Is "Actual Behavior Data" Really Usable? ―Hacking Customer Discovery―

In this era of "marketing IT," "Brand Growth Hacking" captures sustainable brand growth from a customer development perspective and achieves it by leveraging advanced technical capabilities (data, technology). Over three installments, we will introduce the three core processes that form its foundation: "Find," "Nurture," and "Optimize."

This time, we focus on the first and most crucial process: "Identifying" the customers who should be approached now. While "customer creation" remains an eternal theme in marketing, the IT-driven transformation of marketing has made it easier to capture customer behavior through data. This has also led to the emergence of new methodologies beyond traditional web surveys, group interviews, and behavioral observation.

Ⅰ Approaching Customer Creation with a 360-Degree Perspective

In "Brand Growth Hack," we first identify the opportunities and challenges to tackle in customer creation through a 360-degree perspective.

Throughout this journey to turning prospects into loyal customers, we explore questions like: "Where exactly are the current opportunities and challenges?" and "Which areas demand priority attention?" Even if you grasp these concepts theoretically, putting them into practice can be surprisingly difficult. Questions like "Even if someone shows high brand affinity or purchase intent, will they actually buy?" or "If someone made a purchase and was highly satisfied, will they truly buy again?" remain uncertain based solely on traditional web surveys measuring awareness levels. The solution to this uncertainty lies in "actual behavior data"—data capturing customers' real actions.

Actual behavior data includes: * Purchase Data: Reveals who actually bought what, when, and where. * TV Commercial × Digital Behavior Data: Shows what actions viewers took online after seeing a TV commercial. * Web Browsing Data: Tracks actual browsing behavior on websites other than the company's own. * SNS Data: Identifies who actually tweeted about the target brand. * CRM Data: Provides insights into the actual purchasing status of existing customers.

Before smartphones, the purchasing process was straightforward. To put it bluntly, if TV commercials boosted awareness and brand image, the path was linear: "mass advertising → web search → purchase." Marketing could be managed by tracking brand awareness and image through web surveys, and products sold. However, with the advent of smartphones and social media, the purchasing process has become more complex and opaque. Influenced by the opinions of close friends and acquaintances, third-party reviews, and various digital content (including competing products), simply improving awareness or brand image often fails to drive purchases for many products.

Yet, leveraging this "actual action data" visualizes the complex paths to purchase and reveals the reality of repeat buying, pinpointing bottlenecks. Improving these areas directly impacts sales figures, making it a strategy too valuable to ignore.

While the proliferation of smartphones and social media has complicated the purchasing process, big data analysis of "actual action data" now allows us to visualize this process and design more precise marketing strategies. Visualizing the target's path to purchase makes the progression from "potential customer → prospect," "prospect → customer," and "customer → loyal customer" much clearer, making it easier to identify opportunities and challenges for increasing sales. Handling "actual behavior data" is becoming an essential skill for anyone involved in marketing.

Ⅱ How to Use "Actual Behavior Data"

So how do you actually use "actual behavior data" to explore customers? Here are three approaches.

① Discover New Customers Using "Social Media Data"!

A client selling whitening cosmetics faced declining sales due to an aging customer base and increased competition. They collected social media posts about "whitening" from the past year. This revealed comments like, "Why did I go so goth-tanned in high school? Now I regret it because of the spots," from several women in their late 30s who had been goth-tanned gals. With web surveys, small sample sizes often miss such latent customers. Companies might also overlook them entirely, assuming "our target is late 40s and up." Until we gathered a year's worth of SNS posts, we hadn't realized there was a group just below the 40s demographic—women in their 30s genuinely troubled by age spots. This project truly highlighted how assumptions can lead to missed opportunities.

The decision of whether to target this demographic – "should we pursue them or not?" – and whether they could serve as a word-of-mouth catalyst despite their smaller volume, is a crucial strategic point. Today, however, we can precisely target this audience not just through mass advertising but also via digital initiatives. Consequently, the initial approach costs are lower than before, making it easier to take action – a defining feature of the IT-driven marketing era.

② "TV Commercials × Digital Behavioral Data": Convert Prospects into Customers!

Recently, it has become possible to visualize the relationship between TV commercial viewing and subsequent website behavior. For example, we can now determine what percentage of people who saw a brand's TV commercial actually visited that brand's website, and further, whether those visitors purchased the brand's product. We can also identify the attributes of the target audience, revealing what kind of people responded to the TV commercial and visited the site afterward. Additional surveys can even uncover reasons like "I was interested but didn't end up purchasing." This data can then be used to improve future products and aim to convert interested prospects into customers next time.

