Tracking today s online consumer How DATA MINING helps marketers uncover hidden patterns, build customer engagement, and drive repeat sales

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1 Tracking today s online consumer How DATA MINING helps marketers uncover hidden patterns, build customer engagement, and drive repeat sales Half the money spent on advertising is wasted; the problem lies in figuring out which half. It s a quote attributed variously to US merchant John Wanamaker and to English industrialist William Lever, and whoever said it was correct at the time. But increasingly, digital technology is solving that problem. Data mining has been especially helpful in this quest. Data mining explores patterns, trends, and relationships hidden inside different forms of data. Where marketing is concerned, it looks for patterns in campaign results, of which there are two kinds: 01

2 Trackable results come from communications that leave tracks, such as a direct marketing campaign that uses a unique source code printed on a piece of mail (paper or electronic) and sent to a target customer. When the customer makes a purchase, the sale is attributed to that campaign. Non-trackable results are generated by a customer who walks into a retail store and buys something a buyer who was certainly influenced by one or more marketing messages. More often than not, marketing looks at communication efficiencies through trackable results only. Using statistical modeling and analysis, we can now make highly informed guesses about which communications influenced the purchasing decision. Data mining answers two questions of crucial importance to marketers: How did different advertising and marketing exposures interact to produce a buying decision? And, is the marketer spending the right amounts of money at the various consumer touchpoints to produce the desired, and indeed, the optimal outcome? Even if current marketing efforts are satisfactory, might the right adjustments produce the same results at a lower cost or better results at the same cost? Data mining helps identify where potential customers are in the buying cycle, and lets marketers customize messages to those buyers at their point of maximum receptivity. Today as never before, technology enables us to construct a model of a company s target consumer that captures multiples dimensions of her life, says Li Hong, Senior Partner and President of FleishmanHillard in China. We can find out who she knows by looking at which social media sites she visits. We can find out what she does at 8:00 a.m. vs. 11:00 p.m. by watching what she clicks on at these hours. In this way, we know what she s receptive to, and when, so we know what to show her, and when to show it. For example, we may learn that she doesn t respond to TV commercials, so we ll make sure she sees product placements or branded content. Maybe we learn that she doesn t consume media passively, so we hit her with something 02

3 interactive. Using the latest data analytics technology gives us an information-rich picture of the target consumer. And because there are others just like her out there, we can help clients build segments or personas for their target consumers and refine them over time. Data mining 101: Techniques you should know about Tactic Definition Sourcing analysis At the initial customer acquisition stage, when companies may be using contact lists for lead generation, analytics can optimize targeting criteria, helping companies identify the types of lists or publications that work best for a particular type of product or consumer. Campaign management and reporting Analytics can be used to test marketing campaigns and see which promotions or offers are most effective (this is sometimes called split testing or A/B testing) Behaviorbased tactics Compare one-time customers with repeat buyers, allowing companies to see what makes people become brand-loyal customers who keep coming back. Deep dive data analysis Looks at the effectiveness of incentives such as reward programs. By examining the behavior of people who redeem coupons, for example, companies can see whether customers who receive them typically spend more on each purchase. Predictive modeling Analyzes past campaigns and builds statistical models to rank-order people who are more likely to respond. For example, a company may have a limited budget for a promotional mailing and wish to send a catalogue only to half of the people in its database (the half that s more likely to buy). Analytics can identify which half should receive the catalogue. Segmentation Helps companies spot natural groupings that allow more focused communications to narrowly targeted audiences. Customer life cycle management Once customer loyalty has been established, affinity or other loyalty programs can be used to keep customers coming back over the long term. Data analytics help by keeping the brand relevant to these important customers by giving them the rewards they find most valuable. In addition to retaining high-value customers, life cycle management also helps identify customers in mid- or lower-value segments who can be grown into engaged, brand-loyal customers. 03

