Do more with your data after busy shopping periods: five minutes with the data gurus

Keep your eyes peeled for part two.

Congratulations! Your business has weathered one of the busiest shopping periods of the year. But whether it’s Black Friday, Christmas, January,scorching summer sales, or even Halloween bonanzas, we all know that the work doesn’t stop post-event. It’s not just distribution and accounts that will have their hands full now – at least it shouldn’t be. As a marketer or a CX professional, this is where you can really get your hands dirty with data.

We speak with Ian Pollard, one of our senior product managers, and Sam Crawley, a product data scientist, to uncover what you should pay attention to once the dust has settled on a busy shopping period, and how to make the most of it in Engagement Cloud.

What are the most common challenges you hear about from retail customers following a surge in sales? Did this inform the product design of Engagement Cloud?

Ian: If you’ve just run a big sale, you’ll have some newly acquired customers. Having spent big on acquisition and likely taken a margin hit with discounting, you don’t want to lose them. Getting that all-important second purchase is the difference between never hearing from them again and building loyalty. This was what led us to build out the ‘single purchase customer’ tile as one of the nine metrics Engagement Cloud users can keep a close eye on and drill down into. 

Sam: There are nine tiles in total and things to learn from all of them, especially after a big sales event, as long as you keep context in mind! The ‘average items per order tile’, for example, might show that during the sale people were either picking up several discounted items or in fact buying lots at once. The latter might indicate a successful use of on-site product recommendations.

As Ian mentioned, the acquisition of new customers is a major part of these events, and many of them may not end up re-purchasing at all. It’s important to keep the average value of these newly acquired customers in mind, especially when comparing to the amount of money and effort that went in to acquiring them. Drilling down into the CLV tile, for instance, might give an idea of the ROI you’d expect compared with a standard period.

Now if you were head of marketing or customer experience, what would you do with this data? How would it help you achieve your goals of improving ROI, lifetime value, or overall customer experience?

Ian: Our segment builder lets you target customers by RFM persona. Drag in the RFM data block and target anyone in the ‘recent customers’ persona.

Marketing to these people is difficult at such an early stage of the relationship; all you really know is that they’re a new customer. They may have nothing else in common with each other. For this reason, I would follow up by using the most reliable data point you have — what they just bought. 

To target effectively against that, I recommend using our ‘also bought’ product recommendation. This looks at the highest value item in the recent checkout and finds other shoppers who have also purchased it. Within that group of shoppers, Engagement Cloud will then find other products they have bought and recommend the most popular. 

Sam: There is no magical method to improve ROI or lifetime value, but different marketing methods can be optimized and refined over time in order to see more success. This is where context becomes important.

We’ve given you the ability to filter the metrics and drill down reports on specific segments or RFM personas. What this means is actually really cool. You can trial different methods on different categories of customers. Then you can compare the effects on CLV and ‘average delay’ over time by selecting different date ranges.

Use these tools to find what works best for you and your customers.

Ian, what was the drive behind developing the recency, frequency, and monetary (RFM) personas (as well as the persona movement reports) in Engagement Cloud? What value do these data-driven metrics bring to a business?

Ian: RFM had been in our plans for a while and we knew it was a popular wish-list feature with customers. The ability to manually create RFM-like segments had always been possible in Engagement Cloud, so the decision to make a formal data model for it wasn’t something we rushed into.

 I’m really pleased with our model: it took a lot of thought, but I think it’s the right balance of power and simplicity. The core model is built around six very-easy-to-understand personas grouped across a lifecycle timeline familiar to any retailer — inactive, lapsing, active. It’s incredibly valuable to anyone wanting to do behavior-based targeting or reporting.

The movement reporting came from insights we uncovered whilst building the RFM model. Some of our customers were really interested in how customers moved through personas over time. That stuck with us and we started modelling these movements and found interesting stories in the data. Finding a way to show this to our customers was a little more of a challenge. We have some big opinions on data visualization in the team, but I think we’re all happy with where we ended up. Even if we did need to define a whole new color palette to make it work!

Which personas should businesses keep an eye on? And how should they be treated after a large sales event?

Sam: New customers, for sure.  After a large sales event you are likely to have a much larger chunk of new customers than normal, and this represents a great opportunity to increase your loyal customer base.  You should focus on marketing to these people, with the aim of converting them into repeat customers.  Make use of the persona movement report to keep track of them and figure out which tactics work best.

Any other advice on doing more with data for businesses using (or thinking of using…) Engagement Cloud?

Ian: We have a great feature called web behavior tracking (WBT). It tracks page views, and, when we can identify the contact, it matches those web sessions with them. If you combine WBT with RFM, you get the ability to identify emerging purchase intent. 

Why does that matter?

Ian: Think about a win-back campaign for your inactive customers. You’ve already paid to acquire them and they’re now giving you every sign that they can realistically be won back. They’re worth spending money on, they’re your best leads.

I would create a multi-stage and multi-channel campaign. If they don’t buy or engage via email, then re-target via Facebook, Instagram, or Google (which can you do via our program builder). If they engage again on your website but still don’t buy, then it may be worth looking at a coupon campaign. 

Any top tips Sam?

Sam: Try combining automation and programs with the persona movement report.  The report isn’t just useful for tracking what happens to your new customers after a sale, but can be used to see what the overall engagement lifecycle of your customers looks like. Filtering based on segments might reveal insights into what can be improved in your automation and programs, or where you are excelling.

Thanks both!

Want to hear more from Ian and Sam? They’ll be speaking at our dotlive event on Wednesday 11th December.

And don’t forget, this is the first of our three-part series in what to do after a surge in sales. Check back soon for part two, or sign up for blog updates and more here.

The post Do more with your data after busy shopping periods: five minutes with the data gurus appeared first on dotdigital blog.

Reblogged 8 hours ago from blog.dotdigital.com

Customer Engagement: Brands need to focus more on data to engage with customers better

Customer engagement has been an industry-wide marketing term for around a decade now. It encapsulates the change in marketing philosophy brought about by three effects of digital media:

  • Rise of marketing automation
  • Move from one-to-many broadcast marketing to one-to-one conversational commerce
  • Proliferation of transactional and programmatic messaging

Customer Engagement pressures: Marketers need new ways to engage

According to the report, marketers have found that they need to find new ways to communicate with customers and build lasting relationships, or face crippling competition from more capable rivals.

Those that adopt a customer-engagement approach to their marketing strategy typically see improved customer satisfaction, resulting in improved customer retention and financial performance.

