8 Steps to Achieve Personalized Ecommerce for Better Sales

Customers have come to expect personalization when they’re shopping online. They’re starting to see this as a standard service and appreciate receiving special, tailored offers.  Ecommerce personalization enables you to treat every customer like a VIP. And when customers feel like VIPs they’re more likely to stay loyal to your brand.  Ways to keep customers…

Customers have come to expect personalization when they’re shopping online. They’re starting to see this as a standard service and appreciate receiving special, tailored offers. 

pie chart on how personalization can improve customer engagement
Image Source: [1]

Ecommerce personalization enables you to treat every customer like a VIP. And when customers feel like VIPs they’re more likely to stay loyal to your brand. 

Ways to keep customers happy include providing them with a seamless customer service experience. For example, implementing VoIP technology is a great way to handle incoming calls. Other ways to keep satisfaction levels high include offering a customized shopping experience like no other. 

Follow these eight steps to create a winning website personalization strategy.

1. Encourage customers to create accounts 

In order to build a customer profile (or buyer persona) you need to gather data from your existing subscribers and buyers. One good source of data is the information that’s input when a customer signs up to buy or subscribe to your newsletter. When you have amassed enough data you can dig through it to identify common traits that many of your customers have. Such as, if they are in specific age groups or genders. Then you can use this information to help you create an ideal customer profile and targeted campaigns for people in those demographics. 

Having customer profiles will help you implement personalization strategies. It will help you monitor your customers’ behaviors (This is something that customers are generally happy[1] for you to do, so long as it leads to them having an improved shopping experience). 

Once you can identify customers by their profiles, you’ll be able to offer them products or information that may be of interest to them. 

When you ask people to create accounts, make it easy for them to do so by adding a checkbox at the checkout that nudges the customers to add their details. 

2. Segment email subscribers

You can segment your email lists by geographic data, demographic data, psychographic data (lifestyle, activities, etc.), and behavioral data (based on purchases, browsing behaviors, etc.)

the key pillars of segmentation
Image Source: [2]

Once you’ve segmented your lists you can make sure you send appropriate content, for instance, based on a customer’s location. Say you’re promoting a ‘winter sale’. You only want to send out emails to subscribers in locations that are currently, or are soon going to be, wintry.

Pair your email campaign with a landing page to boost conversion rates. Send subscribers an email that reflects their purchasing history – along with a link to a landing page that tells them more about those products or services. Linking them to a specific landing page (rather than the generic homepage) increases the likelihood of them taking action. 

Personalize email subject lines to include subscribers’ first names and send out celebratory birthday emails with special money off vouchers.

Other ways of personalizing include sending emails inviting customers to leave their opinions. These business review examples can give you an opportunity to reply personally to subscribers who love your brand as well as respond to criticisms. 

3. Create personalized homepages

Homepages are your online store’s front door. So apart from making sure, your landing page is optimized, make sure you give customers a warm personalized welcome based on their purchases or browsing history. By using tracking cookies you can see which pages a previous user has visited and present them offers that might be relevant to them. 

If, for example, someone has previously visited a blog on ‘how to start an ecommerce business’, next time they visit you could invite them to download an eBook on order management systems.

Or, for instance, if someone has previously browsed the ‘15% off boots section’ on the ‘women’s sale’ page you can show them ‘new women’s boots just-in’’ on the homepage next time they browse your site. 

4. Provide personalized online store assistance 

Invite customers to participate in quick quizzes around the size or style of items they’re looking to buy. By storing the results, you can personalize product suggestions. If you’re a fashion e-retailer you could provide personalized wardrobe suggestions that fit budgets, sizes, and tastes. 

Having this information also opens up further marketing opportunities in terms of email updates about new products that might fit the bill. If, for example, a customer has expressed an interest in creating sales literature for their website, you could send them an email inviting them to use your online digital brochure maker.

5. Personalize product pages using location data

So whether you use cookies or ask for customer information you probably know where your visitors are located. This is valuable information that can vastly improve a customer’s shopping experience. 

For example, you can personalize sizes and currencies based on the visitor’s store selection. This means shoppers no longer have to use size conversion charts for each product category if they’re shopping from abroad. 

If a visitor has already selected the US on the top bar on a previous visit, you can make sure they’re taken directly to this store next time they visit.

6. Capture visitors when they’re about to leave

When visitors are about to leave your site, show them a personalized offer based on their browsing activity – either to complete a purchase, sign up for your newsletter, etc. 