4 Growth through Customer Life Cycle Management Customer Life Cycle Management/ Loyalty Predictive Modeling Growth Engagement Retention Acquisition Deep Dive Analysis/ Data Mining Customer Behavior Based Retention Tactics Campaign Management & Reporting New Customer Sourcing Analysis Data analysis drives customer growth and engagement Case study: Data mining helps retail chain maximize customer engagement on Sina Weibo A chain of retail shops based in northern China actively uses Sina Weibo, China s dominant microblog, to promote special offerings and engage customers. About two-thirds of its official Sina Weibo messages ( tweets ) were re-posted by fans, usually between one and 10 times; the client wanted to magnify the impact of its Sina Weibo platform. Javelin, an Omnicom marketing agency focusing on analytics, mined a year s worth of tweets in search of data patterns that might improve the chain's re-posting rate, thereby deepening its Sina Weibo communication reach and customer engagement. Javelin didn t look at the actual content of the tweets only structured data such as the time of day, day of the week, and what region the chain s fans came from. Its analysis revealed that although the chain typically posted messages around 9:00 a.m., its followers rarely got around to re-tweeting the posts until early evening. Clearly, that was the time when its followers were most active, and therefore most likely to re-tweet the information. An additional factor: as followers received more tweets throughout the day, the chain's messages got pushed further down the stack, reducing the odds that followers would even see them. 04

5 By sending out its official tweets later in the day, the retail chain found that it could increase the reach of its Sina Weibo communications. Javelin s analysis also revealed an opportunity for the chain, many of whose most active fans lived in southern China where the chain had no retail presence. In addition to helping the client deepen engagement with its customers, the analysis provided useful intelligence for the store s future expansion plans, said Ingrid Guo, VP Analytics and Managing Director of Javelin Beijing. The data showed that southern locations would be likely to prosper, since the chain already had a significant fan base in that region. Advanced analytics: Fraud detection Data has been called the new oil and, like oil, it has various grades of quality. Marketers rely on companies that sell media space to provide accurate data about their online inventory, and about the traffic generated by their sites. Since sellers earn more money from additional clicks, unscrupulous sellers may fraudulently inflate their site traffic figures in order to charge more for their inventory. Indeed, a study by R3, a consultancy based in Asia Pacific, shows that nearly half of marketers surveyed (48%) say that accuracy of site traffic is their chief concern when deciding what share of their budget should be allocated to digital media. To get high-quality data about how their ads are performing, marketers can work with advisors who analyze data to verify publishers claims. For example, if an analysis shows that one user has clicked twice in one second on a single online ad, it s likely that the user is actually a computer program, not a real person. This information about data quality helps marketers optimize their site selection and negotiate better rates with publishers. Case study Attribution modeling helps China HR optimize marketing spend Plenty of tools exist to measure online advertising performance. But many of them only measure the final click a consumer makes before buying something online. Unfortunately, marketers that use the last click as a metric may be tempted to eliminate ads that don t lead directly to a sale, but are nevertheless important. Display ads such as banners, for example, play an important role in converting prospects into customers. But they may get optimized out by clients who are disproportionately focused on clicks rather than cumulative exposure to marketing messages. You don t want to cut off the top of your sales funnel, says Doug Pearce, CEO of China for Omnicom Media Group. Unclicked-on exposures can be very important they re just harder to measure. Attribution modeling is a form of analytics that takes these un-clicked-on exposures into account. It came in handy for China HR, an online recruitment site, when the company wanted to know which web sites they should advertise on to increase the number of jobs applied for on China HR. 05

6 Benefits of attribution modeling 1. Gives advertiser true visibility into what is driving conversions 2. Undervalued traffic has opportunity to prove its effect in conversion path 3. Helps advertisers allocate budget more effectively Thus far, the company had relied mainly on a combination of search and display ads running on Baidu, China s dominant search engine, and on QQ, a popular instant messaging platform. DoubleClick was being used to monitor ad performance, but the client wanted deeper insight into how different advertising channels combined to drive job applications posted on China HR. Annalect, a subsidiary of Omnicom Media Group, performed attribution modeling to quantify the contribution of all 15 sites on which China HR was advertising. It found that Baidu was 20% less effective than previously thought, and that QQ was 8% less effective. This did not mean that China HR should stop using those sites; however, it did indicate that the aggregate contribution of other, seemingly lesser sites was important, and that advertising on those other sites needed to be maintained. For example, attributional analysis also showed that the client s display ads were 42% more effective and 30% less expensive than originally thought crucial information for a conversion-driven company like China HR. The attributional modeling showed the client what worked, and demonstrated that seemingly unimportant display ads made a critical contribution to conversation rates, while some apparently expensive channels were actually quite cost-effective. By learning what worked, China HR has been able to lower its overall cost per action and increase the effectiveness of its online marketing mix. 06

