On a semi-regular basis, Apple’s executives tout the customer loyalty that its brand has picked up over the years, usually touting how much people love iOS and the iPhone. Continue reading
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The following is a guest contributed post from Iqbal Kaur, co-founder of Zylotech.
In a highly competitive market, it is critical for a marketer to know who the right person is for sending information about their latest marketing campaign. It is not simply looking for any existing customer, but the customer who would be most likely to act on the specific offer provided. By sending the correct message, to the correct person, at the appropriate time, a company achieves the following:
To make a long story short, in today’s world where information is readily available, but quickly outdated, it is hyper critical to stay relevant to your customers.
Now that we understand the need for segmentation, let’s ask the question of how?
Before this process can begin, it’s clear that further information is needed, such as:
Once we have answers to the above, we begin to segment our customers. There are several approaches that can be taken. These approaches depend on the type of information you have and how it is being used. Let’s take a look at a few:
This type of segmentation that groups customers is based on where they are in the course of their lives. Are they young and single? Are they married with children? This is done using demographic data like age, gender, marital status, number of children at home, etc. One interesting thing to note about this type of segmentation is that it lasts for quite some time–at least a few years–until the customer moves from one life stage to another.
What’s the relevance of Life Stage Segmentation?
Your customer, Sara, has recently had a baby. She makes the trips to her favorite retailer to get diapers regularly. One day Sara receives her mail and finds discount coupons for baby clothing. Sara is delighted. How did they know? Sara feels understood, and uses the coupons to buy clothes for her baby. This retailer has managed to personalize, stay relevant, and monetize.
This type of segmentation looks at what types of products customers buy the most. Are they always buying the latest gadgets? Do they prefer branded products? Do they like buying things on sale? Here we mostly use information about the attributes of the products that customers are buying and at what price point.
What is the relevance of Life Style Segmentation?
Your customer, Kevin, loves gadgets. He’s been waiting for the launch of the next big cell phone. He does not want to miss out on information about the pre-launch offers at his favorite retailer. He waits and waits, and never gets any information. Sara, however, does receive this promotion. Sara didn’t need a phone. Kevin is frustrated. He decides to buy the phone from a different store. Having the insight to find your Kevins and Saras on an ongoing basis is a solid basis for promotional strategies.
This type of segmentation is interesting, so let’s explore it in a bit more detail. Here we are looking at a customer’s purchase behavior.
This is the most popular form of segmentation as it yields very useful information from basic transaction data. Let’s take a look at RFM Segmentation using five questions:
Before we start getting into details, let’s do a small exercise. As retailer, which of the following customers is more valuable to you?
At first glance, it looks like Sandra is most valuable. She spent big bucks, but it’s important to note that she hasn’t been to the store in quite some time. Rony he has come in more recently, but has spent much less. How do we crack this riddle? Let’s continue using RFM segmentation to make this question easier. Now that we have set the context, we can move on.
Question 2: What data is required?
Interestingly, we can derive all our fields from our basic POS transaction data. We just have to get the numbers rolled up at the customer level. Let’s see how. Recency is calculated by the last order day subtracted from the current day and, obviously, less is better. Frequency is the total number of orders for a particular customer. Here, more is better. Monetary is the sum of the total sales from a particular customer, and more is certainly better.
Step 1: Data to scores. The first step is to create our individual R, F, and M scores. Here we simply put the data in ascending order and divide the data into five equal parts (Pentiles). Each of these pentiles is then given an ordinal score of 1 to 5. Each customer receives a score for each for the 3 metrics. Now, we concatenate the 3 scores in the order of R, F and M and get a 3 digit score for every customer
Step 2: Scores to segments: Each of the possible 125 scores are then used to identify visual patterns to get a handful of manageable behavior types. The easiest way to identify visual patterns is to put the average value for all the customers in each R, F & M table into a stacked contingency table.
Question 4: How do we get these results?
The whole segmentation is done by an unsupervised method along with a bit of human inspection. Again, this is not a predictive activity, this is a classification activity. Keeping this in mind, we do not have any post analysis diagnostics to validate our results. The best way is to split the data into test and validation periods to see if the segments remain stable. For example, we would build the segments on 2018 data and validate on 2017. Do the segments show very similar profiles? If they do, this method is fine!
Question 5: How does the business use this information?
