There is little question that big data has the potential to generate greater profits. McKinsey and Company studied the use of big data in five different industries around the globe and found that the use of big data can increase operating margin by as much as 60%. At the same time, a study reported by the Marketing Accountability Standards Board (MASB) and marketing research firm Truthset found only modest agreement on basic demographic information across sixteen leading digital targeting data service providers. Agreement on gender was only 51% among these sixteen providers and for age ranges, where more options are available, the agreement was only 7%. The lesson is that big is not necessarily better and in some cases, may be worse because it creates a false sense of confidence.

Big Data’s Strengths

Of course, a great deal of the benefit of big data for marketers has little to do with the identity of individual consumers. Much of the value of big data arises from its ability to make firms more efficient. The ability to quickly summarize transactions, identify trends, and forecast future demand allows firms to be more responsive more quickly to markets. Such responsiveness has two benefits: (1) it enables the firm to be where the action is faster than competitors, thus producing more sales and (2) it allows the firm to reduce the amount of unsold inventory that does not match demand. It is also possible to conduct real time experiments to see what types of advertising and promotions work, improve those that do work and change those that do not, and repeat the process. Direct marketers, using catalogs, telemarketing, and home shopping television channels have used such approaches for years, but it is now possible to do such experimentation on a larger scale and at a more rapid pace. Finally, new technologies provide a means for customer feedback about existing products and services and potential new products and services more quickly and far less expensively than ever before. As a result, products can, at least in theory, be improved and innovations created at a rapid pace. All of these benefits can potentially be realized without a single consumer revealing their identity or providing personal information.

Big Data’s Weaknesses

Successful branding is more than analyzing trends, however. Rather, branding is about building personal relationships. It is about more than simple demographics. Rather, it is about complex behavioral, lifestyle, life stage, and interest attributes. It is about problems and aspirations. Teasing such information from big data is difficult, if not impossible. Big data are often not representative of the market as a whole. Information about a firm’s own customers provides little information about consumers who are not customers. Even information about a firm’s own customers may be bound by time, place, and the customer’s purpose for interaction. Indeed, one limitation of much big data is that it has been collected and structured to serve purposes other than informing marketing decisions.

The Danger In Big Data

While it is clear that big data has large benefits for both firms and consumers, it is not a substitute for other types of data, like talking to customers. Big data that might otherwise be useful is often not in a form that is useful for marketing decisions. A real danger for marketers with access to big data is a complacency that grows from the perception that they know so much about the market. In fact, what they know is only history, and an incomplete history at that. History is a good predictor in the very short term, but a terrible predictor of even the intermediate term especially in a rapidly evolving economic, social, and technological context. Marketers can play a critical role in helping organizations understand the uses and limitations of big data, and the many other tools for engaging with and understanding consumers. There is great potential but much remains to be done to make it a useful tool for managing and growing businesses and brands.

Contributed to Branding Strategy Insider by: Dr. David Stewart, Emeritus Professor of Marketing and Business Law, Loyola Marymount University, Author, Financial Dimensions Of Marketing Decisions.

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Data do not decide; people do. Data do not think; people do. However, interpretation of data is a skill. Judgment about what data mean takes confidence and insight.

Business executives often expect data analytics to reveal answers. Data do not take into consideration mission, context, policies or priorities; people do.

Data Misses The Why

In his 2003 book, How Customers Think, Harvard Professor Gerald Zaltman wrote that marketers and C-suite executives must remember that data do not speak for themselves, people do. He said, “Managers’ interpretation of data is the meaning they extract from the data. Rarely does a number by itself have meaning. It is the manager’s prior experience or the consensus of the group of managers that makes a number meaningful.” Management by metrics has become the order of the day. We are mesmerized by metrics.

While there is much that we can measure, there is also much that is not measurable. Unfortunately, as business becomes more demanding, business becomes more defensive. In a world where budgets are squeezed by limited resources, managers and marketers lean towards an over-reliance on the mystical muscle of measurement to take over the role of marketing expertise and experience.

In 2017, Professor Jerry Z. Miller, at Catholic University of America, wrote a book titled The Tyranny of Metrics. Professor Miller focused on “metric fixation.” Metric fixation is our tendency to become mesmerized by metrics. As discussed in a Bloomberg Opinion piece Justin Fox wrote that Professor Miller was critical about our belief in the overarching power of data. Mr. Fox explained that Professor Miller sees one of the main characteristics of metric mania as the idea that “… it is possible and desirable to replace judgment… with numerical indicators of comparative performance based on standardized data.”