B2Cs, for instance, measure impact in increased order values and reduced marketing costs, while their B2B counterparts use customer engagement techniques to boost acquisition and optimize their lead quality.

For better customer engagement, brands need to connect and communicate smarter

Adopting a customer engagement-focused marketing strategy doesn’t happen overnight. Putting the customer at the heart of the business requires significant, inside-out change throughout the organization. Only 9% of respondents in the survey cited their business at an advanced level.

There are two main barriers to a robust customer engagement strategy:

  1. Difficulties in getting a single customer view (SCV)
  2. Disconnected technology platforms

Many businesses struggle with legacy systems that don’t work together, since driving engagement relies on the seamless connection of all data points. Plus, antiquated organizational structures impede the vital sharing of data across the business. This is another key requirement to creating a consistent and engaging experience along the path to purchase and beyond.

Headline stats highlight customer data silos

How are marketers engaging their customers?

  • 79% of respondents use email platforms
  • 65% use content management systems
  • 62% use social media tools

BUT, creating an SCV proves to be a challenge…

  • Just 65% of companies have integrated email and CRM
  • Only 56% of businesses have integrated email with their didgital analytics

And the levels of integration are far lower for other types of technology.

Consumers expect personalized experiences

Brands are well aware of the growing expectations of consumers. Personalization has, according to 25% of those surveyed, been one of the most important customer engagement-related trends in the last five years. The need to personalize is driving intermediate and advanced brands to focus more on AI as a tool to accelerate the customer experience into new realms of personalization.

More unmissable insights

For a deep-dive into all of the stats, as well as regional breakdowns across the UK & Europe, North America, and APAC, download the full version here.

You’ll discover the true importance of customer engagement, considerations and tactics for B2C and B2B, as well as how the right technology can drive long-term success.

The post Customer Engagement: Brands need to focus more on data to engage with customers better appeared first on dotdigital blog.

Reblogged 4 weeks ago from blog.dotdigital.com

Supercharging your marketing communication with in-store customer data

Businesses with a retail presence know very well that a strong omnichannel strategy lives and dies on having a single customer view. Storing offline and online shopping history in one place sounds awesome on paper but collecting personal information in-store is quite a challenge. 

When buying online, customers have all the time in the world, so they’re more inclined to type-in their contact data or register to a loyalty program, especially since their phone or computer can auto-fill certain fields. 

But in a brick-and-mortar environment, the same customers are less patient, unwilling to waste time on surveys in the middle of a shopping spree. Not to mention that manually writing down their information is just not a good user experience. 

A loyalty program could provide the necessary incentive since an overwhelming majority of people are willing to share their personal information and have their activity tracked in exchange for personalized rewards.

But to successfully seal the deal, you need to ensure that the in-store user experience is up to snuff. We at Antavo offer three solutions to engage guest shoppers the 21st-century way:  

Incentivised Product Interaction 

Associate each product with a unique tag that customers can find in a little sachet attached to the product. By scanning the tag, people will be redirected to the loyalty program’s landing page where they can register or sign in.

Link each product tag to an instant reward that customers can unlock by accessing the Loyalty program. Such rewards can include coupons or little gifts (like a free lipstick or custom laces) that can be enjoyed together with the purchase.

This strategy works because customers need to have an account in order to redeem the reward. And if they don’t, the fear of missing out will motivate them to quickly enroll in the loyalty program. Pro tip: when scanning the code, redirect shoppers to a page displaying an image of the reward to further emphasize the value of the incentive.

Mobile Passes

Mobile Wallets are native applications that are present in both iPhone and Android phones. Your Wallet can hold multiple Mobile Passes, which can be a one-time coupon, an event ticket or a loyalty program membership card.

Customers can have their Passes scanned by the shop assistant (using a POS device) to redeem a coupon or have their point balance updated. Doing so ensures that they’ leave a footprint after the purchase, giving you valuable insight.  

Another benefit of having a Mobile Wallet system is that you can target customers with personalized push notifications, using location-based technology. In other words, when they’re walking past the store, they receive a message telling them that their favorite product is now in stock. 

NFC-Enhanced Registration

If there’s something customers love even more than being rewarded, it’s being part of a great experience. The Loyalty Experience Kiosk — Antavo’s very own hardware-software solution — aims to turn the process of enrollment into something memorable; an act people genuinely desire to do. 

The Kiosk uses NFC technology to make the registration smooth and exciting. Imagine a large tablet that loops a flashy animation, inviting customers to touch their phone to screen. Once they do it, the animation changes, congratulating them, while the phone opens up the enrollment page.  

But the experience is only beginning. If they follow through and register, they can sync the phone to access various features on the tablet. For instance, after engaging with gamified functions such as the Prize Wheel or Sweepstake, the rewards aren’t shown on the tablet screen but on the phone, and it’s instantly redeemable during the checkout.

In short, NFC tech delivers value on two fronts: it makes the enrollment swift and painless, and at the same time increases footfall due to being a novelty.  

6 Reasons To Give In-Store Enrollment a Chance

With the solutions now at hand, it’s time to see what benefits you could reap from spicing up the store experience.

  • First and foremost, you can significantly expand your marketing database with the contact information of the freshly enrolled buyers
  • Even better, you have the means to retarget and nurture guest shoppers with follow-up messages or newsletters. 
  • In-store shoppers often have different preferences than their online-buying counterparts. Finally learning about their habits, needs, and wants is invaluable to engage them with personalized emails.
  • Having a larger and more diverse pool of contacts also unlocks new possibilities for A/B testing, as you can send out news and coupons with store-related incentives. 
  • Being able to bridge the gap between offline and online purchases highlights customers who buy on both channels, showing you their true purchase frequency. 
  • And let’s not forget that interacting with an NFC-enabled kiosk or redeeming a Mobile Pass are great experiences, convincing people to visit your shop more often. 

Naturally, collecting contact and personal information through loyalty solutions is just the first step towards faster, data-driven customer engagement. dotdigital and Antavo are hosting a marketing seminar, titled “How to boost your marketing with a loyalty program”, so if you’re interested in making your marketing communication more powerful, then book your seat here

The post Supercharging your marketing communication with in-store customer data appeared first on dotdigital blog.

Reblogged 2 months ago from blog.dotdigital.com

Ignoring big data is hurting your customer experience

We’ve all heard the saying before – content is king.

So, what does that make data? If we’re sticking with the analogy
of chess in regards to marketing efforts, where on the board does big data
fall? You might be surprised to know that big data
isn’t a chess piece at all, it’s the clock off to the side of the game that
you’re playing against.

