Blueshift partners with branch to offere personalized deep linking for email, mobile

Branch and Blueshift: Enabling seamless cross-channel personalization

With Blueshift’s personalization and Branch’s deep linking capabilities, marketers can now deliver  frictionless personalized experiences to their users across all channels.

Let’s take the instance of Jane. Jane is browsing through email on her mobile phone during her short break, and an email promotion for a weekend get-away grabs her attention. Clicking on the email leads her to the app’s home page where she spends a few secs looking for the promotional offer. Frustrated, she moves on to the next email assuming the get away is not meant to be, and makes alternate plans for the weekend. Not only did Jane not convert, she also had a frustrating experience with the app, making it less likely for her to go back.

In the example above, if Jane did not have the app installed, she might have been taken to the right page on the mobile website in her browser, albeit without any of the advantages of frictionless transaction on the app. Just like mobile websites have URLs that can “deep-link” to the right content, marketers need the ability to deep-link into mobile apps. Additionally, they need the ability to automatically detect if the customer has an app installed, and route the customer appropriately to the deep-linked content on the website or the app.

That is why we are excited to partner with Branch, the leader in deep linking. Combined with Blueshift’s personalization and recommendation capabilities on email, mobile apps and mobile websites, this provides the modern marketer a way to deliver seamless personalization to the perpetually connected customer.

Deliver frictionless experience with deep links

Deep links automatically take a user to in-app content or to the web if the app is not already installed. Mobile apps with deep links show 3x higher conversion. Blueshift supports deep links at scale in both email (for web and mobile) and SMS/push notifications and provides full attribution from first campaign to final conversion.

Here is an example of Blueshift’s native support for deep-linking:

 

Deepen engagement with personalization

Unlike other marketing platforms, Blueshift’s AI powered platform listens to every behavior in real-time and allows you to automatically add personalized content and products that are most relevant to the user in any channel. You can lead users to personalized in-app content or landing pages by adding deep links to your email, SMS or push notifications – driving higher conversions across. Track and optimize campaigns as you go with full attribution across all your channels.

With data, automation and AI – all in one platform, and integration with Branch.io, Blueshift allows you to deliver highly relevant and frictionless cross-channel experiences that lead to higher engagement, conversion and thereby, revenues.

The 4 Best Growth Marketing Campaigns That Delight Travelers

This series of blogs goes into detailed campaigns that growth marketers can run for specific industries. These campaigns are tailored towards goals and revenue that growth marketers are responsible for. Our third industry deep dive takes a look at the digital travel booking industry and campaigns specifically tailored for growth marketers to move users along the buying cycle fast and keep them coming back for more purchases.

The digital travel industry has come a long way in the past decade. What started from a handful of booking sites has grown into thousands of websites all fighting for attention through price comparisons, user experience, loyalty benefits, convenience, etc. Everyone is working hard to differentiate themselves from their competition. What they all have in common is thousands of people coming to their site everyday, ever changing inventory and prices, and millions of unique searches of what people are looking for. This creates the perfect recipe for growth marketers to cook up something new in digital engagement campaigns.

Below are 4 personalized email and notification campaigns growth marketers at digital travel companies launch to reduce churn. 

Abandoned Search

For your known users who make a search on your site and do not make a purchase, you can recommend fares based on their recent search with the dates and location from the search. This has to be sent out 1, 3, & 7 days after the search since it is a time sensitive search.

Add a Hotel/Car

A great up-sell campaign for customers who have recently booked a flight on your site is a personalized offer to add a hotel or car to their booking on those same dates based on the flight location/dates. This has to be executed immediately or between 1 and 3 days of the customer booking the flight.

Trending Getaway Deals

This is a great evergreen campaign for all your users to send them the latest and trending weekend getaway deals personalized based on their specific location. It can be sent on a weekly or monthly recurring basis. below is an example using a visitors location to deliver weekend getaways within relatively close distance to them.

Screen Shot 2017-03-01 at 12.11.38 PM

Location based recommendations

 

Promotions

Screen Shot 2017-03-01 at 12.04.36 PM

Promotional Sale

Another great evergreen campaign that requires little work on the marketers part is a promotions campaign. Airlines and hotels put out promotional offers every now and then and those can be used to send personalized offers of deals from nearby airports/locations based on the user’s location to your active customers on a weekly or monthly basis.

 

 

 

 

 


Watch out for more posts about growth marketing, and check out our comprehensive guide here for everything you need to know about the subject.

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Lifecycle Stages for the Growth Marketer Part 1 – Activation

Customer lifecycle is a term used to describe the progress of a customer as they go through consideration, engagement, purchasing, and maintaining loyalty to a product or service. It starts from the first time you get a user’s attention to your product and then keeping them as loyal customer. The customer lifecycle is often depicted by an ellipse because the goal of customer retention is to get them to move through the cycle again and again.