Another example is "web navigation data," which tracks browsing activity on websites other than the company's own. For a certain financial product, we discovered that people who came to the client's site via a "comparison site" ultimately did not contract that client's financial product. The comparison site had given the product zero positive ratings. Seeing this killer data, the client immediately decided to develop a new product that addressed the weaknesses highlighted on the comparison site. This was the moment when a single piece of actionable data drove a business decision. Because it's data reflecting actual consumer behavior online, it's persuasive and easily translates into management decisions and action.

③ Turn Customers into Top Performers with "Purchase Data"!

Can you recall the beer brand you purchased most frequently in the last three months, and the second most frequently purchased brand? Few people can answer this with clear confidence. However, using "purchase data" allows us to precisely and easily identify "people who actually purchased Brand A most frequently and Brand B second most frequently in the last three months." The more precise the target audience is narrowed down, the more precise the analysis results become. Trying to gather such a target audience through web surveys requires them to recall past purchase memories, meaning it might not have been possible to gather a truly precise target audience as intended.

In one case involving a health-functional beer, we discovered that the main purchasing group was "health-conscious individuals," followed by "those who enjoy rich, hearty dishes." It became clear that consumers were drinking it not primarily for its health benefits, but because its light taste complemented rich foods. Furthermore, "purchase data" allows tracking purchases over time, revealing patterns like a tendency to switch brands to other light-tasting beers. This enables immediate countermeasures. In this case, strategies to retain these customers and turn them into loyal patrons were promptly considered.

The above is an example of customer exploration using "actual behavior data," which is gradually gaining traction. "Actual behavior data" provides precise analytical results for marketing strategy development, deepening customer understanding. However, there are two key points to note when handling "actual behavior data."

First, the reasons for purchase are often unclear. Why did they choose that brand? Actual behavior data can only provide inferences. Therefore, supplementary research like web surveys or group interviews is necessary. Clarifying purchase motivations is essential to pinpointing the causes of success or failure.

Second, relying solely on past purchase behavior results can sometimes make it difficult to determine what actions to take next. For example, even if we precisely know that 60% of customers are Type A and 40% are Type B, should we focus next on the larger volume of Type A customers? Or on the potentially growing Type B? "Actual action data" alone cannot make this judgment. The strategist's future outlook is crucial.

Questions like "WHY SO?" (Why is this happening?) and "SO WHAT?" (So, what should we do next?) are hard to glean from analyzing "actual behavioral data." Some say, "We can't learn anything from big data on actual behavior; it has no value." This is partly true. It remains vital to make judgments by cross-referencing this data with "quantitative awareness data" from web surveys and "qualitative content" from group interviews.

Ⅲ Triggering Small Innovations with "Actual Behavior Data"

Analyzing "actual behavior data" and digitizing marketing has made it easier to trial new digital initiatives at low cost for new target audiences. Therefore, rather than spending time solely on analysis and hypothesis building, you can test digital initiatives at low cost, verify hypotheses, and refine their accuracy. Previously, "analysis" and "initiatives" were disconnected, but introducing "actual behavior data" has made collaboration easier.

In the earlier examples: "Would former ganguro gals in their late 30s genuinely be interested in our brand? Though few in number, could they become word-of-mouth influencers?" "Would visitors to comparison sites actually sign up for new financial products?" "Will people who chose functional beer for its light taste apply for a rich food gift?" These hypotheses can be rigorously tested using "actual action data" by running minimal banner ads or digital campaigns.

By testing and achieving good results through digital initiatives like this, you can then make the decision to go big with mass advertising campaigns. In other words, combining "actual action data" analysis with test trials creates an environment conducive to innovation at low cost.

However, there's a crucial point to remember here. If digital initiatives focus excessively on individualized, one-to-one approaches, the brand risks becoming a "pleaser to everyone," diluting its core value. This ultimately increases the likelihood of customers drifting away.

In this era of "marketing IT," brands require hybrid management—balancing what must remain unchanged with what must be tailored to each customer. It's a question of balancing holistic brand value design with individualized design. Next time, we'll explore recognizing this challenge and introduce the "nurturing" process of brand growth hacking: "hacking customer development through new brand value experiences." Stay tuned.