In the game of chess, you can lose if you focus too much on the moves your opponent makes and not enough on how much time you have left – the same is true for marketing.

Technology is changing the way we do business. We have more data at our disposal than ever before, and we’ve reached the point that not utilizing that data could eventually render your business obsolete. Passively tracking the latest data tools and tips isn’t enough anymore, you need a proactive approach to integrating big data into your customer experience plan.

How does ignoring big data affect your business?

Software is eating the world and it’s fueling itself with user
data.

In just the span of a year, the number of marketing automation software
products listed on G2’s website jumped from 129 to 260. That’s an increase of
201.55% and that number is expected to keep increasing. The biggest names in
marketing are investing more time and resources into gathering customer data to
drive customer engagement
and improve the consumer experience.

That’s not all – they’re also seeing a huge return on their investment. The total revenue generated from hardware, software, and professional services associated with big data will reach $92.2 billion by 2026. And that’s a conservative estimate, some experts predict that number to spike even higher.

If you’re not already making data a priority for your marketing strategy, you may be falling behind the competition without even knowing it. While you’re watching the pieces on their chess board, the clock is ticking and you’re running out of time to switch your strategy in time.

How can you harness big data to engage your customers?

Knowing why you need data for your customer experience is just one piece on the board. The real question is do you know how to move the pieces to win the game? In order to beat your opponent, you’ll need to understand the different ways you can use data to improve your customer experience and drive conversions.

Think of these strategies as your chess openings. There are three
main uses of big data in relation to the customer experience:

1. Using big data to increase customer acquisition

2. Using big data to increase customer retention

3. Using big data to drive customer expansion.

Let’s break these down one by one.

Using big data to increase customer acquisition is pretty straightforward. You can use big data to learn more about potential leads, what motivates them, what they like and dislike. Big data can provide you with information about their purchasing behavior, which of your competitors they’ve checked out, and more. All of this information can be given to your sales team to help them close deals and acquire new customers.

Once you acquire customers, you can use big data to keep them
engaged. Netflix is at the
forefront of the conversation when it comes to harnessing big data for a unique
customer experience. They use AI and predictive analytics to pinpoint what
their customers like and offer them new features and products before customers
even know they want them. With the right data, your company can do the same
thing.

Attracting and keeping customers isn’t all big data can do – it can help you save time and resources by knowing which leads aren’t worth pursuing. You can use CRM software to track your customer data and build consumer profiles that will help you decide the likelihood a lead will convert to a sale.

You can save your employees time, money, and frustration by using
data to equip them with the tools they need to be more effective. And in the
same breath, you can create a more personalized user experience to your
customers. Unlike chess, this is a game where everyone can be a winner.

Don’t let the clock run out on your company

Change isn’t coming, change is already here. Thankfully, it’s not too late to make a shift and get back in the game. If you haven’t taken a hard look at how big data plays into your marketing strategy, make it a priority. You’ll be having your competition in checkmate before you know it.

The post Ignoring big data is hurting your customer experience appeared first on dotdigital blog.

Reblogged 7 months ago from blog.dotdigital.com

3 ways to drive conversions with web behavior data

Session length, clicks, and product views are among the most actively tracked ecommerce metrics. And for good reason, too. Whilst numbers vary greatly, most e-tailers report that their average session duration is between 2 to 5 minutes. During that time, a customer will browse multiple product pages, mere clicks away from conversion.

The average conversion rate on a product
description page is over 8%, meaning it’s important that merchants track
online visitors
even before they abandon their cart.

Beyond simply tracking these metrics, merchants and marketers can inform their merchandising and marketing with these web behavior insights. Why not resurface popular products in your next email?

Implementation

The first step is to implement web tracking script on your site – this can be done manually via your CMS or Google Tag Manager. Both methods are referenced in our support implementation guide.

Once the script is live you’ll start collecting session data (from contacts who’ve clicked through from an email) in your WebInsights. One contact can have many web sessions, and each can have multiple page visits recorded. There are lots of data attributes available which you can view within an individual contact record – duration of page views (in minutes) is a good one. You can apply segmentation rules based on these attributes, too.   

Here are three ways to start leveraging web behavior data with dotdigital Engagement Cloud:

Abandoned browse email

Truth is – everyone gets distracted online
(who’s multi-tasking right now?). If a customer has viewed a product but hasn’t
purchased, sending a relevant and targeted follow-up makes complete sense. So,
creating an abandoned browse campaign should be on your radar.

Obviously, it’s impractical to set up triggers for all products; select a popular high-value product, or a product with a big margin, as a first step when testing the abandoned browse waters.  

Ready for the next level? Why not set up an abandoned browse based on a set of more generic rules like total number of WebInsight collections or a ‘url contains’ rule. 

(!) Just remember to exclude anyone that has already purchased!

The program-entry segment might look something like:

The program flow might look something like:

Product recommendations

Product recs are a great tool to inspire longer and
repeat website visits. Shoppers
buy recommended products
– whether they’re bestsellers or tailored uniquely
to them. This unmissable sales tactic accounts for 31% of ecommerce site
revenue (Barilliance, 2018).   

If you are storing your products in our insight data collection (this product catalog will sync over as part of our commerce connectors: Magento, Shopify Plus, WooCommerce, BigCommerce, Shopware, and Salesforce Commerce Cloud), then once you begin tracking web behavior you can start using most viewed and trending product recommendation blocks in either BAU campaigns or in automated triggers.

The content block will dynamically update with the latest most-view products based on the logic you set. For example, ‘only show most viewed products over a certain price’ (and if you have stock levels, you could exclude any products that have a low inventory).

A simple rule might look like:

Continue your journey

Rather than target online visitors based on
specific pages, you could drive a more generic follow-up; one that prompts them
to continue their journey. Something like: ‘We hope you liked what you saw’ – a
triggered campaign sent to those who’ve viewed pages numerous times but haven’t
yet purchased or converted.

If you have an online store, get your subscribers to create an account using a dynamic content block that’s only visible to them (and not customers). This email campaign would have a light touch and is about getting your contacts to come back to your site to create an account and/or make a purchase.

One simple script.

Engagement Cloud is powerful, but we can’t collect web behavioral insight unless you tell us to with a simple piece of script. To find out more about implementing web tracking on your site, check out our Knowledge Base here.

Once the script is live, Engagement Cloud
will start collecting session data for you (contacts who have clicked through
from an email) and store it in a web insights collection.

