The CMO Guide to Big Data and Analytics shows how CMOs can use exploding digital social and transaction data from websites, enterprise systems, smartphones, tablets, cars, appliances, devices and the Internet of Things in surprising ways that have more to do with customer insight than technology.
The digital social world and modern cloud-based technologies have reinvented the practice of “analytics” so that it’s faster, cheaper and more open. I’ll paint a broad picture of this new world before referring you to other resources where you can drill down.
Big data is usually discussed from a big-ticket I.T. perspective, but new technology enables marketers to practice “lean data,” which starts small and proves/iterates hypotheses before scaling with large investments. The biggest barriers are knowledge and imagination, not technology.
Big data elevates opportunities for marketers because they can know their customers and serve them better based on their actions, not only what they say. Conversely, big data is increasing customers’ expectations for being treated as individuals, not “consumers.”
Big Data & Analytics Overview
“Big data” is in hype mode, but there’s serious meat behind it and exceptional opportunity for chief marketers. Most readers are probably somewhat familiar with the rather painful legacy way that “analytics” has been practiced up to now. Several things are changing the situation in ways that favor marketers:
- Modern technologies like cloud make processing, storage and other computing resources far less costly, and start-ups are providing a dizzying array of tools to make analytics easier. Massive computing power is available by the second or the transaction.
- Digital social technologies have made it exceedingly easy and fun for people to interact digitally about everything. For the first time ever, the whole spectrum of people’s fears, dreams, desires, frustrations and exhilarations are online, searchable and forever. Social data is the key to understanding the context of customer satisfaction.
- Open data is exploding because governments, universities, people and enlightened firms are making their data available for free.
- Mobile technologies are creating digital data that fuse transactions (what people are doing) with where and when they are doing it, creating an unprecedented view of behavior.
The above points make big data a new phenomenon. Its official definition refers to “the four Vs”: volume, velocity, variety and veracity. More types of data are available than ever (“variety”), and the volume of data is growing exponentially (“velocity” and “volume”). Veracity refers to the degree to which the data can be trusted.
Some Examples of Big Data Sources
- Social network data, email response rates, geosocial data (i.e. Google, Yelp, Foursquare checkins) ecommerce transactions.
- Chemical composition of sewage, shopping cart abandonment, call center logs, warranty data, weather, sports schedules, disease outbreaks.
- How many Parisians ran sub six-minute miles between 6-9 a.m., natural disaster data, school closings, import/export data, city license renewals. The main barrier is imagination, which is a great place to be.
Big data will be huge data next week because mobile devices follow people everywhere, and apps encourage people to interact with their surroundings or as they pass through their surroundings. When you rent a bike, pay for something, text someone, even walk down the street, you are creating digital information that can be gold to certain firms or people.
Big Data: How to Get Started
Get Grounded in a Customer Context
To use big data most effectively, companies and marketers need to reorient themselves to their customers (clients, employees and other stakeholders). All the types of data mentioned here are rapidly changing customer expectations: Because people are sharing a lot, they increasingly expect to be treated as people, not “segments” or “demographics.”
The customer context also means recognizing that people are less interested in products and services than in the outcomes they can attain by using products and services.
The new “customer context represents a profound shift for marketers. Up to now, it hasn’t been practical for companies to know their customers individually, so no company did and customers couldn’t expect it. Now, digital social and big data make it efficient and practical to “know” customers individually.
It is now possible for companies to treat their customers personally, at scale.
So focus your big data initiatives on learning about and supporting the outcomes your customers want. Don’t let the product focus be a barrier between your company and the customer. The current state is “brands are from Mars and customers are from Venus.” You don’t have to do this anymore. Align with customers by focusing on what they really want: outcomes of using your product.
For example, if your company or brand involves camping equipment, focus on helping people have better camping experiences. This has to be specific: family camping is different than cross-country cycling, college expeditions… In another example, women wear hats in various situations, so learn about what outcomes they want from wearing hats in situations that are most relevant to the hats you offer.
Don’t Get Blinded by Technology
The over-riding promise behind big data is seductive: machine-derived learning about customers or other stakeholders. Big data enables better decision making by selectively replacing managers’ “impressions” or “gut feel” with “data” that are presented using amazing visualization tools. This is all true, but it’s still a long way from producing a profitable return on investment.
All organizations are sitting on data, and cloud and modern tools make it easier than ever to work with structured data (often from internal data bases) and unstructured data (social network data, other external data). The question is not whether an organization can “create value”; rather, can it create enough business impact to justify the investment within a certain time frame?
Be Creative with Data
Most teams start from the organization perspective, “What do we have, and how can we use it?” I’ll suggest that a more direct path to success is to approach from the customer point of view by asking, “What do our best customers want to accomplish by using our products/services?” What do they need to know to attain the outcomes they want? To really get this, the company must put customer outcome first and product sales second. Companies that do this will sell more at higher margins.
Most big data thought leadership emphasizes creating and testing hypotheses. Take this further by focusing on outcomes like these:
- The camping equipment brand thinks, “If we could analyze transaction data, we could learn when customers go on extended family camping trips, and help them to have rewarding experiences. By helping them to have more fun, a greater portion of them will become more avid campers. We could help campers by analyzing weather patterns, festivals, sporting events and other events that influence traffic and business at camping areas.”
- The hatmaker team hypothesizes, “By analyzing transaction and returns data, we can discover that an important portion of women who buy our exotic hats do so for weddings and graduations. In certain geographies, intermittent rainfall can ruin certain models, so we can analyze geographic and weather data to make suggestions for them to look smashing without having their hats ruined.”
- A commercial bank team asserts, “By analyzing small business and personal accounts, we could explore how the success of a business affects customers’ use of more of our services. We could look for patterns and triggers in their businesses’ websites and offer to help them grow their businesses, say, by making introductions to other clients in complementary areas.”
The Social Angle
The conventional wisdom says that the teams need to test their hypotheses by piloting big data, and there are elegant ways to do that. However, in most cases, teams can use digital social venues to test the value proposition embedded in the hypotheses, to pre-validate the project at a lower cost. For example:
- The camping team obtains pre-existing reports that show what kind of events produce the largest camping equipment purchases and returns. It analyzes these data to deduce events with extended families. For example, families camping with grandparents probably buy certain things. The team analyzes the digital social ecosystem to find venues in which people are talking about camping trips. It learns, fast, what’s important, what information has the most impact on outcomes.
- The hatmaker and bank teams can do the same thing. It’s important to realize that the traditional method of surveying people is far less useful than observing people talking among themselves. When a business asks an individual what kind of information would have made a difference, that is an explicit question, and few people have the awareness to give a complete response. However, when people are interacting among themselves, far more implicit information emerges.
- Working with big data requires an unusual combination of expertise and skills, and its relatively sudden growth means that there is a serious people shortage. However, you can mitigate this risk by being creative with how you access the expertise you need.
- If you ground your big data initiatives in helping customers attain desirable outcomes, you will find that internal data may be less relevant than you might have assumed at first. You will waste less time chasing skills you may not need.
- In most cases, creating a cocktail of internal and external data will add the most value. However, you can use a small core of data science experts and build customer-focused people around them. Contracting might make more sense than you think, especially during the pilot phase.
- Include social business people on your team; they are people that specialize in deep interaction more than promotions and marketing.
- Too many companies don’t take privacy seriously, and they are on a time bomb because it’s only a matter of time before they are outed by customers, government or geeks. Establish standards that transcend various teams. Customer data is key to trust.
For more practical guides on using big data to transform customer experience, see the Big Data & Analytics Competency Center.