7 Today, advertising is bought and delivered in real time Until recently, online advertising space was sold in much the same way that print ads traditionally were sold. If a company wanted to target a particular type of consumer, it called its ad agency, whose media planners constructed a profile based on age, income, gender and other factors. That profile went to a media buying company, which wrote an RFP that went out to vendors the companies that owned media space, such as TV networks. The vendors matched the profile of the target consumer with the available media space, on which advertisers then bid, in a process that unfolded over a period of weeks or months. Today, in a process known as real-time bidding, it is possible to use client data to assemble actual target customers (segmented by demographics, psychographics and other criteria) and link them directly with publishers inventory management systems. In other words, it is no longer necessary to use a web site or any other medium as a proxy for the target consumer you want to reach. Instead of buying the proxy the advertising space you buy the audience. How real-time bidding will transform digital advertising Now Future Benefits Focus on planning and buying Focus on analytics and optimization 42% lower cost Buying pages Buying people 47% better targeting Post-campaign reporting Real-time analytics and impression-level decisions 48% performance lift Duplication from multiple vendors De-duplication and optimum frequency 33% less wastage Flat post-campaign data Interactive data visualizations More insightful learnings Based on Accuen client feedback. 07

8 The right audience may be determined by data that suggests purchase intent, such as information about auto leases. For example, car companies can advertise directly to people who have been leasing automobiles and will soon come off lease, meaning they might be ready to buy a car. This target group is matched with available ad space and competing advertisers bid for the right to advertise to that group. This is done online in real time, so the bidding lasts mere seconds and generates huge efficiencies in cost by eliminating 30% to 40% of the targets that would have been generated by standard age and income type profiling. Then, also in real time, ads are immediately served to the target customers. They can be customized based on audience characteristics, tested to see which headline or promotion works best, and refined on the basis of those test results. With today s technology, you can wait for just the right combination of inventory and targets to become available and then hit them at the point of maximum receptivity and impact, says Doug Pearce, China CEO of Omnicom Media Group. So basically it s like, I ve got $10m and I m looking for car owners who are just coming off-lease. Have you got any of those? How many can you sell me? And you can deploy that $10m to reach those consumers within the space of an hour. This becomes a lot more like direct response, in that it s more measurable, and a lot more focused on ROI, which is what clients want. Data Management Platform (DMP) A Data Management Platform (DMP) is specialized database containing information about users online behavior. Each user is represented by a cookie, a text file containing information about what sites that person has visited. Each cookie, in turn, contains hundreds or even thousands of distinctive data points about the user based on his or her past behavior. For example, Accuen, part of Omnicom Media Group, has developed a DMP called the Audience Intelligence Platform, which contains 3,000 data points for each user. Sophisticated algorithms take those data points and categorize them into a user profile that assigns a value to the user based on interests, purchase intent, and demographic profile. The new data landscape Today s new analytics-oriented landscape has shaken traditional marketing paradigms. In the past, marketers typically looked at a very small target audience sample usually through a survey or a focus group. Today, with real-time bidding and other technologies, marketers can buy entire audiences and collect thousands of data points about every consumer they target. Given this ocean of data, the goal cannot be analysis for its own sake. Instead, data mining and analytics offer a way to respond to growing customer demand for timelier, more personalized service demand that can be especially keen in China. That s why companies must do more than simply track and collect data. They must also analyze it for the insights that will enable them to create customized communications that resonate more strongly with target consumers. 08

9 To learn more about how your company can leverage the opportunities presented by data analytics, please contact Li Hong, Senior Partner and President (China) of Fleishman-Hillard, tel , or Winter Wright, Vice President of Fleishman-Hillard, tel , Omnicom and FleishmanHillard would like to acknowledge the following agencies, all part of the Omnicom Group, for their valuable contributions to this paper: DDB Group Accuen and Annalect (part of Omnicom Media Group) Javelin Marketing