Data Collection: Essential for Success

Data Collection:  Essential for Business

Companies are gradually waking up to the fact that in the age of digital technology, social media and smart phones, the ability to collect and effectively analyze data about customers and their friends is becoming an essential tool for success.  In some circles, the quest to utilize data to develop competitive strategy, design new products or fine tune brands is becoming the Holy Grail.  For internet and mobile businesses, it is an absolute must because knowing as much about what the consumer wants and when he/she wants to see it is the key to generating future advertising growth.

Data Collection:  Businesses react differently

Recent studies suggest that companies are reacting differently to the competitive challenges in this era of big data.  Even so, there is a widely held belief that developing the ability to collect and collate data and then develop action plans based on that data is a competitive necessity for all companies, whether B to C or B to B.  And the challenge in this era of big data is not only determining what data to collect but how to prioritize that data.  Countless companies, from giants like Google down to small VC backed startups, are working to contextualize data because the development of effective and imaginative algorithms will ultimately lead to billions of dollars of additional ad spending and investment as well as sophisticated new management tools.

According to a study by David Rogers of the Columbia Business School, Marketing ROI in the Era of Big Data, there is a general acknowledgement about the connection between data and success.  49% of the companies surveyed believe successful brands use data effectively but 39% say that their company collects data too infrequently or not in real time.  The study also points out that only 19% of large firms were likely to collect new forms of digital data such as from mobile phones and these same large companies continue to put their emphasis on more traditional and simple forms of customer data based on demographics. This Rogers lists four main recommendations: 1) collect meaningful data from a variety of sources, including real time; 2) link data to metrics developed for measuring ROI; 3) share data across the organization and 4) utilize shared data to effectively target and personalize marketing efforts to consumers.

Data Collection:  Contextual

I don’t think there has ever been a time in history when businesses have the potential access to so much raw personal data on their customers.  Consider the granular depth of knowledge that can be developed on consumers when you can collate Google based search analytics with cookies, social media metadata from Facebook and Twitter tweets, transactional information on purchases with location based mobile telephony patterns, opt-ins and search.  The metadata from Facebook theoretically can provide a deeper level of engagement based on a person’s social index of activities and profiles cross referenced against the social graph of their friends and the activities and contacts of their friends.  From a purely entertainment point of view, the more contextualized data you have on a person and his/her group, the greater chances of serving more relevant content and targeted advertising which leads to higher engagement and CPMs.    The graphic below illustrates the organization:

Data Collection


Data Collection:  Power to transform

Data has the power to transform hypothesis or intuitive business decisions into those that are supported by or in fact driven by contextualized data.  But data collection and synthesis is only a means to an end, not the end itself.  Measures need to be taken to insure that decision makers don’t pick the data to match the hypothesis because that makes for bad business decisions.  Anyone that has dealt with raw data or built business models knows that giving more importance or weight to one set of projections can skew the findings of the entire model.  One thing to keep in mind is that in building models or algorithms, the goal should not be predicting whether a product will work or who will win an election.  The goal should be to give marketers, product designers and chief executives the data tools to better understand and predict consumer behavior and to anticipate the needs of their customers.  I agree with Douglas Merrill, former CIO/VP Engineering for Google who wrote: “I’m certain that the advancements and triumphs in both science and business will come from cool minds developing imaginative models and theories and testing them with the help of new big data tools and technology”.  For companies competing for consumer’s hearts and minds in this age of big data, having too little data is a far riskier proposition than collecting too much data and struggling to develop proprietary models to understand its meaning.  The difference can be quite stark as between trusting business decisions and strategies based on informative data or flying blind.




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