Customer Centricity Fader Pdf 23

Pete Fader is the Frances and Pei-Yuan Chia Professor of Marketing at The Wharton School of the University of Pennsylvania. His expertise centers on the analysis of behavioral data to understand and forecast customer shopping/purchasing...

"'Of the portfolio things we sell, here's the next thing that you should buy.' That's not customer centricity. I mean, it can be if they love us, and they really do want to buy all of our things in sequence, but in a lot of cases, that's not the way it works."

As Covid-19 declined, a European multichannel retailer observed a decline in its online revenues, which caused alarm. But then they looked at the data a different way, focusing on transactions by individual customers. When they sliced the data in this manner, they realized that their customer base was actually healthy, but that their channel behavior had shifted: Online purchasing, which had become unnaturally accelerated during the pandemic, was now returning to a more normal pattern of online and offline purchasing.

What do these two examples have in common? Companies often look at their business by focusing on geographic regions, specific brands or products, or by sales channel. This makes sense, because this data is always at hand, and organizations are often structured around geography or channels. But by looking at data and business problems from a frame of reference in which the customer is the atomic unit for analyzing revenue and profitability, these firms were able to gain a new perspective on the problem they were facing, either properly diagnosing the problem or stopping themselves from making a bad decision.

At too many firms, analyzing the data of individual customers gets short shrift. Management reporting systems make it easier to focus on other things, and the organizational structure can make other metrics a priority. (If you have a person in charge of online sales, it feels natural to judge his or her performance by channel metrics.) This lack of focus on individual customer data is often a mistake. Revenues are generated by customers pulling out their wallets and paying for your products and services. Revenue is the sum of the value of all the customer transactions that occurred in a given time period.

How do we approach the task of gaining insight from such a customer-level summary? As we reflect on the various questions that are asked when leaders seriously engage with the idea of understanding the performance and health of their business using the customer as the atomic unit of revenue and profitability, five broad themes appear, which we call the five lenses of a customer-base audit.

If we reflect on a single vertical slice of the table, say the columns associated with last year, the following types of questions come to mind. How many customers did we have last year? How do these customers differ in terms of their value to the firm? For example, how many customers purchased from us just once last year? How many customers accounted for half of our revenue last year? Half of our profit? If we compare, say, the 10% most profitable customers to the 10% least profitable, what lies behind these differences? To what extent are they driven by differences in the number of transactions, the average value per transaction, and average margin per transaction? Digging deeper, what about differences in the types of products they purchased?

The set of simple analyses that explore how different our customers are from each other lead to a fundamental conclusion: customers are not equal. Most people underestimate just how unevenly revenue and profit are distributed across customers.

Yet without a solid understanding of the buying behavior of your customers, including an appreciation of how they differ in their value to the firm and a solid understanding of how their behavior is evolving over time, how can you be expected to ask the right questions and make informed decisions?

Abstract:Firms are increasingly organized around the client. At the same time, there is customer pressure on green and sustainable organizations. The purpose of this paper is to map the current state of the research in the domain of customer-centric organizations from a sustainability perspective. We conducted a bibliometric analysis from published documents between 1990 and 31 July 2020. Key findings indicate that research on customer centricity and sustainability has increased in recent years, finding some trends and that the topic is structured into three clusters: (1) Sustainable Development, Customer-Centric Perspective, and Sales; (2) Sustainability and Commerce; and (3) Customer-Centricity and Sustainability Trends. The implementation of a bibliometric methodology and the focus given to the definition, the relationships, and the evolution of the three main clusters within the topic are the characteristics that differentiate our study from other publications or reviews in the field of research. In addition, all the documents that refer to practical cases were identified, and the main ones were analyzed, to provide highlights to practitioners who aim to deploy the customer centricity approach in their firms from a sustainable perspective and seeking that the corporate purpose is followed.Keywords: sustainability; customer centric; business strategy; marketing; bibliometric analysis

Rather than showing how to wear the product as the 1900s does above, marketers now talk about how the product will benefit the life of the customer - and not cause bodily abnormalities for the sake of appearance.