A growth marketer’s prime objective is to drive user engagement with the product. The key to driving engagement is understanding the customer’s lifecycle stage and messaging them accordingly over time to keep them as an active customer. The first form of engagement is activating new customers. Activation is a stage when the user completes an action that indicates them getting value out of a product. This goal can be different for different business models e.g. an app like twitter might consider a user activated when they follow a certain number of other users within a given time-period; a retailer might consider a user to be active when they make their first purchase, or on a rolling basis.

Activation is the first step of the customer lifecycle when they fully experience the product or derived value from it. It is important to get users to activate faster because they can experience the product and see the value it provides. Users who don’t get activated quickly might never return since they never derive any value from the product in the time you have their attention. The core product experience is key to higher activation rates and growth marketers can help increase activation rates by extending the experience into marketing channels.

 

Below we go into some detail about the 2 ways in which growth marketers drive activation.

Welcome Series:

Welcome series from Flipboard

Welcome series from Flipboard

Almost every company or app has a welcome series of messages for activating and educating new customers. Such on-boarding emails have a 3X higher click thru rate than batch and blast emails. Growth marketers can take this strategy one step further by including the elements of product or merchandising in their emails or push notifications. A good example of this strategy is the app Flipboard. Their on-boarding process includes asking users about their interest in order to know what they like and personalize their experience in the app accordingly. This way they are able to onboard a new customer, educate them, and deliver a product that is personalized specifically for them. The welcome series is drawing the user deeper into the product and turning them into engaged users.

 

Abandoner re-targeting:

Guiding customers along their journey is very effective to activate them. This can also take the shape of re-targeting the user with a piece of the product or content if they do not activate the first time. Bringing a user back once they have abandoned is comparatively harder than connecting with first time visitors. For growth marketers to be successful at re-targeting they have to engage customers with very meaningful and compelling content to bring them back in the cycle. Abandoned cart items is an easy example of that or in the case of Flipboard it is the reminder of signing up with them to save your preferences in order to access it from the web or a different device.

Here retargeting is not only acting as a trigger to bring them back into the customer journey but also improving loyalty to the brand, stickiness of the product, and their overall lifetime value.

 


Watch out for more posts about growth marketing, and check out our comprehensive guide here for everything you need to know about the subject.

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Listening to your users: Inferring Affinities and Interests based on actual time spent vs clicks or pageloads

Personalized recommendations rely on the idea the you know the interests of your audience. In absence of explicit feedback, interests are generally derived from clickstream data: session and event (e.g. click) data. But given that sessions can be short lived (bounce) and clicks can be unintentional, they are unlikely to reflect true interests of your audience if you simply count them.

At Blueshift, we choose to actively follow along the individual’s storyline and extract intelligence from each event to gather insights of the user’s intent and interests, so we can provide better recommendations.

Let’s look at a real user example

In the table below, we see an actual clickstream of events from a user on blueshiftreads.com.

Timestamp Session_id Event Category Book title
12:30:24 session_id1 view Biography & Autobiography > Personal Memoirs Eat Pray Love
12:31:29 session_id1 view Drama > American > General Death of a Salesman
13:48:49 session_id2 view Science > Physics > General Physics of the Impossible
13:49:02 session_id2 view Biography & Autobiography > Personal Memoirs Eat Pray Love
13:49:09 session_id2 view Health & Fitness > Diet & Nutrition > Nutrition The Omnivore’s Dilemma
13:49:19 session_id2 view Health & Fitness > Diet & Nutrition > Nutrition The Omnivore’s Dilemma
13:49:35 session_id2 view Poetry > American > General Leaves of Grass
14:09:47 session_id2 view Poetry > American > General Leaves of Grass
14:10:02 session_id2 add_to_cart Poetry > American > General Leaves of Grass

This specific user interacted during two different sessions, browsing books from different categories. If we try to come up with the top categories for this user, based on total number of sessions, we get:

Rank Category Session count
1 Biography & Autobiography > Personal Memoirs 2
2 Health & Fitness > Diet & Nutrition > Nutrition 1
3 Poetry > American > General 1
4 Science > Physics > General 1

As you can see in the table above, Personal Memoirs is the top category while the three other categories tie to second-place (they have been alphabetically ordered in that case), but other tie-breaking rules can be applied.

Time spent ranking

At Blueshift, we developed algorithms to re-rank these categories according to the time the user actually spent on your products and categories:

Rank Category Time spent
1 Poetry > American > General 1212
2 Biography & Autobiography > Personal Memoirs 72
3 Health & Fitness > Diet & Nutrition > Nutrition 26
4 Science > Physics > General 13

Here, we rank ‘Poetry > American > General’ above the other categories. Note that at the end of the original event stream above, the user actually did add the book from that category to the cart. Even if we would have ignored that event, our time based ranking would have indeed capture a category of interest to this user.

There’s more: decayed time spent

You should be careful not to rely on detailed information from a single user on a single day: if the user indeed bought the book he added to the cart, that might just be an indicator of no longer being interested in that specific category of products. Furthermore, you would want to adapt to changing user interest over time.

That’s why we implemented what we call a decayed time spent algorithm, that combines the time spent by users over a certain period of time (say last week) and that weighs recent time spent as more important to the ranking than time the user spent before (say 14 days ago).