There are many web behavior attributes available for use, this overview should help you learn more about them and web behavior in general.

Getting started

  1. Think big, start small, and
    scale quickly
  2. Focus on high value / bestsellers
    / biggest margin products
  3. Put data to use with customer
    segmentation
  4. Remember to apply frequency
    rules (gauge engagement based on a minimum number of visits in 7 or 14 days) and
    exclude recent purchasers

Want more information on WebInsights? Speak to your account manager today.

The post 3 ways to drive conversions with web behavior data appeared first on dotdigital blog.

Reblogged 8 months ago from blog.dotdigital.com

Big changes, big data, BigQuery

Our customers store diverse data in dotmailer, including product catalogs, orders, reviews, carts, web analytics, and more.

They do this thanks to our insight data features, which are a flexible way to store and use any type of data. Today, thousands of dotmailer customers use it to power their segments, automations, campaign personalization, GDPR consent, and reporting.

It’s fair to say that the feature has been very popular.

We’ve seen individual customer insight data grow from thousands of rows, to hundreds of thousands, to millions, to tens of millions. To support that data growth, and deliver some exciting big data-powered features, we have partnered with Google to launch a new version of insight data built on their BigQuery data warehouse platform.

BigQuery is being made available in staged migrations: US accounts are already migrated, EMEA is in progress, and APAC will migrate once BigQuery is available in that region (this is expected in October).

What BigQuery gives you today

If you’re a bit geeky, you’ll be excited by BigQuery’s technical headlines: it’s an infinitely scalable, server-less, data warehouse.

However, if you’re more interested in the tangible features and benefits, here’s what BigQuery gives you today in dotmailer:

  • GDPR-compliant consent insight storage
  • Product recommendations that generate powerful dynamic content for your campaigns
  • Segments that can now be infinitely complex and will consistently return results in < 15 seconds
  • Segments that can now use a powerful ‘contains’ operator

There really is no limit to how much data you can store in dotmailer, how you can query it, or what you can do with it.

Tomorrow looks very exciting

BigQuery is an enabling technology for dotmailer and our customers. By allowing you to store more data, and use it more easily, our engineering teams are free to build ambitious new features to power your omnichannel marketing campaigns.

Central to the new features that we’re working on are predictive marketing capabilities powered by machine learning and artificial intelligence. With what we’re building, we think dotmailer will fundamentally change the way you think about, build, integrate, and report on your marketing campaigns.

For our ecommerce customers, we are also bringing you many new features for product recommendations. These include new recommendation types, a powerful filter builder, and ground-breaking machine learning functionality for predictive recommendations.

We can’t wait to show you the new BigQuery-powered features we’re developing.

Does this sound good to you?

If you want to know more about what we’ve got under the hood, contact your account manager or book a demo today.

The post Big changes, big data, BigQuery appeared first on The Marketing Automation Blog.

Reblogged 1 year ago from blog.dotmailer.com

How to use rewards data to improve your customer experience

As eCommerce retailers find it more time-consuming and expensive to generate new customers, they are increasingly looking to their loyalty programs. And customers are certainly eager to sign up. In 2017, there were 3.8 billion memberships of loyalty programs in the US alone.

But overall growth has also slowed. Many retailers are struggling to retain members. They’re also finding it difficult to prompt them to take meaningful actions like make purchases and send referrals.

So what’s the solution?

One option is to use data derived from your rewards program to improve the experience of those who have signed up.

By leveraging a number of data-points, you can build a program that boosts engagement while also driving a number of key metrics, like purchase frequency, average order value, referrals, lifetime value and more.

In this post, we’re going to identify the most important types of data and how to use that data to create meaningful changes.

What data can you generate from a rewards program?

 

  • Segmentation dataThis is data about the demographic makeup of your loyalty program membership, and encompasses age, location, marital status, gender etc.
  • Reward-specific dataWhich rewards, promotions and giveaways are most popular? Determining which products and voucher codes are redeemed most often is usually a relatively simple process.
  • Membership activityActivity refers to the degree to which your members are interacting with your program. How many points have they redeemed? How many have been left sitting? How many vouchers have been used? This data is immensely useful for deciding which members to prioritize.
  • Personal detailsThis is individual data that you have extracted on the basis of membership of your loyalty program. It can include birth dates, reward preferences, specific location and so on.

 

So how do you get started? Here are four data-based ways to improve the customer experience of members of your loyalty program.

1. Segment rewards by activity and demographics

 

Segmentation works for both VIP members, who have high purchase frequency and regularly redeem their points, and for members that do not exhibit a high level of engagement.

For your top members, offering high-value rewards will encourage engagement with your program over the long term. By picking and contacting certain groups, and even individuals, for exclusive rewards, you can provide the best possible incentives in a cost-effective way.

Showcasing unique rewards and giveaways via email to members that are inactive, under-engaged or sitting on a large number of unredeemed points will also further increase retention among those most likely to drop off. It’s usually viable to allocate extra resources to this segment because they represent a high-potential group – they’re existing customers who have already signed up – with the greatest contribution to your overall churn rate.

Segmentation can also work effectively when unique promotions and rewards are designed on the basis of demographic information like age, location, marital status etc. By tailoring reward initiatives to meet the unique preferences and needs of specific sections of your customer base, you are much more likely to drive action (and thus engagement). Amazon used this strategy to immense success by targeting students for its Prime program.

2. Create highly personalized initiatives

Personalized reward initiatives

 

Personalization is a hugely under-leveraged strategy. It’s one thing to include a personal name at the beginning of an email. It’s another to encourage members to enter the birth dates of family members at sign-up and use that information to send tailored discounts and offers in the run-up to the big event.

Most managers responsible for running loyalty programs don’t take advantage of the huge array of personal details at their disposal. Customer experience can be dramatically improved when you tailor email promotions and rewards to include personalizaton; think relevant buying holidays (such as Mother’s or Valentine’s Day), personal celebrations, specific genders, locations and so on.

We’re not talking about general demographic or segmentation data here, but rather individual-specific details that you can use to automate highly targeted promotions or reward offers.

An added benefit of sending these highly personalized rewards is that they will increase trust over the long term. If you send your customers free points via email on their birthday or favorite shopping holiday, particularly when your competitors don’t, you’re much more likely to stand out.

3. Tailor your program to preferred platforms

Tailor your programs

 

Which platforms are your members using to check and redeem their points? Data about the kinds of devices and channels your customers prefer can be useful for deciding which platforms to prioritize.

