Analytics 2.0: Big Data, Big Testing, and Big Experiences — Part 2
July 31, 2013
In Part 1 of this two-part series, I discussed a Harvard Business Review article written by Wes Nichols, cofounder and the CEO of MarketShare, a global predictive-analytics company headquartered in Los Angeles. [“Advertising Analytics 2.0,” March 2013] In that article, Nichols argues that old analytic techniques are simply too limited to meet the complexities of today’s marketplace. He asserts that analytics 2.0 involves three broad activities: attribution, optimization, and allocation.
“As Bob explained, Buyer Experience Management (BXM) means understanding how buyers perceive their interactions with a brand, and then delivering value during those interactions so buyers (and non-buyers) become brand advocates. Thinking in terms of BXM helps tear down silos, especially the ones between sales and marketing. ‘Keep in mind that one of the big problems in B2B marketing/sales is the silo mentality,’ Bob told me. ‘Marketing generates leads, sales closes deals. Each has their own set of goals and processes. Left out is an appreciation for what buyers are going through as they navigate from marketing to inside sales to field sales.’ A derivative of Customer Experience Management (CEM), BXM focuses on the marketing/sales function. It’s a way of looking at the buyers’ complete journey as they perceive it. ‘The core issue is that marketing is still viewed by most as pushing a message and/or generating leads. It’s part of the “CRM” mentality which is really company-centric – designed to extract value, not add value,’ Bob said. ‘BXM is about taking a customer-centric view of the buyer’s journey, and asking how the buying experience is adding value and creating loyalty, even with prospects that don’t end up buying.'”Also in that post, I discussed Scott Brinker’s view of how big data should be used. He stated that marketing’s future involves using big data and big testing to provide the consumer with a big experience. [“The big data bubble in marketing — but a bigger future,” Chief Marketing Technologist, 21 January 2013] As I stated in that post, only analytics 2.0 is capable of dealing with all three “big” activities. If you don’t believe that “big experiences” are important, Lisa Arthur might convince you. She reports, “Poor customer experiences result in an estimated $83 billion loss by US enterprises each year because of defections and abandoned purchases.” [“Four Ways To Improve The Buyer Experience Starting Today,” Forbes, 26 June 2013] “Unfortunately,” she continues, “most marketers remain unsure about how to improve relationships with today’s empowered consumers. What ‘exactly’ should you be doing? How can you start creating a better customer experience?” In an interview, Bob Thompson, CEO of CustomerThink Corp., told Arthur, that “the first step towards creating a better customer experience is to put the customer at the center of all you do.” His first recommendation for doing that involves “thinking in terms of Buyer Experience Management.” Arthur writes:
Clearly, in order to understand how buyers perceive their interactions with a brand, companies need to gather and analyze big data. Getting to know your customer (i.e., taking a walk in your customer’s shoes) is the next recommendation Thompson related to Arthur. She continues:
“Bob estimates that less than 10% of B2B firms truly understand what experience buyers receive, even though virtually all agree that experience is important to revenue performance. Granted, B2C firms may not be quite as infected, with ‘silo-itis,’ but Bob suspects similar problems exist for B2C marketers, as well. … ‘Good customer/buyer research is essential to figuring out what the target market really values, and what they are experiencing on their journey. You can’t be “value adding” unless you know what customers think is valuable!’ I wholeheartedly agree. It’s time to stop walking the talk.”
Thompson told Arthur that, in order to achieve the desired goal of understanding the customer’s path to purchase, they need to “eliminate touchpoint amnesia.” Arthur explains:
“A truly satisfying omnichannel experience requires an integrated approach. According to Bob, a lack of channel integration clashes with consumer expectations –and it negatively impacts sales. ‘Companies have automated channels bit by bit, so they can claim to be multi-channel. Yet customers find (about 80% of the time, in my research) that a multi-touch experience is not remembered. I call this ‘touchpoint amnesia and found that it has significantly reduced customer loyalty and propensity to buy,’ he told me. ‘Omni-channel experiences should make it easy to customers to navigate channels as they wish, and not lose information the customer has already provided.'”
Data integration is not easy, but the kind of integration Thompson is talking about is more structured than many kinds of data and should be relatively easy to accomplish. Customers certainly believe that, which is why they find it frustrating when they have to provide the same information time and again. Thompson’s final recommendation for companies is to “keep learning.” Persistent learning can only be achieved through the collection and analysis of big data. This subject interests me because many of my company’s offerings use the Enterra Cognitive Reasoning Platform™, which ingests structured and unstructured data, understands the nature of the data, learns from known and discovered relationships, and takes actions within decision cycles to obtain desired outcomes. The Platform addresses all four of Big Data’s dimensions: Volume, Velocity, Variety, and Veracity.
- Volume: This dimension addresses the size of data. In today’s world, that volume is enormous and getting larger.
- Velocity: This dimension addresses the timeliness of information. There was a time when quarterly reports were fast enough to keep up with the clock speed of businesses. Today, for truly time-sensitive data, two minutes may be too long.
- Variety: This dimension addresses the fact that useful data can be messy. It is no longer found solely in neat rows and columns of spreadsheets. Most new data is unstructured and must be filtered and understood to be of value.
- Veracity: This dimension addresses the trustworthiness of data. If you can’t trust the data, you can’t act confidently act upon the insights obtained from it. As the number of sources of data grows, challenges associated with the veracity of that data also increases.
Some analysts add two more “Vs” to that list: Visualization and Value. If information is presented in a way that is not easily understood, it is little better than having no information at all. Value is not so much a dimension as it is an outcome of the other “Vs” discussed above. Enterra’s Cognitive Reasoning Platform brings together two distinct philosophical and technological computing camps (i.e., Mathematical Optimization and Reasoning). This melding of methods allows the Platform to perform rapid computations as well as discover and explore new relationships.
Enterra’s Cognitive Reasoning Platform addresses marketplace needs for today’s computer systems to be able to Sense, Think/Learn, and Act™ about the environment (industry/domain) in which they operate. At the level of so-called “Small Data,” the amount of data can be too overwhelming for a human to process. At the level of Big Data, it is impossible to manage without some kind of artificial intelligence help. Therefore, computers need to evolve from executing tactical instructions to thinking and making sense of the data in a way more similar to humans. And they can’t take excessively long periods of time to conduct the analysis. Enterra’s Platform attempts to do this by pairing an ontology and rules engine with the muscle of dedicated analytic data processors to perform rapid computations.
As Wes Nichols made clear in his article about analytics 2.0, it’s easier to talk about doing it than actually being able to accomplish it. As he puts it, “The opportunity is clear, but so is the challenge.” At Enterra, we are excited about helping develop some of technologies that can rise to the challenge and help consumers have a big experience by better understanding them.