M A R K E T I N G A N A L Y T I C S
UNDER THE SCOPE
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BEYOND THE MATTER
ecause data is becoming more widely available , it appears that analytics should be able to deliver on its promise of value creation . Data , on the other hand , grows on its own terms , and this expansion is frequently fueled by IT investments rather than by well-defined
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BREAKTHROUGHS
How is it possible that companies haven ' t witnessed an increase in how analytics contributes to corporate performance yet are still intending to spend so much more ? Two conflicting forces explain this mismatch , according to our work with Marketing Science Institute member companies : the data used in analytics and the analyst talent providing it . We ' ll go over how each of these forces has prevented companies from reaching the full potential of marketing analytics , as well as practical prescriptions for better aligning analytics results with greater investment . marketing objectives . As a result , data libraries resemble the classic cluttered closet , where it ' s difficult to tell what ' s important from what ' s
not .
Worst of all , data is frequently not causative . While it is true that search advertising can be linked to purchases because buyers are in a buying mood , this does not mean that the advertisement triggered sales . Consumers are motivated to buy even if the company does not market , so how can one tell if the commercials were effective ? Worse , as data expands , these issues get more severe . No amount of investment will yield insights unless the correct analytic approach is
used .
Technology Now | Issue 70 | 234
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INSIGHT CENTER DATA-DRIVEN MARKETING THE SCIENCE OF STORYTELLING AND BRAND PERFORMANCE .
In most companies , data is not integrated . Data collected by different systems is disjointed , lacking variables to match the data , and using different coding schemes . For example , data from mobile devices and data from PCs might indicate similar browsing paths , but if the consumer data and the data on pages browsed cannot be matched , it is hard to determine browsing behavior . That ’ s why understanding how data will ultimately be integrated and measured should be considered prior to collect the data , precisely because it will
Furthermore , most businesses have massive amounts of data to handle , making it difficult to do so in a timely manner . When combining data from a large number of consumers and interactions , “ translating ” code , systems , and dictionaries is required . Massive volumes of data can overwhelm processing capacity and algorithms after they ' ve been combined . Many methods for scaling analytics exist , but gathering data that cannot be analyzed is inefficient . lower the cost of matching .