Another product that has moved from product-centric to customer-centric over time is the watch. If you look at this Accutron ad from 1966, you can see how they highlight every single innovation that is a functional quality of the product.

In 2019, Apple released its Patently Apple watch. 50 years after Accutron and watches save lives. Sure, this evolution shows off the exponential rise of technology. But it also shows a dramatic shift in the language of marketers from product-centric to customer-centric.

We see a similar evolution of sports clothing: from product-centric, e.g. selling petticoats and sandals for weightlifting (I have so many questions about this), to customer-centric, e.g. the trend of athleisure and its exponential rise in recent years as shown in the graph below.

These three examples of the product-centric vs. customer-centric in retail are in fact the top trending fashion niches of 2019: shapewear, smartwatches, and athleisure. Each demonstrates a move from marketing the product to marketing for the customer.

There is a new age upon us, but one that is already happening on the subtle peripheries of eCommerce. This is the intersection of both the product-centric and customer-centric approach.

Product centric vs. customer-centric is an age-old way of thinking about selling products. By removing this belief in one versus another, you can do more with both your customer and product data.

That being said, the safest way to continue to collect data to optimize your webshops and personalize the CX is to shift the focus to your products and what your customers love about them. This is your data feedback loop in action.

Another reason for synthesizing the product-centric vs. customer-centric approaches is due to the sheer amount of choice that exists in the market. Consumers have a vast array of product assortments at the tip of their fingertips, which means they can shop for anything, anywhere, and at any time.

In practice, product intelligence will help optimize your entire supply chain. It supplies CDPs and PIMs with relevant product information, whilst saying something about who your customers are as people.

So Asics can further recommend trail running shoes in email campaigns or product recommendations, offering relevant tips and tricks related to their customers' running goals. This supports the overall CX.

Another case study that uses product intelligence is by the eCommerce tool YotPo. YotPo provides the retailer Greats with a review and rating box that customers can fill out when they purchase a sneaker.

For Bonobos and other e-tailers such as eyeglass seller Warby Parker, the decision to add physical stores represents an effort to become truly omnichannel, to mirror the way their customers actually shop. While retail stores need to be reinvented, they still have value. For while customers love to research potential purchases online, many do their actual purchasing in a store. According to a report by The International Council of Shopping Centers, customers are four times more likely to follow through on a purchase in-store than online. Moreover, seven out of 10 online shopping carts never make it to checkout, according to the Baymard Institute.

Yet, even for those enterprises considered among the best when it comes to omnichannel strategies, accompanying customers on their commercial journey can have challenges of its own. One of the biggest concerns is that most omnichannel efforts require some kind of opt-in from customers such as joining a loyalty program, downloading a mobile app, applying for a business credit card or providing an email address or mobile number.

The Crawl-Walk-Run StrategyAnother obstacle to developing an effective omnichannel strategy lies in the way operations are organized within a company. If they are compartmentalized based on channels rather than product or even type of customers, executives representing each channel end up competing with each other, thus hampering the creation of a true omnichannel experience.

The purpose of this paper is to explore the role of WeChat mobile-payment (m-payment)-based smart technologies in improving the retail customer experience and to develop an integrated framework of the smart retail customer experience including antecedents, consequences, and moderators. Based on the stimulus-organism-response (SOR) paradigm, we investigated the relationships among socio-technical stimuli, the smart retail customer experience, and relationship quality. We also developed hypotheses regarding the moderating role of customer lifetime value (CLV), which is considered an important customer characteristic. The proposed framework was empirically tested based on transaction and survey data of 462 WeChat m-payment retail customers. The results showed the following. (1) WeChat m-payment-based smart retail technology can enhance the customer experience by improving customer-perceived relationship orientation, employee-customer interaction, and communication effectiveness. (2) CLV has a positive moderating effect on the relationship between socio-technical stimuli and the customer experience. (3) The customer experience has a positive influence on relationship quality in the retail industry. Retail managers should make full use of smart retail technologies to improve the customer experience. In addition, they should emphasize the increase in CLV, as this increase enhances the positive relationship between socio-technical stimuli and the customer experience, making customer experience management more efficient. 2b1af7f3a8