, and the investment community make fact-based decisions on technology purchases and business strategy. More than 1,100 IDC analysts provide global, regional, and local expertise on technology and industry opportunities and trends in over 110 countries worldwide. For 50 years, IDC has provided strategic insights to help our clients achieve their key business objectives. IDC is a subsidiary of IDG, the world's leading technology media, research, and events company. IDC U.K. 5th Floor, Ealing Cross 85 Uxbridge Road London W5 5TH, United Kingdom idc-community.com Copyright and Restrictions Any IDC information or reference to IDC that is to be used in advertising, press releases, or promotional materials requires prior written approval from IDC. For permission requests contact the Custom Solutions information line at or permissions@idc.com. Translation and/or localization of this document require an additional license from IDC. For more information on IDC visit For more information on IDC Custom Solutions, visit Global Headquarters: 5 Speen Street Framingham, MA USA P F Copyright 2017 IDC. Reproduction is forbidden unless authorized. All rights reserved.