Home Data-Driven Thinking Big Data’s Real Challenge: Consumer Value

Big Data’s Real Challenge: Consumer Value


dunnhumby-keylock-usethis“Data Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Matthew Keylock, SVP and global head of data at dunnhumby.

Big Data is enabling breakthrough innovations in national infrastructure, biology, medicine and national disasters. It’s also good for business, making it easier for companies to understand their customers and deliver value based on that knowledge. But, so far, Big Data hasn’t succeeded in making advertising easier on brands or on consumers.

The plethora of vendors and data stakeholders between the consumer and the advertiser are siphoning most of the value toward their profitability. As a result, while the industry is booming, neither consumers nor brands have really benefitted from Big Data as much as they should.

The technology-driven challenges are dominating thinking and investments, distracting us from articulating ad-tech’s value to the consumer. And that value, I’d argue, is the true objective of Big Data. This is not just a “do good” message; it’s also imperative for survival. Very soon, companies that are able to deliver incremental and explicit value will dominate the landscape and the consumer relationship.

There are four key areas where we can play a bigger role in delivering value more explicitly:

1. A More Seamless, More Rewarding Experience

The AdTech world is in transition. Yesterday’s methods and approaches are colliding with today’s assets and methods. The user experience, though, hasn’t yet become more seamless or more rewarding; engagement is still event-based and fragmented, not personalized and connected over time or channel.

In commerce too, worlds are colliding. Retailers are testing apps, video technology, social-enabled customer service and mobile payments. Most are still trying to work out how to navigate and successfully integrate these new capabilities with new assets. In many cases, e-commerce-led companies that designed data integration into their systems from the start have been leading the charge.

TaoBao, China’s leading online marketplace is a great example of how technology, data and great retail practices can create new value for consumers. Not only does it enjoy 96% of the online market share, it also has a remarkable 71% loyalty rate. New research by Temple University links this success with its ability to stimulate “swift guanxi,” a quickly-formed interpersonal relationship that plays a vital role in business success within Chinese culture.

Using a computer-mediated communication platform similar to instant messaging, TaoBao allows sellers to interact directly with buyers throughout the negotiation process, eventually building relationships in a way that many vendors haven’t been able to replicate offline. This allows both parties to participate and collaborate along the process, not just during the actual purchase. Sellers that are able to establish swift guanxi are more likely to have repeat purchases, higher sales and build long-term loyalty with their buyers. And by enabling the buying and selling of a product to extend beyond the moment of purchase, this relationship becomes more meaningful and more personal.


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Tracking consumer-brand interactions like these enable ad tech to deliver highly personalized value in real time while giving consumers greater control over that experience. Offers and experiences need to deliver value to me as an individual, not people like me. For my wife, for example, instead of simply purchasing a bottle of her favorite wine, Cloudy Bay could engage her personally by providing information on where and how it is made and which food pairings she might like. For someone else, the message could be very different. Delivering incremental value like this requires the ability to learn what works in a closed-loop system. All this means that aggregate results of shopper behavior, profit or campaign performance become irrelevant; what matters is the entire branded experience.

2. Collaborative Innovation

One of the greatest potential impacts of Big Data could be the emergence of a data-enabled community. More data, greater data awareness, greater mobility and new technologies are creating opportunities for bridging and collaboration across disciplines and business verticals. Whether it’s through open-crowdsourcing platforms or new partnerships, data is bringing vendors from across the marketing-technology landscape together to rethink the future of our industry.

Yet there is still work we need to do to improve the quality of the insight. For example, marketing automation is enabling brands to achieve greater sophistication at scale, but the silos and the campaign-based approach continue to limit the value to consumers. We’re creating more data and also more islands of data that all tell different stories. Variety, more than volume and velocity, is the greatest challenge to drive value from Big Data. Unfortunately, most data folk are too technology-oriented, focusing on the systems and incremental integration rather than rethinking the quality of our collective insight. How can we reconnect brands with consumers? Could we design databases to better understand what consumers need and value? If we, as an industry, structure our insight around the quality of personal engagement over the quantity of people we’re targeting, we have the ability to improve that insight and make it more precise.

3. Computer-Augmented Customer Knowledge

Big Data is enabling amazing advancements within the field of analytics and computing. Cognitive computing, for example, already has had a major impact in diagnosing disease and improving our understanding of cancer. Meanwhile, in marketing, analytics advancements are playing a major role in connecting different types of data and liberating more value from the data. Without automation and analytics, it wouldn’t be feasible to personalize offers for 10 million customers, for example. However, the technology can’t do this alone. As in every other field, the collaborative innovation between man and machine depends on human direction. It’s analysts’ creativity combined with those analytics that enables us to better understand human behavior in all of its non-linear wonder.

Personalizing coupons, offers and advertising at scale — to a set of 10 million customers, for example — require algorithms and models built around human-generated questions designed to answer questions, such as “What do they buy?” and “How and when do they shop?” Existing customer knowledge through numerous sources and types of data, such as historical campaign responses, transaction data and custom segments, can then be incorporated into the models to produce highly accurate and robust predictions. Ultimately, personalization should be a continuous program that builds on the past and blends smart algorithms with human direction and leadership to grow customer knowledge and value over time.

4. Business Transparency And A Cycle of Trust

More customer awareness of Big Data — and all the related privacy concerns — is forcing companies to become more transparent and provide customers with full disclosure about how their data is used. More importantly, customers have gained more control in sharing in the value of their own data. In his book, “The Intention Economy,” Doc Searls makes a key point about this transition to consumer power: Personalized advertising is not enough to satisfy consumers in an economy where consumers are in control. Only those companies and business models that are able to deliver the value that consumers want will be able to develop the close, personal relationships needed to thrive in this economy.

Businesses — especially brand advertisers — need to buy into the cycle of trust. The more value that brands are able to deliver to their end consumers, the more willing consumers will be to share their data with those brands — and the better they’ll feel about sharing it. And with every churn of the cycle, brands will be able to deliver increasing value to those customers.

Follow Matt Keylock (@mattkeylock) and AdExchanger (@adexchanger) on Twitter.

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