In the first segment of this two-part series on predictive analytics, I discussed the potential of analytics for providing a better ROI for marketers as well as how to select the right databases to analyze and what to do with them once they were identified. McKinsey & Company partners Jonathan Gordon, Jesko Perrey, and Dennis Spillecke report:
“Some companies are already turning that Big Data promise into reality. Those that use Big Data and analytics effectively show productivity rates and profitability that are 5 – 6 percent higher than those of their peers. McKinsey analysis of more than 250 engagements over five years has revealed that companies that put data at the center of the marketing and sales decisions improve their marketing return on investment (MROI) by 15 – 20 percent. That adds up to $150 – $200 billion of additional value based on global annual marketing spend of an estimated $1 trillion.” [“Big Data, Analytics And The Future Of Marketing And Sales,” Forbes, 22 July 2013]
Andrew Gill, CEO of Kred, was first impressed with the potential of predictive analytics when he saw a presentation about how law enforcement organizations were using Big Data to predict potential criminal activity and then used those predictions to make arrests leading to convictions. It convinced him that “the marketing community can use cues from social big data and purchase history big data to predict future purchase patterns.” [“Using Big Data to fight crime and predict what products consumers might purchase in the future,” London Calling, 4 June 2013] He concluded:
“I believe that if individual brands start to harness the power of big social data (and that means becoming a social business), then they can start to pull ahead of their competition. Angela Ahrendts, CEO of Burberry, was quoted in a Capgemini consulting report recently as saying ‘Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.’ Placing a bet on big data is not for the feint hearted. Those brands that will lead the big data race have already started though.”
Alex Bulat offers “some practical ways of applying predictive personalization.” [“Predictive Personalization as a Way to Increase Conversion,” Template Monster Blog, April 2013] His first suggestion is to “provide relevant content.” Targeted marketing (i.e., the implementation of predictive personalization) is all about providing the right offer to the right person at the right time in the right circumstance. In other words, it involves both content and context. Concerning content, Bulat writes:
“Website personalization simplifies the search of the content. This technology adequately identifies key characteristics of each visitor, and categorizes them based on predefined rules. At the same time, visitors feel that website is personalized and enjoy the benefits of ‘noise reduction’ (irrelevant information) and see only most interesting content.”
Targeted marketing, of course, involves more than website content. It also includes targeting advertising. On that subject, Bulat writes:
“This technique means building up ads depending on customer’s needs, based on the history of their interaction with the website. It sufficiently increases customer satisfaction and leads to an increase of conversions. While the history of user interaction with the site accumulates, this data can be used to develop unique, relevant offers in [the] future, as well as group visitors with similar interests.”
Bulat believes that “all personalization techniques can be divided into two categories.” Those categories are:
1. Rule-based and user segment personalization.
2. Personalization based on predictive analytical algorithms.
Concerning rule-based and user segment personalization, Bulat writes:
“This type [of personalization technique] is based on the rules (i.e., the practice of using history data, behavioral data and environmental data for creating unique proposals based on those predefined rules). Typical personalization rule takes the following form: ‘If a visitor makes a follow-up, show the X offer.’ One example of easily tracked and segmented client’s characteristics is geographic location. If a customer visits the site selling cloths in New York, user will be offered personalization ads based on his IP address and will see coats and jackets, but if the IP address belongs to Las Vegas they will be offered sandals and slippers.”