If, for example, the majority of your eCommerce visitors shop on mobile, it makes sense to make your loyalty program directly available through mobile devices. Research by Exodus shows that 31% of consumers use an app to manage their loyalty rewards, so there is clearly a preference for certain access-points.

Most loyalty program managers take an omni-channel approach. And while this is certainly a laudable strategy, it usually falls short. The key is to hone in and optimize those channels that are most effective at engaging your membership.

4. Build feedback into your program

Build program feedback

 

Do you have any feedback mechanisms in place to determine unserved needs and pain points among your members?

Indirect feedback exists in the form of data about your most popular rewards and promotions. You can use this information when creating new rewards or putting together future promotions. If, for example, most members swap their points for cash-back rewards, then you can offer variants and similar offers going forward.

But it’s also important to utilize other ways of collecting feedback. How often do you send email surveys to your loyalty members or include survey questions on your rewards pages? Are you listening to customer service recordings? Do you undertake user testing?

This is one of the big reasons that retailers often experience high rates of churn. They apply a rigorous set of methods to pinpoint customer needs and pains related to the buying process but none to the customer experience of their loyalty program members, where a unique set of issues are often present. If you want to boost retention, it’s vital that you listen closely to your existing members.

Conclusion: Loyalty programs are a powerful but underutilized tool

Loyalty programs are so popular among eCommerce retailers because they work. But it’s also vital to keep in mind that the market is incredibly saturated. The average American is a member of over 14 programs.

As ad costs soar and search engine traffic becomes scarcer, holding onto your existing customers is ever more important. This is why a data-driven approach to improving the customer experience of your loyalty program will almost certainly be relevant.

On the one hand, it will enable you to generate concrete insights for reducing churn. On the other, you have an opportunity to create a key competitive advantage by building a rewards program that is genuinely based on customer needs and preferences.

Now, time to start mining that data.


This is a guest post written by Skubana. Skubana provides an omni-channel eCommerce platform for unifying all aspects of your store’s operation. Skubana’s tools make it easy to manage inventory and shipping, automate laborious tasks and generate meaningful insights from on and off-site data.

 

The post How to use rewards data to improve your customer experience appeared first on The Marketing Automation Blog.

Reblogged 1 year ago from blog.dotmailer.com

It doesn’t need to be spring to give your data a good clean

Here are some of the reasons why…

  • Sending to lapsed data is bad for your deliverability – it’s easy to damage your deliverability but hard to fix it
  • You’re more likely to see complaints, unsubscribes and spam trap hits from this type of data
  • You’re wasting your money by sending to people who no longer open
  • You are automatically, before you even hit send, degrading your engagement metrics
  • It enables you to have a conversation with the people that want to talk to you and are listening

Hopefully the above is enough to convince you that you need to take action if you haven’t already. There are now two things you need to plan for: how to clean up the data that has already lapsed and how to manage lapsing data moving forwards.

Step-by-step guide to handling those who are already playing hard to get

  • Run a segment to find those who’ve become unengaged – I suggest you look for contacts who have been sent multiple campaigns in 180 days yet haven’t opened anything they’ve received (if you are unsure how to do this, your dotmailer Account Manager can help).
  • If you have a large number of contacts who are unengaged, do not send to them all in one go; this could be disastrous! Instead, take a very small chunk of them and test what impact this has (you could use dotmailer’s random sample tool).
  • If you see a high number of unsubscribes or next to no positive action, it might be worth taking the data out and accepting the loss.
  • Next, we need to build your “Don’t leave us” or “We miss you” email.
  • The email must contain a link to be clicked to show that they wish to remain on your mailing list – DO NOT assume an open is enough; it’s not. You need explicit opt-in and the only way to do this is to have them fulfil an action, and this link needs to go to a landing page saying “Thank you for remaining subscribed”. This is now your chance to collect updated preferences and set new expectations.
  • You need to clearly state “If you do not click this link, we will no longer email you – you have 7 days till D day” (or something along those lines).
  • After the desired time period, you need to run a segment or have a decision node in your program to find those who’ve not clicked the link – then whip those clients out of your account!

What to do with those becoming lapsed

Basically, do exactly the same as the above, except ensure that your processes are built into a marketing automation program. Set up your program so it pulls in wavering contacts on the day you think they’re in danger of becoming lapsed. For instance, it could be that you want to capture all contacts who’ve not opened your last 10 email campaigns. It’s at this point that you then send them your lapsed customer campaign.

One thing you need to be conscious of is how you treat the people who are enrolled into your program. It’s worth setting expectations like “If you choose not to stay, we’ll take you out of our marketing list in 7 days”. As it’s an automation program, remember to add in a ‘delay’ node or a ‘decision’ node that holds them for X number of days (i.e. however long you want to give them to take an action). Based on the link they click, send them down a lapsed path or a re-engaged path.

If you choose to exclude lapsed contacts from ‘business as usual’ emails, you should flag those contacts currently going through the lapsed journey and add them as an exclusion rule in your usual send segments. You can do this using the subscription node and enter them into a lapsed address book when they enter the program. Alternatively, you can use the ‘update contact’ node and update a data field to show they’re going through the journey, using the relevant address book or data field in the exclusion box. Please be aware that if they click the link to remain subscribed, you then also need to reverse this and update the field again, or remove them from the “going through lapsed” address book.

If you’ve managed to keep them then WOHOO! Make sure you capture their preferences and ensure you honour these options so you do not have to put them back into the program later. What you should be left with after this is a beautifully engaged pot of data, a far less risky email program, and much nicer email reporting stats!

If you’re interested in other ways to keep your reputation and deliverability in tip-top condition, get a free copy of our deliverability guide.

The post It doesn’t need to be spring to give your data a good clean appeared first on The Marketing Automation Blog.

Reblogged 2 years ago from blog.dotmailer.com

The Beginner&rsquo;s Guide to Structured Data for SEO: How to Implement Structured Data [Part 2]

Posted by bridget.randolph

Welcome to Part 2 of The Beginner’s Guide to Structured Data: How to Implement Structured Data for SEO. In Part 1, we focused on gaining a high-level understanding of what structured data is and how it can be used to support SEO efforts.

(If you missed Part 1, you can go check it out here).

In Part 2, we’ll be looking at the steps to identify opportunities and implement structured data for SEO on your website. Since this is an introductory guide, I’ll be focusing on the most basic types of markup you can add and the most common use cases, and providing resources with additional detail for the more technical aspects of implementation.

Is structured data right for you?

