5 Unexpected Ways Apple’s ARKit Is Changing the Customer Experience Across Industries

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Apple’s ARKit has great implications for e-commerce. It’s the next best thing to literally trying before you buy, and you can do it anywhere you have your iPhone with you. Thanks to AR, consumers get a better feel for products before purchasing, increasing cart checkout totals and reducing returns.

But the capabilities of AR promise big things for other industries, too. Here are five surprising ARKit applications that are changing the customer experience for the better.

1. Car Showrooms

Thanks to e-commerce and review sites, the car industry has been slowly shifting to be more customer friendly.

Augmented created a virtual showroom experience of a Mercedes sedan using ARKit. Users can walk around the car, open and close doors, and try out different paint colors, allowing them to get a feel for the car without having a pushy salesman breathing down their shoulder.

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Once they choose a model and color, users can take it for a test drive with Vincenzo, and imagine what the car would look like parked next to their apartment building.

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2. Ordering Food

Alper Guler of Kabaq Food Technologies created a food ordering app that helps users envision ordering food at a restaurant.

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It’s easy to apply the same technology to food delivery and meal kit services like Blue Apron. If consumers can imagine what the finished meal will look like on their table at home, they might be more quick to order or subscribe.

3. Event Marketing

At Disney’s D23 expo this summer, Steve Lukas dropped a “live” Mickey Mouse in front of the conference entrance.

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Companies can do something similar to enhance their brand presence at events, creating a virtual game or scavenger hunt experience to direct attendees their way.

4. Online Tutorials

Whether they’re hardcore DIYers or just want to take on a new craft project, one thing that gives customers pause is whether they’ll be able to actually assemble the final advertised product. Online tutorial videos exploded in recent years to help address this problem.

AR transfers the tutorial from YouTube to your very own craft table or garage, helping you put the product together yourself. Artists can gain inspiration from AR experiences like this one, which shows a friendly blob creature drawing a flower according to your command:

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5. Product Promotion

From logo decals to product samples, companies are always thinking up new ways to give you freebies along with what you purchased. AR unlocks yet another freebie option, allowing customers to bring the brand to life in their own home. For example, upcoming DVD releases of Star Wards might come with an AR version of BB-8 to watch the film alongside the purchaser:

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Alternately, brands can delight customers and cement their loyalty by doing crazy things with their product, like raining endless cans of La Croix down on the floor:

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Conclusion

ARKit will come to all iPhones this fall. Until then, stay up to date with the latest ARKit developments by following MadeWithARKit, a Twitter account and blog showcasing the best submissions so far.

Michael Quoc is the founder & CEO of Dealspotr, an open shopping platform bringing together up-and-coming brands, influencers, and savvy shoppers around today’s best deals. He was previously the Director of Product Management for Yahoo’s media lab, spearheading the launch of several innovative live video and mobile social networking services. Michael has been awarded nine patents relating to mobile and social network applications and technology. Follow him on Twitter at @michaelquoc.

The post 5 Unexpected Ways Apple’s ARKit Is Changing the Customer Experience Across Industries appeared first on ReadWrite.

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Panera Bread left millions of customer records exposed on the web

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Add another big-name brand to the list of those who've left customer data exposed online. Thanks to security researcher Dylan Houlihan, KrebsOnSecurity has discovered that Panera Bread left millions of customer sign-up records (possibly 37 million) i…
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Why for manufacturers the customer must be at the centre of the digital strategy

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It’s easy to get obsessed with technology – it is the key enabler of today’s revolution – but the harder part of the transformation is cultural.

It’s about putting the customer experience and their business outcomes at the centre of everything. That means realigning engineering, manufacturing and the supply chain around delivering a world-class sales and service experience. It means new thinking about optimising customer satisfaction and loyalty metrics such as Customer Satisfaction Scores or Net Promoter Scores rather than production efficiency.

The list of technologies that can help this realignment is endless. Manufacturers should be investing in, or at least exploring, the internet of things (IoT) and industrial automation, cloud, big data, artificial intelligence (AI), robotics, 3D printing and more. Microsoft partner Columbus recently published a major industry report exploring how these emerging technologies fit into the wider digital transformation push of manufacturers, as part of a ‘Manufacturing 2020’ strategy.

But at the heart of the revolution is data. We are putting telemetry on everything, creating a data-driven culture with a single version of the truth. Fundamentally the fourth industrial revolution is being powered by the ubiquity of IoT data coming from sensors in the factory combining with data pouring in from the outside world, such as the wealth of information being generated by smart cities, smart buildings, smart offices and even connected cars. Choosing an IoT platform is a big decision; start by identifying one that can match the scale of your ambitions.

There is another convergence that is driving business transformation. Inside the firm, the digital technologies used by IT, operations and engineering are converging. By embracing the digital transformation, manufacturers are empowering employees to be more productive in modern workplaces with apps and intelligent working methods such as the use of cobots, where employees and robots co-operate “shoulder to shoulder”.

