Consumer Data: It’s Not Just What You Have, It’s Also Knowing What to Do With It.
As the time that consumers spend on line continues to grow, particularly on mobile devices, the information that various companies are able to collect about their users is also growing. The value of that information – for commercial advertising, commerce, and personalized services – allows those companies to better address markets that are collectively worth $17T. We have built a taxonomy of that information – demographics, interests, actions, locations and connections – and assessed the degrees to which each category might be valuable. We then created a framework to assess various companies with stakes in the flow of consumer data based on their access to data, their ability to collect and analyze the information, and their potential for monetizing it. Not surprisingly, GOOG is far ahead in all regards – the extensive data they are privy to across the taxonomy, their prodigious institutional skills in data collection and analysis, and the levers for monetization that they are able to pull. Amongst the rest – AMZN, FB and TWTR are obvious leaders. AAPL, and NFLX are constrained by their ability/willingness to monetize. Other internet players – YHOO, AOL, YELP, EBAY, etc. – lack scale and depth in the data that they are able to collect and reach in the vehicles used to monetize it. The same is even more true of the incumbents in industries in the path of digital competition, including banks, retailers, media companies, and others.
Ubiquitous mobile computing yields a trove of data on individual consumers. The growing use of cloud-based services wherever and whenever a consumer goes throughout their day affords companies involved the opportunity to collect data. We have categorized that data into 5 buckets: Demographics, Interests, Actions, Locations and Connections. In each case, the collected data can be assessed by the breadth of the attributes collected, the length of time that the data covers, and the quality (accuracy, sharpness, etc.) of the data itself. It is also important to consider the degree to which the breadth of the data is integrated into unique profiles directly tied to specific individuals vs. generalized by segment.
Consumer data key to online attack on markets worth up to $17T. Detailed data profiles allow online companies to target specific, high likelihood consumers in merchandizing and to tailor services to maximize their value for individuals, advantages that expand dramatically with the reach, breadth, quality and time span of the data collected. Currently, digital advertising ($140B worldwide), e-tail ($1.5T), and digital content ($57B), are the biggest businesses constructed on this data, but there is significant room for future growth in each of these categories and new opportunities to exploit data, including personal financial services, health care/fitness, home/energy management, transportation, and others.
We evaluated 20+ large cap companies for their potential to exploit consumer data. Many companies have access to information about consumers stemming from mobile platforms, media distribution, search/navigation tools, financial transactions, and communications. We assessed leaders from both online and traditional business models for their access to valuable data, their ability to collect and analyze that data, and their potential for monetizing it. This exercise reveals a world of haves and have nots, where the few companies able to draw unusually high quality data from frequent engagements with a large population of attractive consumers over a sustained period reign supreme.
GOOG, FB and TWTR are winning the ad game. GOOG has a hammerlock on search data, the gold standard for flagging interests and purchase intentions. Add in the Android platform funneling users to GOOG’s oft used, well integrated, and ad friendly applications under a single sign-in, a peerless data analysis infrastructure and a hand on every monetization lever, and GOOG takes the overall prize. FB has an enormous horde of data on its 1.2B fanatically engaged users, but the data is a step below in quality and monetization is uneven vs. GOOG. TWTR has a stronger signal for its users’ interests and clearer context for advertisers than FB, but a much smaller universe of less engaged users.
AMZN, AAPL and NFLX have narrow data strategies. AMZN captures demographic, interest and purchase data from its 162M MAUs, using it to recommend products and build engagement/loyalty, key drivers for its $70B e-tail franchise. It is exploring monetizing through advertising, but remains a relative neophyte. AAPL, with a loyal base of 200M iPhone users and 800M cards on file, has considerable reach but is unprepared to collect or exploit data about its customers. NFLX uses a deep understanding of its customers’ viewing preferences to acquire content and guide recommendations, but eschews advertising and has a very narrow set of data that is tied to accounts rather than individuals. Other internet players – e.g. YHOO, EBAY, PCLN, YELP, AOL, etc. – lack data on multiple dimensions, and will be disadvantaged in competing in their core businesses and in entering new ones vs. the data leaders.
Traditional players will be severely challenged. Media, retail, and financial services players, amongst others, face significant long-term challenges from data rich online competitors. Typically, traditional companies have limited data sets, lack the skills to collect and analyze that data, and are inexperienced in monetizing it. TV networks and distributers hope to use digital ad insertion to target consumers, but progress is slow, online video viewership is rising, video ads on FB, TWTR, etc. have gained traction, and NFLX could eventually include an ad driven alternative. Retailers are behind in tracking and targeting consumers, overmatched in their IT capabilities, working with data limited to their own transactions and often, tied to credit card numbers which will be obsolete once tokenization is fully implemented. Banks and card nets lust after advertising, but operate under strict regulations, may be overestimating the quality of their data and their abilities to analyze and monetize it, and are at longer term risk of disintermediation as mobile payments brands (a la Apple Pay) slip in front of them with consumers and merchants.
Privacy little obstacle for consumer data leaders. While sharp criticism of top consumer data driven companies’ data policies has raised concerns amongst consumers, it has not hampered engagement with these services and very few users choose to opt out of data tracking when given the chance. We believe that the long term value of consumer data is huge, and that the economies of scale, scope and skill are broadly separating a few winners from many losers. We see GOOG, FB, AMZN and TWTR as obvious winners, with AAPL and NFLX, perhaps shortsightedly, standing on the sidelines despite strong potential data assets. We are sanguine about other online consumer data plays, e.g. YHOO, AOL, YELP, etc., given the severe scale disadvantages. Most traditional players will struggle as consumer data becomes increasingly valuable to their core businesses and online competition becomes ever more powerful.
For our full research notes, please visit our published research site.