Fashion brand Minimum offered the following incentive to customers in order to get them to complete an order. The campaign was wholly successful with a conversion rate of 37.4%.

personalized offers in pop-ups on exit intent
Image Source: [3]

7. Offer incentives to win back customers

Personalization can not only help you win and retain customers, but it can help you win back old ones. If certain customers haven’t purchased from you in a while, use retargeting ads. Include offers based on previous purchasing history along with incentives such as special one-off discount codes to give them a reason to shop with you again. 

8 Use live chat software or chatbots

Most companies still use the same pop-up chat messages for all visitors on their site. But it makes far more sense to personalize the introduction message based on a visitor’s URL, their behavior, or any other data you can collect. 

Once you understand behaviors you can deliver price discounts or other incentives to persuade a visitor to proceed to purchase. 

eCommerce is here to stay and shoppers are demanding an ever more personalized shopping experience. You need an eCommerce personalization strategy to help you attract and retain customers.

Which aspects of personalization you choose to invest in or focus on is a major challenge. This is primarily because personalization needs data and infrastructure, which takes time and budgets to build. 

While there can be hundreds and thousands of variables that feed into personalization, it’s important to know which of these variables make sense to you. You can identify these variables through A/B tests. These A/B tests may be front end driven or may even be driven by the server side

This is where an A/B Testing tool comes in very handy – to run experiments at scale with statistical reporting to precisely know the impact of each tested variable. Once the personalization variables of value are known to you, you should then set out to invest in them.   

 

The state of machine learning in web experimentation and testing

Machine learning is one of those buzzwords that can get almost anyone to either raise their eyebrows — or roll their eyes. The hype around machine learning (sometimes inaccurately referred to as “AI”) has been so overblown for so long that many have become grizzled cynics, believing that machine learning represents not a panacea but […]

Machine learning is one of those buzzwords that can get almost anyone to either raise their eyebrows — or roll their eyes. The hype around machine learning (sometimes inaccurately referred to as “AI”) has been so overblown for so long that many have become grizzled cynics, believing that machine learning represents not a panacea but a pointless money-hole for business.

The reality is actually somewhere in between. Machine learning is indeed a powerful tool for all kinds of applications, including experimentation and web experience design, but it needs to be directed by the same sound strategy as any other initiative. The core lie of the machine learning hype isn’t that it can solve your problems, but that it can solve your problems on its own.

To come to a better understanding, we have to dive into just what machine learning is, and how it is used in experimentation and experience design.

What is machine learning?

Put simply, machine learning is the automation of software design. One program observes the activity of another, usually a so-called “neural network,” and makes small, iterative edits to that neural network’s design. Most of these edits are about deemphasizing aspects of the neural net that are associated with bad outcomes, and reinforcing those associated with good ones.

This simple idea has had an incredible impact over the past decade or so, allowing the creation of programs that could never have been created directly. Data scientists spent decades trying in vain to teach computers to quickly transcribe spoken language, before finally giving up and passing the problem to machine learning; while each of the scientists had a brain that could of course translate spoken words themselves, their lack of awareness of the process by which that happened made them incapable of teaching the process to a computer.

Machine learning, on the other hand, was so successful in tackling this problem that we now take language-capable smartphones for granted. The neural nets doing that work were architected (and continue to be architected as new data comes in) by machine learning algorithms. Many in the business world have been told that machine learning can help them create equally revolutionary pieces of software, solving hard problems and overcoming seemingly impossible complexity.

Sounds great, so how does it actually work?[?bp_heading]

If we understand how machine learning works, however, we know that it only really applies in specific types of situations. The biggest hurdle in most cases is the need to create a dataset of so-called “training data” to be analyzed; if we want to teach a computer to find pictures of cats, the dataset is a collection of pictures that either do or do not contain a cat. This dataset must also be meticulously appended with metadata containing whatever information the machine learning algorithm requires — in the case of cat-pictures, this means a tag showing whether there really is or is not a cat in each picture. In web design, the dataset is often composed of user journeys, and a means of determining whether this journey ended positively or not.

So, you can’t simply tell a machine learning algorithm to become better at “making me money;” you have to be able to express your needs clearly, knowing in advance the exact process to be improved, exactly what a better or worse outcome will look like, and specifically what endpoint you’d like the process to achieve. It often requires human supervision or maintenance at some step in the process — which, if you’re keeping score, seriously undermines the whole “automated” argument for machine learning. “ML” can be expensive and time-consuming, requiring heavy investment before you can even get the process started.

It should be obvious that while machine learning is powerful, it is also poorly suited to many applications; it’s far from a silver bullet for digital experiences and growth. Neural nets only know what they’ve seen, and tend not to be able to deal with deviations from the content of their training dataset. This means that generalizing machine learning solutions to similar tasks in different projects can be difficult, and that they are often incapable of dealing with drastic change in the environment — say, a global pandemic and the mass changes in human behavior that come with it.