Generally speaking, implementing structured data for SEO is worthwhile for most people. However, it does require a certain level of effort and resources, and you may be asking yourself whether it’s worth prioritizing.

Here are some signs that it’s a good time to prioritize structured data for SEO:

  • Search is a key value-driving channel for your business
  • You’ve recently audited your site for basic optimization issues and you know that you’ve achieved a competitive baseline with your keyword targeting, backlinks profile, site structure, and technical setup
  • You’re in a competitive vertical and need your results to stand out in the SERPs
  • You want to use AMP (Accelerated Mobile Pages) as a way to show up in featured areas of the SERP, including carousels
  • You have a lot of article-style content related to key head terms (e.g. 10 chicken recipes) and you’d like a way to display multiple results for those terms in the SERP
  • You’re ranking fairly well (position 15 or higher) already for terms with significant search volume (5000–50,000 searches/month)*
  • You have solid development resources with availability on staff and can implement with minimal time and financial investment
  • You’re in any of the following verticals: e-commerce, publishing, educational products, events/ticketing, creative production, TV/movie/book reviews, job listings, local business

*What is considered significant volume may vary according to how niche your market is.

If you said yes to any of these statements, then implementing structured data is particularly relevant to you! And if these criteria don’t currently apply to you, of course you can still go ahead and implement; you might have great results. The above are just a few of the most common indicators that it’s a worthwhile investment.

Implementing structured data on your site

In this guide, we will be looking solely at opportunities to implement Schema.org markup, as this is the most extensive vocabulary for our purposes. Also, because it was developed by the search engine companies themselves, it aligns with what they support now and should continue to be the most supported framework going forward.

How is Schema.org data structured?

The way that the Schema.org vocabulary is structured is with different “types” (Recipe, Product, Article, Person, Organization, etc.) that represent entities, kinds of data, and/or content types.

Each Type has its own set of “properties” that you can use to identify the attributes of that item. For example, a “Recipe” Type includes properties like “image,” “cookTime,” “nutritionInformation,” etc. When you mark up a recipe on your site with these properties, Google is able to present those details visually in the SERP, like this:

Image source

In order to mark up your content with Schema.org vocabulary, you’ll need to define the specific properties for the Type you’re indicating.

For example:

If you’re marking up a recipe page, you need to include the title and at least two other attributes. These could be properties like:

  • aggregateRating: The averaged star rating of the recipe by your users
  • author: The person who created the recipe
  • prepTime: The length of time required to prepare the dish for cooking
  • cookTime: The length of time required to cook the dish
  • datePublished: Date of the article’s publication
  • image: An image of the dish
  • nutritionInformation: Number of calories in the dish
  • review: A review of the dish
  • …and more.

Each Type has different “required” properties in order to work correctly, as well as additional properties you can include if relevant. (You can view a full list of the Recipe properties at Schema.org/Recipe, or check out Google’s overview of Recipe markup.)

Once you know what Types, properties and data need to be included in your markup, you can generate the code.

The code: Microdata vs JSON-LD

There are two common approaches to adding Schema.org markup to your pages: Microdata (in-line annotations added directly to the relevant HTML) and JSON-LD (which uses a Javascript script tag to insert the markup into the head of the page).

JSON-LD is Google’s recommended approach, and in general is a cleaner, simpler implementation… but it is worth noting that Bing does not yet officially support JSON-LD. Also, if you have a WordPress site, you may be able to use a plugin (although be aware that not all of WordPress’ plugins work they way they’re supposed to, so it’s especially important to choose one with good reviews, and test thoroughly after implementation).

Whatever option you choose to use, always test your implementation to make sure Google is seeing it show up correctly.

What does this code look like?

Let’s look at an example of marking up a very simple news article (Schema.org/NewsArticle).


Here’s the article content (excluding body copy), with my notes about what each element is:

[posted by publisher ‘Google’]
[headline]Article Headline
[author byline]By John Doe
[date published] Feb 5, 2015
[description] A most wonderful article
[image]
[company logo]

And here’s the basic HTML version of that article:

<div>
  <h2>Article headline</h2>
  <h3>By John Doe</h3>
    <div>
    <img src="https://google.com/thumbnai1.jpg"/>
    </div>
  <div>
      <img src="https://google.com/logo.jpg"/>
      </div>

If you use Microdata, you’ll nest your content inside the relevant meta tags for each piece of data. For this article example, your Microdata code might look like this (within the <body> of the page):

<div itemscope itemtype="http://schema.org/NewsArticle">
  <meta itemscope itemprop="mainEntityOfPage"  itemType="https://schema.org/WebPage" itemid="https://google.com/article"/>
  <h2 itemprop="headline">Article headline</h2>
  <h3 itemprop="author" itemscope itemtype="https://schema.org/Person">
    By <span itemprop="name">John Doe</span>
  </h3>
  <span itemprop="description">A most wonderful article</span>
  <div itemprop="image" itemscope itemtype="https://schema.org/ImageObject">
    <img src="https://google.com/thumbnail1.jpg"/>
    <meta itemprop="url" content="https://google.com/thumbnail1.jpg">
    <meta itemprop="width" content="800">
    <meta itemprop="height" content="800">
  </div>
  <div itemprop="publisher" itemscope itemtype="https://schema.org/Organization">
    <div itemprop="logo" itemscope itemtype="https://schema.org/ImageObject">
      <img src="https://google.com/logo.jpg"/>
      <meta itemprop="url" content="https://google.com/logo.jpg">
      <meta itemprop="width" content="600">
      <meta itemprop="height" content="60">
    </div>
    <meta itemprop="name" content="Google">
  </div>
  <meta itemprop="datePublished" content="2015-02-05T08:00:00+08:00"/>
  <meta itemprop="dateModified" content="2015-02-05T09:20:00+08:00"/>
</div>

The JSON-LD version would usually be added to the <head> of the page, rather than integrated with the <body> content (although adding it in the <body> is still valid).

JSON-LD code for this same article would look like this:

<script type="application/ld+json">
{
  "@context": "http://schema.org",
  "@type": "NewsArticle",
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "https://google.com/article"
  },
  "headline": "Article headline",
  "image": {
    "@type": "ImageObject",
    "url": "https://google.com/thumbnail1.jpg",
    "height": 800,
    "width": 800
  },
  "datePublished": "2015-02-05T08:00:00+08:00",
  "dateModified": "2015-02-05T09:20:00+08:00",
  "author": {
    "@type": "Person",
    "name": "John Doe"
  },
   "publisher": {
    "@type": "Organization",
    "name": "Google",
    "logo": {
      "@type": "ImageObject",
      "url": "https://google.com/logo.jpg",
      "width": 600,
      "height": 60
    }
  },
  "description": "A most wonderful article"
}
</script>

This is the general style for Microdata and JSON-LD code (for Schema.org/Article). The Schema.org website has a full list of every supported Type and its Properties, and Google has created “feature guides” with example code for the most common structured data use cases, which you can use as a reference for your own code.

