Internet Data for Marketing Measurement
David M. Raab
DM Review
February 2009
Marketers know what they spend and their companies know what gets sold. The challenge of marketing measurement is connecting the two. Which customers were touched by which marketing programs? How did those programs affect behavior? What non-marketing factors contributed to the final result? This data has often been missing.
Today, find the Internet can fill in many of these gaps. Marketers can directly measure the Web traffic generated by online and offline campaigns. They can count search queries and blog mentions to see what’s on consumers’ minds. They can capture attitudes based on message contents, stuff measure engagement by willingness to download content, and identify influential voices based on their audience. More prosaically, online surveys can assess consumer reactions to campaigns and make appropriate changes. In a growing number of cases, they can relate individuals’ Internet experience to their actual purchases, providing the final link in the chain between marketing efforts and sales results.
Taking advantage of this data poses new challenges. Here are some issues and solutions to consider.
– changing data sources. Two years ago, few people had heard of Twitter. Today, many companies are obligated to monitor it on a regular basis. The measurement challenge is not simply that new channels keep appearing, but that you may need to start tracking one literally overnight. Imagine, for example, that you are featured in the next popular YouTube video or a prominent social network discussion. Or, that marketing asks you track downloads of that new widget they released yesterday without telling you. Coping with these demands requires very flexible, Web-friendly data integration tools that can adapt to new sources with minimal effort. It also requires analytical databases and reporting tools that can incorporate the new data without major projects to redesign data models, reload old data, or rebuild standard queries and reports.
– high data volumes. Internet sources can generate massive data streams, particularly when individual data is involved. Your reporting system must be able to load and store large volumes, including sudden spikes to many times the size of base levels. You may also need automated tools to scan and summarize incoming data and to issue alerts about specified conditions. All this has to happen without degrading performance or losing downtime for database reorganizations.
– ambiguous data. A few Web sources are nicely structured: perhaps your marketing department takes well organized surveys. But most Web data is fundamentally anarchic, involving not simply free text, but free text that’s misspelled, unpunctuated, in several languages, and filled with ever-morphing abbreviations, slang and emoticons. 🙁 indeed. Now add in audio, video, maps, graphics and other formats that not text-based at all. You’ll need powerful data quality tools that can identify unfamiliar elements and resolve them automatically when possible. You also need tools to interpret and classify the these contents so you can manage them without paying someone to read every word, listen to every podcast, or look at every picture.
– new attributes. New media contain new types of data such as social media rankings, connections within a network, paths through a Web site, links to a Web page, and numbers of subscribers to a blog. Your systems must capture and interpret this information, monitor changes over time, and build it into meaningful reports. This will require sophisticated data parsing, queries, trending, and calculations.
– identity resolution. Internet identities have less to do with name and mailing address than network nodes, cookie IDs, user names, biometrics, and self-identifying devices such as smart cards, cell phones, electronic license plates. Within legal and ethnical privacy limits, you’ll want to link these to gain a comprehensive picture of customer media consumption, behaviors and purchases. The sheer variety of potential identifiers will require radical changes to identity resolution techniques. Clever algorithms to find similarities in text strings will not longer suffice.
– mashups. The best way to make sense of Web data may be to view several sources in combination. Many Web products expose their data through APIs designed to make this possible. Your marketing reporting systems will need to take advantage of these capabilities. In some cases, one of the Web systems themselves may provide a platform to mash together several types of information.
– data exploration. To handle an endless stream of new and shifting data sources, your marketing measurement system must give users tools to explore each source and uncover significant relationships among them. This implies a heavy dose of ad hoc inquiry, data mining and visualization capabilities, all designed for relatively non-technical users: that is, marketing analysts, if not necessarily the senior marketing managers themselves. (As someone put it, the users drive a Honda, not the Lexus.)
– data distribution. Different Web data is relevant to different users. Customer support will be interested in complaints, sales will be interested in product questions, and product design will want to see feature requests. Even within marketing, your product managers, promotion managers, publicity managers, Web managers and others will want different items at different intervals and levels of detail. The marketing measurement system must make it easy for analysts to publish a useful piece of information, for users to share comments on the findings, and for users to subscribe to reports they find relevant.
These challenges won’t be all met by any one product. But every partial solution fills in a piece of the puzzle. Over time, marketers will gain an ever-clearer picture of how their efforts contribute to business results.
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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. He can be reached at draab@raabassociates.com.