Agilely Attending to Customers: Using Big Data to Bring Customers and Manufacturers Closer Together
*This article is an edited version of an online seminar that was co-hosted by INTAGE and RnI and held on November 22, 2022, entitled “Agile Marketing: Let’s Hear from Actual Purchasers!”
With all the talk about the need for CX (Customer Experience) and DX (Digital Transformation), you might be wondering where marketers at consumer goods companies should start. This article should help point you in the right direction. We can now gain a quick understanding of product evaluations and usage scenarios from comments collected through shopping and household budgeting apps, and obtain face-to-face “real-life feedback” from customers extracted and selected based on their purchase logs. In this article, we present, through case studies, our efforts to link this understanding of CX to marketing activities.
Engaging with Customers in the Age of Agile Marketing
Agile is an English word meaning “nimble and quick” that was used in the “The Lean Startup”, a book by Eric Ries that was published about 10 years ago, to describe a method of starting a business on a small scale and quickly making improvements while adjusting flexibly in response to customer reactions. Since then, “agile” has come to be used as an adjective in a variety of domains, such as agile development, agile management, agile organization…and agile marketing.
With trends changing so quickly and customer needs difficult to capture through a single approach, it is easy to see why the concept of agile marketing is resonating with many people. In addition to its literal meaning – “quick and nimble” – agile marketing is essentially a steady effort to attend closely to customers while repeatedly course correcting and taking action.
In consumer goods marketing, attending to customers through research and data analysis is not a new practice. However, the expertise required for manufacturers’ marketers to attend to customers and agilely apply user feedback to marketing actions is still in its developmental stages.
Accurately and Quickly Obtaining “Real-Life Feedback” from Customers through Purchase Log-Based Interviews
One way to attend to customers and get “real-life feedback” from them is to conduct 1-on-1 interviews, but there are several issues that need to be resolved in order to proceed in an agile manner. One of the most difficult challenges is to quickly and reliably find people who have bought your company’s products and/or your competitors’ products.
Normally, when recruiting product users for interviews, respondents are identified and selected by means of a questionnaire survey that relies on customer recall to ascertain what products they bought and when and where they bought them. In the survey, respondents must correctly indicate which products they have purchased from a wide array of product choices. However, as the famous “Ebbinghaus’ Forgetting Curve” has shown, human memory is not very reliable: it is said that roughly 56% of what we remember is forgotten after an hour has passed. In addition, according to independent research conducted by INTAGE, more than half of all consumer goods purchases are made on the spur of the moment in the store, and these purchases are even less likely to be remembered (Fig. 1).
Fig. 1
Those who have conducted qualitative studies before have probably had the experience of wondering during an interview, “Did this respondent really buy the product?”
An effective solution to the above issue is respondent recruitment based on purchase logs. By using log data on purchasing behavior (i.e., what was purchased, and when and where it was purchased) to recruit respondents, the issue of quickly and reliably finding people who have purchased your company’s products and your competitors’ products can be resolved.
Let’s consider the benefits for marketers of directly capturing “real-life feedback” by looking at case studies of interviews conducted through recruitment based on the purchase logs of CODE, a shopping and household budgeting app operated by the INTAGE Group company, Research and Innovation.
Food Manufacturer Case Studies
Along with case studies that use the aforementioned purchase log-based 1-on-1 interviews, some of the strengths of this approach are presented below, based on feedback from food product manufacturers who have actually interacted with users of their products.
Case Study: Ajinomoto Co., Inc.
Overview: Purchase log-based online interviews were conducted to learn about purchasers of a newly launched seasoning product. The company’s category manager spoke directly with respondents.
Post-interview impressions: “I could literally hear the actual ‘voices’ of purchasers.” “It was good that I could focus on what I wanted to hear about and what I was interested in (since I was the interviewer).” “It’s good that we can casually interview people from various areas and life stages. It might also be good for understanding the characteristics of each area.”
The study was well-received for allowing marketers to get “real-life feedback” at their own discretion from actual purchasers of the new product. It should also be noted that, the product in question being a seasoning, the target included mothers of small children, and while it would have been difficult for them to travel to an actual research venue due to time restrictions, the online interview format allowed them to cooperate in their spare time from their own homes. This approach, which would have been unthinkable before the pandemic, is a boon resulting from the DX of qualitative research.
