growing your email list
There are several common practices online retailers use to grow their email lists. Offering first time visitors a coupon is probably the most effective one and it can also create a nice conversion lift for this particular segment.
Yesterday, I came across a less common practice which could be also very effective. The merchant added an “email cart” button on the shopping cart page.
email my shopping cart
Most shoppers who abandon the shopping cart do not even start the checkout process and by using the “email my cart” value proposition, you can grow your email list, but even more importantly you get a chance to remarket to these shoppers .
Cart abandonment emails, the feedback opportunity
Most merchants think about cart abandonment emails as an opportunity to recover lost sales and that is indeed the ultimate goal. But following up with this segment of visitors presents another opportunity: getting their feedback.
Shoppers who started the checkout process but and have not completed the order is one of the most lucrative segments for feedback solicitation. From this segment, you can understand if your checkout process is too long, or if you charge too much for delivery or if first time visitors don’t feel confident buying from you.
One of our customers used this verbiage in his cart abandonment emails: “We’ve noticed that you recently tried to purchase some items from our website but for some reasons you haven’t finished the purchase. If we’ve done anything wrong please let us know by replying to this email or by calling us on …”
Guess how customers responded? many of them replied saying that the customer did not do anything wrong and provided additional information about their situation. From these emails our customer started conversations with these prospects that either led to sales or provided important feedback.
So when you craft your remarketing emails, think about them also as an opportunity to start a conversation with your prospects/customers and use the right tone to spark such conversations.
personalized email recommendations without integration
We’ve been supporting email recommendations for a while now but it required our customers to use our apis which also meant – an “IT project”. We noticed that this approach creates a barrier for adoption of the technology and we thought that there must be a better way to add product recommendations to emails without pushing the integration burden to our customers.
We are excited to announce that in the spirit of our “zero integration” philosophy we came up with an innovative way that allows our customers to add personalized product recommendations to emails without any coding. And even better, our new product works with any email marketing platform that supports custom fields.
As a marketer all you need to do is login to your Barilliance account and create an email recommendations widget and then:
1. Select your email provider
2. Customize the widget if you want to
3. Copy and paste the HTML into your email marketing template
4. That’s it!
After you hit “Send” in your email marketing software , your emails will contain personalized product recommendations based on customers’ recent activity on your website.
shopping cart abandonment service released
Last week we released the latest member of our e-commerce personalization products suite: a shopping cart recovery tool that sends automated emails to shoppers who left the checkout process without making a purchase.
Of course we maintained our zero integration philosophy which means that online retailers can implement the service in a few minutes. Our code snippet reads the content of the shopping cart without special tags!
Currently we provide an end-to-end solution and our system sends out the emails but in the future we will add “connectors” to popular email marketing platforms.
If you are using our product recommendations or segmentation products, there are exciting synergies between the 3 products. For example if you offer a promotion in the email, you can upload a matching banner on the shopping cart page that will be served on for those customers who clicked on the link embedded in the cart abandonment email.
Drop us a line if you want to give it a try.
What does Facebook’s new like button mean?
A few days ago Facebook announced their latest platform direction, the “Open Graph”. If you remember at some point Facebook tried “Beacon” which handled privacy miserably and failed. 2 years ago Facebook Connect was announced and was supposedly a big success but is now renamed “login” and is part of the new Open Graph and social widgets initiative.
The major change that Facebook is introducing is the “like” functionality which allows users to interact with the site and to see friends’ activity without connecting to Facebook if they are currently logged-in to Facebook. This is similar to “share this” in many ways. While this is probably going to increase engagement on the site, it is important to understand that as an e-commerce site you don’t establish relations with the user and don’t get access to the his/her data in this “like” model. Only when user connect or log- in to Facebook on your site, you get access to their data. So while the Open Graph could make e-commerce sites more social and engaging (see www.levi.com for example) it is recommended to drive users to login using Facebook on your site so that you can establish direct relationship with the user.
Last year we introduced our social widgets. Here is an example for our social wishlist widget:
We used Facebook connect and therefore participating sites had direct relationships with the users.
This is how it looks when I “liked” a product on levi.com
In this case only Facebook knows that I like the product but Levi.com does not, as I have not logged-in.