Mr. Fox pointed out that Professor Miller is not alone in his concerns about data reliance. Billionaire John Doerr wrote a book also dealing with the problems of over reliance on metrics and performance measurement. Mr. Doerr stated that data reliance without judgment and without knowing “why” people behave the way they do, is the wrong approach. Mr. Doerr is not against metrics. But, he told us that the best metrics are used to inform rather than form opinion.

The problem with using data to replace judgment is that we will only focus on the “what” and not the “why.” Data are a resource. Data can provide correlations. Data do not attest to causality, even though we may think so. Data do not say, “A caused B to happen.” But, data can suggest a relationship between A and B.

Insight Reveals A Hershey Strength

For example, as reported on CNN, Hershey, maker of the ubiquitous, iconic Hershey Bar, Reese’s Pieces and Reese’s Peanut Butter Cups, saw data showing that areas of the country with high numbers of Covid-19 cases also had high chocolate sales. The Hershey data revealed that those zip codes with numerous Covid-19 cases saw demand for milk chocolate soar by 40% to 50%. The data revealed a correlation but could not tell Hershey “why” this was the case. The data could only point out people’s behavior.

Hershey used the data to focus on understanding the “why.” Hershey used the data to figure out what was driving the relationship between coronavirus and chocolate. Hershey determined that due to the virus, people were spending more time outside. One great outdoor activity is making S’mores. The ingredients in S’mores are graham crackers, marshmallows and Hershey Bars. The marketers at Hershey used the data as a platform for creativity, not as the creativity itself.

This Hershey approach is cited as one of the reasons Hershey performed so well in the third quarter of last year. According to The Wall Street Journal, Hershey stated that its brands increased share due to its adapting to changing customer behavior, part of which was producing more Hershey bars for S’mores. Hershey’s North American comparable sales increased 5.5%. Hershey’s net income rose from $325.3 million to $447.3.

Lack Of Insight Reveals An Amazon Weakness

On the other hand, for all the wondrous personalization from Amazon, its data do not understand time. For Amazon data, last time is all the time. For example, if you buy baby gifts for your new grandchild in April, you will still receive suggestions for baby things a year later. If you buy your son-in-law tools for his outdoor grill as a holiday gift, you continue to receive grill tool sets suggestions for time immemorial. Amazon knows what you bought, but is less certain why you bought. Amazon data do not seem to care that time goes on, babies grow up and one gift set of grill tools is sufficient.

In the seminal work, Big Data by Schönberger and Cukier, in 2013, the authors pointed out that data can lead to both understanding and misunderstanding. Data can spur innovation. But, “the spark of invention comes from what the data do not say. In a world of big data, it is our most human traits that will need to be fostered… since our ingenuity is the source of our progress.”

For example, one appliance company saw data indicating that noise was a huge problem with vacuum cleaners. Taking the data as decisive, the engineers developed a near-silent vacuum cleaner. The silent vacuum cleaner did not sell. Why? Customers perceive certain sounds as conveying powerfulness: think Dyson. Without sound, customers thought the vacuum’s suction would be weak. The loud sound was not a problem: it was evidence of a powerful vacuum. Data did not provide this critical piece of information.

In this increasingly competitive, uncertain marketing world, there is a pervasive fear of taking a leap of faith based on judgment. Informed judgment is not guesswork. Marketers must use their expertise and their judgment and their creativity to make reasoned, informed and insightful decisions.

Data do not create ideas. Data show areas where ideas are possible. Creativity is a mindset, not a metric. Real, actionable insight will not come from superior data collection and analysis. Superior analysis provides understanding of where we are, and how we got to where we are. It does not provide insight into what kind of future we can create. It does not tell me “why”. Marketers must use their expertise, their judgment, and their creativity to make reasoned, informed, insightful, and bold decisions. Data can provide direction for decision-making. Data inform decisions. Data do not make decisions.

Data do not decide; people do. Data do not think. People do. Do not let data become the decision-maker.

Contributed to Branding Strategy Insider by: Larry Light, CEO of Arcature

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But getting to that data can be tough for brands. With more customers spending time on platforms like Facebook, Amazon and Weibo, etc., those platforms end up owning and controlling data that brands only get access to when they pay premiums for advertising. And even then, it’s not all the data. Also, because the platforms own the data, there’s nothing that can stop them from using it to launch new products, even those that compete with yours.