[bp_heading]Machine learning x Experimentation

In experimentation on web experiences, there are three major uses we see most often. These tactics represent the easiest ways to incorporate machine learning into your business.

  1. Traffic allocation

    Uses so-called “multi-armed bandit” algorithms to determine best distribution of traffic to maximize conversions, traffic, or whatever metric it is told to attenuate. Training data is generally gathered on an ongoing basis as site traffic occurs during the test period. It is able to correlate many attributes of different user journeys, and Frankenstein together the strongest overall journey or journeys, based on its programmed goals.

    This article offers a good primer on how multi-armed bandits function, and how a different mathematical approach to the solution can lead to drastically different outcomes.

  2. Dynamic experience design

    Rather than targeting small user groups, here a machine learning system takes aggregate traffic patterns and determines the ideal experience for the group, overall. This can hone the core version of the site toward a baseline experience that should provide solid results for the vast majority of users. With such a design as the default, more user-specific journeys can be created.

    For example, ML could identify specific product categories that are most correlated with conversion, and reorder product categories to display these more prominently.

  3. Predictive user scoring

    Here, training data is observed through visitor engagement with the site, judged against specific engagement metrics. The computer derives patterns from this data and applies them to predictively calculate engagement metrics for users. These scores can be used to shunt traffic between multiple journeys tailored to user type.

    For example, a company might want to use predictive user scoring to guess how susceptible each user is to becoming annoyed with bugs on a site — leading to so-called “rage clicks” and a high bounce rate. Users with a high rage score can be shunted to a simpler, less advanced journey that minimizes the risk of bugs, while users with low scores can be more safely shunted to test versions of the site.

Starting with just these three applications, any business can start considering strategies that are enabled, or enhanced, by machine learning. But if you’re going to be your company’s evangelist for these advanced techniques, be sure to make it clear that you actually understand the process. In a recent post, Google’s Cassie Kozyrkov went into more detail about the many situations in which machine learning isn’t the answer.

Clearly explain what machine learning can do to improve the company’s bottom line, and you’ll have even the cynics singing your praises.

How One Simple Strategy Changed the Candy Industry

A century ago, Edward Noble sold billions of Life Savers in a few years with a different approach to marketing mints.
The post How One Simple Strategy Changed the Candy Industry appeared first on Neuromarketing.

pep-o-mint mobile

A century ago, Edward Noble sold billions of Life Savers in a few years with a different approach to marketing mints.

The post How One Simple Strategy Changed the Candy Industry appeared first on Neuromarketing.

When Lead Generation Trumps Helping Customers | #FrictionHunter

Turning a chat request into a lead generation process adds friction and annoys customers.
The post When Lead Generation Trumps Helping Customers | #FrictionHunter appeared first on Neuromarketing.

Frustrating customer experience #CX

Turning a chat request into a lead generation process adds friction and annoys customers.

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Customer experience tweaks that boost restaurant results

Restaurant guest experience depends on more than good food and quick service.
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Restaurant guest experience depends on more than good food and quick service.

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Free Guide: How to Strategize & Execute Profitable Personalization Campaigns

When I speak with our clients, it often strikes me how many of them feel overwhelmed by the very idea of personalization. Our imagination, often fueled by the marketing teams of various software companies, creates a perfect world where personalization enables every interaction to be completely custom for every individual. In this dreamland, artificial intelligence […]

The post Free Guide: How to Strategize & Execute Profitable Personalization Campaigns appeared first on Brooks Bell.

When I speak with our clients, it often strikes me how many of them feel overwhelmed by the very idea of personalization.

Our imagination, often fueled by the marketing teams of various software companies, creates a perfect world where personalization enables every interaction to be completely custom for every individual. In this dreamland, artificial intelligence and machine learning solve all our problems. All you have to do is buy a new piece of software, turn it on, and…BOOM: 1:1 personalization.

As a data scientist, I’ll let you in on a little secret: that software only provides the technological capability for personalization. Even further, the algorithms found within these tools simply assign a probability to each potential experience that maximizes the desired outcome, given the data they have access to. Suffice to say, they’re not as intelligent as you are led to believe.

If you caught our first post in this series, you already know that we define personalization a bit more broadly, as any differentiated experience that is delivered to a user based on known data about that user. This means personalization exists on a spectrum: it can be one-to-many, one-to-few, or one-to-one.