How to identify structured data opportunities (and issues)

If structured data has previously been added to your site (or if you’re not sure whether it has), the first place to check is the Structured Data Report in Google Search Console.

This report will tell you not only how many pages have been identified as containing structured data (and how many of these have errors), but may also be able to identify where and/or why the error is occurring. You can also use the Structured Data Testing Tool for debugging any flagged errors: as you edit the code in the tool interface, it will flag any errors or warnings.

If you don’t have structured data implemented yet, or want to overhaul your setup from scratch, the best way to identify opportunities is with a quick content audit of your site, based on the kind of business you have.

A note on keeping it simple

There are lots of options when it comes to Schema.org markup, and it can be tempting to go crazy marking up everything you possibly can. But best practice is to keep focused and generally use a single top-level Type on a given page. In other words, you might include review data on your product page, but the primary Type you’d be using is Schema.org/Product. The goal is to tell search engines what this page is about.

Structured data must be representative of the main content of the page, and marked up content should not be hidden from the user. Google will penalize sites which they believe are using structured data markup in scammy ways.

There are some other general guidelines from Google, including:

  • Add your markup to the page it describes (so Product markup would be added to the individual product page, not the homepage)
  • For duplicated pages with a canonical version, add the same markup to all versions of the page (not just the canonical)
  • Don’t block your marked-up pages from search engines
  • Be as specific as possible when choosing a Type to add to a page
  • Multiple entities on the same page must each be marked up individually (so for a list of products, each product should have its own Product markup added)
  • As a rule, you should only be adding markup for content which is being shown on the page you add it to

So how do you know which Schema.org Types are relevant for your site? That depends on the type of business and website you run.

Schema.org for websites in general

There are certain types of Schema.org markup which almost any business can benefit from, and there are also more specific use cases for certain types of business.

General opportunities to be aware of are:

  • Sitelinks Search Box: if you have search functionality on your site, you can add markup which enables a search box to appear in your sitelinks:

Image source

Image source

  • VideoObject: if you have video content on your site, this markup can enable video snippets in SERPs, with info about uploader, duration, a thumbnail image, and more:

A note about Star reviews in the SERP

You’ll often see recommendations about “marking up your reviews” to get star ratings in the SERP results. “Reviews” have their own type, Schema.org/Review, with properties that you’ll need to include; but they can also be embedded into other types using that type’s “review” property.

You can see an example of this above, in the Recipes image, where some of the recipes in the SERP display a star rating. This is because they have included the aggregate user rating for that recipe in the “review” property within the Schema.org/Recipe type.

You’ll see a similar implementation for other properties which have their own type, such as Schema.org/Duration, Schema.org/Date, and Schema.org/Person. It can feel really complicated, but it’s actually just about organizing your information in terms of category > subcategory > discrete object.

If this feels a little confusing, it might help to think about it in terms of how we define a physical thing, like an ingredient in a recipe. Chicken broth is a dish that you can make, and each food item that goes into making the chicken broth would be classified as an ingredient. But you could also have a recipe that calls for chicken broth as an ingredient. So depending on whether you’re writing out a recipe for chicken broth, or a recipe that includes chicken broth, you’ll classify it differently.

In the same way, attributes like “Review,” “Date,” and “Duration” can be their own thing (Type), or a property of another Type. This is just something to be aware of when you start implementing this kind of markup. So when it comes to “markup for reviews,” unless the page itself is primarily a review of something, you’ll usually want to implement Review markup as a property of the primary Type for the page.


In addition to this generally applicable markup, there are certain Schema.org Types which are particularly helpful for specific kinds of businesses:

  • E-commerce
    • including online course providers
  • Recipes Sites
  • Publishers
  • Events/Ticketing Sites
    • including educational institutions which offer courses
  • Local Businesses
  • Specific Industries (small business and larger organizations)
  • Creative Producers

Schema.org for e-commerce

If you have an e-commerce site, you’ll want to check out:

  • Product: this allows you to display product information, such as price, in the search result. You can use this markup on an individual product page, or an aggregator page which shows information about different sellers offering an individual product.
  • Offer: this can be combined with Schema.org/Product to show a special offer on your product (and encourage higher CTRs).
  • Review: if your site has product reviews, you can aggregate the star ratings for each individual product and display it in the SERP for that product page, using Schema.org/aggregateRating.

Things to watch out for…

  • Product markup is designed for individual products, not lists of products. If you have a category page and want to mark it up, you’ll need to mark up each individual product on the page with its own data.
  • Review markup is designed for reviews of specific items, goods, services, and organizations. You can mark up your site with reviews of your business, but you should do this on the homepage as part of your organization markup.
  • If you are marking up reviews, they must be generated by your site, rather than via a third-party source.
  • Course markup should not be used for how-to content, or for general lectures which do not include a curriculum, specific outcomes, or a set student list.

Schema.org for recipes sites

For sites that publish a lot of recipe content, Recipe markup is a fantastic way to add additional context to your recipe pages and get a lot of visual impact in the SERPs.

Things to watch out for…

If you’re implementing Recipe Rich Cards, you’ll want to be aware of some extra guidelines:

Schema.org for publishers

If you have an publisher site, you’ll want to check out the following:

  • Article and its subtypes,
    • NewsArticle: this indicates that the content is a news article
    • BlogPosting: similar to Article and NewsArticle, but specifies that the content is a blog post
  • Fact Check: If your site reviews or discusses “claims made by others,” as Google diplomatically puts it, you can add a “fact check” to your snippet using the Schema.org/ClaimReview.

Image source

  • CriticReview: if your site offers critic-written reviews of local businesses (such as a restaurant critic’s review), books, and /or movies, you can mark these up with Schema.org/CriticReview.
    • Note that this is a feature being tested, and is a knowledge box feature rather than a rich snippet enhancement of your own search result.

Image source

Things to watch out for…

Schema.org for events/ticketing sites

If your business hosts or lists events, and/or sells tickets, you can use:

  • Events: you can mark up your events pages with Schema.org/Event and get your event details listed in the SERP, both in a regular search result and as instant answers at the top of the SERP:

  • Courses: If your event is a course (i.e., instructor-led with a student roster), you can also use Schema.org/Course markup.