Van Nikerk highlights three trends behind the market consolidation we are seeing today, and why an understanding of them will define the winners and losers of the future.

Trend #1: First-Party Data And Privacy

Data that has not been properly handled or lacks the right permissions is useless. First-party data is at the core of your brand, revealing how and when your customers spend time and money in channels you own. When properly setup and managed, first-party data will go a long way to optimizing the customer experience and even anticipating future needs. The mergers between creative agencies and data firms reveals that there is a need for deeper expertise in this area. It’s amazing how much data many brands are sitting on that goes unused because it is thought too costly to connect it all.

Trend #2: Identity Management

Identity management allows your brand to know who your customers are, their preferences, where and how to reach them, and what kinds of offers they will likely find valuable. The problem is, managing identities across fragmented systems can be a challenge, which explains the trend wherein brands are looking for partners that can help solve this problem. Above all, identity management expands on your limited first party data to allow richer insights to be surfaced to meet customers where they are.

Trend #3: Machine Learning And AI

AI is a buzzword, but machine learning is a more practical subset of AI that holds tremendous potential for marketers, especially when we want to learn things from larger data sets. Thanks to cloud computing, putting this technology to work is within the reach of every marketer. If you target audiences using look-a-like models, you are using AI. Marketo, one of the leading marketing automation platforms, rolled out content AI which uses machine learning to deliver engaging content based on past buyer behavior. It can even predict the ten most interesting pieces of content for a particular audience in real time.

There’s a few examples of brands that are using advanced data techniques to grow their business:

• Target predicted pregnancy when they hired a statistician, Andrew Pole, to analyze shopper data and create a model that could predict which shoppers were likely to be pregnant. This data was later cross-referenced with women who had registered for Target’s gift registry to identify common patterns. But it also exposed a weakness in governance when they started sending coupons to a 16 year old who’s father found out about the unintended pregnancy through the mailings.
• Outdoor clothing retailer North Face partnered with IBM Watson to offer a highly personalized shopping experience. The virtual assistant walks users through a series of questions and learns to offer shoppers the most relevant products based on their needs.

In his 1982 book Megatrends, John Naisbitt wrote, “We are drowning in information but starved for knowledge.” The consolidation trends we are seeing marrying marketing and data show that, at least a few brands have a serious desire to get wiser.

Wiser. The goal for every brand today.

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Below are some of the limitations of big data and solutions to help brand managers extract the most value from these large data sets.

The Value Of Big Data As Stand-Alone Datasets Is Limited

By definition, behavioral data tells you what people do but not why they do it. This can be very misleading, especially if you are trying to pinpoint buying signals. Think of lifestyle products, such as luxury cars or watches. Of the thousands of people who google Ferrari or Rolex every day, how many are really in the market to buy one in the near future? Many of these users research lifestyle brands to daydream and self-actualize, with no real intent to buy these products anytime soon.

That said, behavioral data can be helpful to drive an impulse purchase or sale of lower price items. For example, retargeting (a form of advertising by which the ad is targeted at consumers based on their previous internet actions) can prove to be very effective. If you’ve looked at a book on Amazon this morning and keep getting retargeted with an ad for this same book, chances are you will end up ordering the book, especially if its price has dropped.

But retargeting can also turn into a waste of money, when marketers retarget prospects with products they have already bought. This points to another limitation of big data, whereby marketers often track search activity independently from conversion. If your customer just bought a coffee maker, it is highly unlikely he will buy a second one in the near future. The downsides are obvious: you are wasting your advertising dollars, damaging your brand by annoying your new customer and missing an opportunity to cross-sell companion products (such as coffee cartridges).

Never Lose Sight Of The “Why”

Seduced by big data tactics that seem scalable, instantaneous and cost-effective, brand managers often lose sight of the “why”. That is, why do people do what they do? And how can you seamlessly insert your brand in their thought process? The answer can’t be found in your consumers’ last few clicks.

Today’s consumer journey has 22 touchpoints on average. It is the strength of your brand that leads consumers to search for and select your product, more so than their last two or three clicks. Kantar Millward Brown’s Meaningfully Different Framework highlights that to be recognized and trusted, your brand must be meaningful, different and salient. It is these three qualities, in varying combinations, that predispose consumers to choose your brand and pay for its premium.