Case Study: House Foods Group Headquarters
Overview: The spice market has grown due to the increase in home cooking occasions brought on by the coronavirus pandemic. The product development manager talked with respondents to gain a better understanding of how the spice seasoning products recently launched by House Foods are actually being purchased and used.
Post-interview impressions: “It boosts the morale of product planning staff to hear directly from repeat purchasers that they appreciate (the new products we developed).” “It was good to be able to ask about detailed nuances during the conversation, as I could confirm directly with the speaker whenever I needed to.” “In the course of the conversation, I was able to probe more deeply into the points that were of interest to me as a developer, and I learned about usage methods and areas of attention that I hadn’t anticipated.”
The spice seasonings targeted in this study did not have a high incidence rate, but thanks to the careful identification and selection of respondents after confirmation of multiple purchases in the purchase log big data*, the interviews were a success. Moreover, as the Ajinomoto team members also noted, the online, one-on-one dialogue format allowed them ask in detail about customer experiences with the product at their own discretion, which they found refreshing. (Fig. 2) *There are approximately 300,000 registered monthly shoppers on CODE, a shopping and household budgeting app.
Fig. 2
Quick Formulation of Hypotheses Using Product Review Data
There is another effective means of agilely attending to “real-life feedback” from customers: comments and product review data. The information that can be obtained through comments, however, is limited if the comments are highly anonymous and spontaneous, as is the case with social media. Therefore, it is essential to collect “real-life feedback” on purchased products from consumers whose attributes, such as gender, age, and area of residence, are identifiable*. *Attribute information of CODE app users is obtained when they register for the app.
CODE uses the process shown in Fig. 3 to collect real-life comments on and reviews of products that have actually been purchased.
Fig. 3
To illustrate what can be done with this word-of-mouth and product review data, let us present an example of how hypotheses about a certain nutritional food, Product A, were constructed using data obtained from CODE.
First, let’s look at the words that appear frequently in the “word cloud” (Fig. 4). One distinctive feature is the prominence of the word “Nutrition”. We can also see words related to how it is eaten, such as “heat”; ingredient descriptors such as “chia seeds”; and onomatopeic words describing the texture such as “crunchy (puchi puchi)” and “pasa pasa (dry)”.
Fig. 4
Next, let’s examine the reviews, separating those who gave the product a high rating (4.0 or higher on a 5-point scale) from those who gave it a low rating (3.5 or lower). The key factors in the favorable reviews were nutrition, convenience, and texture. On the other hand, those who gave the product a high rating showed reactions such as “The taste is not bad”, as if to remind themselves that they should not expect much from nutritional food products in terms of taste. Regarding the packaging, one user complained that it was difficult to tell what flavor it was (Fig. 5-1). The reviews of those who gave low ratings tend to focus on the “disappointing” taste and/or texture. There are also people who concluded that, in terms of taste and/or texture, other nutritional balance foods would probably be more satisfying. (Fig. 5-2)
Fig. 5-1
Fig. 5-2
Based on the above, preliminary hypotheses with respect to marketing actions for this product can be quickly formulated without even conducting a market survey (Fig. 6).
Fig. 6
Using this hypothetical framework, marketers can interact with product purchasers, as described above, to clarify points that need to be checked and gain insights that are actionable.
Using Big Data to Bring Customers and Manufacturers Closer Together
In this article, we have presented examples of purchase log-based 1-on-1 interviews and product review data analysis.
Neither 1-on-1 interviews nor data analysis are in themselves particularly novel approaches. However, by utilizing big data that is collected on a daily basis, it is now possible to quickly formulate hypotheses based on comments and reviews, approach more reliable target consumers (actual purchasers), and have them share customer experiences through direct dialogue with the manufacturer’s marketing staff. This may seem modest, but in fact it would not have been possible without recent advances in digital technology.
To put it in trendy business terms, we can describe it this way: The utilization of big data on purchase and comments/reviews, along with online interview DX (digital transformation), has allowed us to gain an understanding of CX (Customer Experience) in an agile manner, and to leverage this understanding for marketing.
We hope you have been persuaded that no “false claims” were made in the title of this article.
Kailog®Talk Kailog®Talk is a service operated by Research and Innovation Inc. that allows users to find and interview panelists who have purchased target products from the Kailog® database, which collects and accumulates daily shopping data such as what was purchased when and where, by whom, and with what other items. A service that allows users to select and interview panelists on their own is scheduled for release in 2023.
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