Things to watch out for…

  • Don’t use Events markup to mark up time-bound non-events like travel packages or business hours.
  • As with products and recipes, don’t mark up multiple events listed on a page with a single usage of Event markup.
    • For a single event running over several days, you should mark this up as an individual event and make sure you indicate start and end dates;
    • For an event series, with multiple connected events running over time, mark up each individual event separately.
  • Course markup should not be used for how-to content, or for general events/lectures which do not include a curriculum, specific outcomes, and an enrolled student list.

Schema.org for job sites

If your site offers job listings, you can use Schema.org/JobPosting markup to appear in Google’s new Jobs listing feature:

Note that this is a Google aggregator feature, rather than a rich snippet enhancement of your own result (like Google Flights).

Things to watch out for…

  • Mark up each job post individually, and do not mark up a jobs listings page.
  • Include your job posts in your sitemap, and update your sitemap at least once daily.
  • You can include Review markup if you have review data about the employer advertising the job.

Schema.org for local businesses

If you have a local business or a store with a brick-and-mortar location (or locations), you can use structured data markup on your homepage and contact page to help flag your location for Maps data as well as note your “local” status:

  • LocalBusiness: this allows you to specify things like your opening hours and payment accepted
  • PostalAddress: this is a good supplement to getting all those NAP citations consistent
  • OrderAction and ReservationAction: if users can place orders or book reservations on your website, you may want to add action markup as well.

You should also get set up with GoogleMyBusiness.

☆ Additional resources for local business markup

Here’s an article from Whitespark specifically about using Schema.org markup and JSON-LD for local businesses, and another from Phil Rozek about choosing the right Schema.org Type. For further advice on local optimization, check out the local SEO learning center and this recent post about common pitfalls.

Schema.org for specific industries

There are certain industries and/or types of organization which get specific Schema.org types, because they have a very individual set of data that they need to specify. You can implement these Types on the homepage of your website, along with your Brand Information.

These include LocalBusiness Types:

And a few larger organizations, such as:

Things to watch out for…

  • When you’re adding markup that describes your business as a whole, it might seem like you should add that markup to every page on the site. However, best practice is to add this markup only to the homepage.

Schema.org for creative producers

If you create a product or type of content which could be considered a “creative work” (e.g. content produced for reading, viewing, listening, or other consumption), you can use CreativeWork markup.

More specific types within CreativeWork include:

Schema.org new features (limited availability)

Google is always developing new SERP features to test, and you can participate in the testing for some of these. For some, the feature is an addition to an existing Type; for others, it is only being offered as part of a limited test group. At the time of this writing, these are some of the new features being tested:

Structured data beyond SEO

As mentioned in Part 1 of this guide, structured data can be useful for other marketing channels as well, including:

For more detail on this, see the section in Part 1 titled: “Common Uses for Structured Data.”

How to generate and test your structured data implementation

Once you’ve decided which Schema.org Types are relevant to you, you’ll want to add the markup to your site. If you need help generating the code, you may find Google’s Data Highlighter tool useful. You can also try this tool from Joe Hall. Note that these tools are limited to a handful of Schema.org Types.

After you generate the markup, you’ll want to test it at two stages of the implementation using the Structured Data Testing Tool from Google — first, before you add it to the site, and then again once it’s live. In that pre-implementation test, you’ll be able to see any errors or issues with the code and correct before adding it to the site. Afterwards, you’ll want to test again to make sure that nothing went wrong in the implementation.

In addition to the Google tools listed above, you should also test your implementation with Bing’s Markup Validator tool and (if applicable) the Yandex structured data validator tool. Bing’s tool can only be used with a URL, but Yandex’s tool will validate a URL or a code snippet, like Google’s SDT tool.

You can also check out Aaron Bradley’s roundup of Structured Data Markup Visualization, Validation, and Testing Tools for more options.

Once you have live structured data on your site, you’ll also want to regularly check the Structured Data Report in Google Search Console, to ensure that your implementation is still working correctly.

Common mistakes in Schema.org structured data implementation

When implementing Schema.org on your site, there are a few things you’ll want to be extra careful about. Marking up content with irrelevant or incorrect Schema.org Types looks spammy, and can result in a “spammy structured markup” penalty from Google. Here are a few of the most common mistakes people make with their Schema.org markup implementation:

Mishandling multiple entities

Marking up categories or lists of items (Products, Recipes, etc) or anything that isn’t a specific item with markup for a single entity

  • Recipe and Product markup are designed for individual recipes and products, not for listings pages with multiple recipes or products on a single page. If you have multiple entities on a single page, mark up each item individually with the relevant markup.

Misapplying Recipes markup

Using Recipe markup for something that isn’t food

  • Recipe markup should only be used for content about preparing food. Other types of content, such as “diy skin treatment” or “date night ideas,” are not valid names for a dish.

Misapplying Reviews and Ratings markup

Using Review markup to display “name” content which is not a reviewer’s name or aggregate rating

  • If your markup includes a single review, the reviewer’s name must be an actual organization or person. Other types of content, like “50% off ingredients,” are considered invalid data to include in the “name” property.

Adding your overall business rating with aggregateRating markup across all pages on your site

  • If your business has reviews with an aggregateRating score, this can be included in the “review” property on your Organization or LocalBusiness.

Using overall service score as a product review score

  • The “review” property in Schema.org/Product is only for reviews of that specific product. Don’t combine all product or business ratings and include those in this property.

Marking up third-party reviews of local businesses with Schema.org markup

  • You should not use structured data markup on reviews which are generated via third-party sites. While these reviews are fine to have on your site, they should not be used for generating rich snippets. The only UGC review content you should mark up is reviews which are displayed on your website, and generated there by your users.

General errors

Using organization markup on multiple pages/pages other than the homepage

  • It might seem counter-intuitive, but organization and LocalBusiness markup should only be used on the pages which are actually about your business (e.g. homepage, about page, and/or contact page).

Improper nesting

  • This is why it’s important to validate your code before implementing. Especially if you’re using Microdata tags, you need to make sure that the nesting of attributes and tags is done correctly.

So there you have it — a beginner’s guide to understanding and implementing structured data for SEO! There’s so much to learn around this topic that a single article or guide can’t cover everything, but if you’ve made it to the end of this series you should have a pretty good understanding of how structured data can help you with SEO and other marketing efforts. Happy implementing!

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Reblogged 2 years ago from tracking.feedpress.it