Dentsu Inc. and Fujitsu's Big Data Business Alliance, and What Happened After...

After a six-month hiatus, I'm resuming this series. I even considered changing the title "Big Data." After all, Gartner's "Hype Cycle for Technologies in Japan: 2014," published in October 2014, commented that "Big Data" would enter the Trough of Disillusionment within about a year.

However, I don't believe "Big Data" has become a dead term like other past IT buzzwords. Rather, it's now being used so naturally that people aren't even consciously thinking about "Big Data." Even data flowing in real-time in diverse formats is being utilized routinely. You hear things like, "It seems various data are being used behind the scenes for this new initiative. Huh? The data volume? Hmm? It might be large. Probably." That's the kind of presence it has become.

To reiterate, it's no longer the proper noun "Big Data." It's starting to be treated as the common noun "data," just like before the "Big Data" boom. So why do I keep using "Big Data" in this title? Because I still think we're in the "peak of inflated expectations" phase (laugh).

One year has passed since the business alliance...

Last May, Dentsu Inc. announced a business partnership with Fujitsu in the big data domain. Please refer to previous articles for the partnership concept. Over a year has passed since then, and during that time, we've had the opportunity to collaborate with diverse companies across utilities, retail, finance, automotive, and more. Through these efforts, a "framework for enhancing client business operations through big data utilization" has taken shape. This framework is uniquely possible because it involves collaboration between two seemingly polar opposite entities: an advertising agency and an IT vendor. That's why we're announcing this release once again.

Creating Optimal Customer Experiences Through Operational Big Data

"Customer Experience" (CX) is a hot topic in today's marketing landscape. While we won't delve into the details here, the core idea is that in this era of product abundance, the value companies should provide to customers is shifting from "product value" to "experience value." Companies must prioritize the customer's experience across all touchpoints with the business—before and after the sale—not just the moment of purchase.

For example, in the automotive industry, the moments of joy differ between the company and the customer during the purchasing process. For the company, specifically the salesperson, the moment of joy is when they "close the deal (receive the order)." After persistent sales efforts under demanding quotas, the moment a customer commits to purchasing a new vehicle must be deeply rewarding.

However, the moment customers are happiest is not the contract signing, but the "delivery" of the vehicle. This timing gap often leads to a diminished experience value, the very service the company aims to provide. Some salespeople, their focus shifting to acquiring new customers the very next day after securing a contract, may make mistakes like delaying notification to the customer when a delivery is delayed. Imagine a customer eagerly counting down the days until delivery, only to receive a delay notice on the very day of delivery... The disappointment is all too easy to imagine.

There are limits to "product sophistication," making differentiation increasingly difficult. Going forward, enhancing customer experience value through "service sophistication" and the accumulation of such experiences will become the competitive edge.

Customer Experience Management through Big Data

A crucial factor supporting customer experience is "customer touchpoint management." This encompasses both digital and physical channels: how can companies make the service experience they provide at customer touchpoints truly exceptional? In the earlier example, the moment a delivery delay is detected, how quickly and courteously does the salesperson communicate using the appropriate channels? Naturally, capable salespeople already do this. Furthermore, it is now critically important for companies to verify whether they can consistently maintain this level of service experience and whether interactions at customer touchpoints are functioning correctly.

While customer touchpoint history has traditionally been collected via websites and optimized in areas like UX, the adoption of systems like SFA (Sales Force Automation) is now enabling the collection of real-world, customer-specific touchpoint history, creating an increasingly verifiable environment. Furthermore, IoT is making it possible to collect history on how customers actually use a company's services and products.

This announcement introduces a framework that defines the data companies can collect as 'business big data'—data generated from operations. Using the expertise and data owned by Dentsu Inc. and Fujitsu, this framework analyzes and verifies this data to devise measures for delivering the ideal customer experience.

A Five-Step Framework

■Step 1: Understanding the Client Company's Operations and Acquiring Operational Data
Primarily through workshops, we understand the company's current customer touchpoints and the mechanisms supporting those touchpoint operations.
We also identify operational data that can be leveraged to objectively verify these points.

■Step 2: Extract challenges in current customer touchpoints using Dentsu Inc. and Fujitsu's know-how and data
We analyze operational data using Fujitsu's big data analytics technology to validate challenges.
We also segment customers to refine initiatives.

■Step 3: Design customer personas and optimal customer touchpoint scenarios to optimize the customer experience
Using Dentsu Inc.'s proprietary marketing data and analytical expertise, we create personas and design scenarios for each customer segment.

■Step 4: Execute real-time scenario initiatives using customer scoring
Continuously analyze daily accumulated operational data to calculate a "purchase score" for each customer based on the purchase funnel.
Execute nurturing and acquisition scenario measures according to the customer's score.

■Step 5: Validate effectiveness through field testing and other methods
Verify the effectiveness of the above framework using operational data after campaign execution and provide feedback.

Shizuoka Gas, recognized for its early adoption of this framework and awarded the "2014 CRM Best Practice Award" by the CRM Council, is now engaged in customer experience management utilizing energy usage data and sales activity histories.
Reference Article: Integrating Full-Count Data and Panel Data Supports Proposals for Better Living (Nikkei Big Data)

As explained at the outset, the primary goal isn't specifically to utilize "big data." Rather, the data became the means to visualize and verify the current customer experience. That's the essence of it. Whether the data was big in volume or not is entirely irrelevant.

In a previous article, I cited the optimization of customer experiences through building corporate "private DMPs" as an example of future activities by advertising agencies and IT vendors. I consider this current initiative to be the first step in that direction. Triggered by the emergence of big data, various "marketing technologies" supporting broad-based DMPs have been born, and I intend to introduce them in this series.

Next time, I'll share insights on how the sophistication of "marketing technology" is even transforming the familiar "4Ps" we know so well.