The location-based example that Bulat provides is a good example of why context as well as content matters. However, some location-based “personalization” has created angst for the companies using it. As I noted in a previous post, Staples, the office supply company, became the poster child for this kind of personalization strategy when Jennifer Valentino-DeVries, Jeremy Singer-Vine, and Ashkan Soltani revealed that “the Staples Inc. website was displaying “different prices to people after estimating their locations. More than that, Staples appeared to consider the person’s distance from a rival brick-and-mortar store, either OfficeMax Inc. or Office Depot Inc. If rival stores were within 20 miles or so, Staples.com usually showed a discounted price.” [“Websites Vary Prices, Deals Based on Users’ Information,” Wall Street Journal, 24 December 2012] The article noted that Staples wasn’t the only culprit, other companies mentioned included: Discover Financial Services, Rosetta Stone Inc. and Home Depot Inc. The revelation was generally met with consumer outrage. The reporters were quick to point out that “offering different prices to different people is legal, with a few exceptions for race-based discrimination and other sensitive situations.” On the subject of personalization based on predictive analytical algorithms, Bulat writes:
“This presumes the use of mathematical systems to monitor visitor behavior to develop predictive models and deliver most relevant content for each visitor. In contrast to the targeting strategy based on rules, algorithmic targeting creates and connects larger, and potentially infinite number of computer-generated micro-segments all of which develop when the model learns.”
Cognitive reasoning systems are being developed that take advantage of machine learning. As these systems mature, predictive analytics will undoubtedly play an ever-larger marketing role. Nevertheless, Bulat cautions, “In behavioral targeting there is no such option as ‘set and forget’. All targeting efforts should … be checked at regular intervals and periodically compared to the control group (which was not personalized to verify the effectiveness of your efforts).” Brian Kardon, Chief Marketing Officer for Lattice Engines, agrees that predictive analytics will become more important. “Right now,” he writes, “virtually all of our marketing data is backward-looking. Clicks, Web visits, open rates, downloads, and tweets all happened in the past. What if we could take this data and use it to predict what customers were going to do next?” [“Predictive Analytics: The Power Behind Next-Gen Marketing,” CMO.COM, 14 August 2013] He continues:
“It is not science-fiction. Right now, the marketing organizations at companies such as ADP, Dell, SunTrust, and Microsoft are doing just that. They are using a variety of statistical techniques to analyze current and historical data to make predictions about the future. It’s called predictive analytics. … Fields as diverse as baseball, insurance, national security, logistics, and (thank you, Nate Silver) presidential elections can now be predicted with stunning accuracy.”
Kardon believes that “predictive analytics is gaining traction for three main reasons.” The first reason is that so much data is being created. He writes:
“Simply put, until recently we didn’t have enough marketing data to confidently predict the future. The amount of data the world produces every two days is equal to all the data produced from the beginning of civilization up to 2003. Today, companies and individuals are spewing out massive amounts of information in social networks, on the Web, and in internal systems (such as CRM and purchase histories). The sheer volume presents an unprecedented opportunity for businesses to gain insights on current and future buying behavior.”
The second reason that predictive analytics is gaining traction is that new technologies are being developed every day to take advantage of Big Data. Kardon explains:
“Advances in technology now allow us to cost-effectively capture, store, search, share, analyze, and visualize data. There have been giant technological advances in computer hardware–faster CPUs, cheaper memory, and massively parallel processing (MPP) architectures. New technologies (Hadoop, MapReduce, and text analytics) can process both structured and unstructured big data. Today, exploring big data and using predictive analytics is within reach of more organizations than ever before.”
Kardon’s final justification for why predictive analytics will find greater future use in marketing has to do with “the democratization of the math.” He explains:
“Until recently, big data and predictive analytics were almost exclusively the domain of highly skilled data scientists. Today, software makes even the most exotic of techniques within sight–from simple linear and multivariate regression to classification and regression trees (CART), conditional mutual information algorithms, random forests, and neural networks. While the range of statistical techniques had widened, the availability of graduate students and software has made it more accessible to more organizations. You do not need a small army of PhDs, but you will need to have some familiarity with these methods.”
Kardon concludes, “The next generation of marketing leaders will be those who effectively harness the power inherent in big data, and the early adapters are already embracing predictive analytics. If you were an early adapter of marketing automation, then I predict that you’ll also be an early adapter of predictive analytics.” In the future, don’t be surprised when you’re shown an offer for something you didn’t even know you wanted, but, once you’ve seen it, fall in love with. That’s the power of predictive analytics.