It’s also about optimising operations through smart factories and supply chain solutions powered by intelligent edge and cloud. It means the transformation of products and business models, using insights from smart connected products, advances in modelling such as “digital twins”, and more agile end-to-end business solutions.

We see manufacturers, and individual businesses within manufacturing organisations, at various stages in their journey to servitisation, transforming products into services. Some are driving more customer engagement through traditional call centres or differentiating their product through (sometimes IoT-connected) field service. Increasingly, though, we are also seeing the transition to full “product-as-a-service”, where they sell flying hours instead of jet engines; car coatings rather than paint; water savings rather than treatment plants; and cleaning services rather than cleaning chemicals.

This journey requires that they break down the silos between internal systems such as ERP, CRM, PLM, and SCM. Instead, they need to connect “things” – people, data and processes – with more agile systems of intelligence that can keep pace with the new speed of business inherent in delivering highly tailored products and services.

Manufacturers need smart factories that can make their smart products and be at the core of much more agile supply chains. They also need intelligent shop floor solutions and business apps that augment people and address the growing skills gap in manufacturing.

IoT platforms are a key enabler, yes. But we also need big data and AI on top to provide the insights that line workers and business decision-makers need. We need both intelligent cloud and intelligent edge technologies to power robots and cobots in the factory of the future.

Big data also needs big compute to accelerate the product innovation unleashed by enhanced insights into customers, enabled by the ability to iterate through digital twins of devices, product designs, supply chains, and customer usage in digital cities. Can your legacy ERP, CRM, PLM and SCM systems keep up with the new speed of business?

At the heart of this digital world, however, lies the simplicity of customer insight. Whether you’ve got a smart product that can beam back data on customer use, or you use traditional client engagement channels, it’s those insights that will differentiate your future products and services –and decide the success or failure of your digital transformation.

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Elon Musk, Customer Service Rep Extraordinaire, Just Deleted the Facebook Pages for SpaceX and Tesla, on Request (Seriously)

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Updated 3/23 at 1:38 PM

Elon Musk just took a stand—a substantial one that’s a giant shot across the bow of Facebook, and a statement Musk’s rabid base of acolytes may or may not take a cue from: In light of the data-gathering scandal that has rocked Facebook over the past week, Musk just dropped the hammer, and deleted the official pages for Tesla and SpaceX from the Book of Face.

It started when Musk played around on Twitter, randomly responding to Signal founder Brian Acton’s Tweet about deleting Facebook:

Someone called Musk out for making light of the situation.

And Musk, as he’s wont to do, (A) trolled Facebook, (B) called this random Twitter user’s bluff, and (C) wiped Space X and Tesla from Facebook:

Indeed, Musk seems to have lived up to his promise. Searching for Tesla on Facebook now reveals just lame stuff about the scientist:

And a search for SpaceX reveals a number of pages, though none seems to be official:

Contrition from Facebook CEO Mark Zuckerberg doesn’t seem to be enough to regain Musk’s trust (to say nothing of a previous beef they once had in which Zuck once blamed Musk for ruining some of his property).

As usual, Musk wins:

If you’re fearing where you’ll get your next Elon fix, check his Twitter and Instagram.

Clearly, Musk won’t be wiping himself from the web any time soon. Those accounts are never not going.

The post Elon Musk, Customer Service Rep Extraordinaire, Just Deleted the Facebook Pages for SpaceX and Tesla, on Request (Seriously) appeared first on Futurism.

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A Small Sample Size Shows Android Has Higher Customer Loyalty Than iOS

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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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Opinion: What’s the deal with Customer Segmentation?

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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:

  1. The best return on marketing effort,
  2. Saving resources by appropriate targeting,
  3. Making customers feel understood and valued.

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.

How do I segment my 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:

  • What is my business objective?
  • How much data do we have?
  • How do we validate our results?

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:

Life Stage Segmentation

 

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.

Life Style Segmentation

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.

RFM Segmentation

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.

  • Recency: When was their last purchase?
  • Frequency: How frequently do they buy?
  • Monetary Value: How many sales do we get from them?

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:

Question 1: What is my purpose?

Before we start getting into details, let’s do a small exercise. As retailer, which of the following customers is more valuable to you?

  • Rony: He made 2 orders, spent $ 200 and came last week
  • Sandra: She made 2 orders, spent $ 1000 and came 5 months ago

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.

Question 3: How do we create these segments?

Here is a step by step look at the model creation process:

 

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

Closing Notes

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.


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Dialog Semiconductor chief believes Apple will stay a power management chip customer until 2020

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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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T-Mobile website bug exposed customer logins to hackers, carrier says no accounts compromised

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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.

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Customer service on Twitter just got a big upgrade

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 […]

(via Cult of Mac – Tech and culture through an Apple lens)

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