And while there are many tools that enable you to do personalization from a technical standpoint, they don’t solve for one of the main sources of anxiety around personalization: strategy

Most personalization campaigns fail because of a lack of a strategy that defines who, where and how to personalize. So I’ve put together a free downloadable guide to help you do just that. This seven-page guide is packed full of guidelines, templates and best practices to strategize and launch a successful personalization campaign, including:

  • Major considerations and things to keep in mind when developing your personalization strategy.
  • More than 30 data-driven questions about your customers to identify campaign opportunities.
  • A template for organizing and planning your personalization campaigns.
  • Guidelines for determining whether to deliver your campaigns via rule-based targeting or algorithmic targeting.

Free Download: Plan & Launch Profitable Personalization Campaigns.

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Who’s Hiring in December?

Here are our picks: Sr. Director – Customer Experience Leader – Equifax is looking for a Senior Director in St. Louis, Missouri, to lead the Customer Experience Team in “intuitive design workflows and overall customer experience as they interact with Workforce Solution products.” Senior Software Engineer, Build Automation – Blizzard Entertainment is “seeking a talented […]

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Here are our picks:

Sr. Director – Customer Experience Leader – Equifax is looking for a Senior Director in St. Louis, Missouri, to lead the Customer Experience Team in “intuitive design workflows and overall customer experience as they interact with Workforce Solution products.”

Senior Software Engineer, Build Automation – Blizzard Entertainment is “seeking a talented and enthusiastic software engineer to join the Hearthstone team” in Irvine, California to improve testing, building and developing Hearthstone through software automation.

Conversion Optimization Specialist – Vivint Smart Home is looking for an “action-oriented thought leader to partner with the digital marketing channel manager to optimize ad creative, product lifts in on-page response rates and improve conversion rates for Vivint’s digital marketing portfolio.” in Provo, Utah.

Head Of Customer Marketing – Kabbage is “looking for an extremely analytical, results-oriented leader to join their data science team in Atlanta with a passion for growing customer relationships and increasing the value of customer marketing.”

Associate Director of Experimentation – Marketing Analytics – Join Walmart in San Bruno, California and “help the World’s largest omni-channel retailer develop, promote and lead execution of a rigorous testing and experimentation roadmap.”

Senior Product Manager, Data & Analytics – In New York, HBO is “looking for someone who has a proven track record of leading teams to identify unique market and consumer requirements, with experience in digital products portfolio management.”

Digital Product Manager – Cole Haan is looking for a manager in New York to “own the front-end digital site experience on ColeHaan.com and drive the overall user experience, optimization efforts and road map.”

UX Manager (E-Commerce) – iHerb is looking for a UI/UX Manager in Orange County, California to “enhance iHerb’s customer experience on their industry-leading, global e-commerce site through design and maintenance.”

Senior Manager, UX Planning & Insights – Join Leapfrog Online’s Strategy & Insights team in Evanston, Illinois and “help lead the strategy and cross-channel, digital user experience planning for Leapfrog clients.

Senior, UX Development – Fidelity Investments is looking for a web developer in Durham, North Carolina to join the User Experience Design team.  This role will be “supporting the Health Care Group’s digital employee and employer platforms, which customers and plan sponsors use to manage their health and welfare benefits.”

Looking for a job or to fill a position?  Give us a shout and we’ll help spread the word in our next careers blog post.

 

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New Features in Illuminate: Impact Analysis, Enhanced Filters, Updated Dashboard & More

Since we launched Illuminate back in May, our team has been working around the clock to develop even more features to help optimization teams better organize experiments, report performance and maximize impact. Today, we’re excited to share a few of these with you. What’s new in Illuminate? Show impact and determine priority Use our new Impact […]

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Since we launched Illuminate back in May, our team has been working around the clock to develop even more features to help optimization teams better organize experiments, report performance and maximize impact. Today, we’re excited to share a few of these with you.

What’s new in Illuminate?

Show impact and determine priority

Use our new Impact Analysis to show the overall impact of your tests by page type and identify where you should be focusing your testing efforts.

Sort and filter by what matters most

Filter your tests by 15 attributes including target audience, page type, start and end date, KPIs, revenue impact and more. Not seeing what you need? Add your own using our new custom tagging feature.

Keep sight of the bigger picture

Our new dashboard view enables you to view your program’s overall performance or view performance by a specific team or line of business.

+ a new tiled layout

If you love a good masonry layout (á la Pinterest), then you’re going to love our updated experiment view. Easily switch between a basic list of your experiments or a super slick-looking tiled layout.

Many of these features were developed in response to feedback from our beta users, bringing more of Brooks Bell’s advanced experimentation methodologies directly into the software.

“With Illuminate, you’re not just getting another test repository,” said Suzi Tripp, Senior Director of Innovative Solutions at Brooks Bell. “You’re getting 15 years of experimentation expertise and proven frameworks to help you do more, and do it better.”

Interested in learning more about illuminate? Learn more on our website or schedule a demo using the form below.

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