We are quite happy to see Facebook actively leading this revolution. As a personalization vendor we recognized the synergies between social networks and personalization early on, and leveraged infomration from Facebook in a unique way that is optimized for e-commerce sites. The new developments from Facebook will allow us to continue and innovate and make shopping more personal and social.
Global expansion
Q1 has been great for us. We deployed our services on 16 new e-commerce sites across 7 new countries (Brazil, France, Germany , Italy, Sweden, Switzerland and the UK) .
We now have customers in 13 different countries in 8 languages, we are excited!
Going the extra mile
Yesterday I stumbled upon this blog post http://www.nonlineblogging.com/blog/2010/2/11/dont-be-mr-average-why-averages-are-a-bad-bad-thing-in-digit.html and it’s right on.
If you think about it, you can’t really improve conversion rates unless you start segmenting your traffic and identifying those under-performing segments. Sure it takes time but compare it to the investment you make in acquiring media and driving visitors to your website. Go the extra mile: segment your traffic, figure out which segments are not converting and target them with relevant and personalized messages. Improving conversion rates for these segments is the only way to increase the average.
Recommendation engine for e-commerce sites, build vs. buy?
Some of the online retailers we talk to, realize the value of cross-sells and up-sells and decide to embark on a “DIY path”. I may be biased here but I think product recommendations are one area in which you don’t want to reinvent the wheel.
There are 2 reasons why a DIY approach does not make sense in this case. The first is the development cost of building a system that works, and the second is the learning curve of optimizing it.
So how difficult is it to develop a recommendation engine? online retailers who decide to build a recommendation engine are not aware of the various components that need to be in place. Here are just a few them:
- The recommendation engine should track every major activity shoppers perform on the site including viewed products, categories and brands; items added to the shopping cart and purchased ; search keywords they used ; traffic source visitors arrived from, Geo-location data, and the list goes on…
- The system must support multiple recommendation types and should be able to display the right one (and more than one on a single page) based on where the user is at the purchase funnel (if you have less than 10 algorithms your system is extremely naive)
- Finding correlations between items/users is easy. The hard part is to choose which correlations should be taken into account and which should be ignored
- The system should have built in a/b testing and reporting capabilities so that it could be optimized and demonstrate its value. This point is very critical as few online retailers actually measure the impact of their homegrown systems
- The system should have an interface that allows marketers to control the outputs of the recommendation engine based on different variables.
The second reason I mentioned is the experience it takes to optimize such a system. There are many things that will determine the impact a recommendation engine will have on the business, and if it’s your first time building one and it’s a one-off project there is no chance you are going to know them or invest the time to learn them. In fact you are probably going to develop a very naive system and you’ll stay with v1 for a long time. You will not test different widget designs or various placements on the page.
The decision to build or buy a recommendation engine should be ROI based. You need to consider the impact it will have on your business versus the cost of development. Building a very naive system may seem cheap but will also likely to deliver poor results and without supporting systems you’ll never know it.
Amazon web services migration completed
We’ve been working hard on moving our infrastructure completely over to Amazon Web Services and we’re happy to announce that the transition is now completed.
Using Amazon Elastic Cloud computing allows us to scale our infrastructure on demand in real time, in response to traffic spikes our customers experience and also scale it down when needed. And the cool thing is that everything is done automatically.
Optimization, segmentation and dynamic targeting
I saw a few discussions on LinkedIn Answers about dynamic content and I realized there is some confusion about the term and what it means. People confused dynamic content with website optimization. I wanted to clarify the difference here.
There is a fundamental difference between optimizing content based on a/b testing using tools such as Google Website Optimizer and serving targeted content based on manual or automatic segmentation rules. The first means someone creating multiple versions of a webpage and then activating a tool to measure which page variation is the most effective in achieving a certain conversion goal.
When we talk about Dynamic content in the segmentation/targeting space we mean that content will change dynamically based on visitor’s attributes and intent: for example promotional banners will change based on search keywords or Geolocation data. Of course it’s important to run testing on dynamic targeted content to measure its impact on conversion rates and sales but this testing is very different than the a/b testing done for optimization.