This information is widely used by marketing teams to identify their most valuable customers. Perhaps the marketing team needs to understand which of their recently inactive customers should be approached with retention efforts. It is easy to identify the best candidates by looking at their RFM segments. Segment information can also be used to identify customers most likely to enroll into loyalty programs. Also, if we have further information on the demographics of each segment. We see a neat profile of the typical high value customer
Hopefully this article has helped you gain a better understanding of the importance of customer segmentation, and also taught you a bit about some of the most popular methods. The two most important questions you must always ask are: What is my business objective? and How do I make the segments relevant for the business and the customer?
The post Opinion: What’s the deal with Customer Segmentation? appeared first on Mobile Marketing Watch.
Apple will continue to be a major customer of Dialog Semiconductor, the chip manufacturer’s chief executive has claimed in an interview, insisting Dialog will continue supplying components for use in a number of Apple products until 2020, despite rumors that the iPhone producer may change how it sources some of its power management hardware.
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In the words of a famous disc jockey: “Another one.” A young hacker-turned-security researcher in England found a critical vulnerability on T-Mobile’s website that basically left records of user logins exposed online for hackers to pillage. The bug was reported and patched in December, and T-Mobile says no customer information was compromised as a result of this flaw.
Kane Gamble, who pled guilty to trying to hack into the email accounts of senior U.S.
T-Mobile website bug exposed customer logins to hackers, carrier says no accounts compromised was written by the awesome team at Android Police.
Twitter has made new tweaks that make it easier for businesses to provide customer service through direct message. Its more relaxed rules mean that businesses can more freely reply to customers who need support without having to worry about reaching a direct message limit. Spam accounts will still find it difficult to bombard users with […]
It seems like there's no end to the data breach stories. Uber covered their problem up, then had to answer to Congress. Equifax's initial response to its massive data exposure added its own security issue. Federal employees were even found stealing d…
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Zaius, a leading B2C CRM that delivers real-time, cross-channel marketing automation and attribution built on a single customer view, today announced immediate availability of its product integration with Zendesk, which builds software for better customer relationships.
By making customer support data available to marketers through this integration, Zaius is delivering on its promise of a complete view of every customer engagement, from marketing channels to support desk tickets.
Zaius’s product integration with Zendesk empowers B2C marketers with a centralized customer database and 360-degree visibility into all customer interactions with their brands. This one pane of glass streamlines the email, web, mobile, advertising and other data companies already collect, so they can better segment and target campaigns, and analyze relationships between ticket activity and customers’ average order value (AOV) or lifetime value (LTV).
“No single view of customer behavior is complete without support data, so the Zaius integration with Zendesk will give us critical feedback and insights that inform our entire marketing approach,” said Nicole Tabatabai, senior director of Digital Marketing at Optoro, whose Blinq.com is a leading overstock and returned product ecommerce business. “Leveraging this integration and the support data for segmentation and personalization lets us truly optimize our customers’ experiences with our brand.”
To learn more about Zaius, click here.
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T-Mobile is adding some new technology to its customer care arsenal. Tupl announced today that T-Mobile is using its Automated Customer Care Resolution (ACCR) tool. The technology uses artificial intelligence (AI) to give T-Mobile customer care reps with detailed cause reports and technical resolutions so that the the reps more quickly help customers. Tupl claims that its ACCR tool is 100 times faster and up to 4 times more accurate than other resolution methodologies. Here’s what … [read full article]
The post T-Mobile adopts Tupl’s AI technology to make its customer care better appeared first on TmoNews.
A recent Adobe survey of U.S. consumers revealed people spend on average 7.8 hours per day engaging with digital content — a figure that jumps to 11.1 hours per day among teenagers.
According to a provided statement from Adobe, brands must produce, execute and iterate on compelling content at ever-increasing velocity to engage with consumers, which is not an easy feat. To solve for these challenges, Adobe today unveiled tighter integrations and seamless workflows between creatives, marketers and data analysts in Adobe Experience Manager, part of Adobe Marketing Cloud in Adobe Experience Cloud.
These advances better enable brands to reach consumers across the full range of devices and channels. Adobe Sensei, the company’s AI and machine learning framework, further automates the delivery of personalized content, empowering marketers to work smarter and faster. New ways to pull creative content instantaneously from Adobe Creative Cloud into Marketing Cloud let brands integrate content and data more closely and deliver a seamlessly integrated experience.
“Content will always play an integral role in building brand loyalty, with personalization, authenticity and design reigning supreme,” said Aseem Chandra, senior vice president, Digital Experience Strategic Marketing at Adobe. “The new content capabilities we are announcing today empower brands to deliver digital experiences that delight consumers and uniquely integrate content and data.”
To learn more about about Experience Manager’s new intelligent content capabilities, click here.
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