Behavioral data is most valuable when connected to other data sets, such as demographics and psychographics. I might not be in the market for a Ferrari this week, but now is a good time to sell me a book on career development (that will get me there) and a Ferrari polo shirt (that will enable me to self-actualize and keep day-dreaming in the interim).

Attitudinal data will provide you with depth and perspective into an otherwise basic outline of your consumer’s behavior. Combined with behavioral data, it will allow you to better understand what your consumers’ expectations and motivations are, refine your brand message and inform your advertising creative process.

Last but not least: when implementing retargeting tactics, add a conversion pixel to the post-transaction page, to stop targeting people with products they just bought and start up-selling them companion products.

In sum, marketing tactics based on behavioral metrics are the most powerful when combined with demographic, psychographic and attitudinal data. Such an approach enables driving action (i.e. enquiry or purchase) while constantly growing your brand’s equity. To measure your progress, it’s a best practice to track your lower funnel activity in conjunction with a robust brand health tracking program.

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A shop with 24 varieties of jam will, as a general rule, sell less jam in total than a shop with only six varieties. The use of big data can overcome this problem, because big data makes possible big personalization. A better focusing on the known preferences of consumers in terms of products, services and communication helps consumers to make their choices. The more personal and relevant the content communicated to a customer, the more likely that the customer will generate positive feedback and eventually make a purchase. Instead of showing your customers all the options in your product range, the trick is to show them just the three options you think will interest them the most. At Amazon, 35 per cent of all sales result from personalized recommendations; at Netflix the percentage is as high as 75 per cent. Personalization is therefore a key step in your data strategy. It is the ultimate way to raise your digital customer relationship to a higher level.

Many consumers are open to the idea of receiving the right (commercial) message at the right time. My own research has shown that 33 per cent are either positive or very positive towards the concept of personalized advertising. Consumers like to buy products. Consumers therefore like to have information about products – but only relevant information provided at the moment when they most need it. The ability to manage big data effectively will be a major tool in helping to achieve extreme customer orientation in the years ahead. The expectations of consumers will continue to evolve. They will almost certainly be less tolerant about receiving the wrong message at the wrong time.

This presents a serious challenge to the modern marketer. Instead of focusing on the average customer, from now on they will need to focus on the individual customer. Classic marketing involves getting to know and understand the average customer. This often resulted in the drawing up of a profile that was so general as to almost be meaningless. Many companies have ‘athletic males between the ages of 25 and 45’ in their target group. This type of profiling was necessary to select the right media and to adjust the message to reflect the target group. But the problem of working with averages is that they also tend to produce average results. One of the classic marketing dictums is that ‘half of the marketing budget is always wasted; the trick is to find out which half’. This is the result of failing to pay sufficient attention to the wants and needs of individual customers.

Personalization Equals Power

There are already many different forms of personalization at work in our modern world. Google searches usually produce different results for different people, because Google can identify what we are looking for. Similarly, the Amazon homepage is different for each visitor. This type of personalization is also becoming increasingly common in communication. When the Belgian energy producer Nuon was bought by Eni, the change of name and the necessary contractual changes were notified to customers in a highly personalized manner. Each customer (roughly 180,000) was sent a new tariff proposal that made clear whether the take-over would mean a price reduction or a price increase for the customer in question. In other words, Eni opted for hyper-personalization on the basis of existing customer data. No two letters were the same and the results were very impressive. For the customers who could expect a price reduction, the company achieved 189 per cent of its commercial objective. For the customers who would have to pay more, it achieved an even more remarkable 159 per cent. The greater the focus on individual needs, the greater the impact.

Not everyone is convinced. You often hear people say that companies should focus on using their small data (well-structured data in a company database) correctly, before attempting to deal with the more serious challenges of big data. But this underestimates the value of the predictive power of big data. Personalizing even relatively minor matters on the basis of existing customer data can mean a major step forward for many companies. For example, I have bought shoes from Amazon on several occasions. So why don’t they now offer me a selection of standard shoes in my size? Things like this can be easily arranged and make the purchase experience so much more pleasant for the buyer. It often seems that brands immediately go in search of the most difficult challenges, instead of dealing with the simple ones first.

Contributed to Branding Strategy Insider by: Steven Van Belleghem, excerpted from his book, When Digital Becomes Human with permission from Kogan Page publishing.

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