Decayed weighting of recency this way allows recommendations to adapt quickly to shifting user interests when they are shopping during holidays and might be looking for gifts for others as well as themselves.

From user-level signal to site-wide signal

Many product recommendations are related to some site-wide top categories of products, like ‘top viewed’. Using our time based algorithms, we can better rank these top categories. Let’s look at another example from blueshiftreads.com where we show you a part (20-25 to be exact) of the top 25 most popular categories.

Using classical session counting, we obtain the following ranking:

category session count
Juvenile Fiction > People & Places > United States > African American 5358
Juvenile Fiction > Girls & Women 5291
Juvenile Fiction > Family > General 5265
Fiction > Contemporary Women 5215
Fiction > Thrillers > Suspense 4971
Fiction > Mystery & Detective > Women Sleuths 4804

However, when we rerank these categories based on actual time spent by the users, we see that ‘Juvenile Fiction > Girls & Woman’ drops from position 21 (above) to position 23 (below), even though it had 76 user sessions more in the 7 days over which this was calculated. User sessions are no guarantee for actual interest (i.e spending time).

category time spent
Juvenile Fiction > People & Places > United States > African American 102164972
Juvenile Fiction > Family > General 100447985
Fiction > Contemporary Women 98897169
Juvenile Fiction > Girls & Women 98340874
Fiction > Thrillers > Suspense 91140081
Fiction > Mystery & Detective > Women Sleuths 87372604

Furthermore, if we rank the categories using our decayed time spent, we see that ‘Fiction > Contemporary Women’ is actually ranked the highest (21) while it was the lowest (23) in the original list. This indicates that this category received the highest time spend by users in the most recent past.

category time score
Juvenile Fiction > People & Places > United States > African American 28461106.29
Fiction > Contemporary Women 28179308.93
Juvenile Fiction > Girls & Women 28068989.26
Juvenile Fiction > Family > General 27608048.02
Fiction > Thrillers > Suspense 26102829.31
Fiction > Mystery & Detective > Women Sleuths 24597921.38
Ok, why bother?

So why bother re-ranking? Well, most catalogs will exhibit a Long Tail in the distribution of popularity of their content: very few items will be very popular while lots of items will be very unpopular. No matter how you rank the popularity of the top-10 categories (sessions, clicks, time, …) out of a 1000 category catalog, these extremely popular categories will always on top. Just have a look at the top 20 categories from blueshiftreads.com:

blog_post_time_spent_top20

As you can see, the top 5 categories do a lot better than the rest. For most businesses there is a lot of value in promoting content from categories other than these few favorites. Therefore, if you can avoid down-ranking interesting categories for users and do this consistently over your whole catalog, you will be able to recommend products from the appropriate category to the users who care for it. In other words, you will avoid the pitfall of recommending an overly popular yet generic product to your users.

But time spent relates to sessions/clicks anyway?

Yes and no. It is true that more sessions correlate to more time users will spend on categories, but not to the same extent: a session length can range from a second to tens of minutes. Have a look at the next graph below.

What we see is the ranking of the 1000+ categories (on the X-axis) for blueshiftreads.com by popularity (on the Y-axis, logarithmic scale) over 7 days, in terms of 3 different metrics:

  • The blue line represents ranking by session count. It is very smooth because it really ranks all categories just in descending order of session count. This is the standard ranking.
  • The red line represents ranking by time spent by the users. It is equally smooth in the beginning (left) because it ‘agrees’ with the session ranking: as mentioned above, the top popular categories will always be on top. But quite soon, the line becomes spiky: the ranking disagrees with session count, and the spikes indicate that this ranking would reorder the categories in a different way (promoting different categories to the top).
  • The green line is the decayed time spent ranking: the same holds as the time spent ranking. This algorithm also disagrees with session count and would reorder lots of categories in the long tail to promote categories of interest to the user.

blog_post_time_spent_ranking_plot

This re-ranking is exactly what you should do to stop recommending the same popular categories to users that might have indicated (time) interest in other categories.

Mobile E-commerce Done Right!

In 2014 the number of mobile users exceeded desktop users for the first time, accounting to 52.1% of all online traffic. (IBM report) This marked an important shift for marketers and how they communicate with their customers. The challenge facing them now is how they can deliver the best mobile experience to their customers.

Large "Add To Basket" button

Large “Add to Basket” button

Simple home page

Simple home page

Simplicity for mobile: When designing your app, less words and big buttons mean easy navigation. Keeping forms simple and short adds to a seamless mobile experience. A great example is the simple design of the Sephora mobile app. The main page only has 4 big buttons, and the item page has a big “Add To Basket” button that sits at the bottom of the screen as you navigate the page so you can press it any time you feel and not have to navigate back up to find the button.

Responsive content: A user can be browsing on a tablet, and use their desktop to make a purchase, and check the confirmation email on their phone. It is necessary to have responsively designed website and emails so it responds to any kind of device accessing it.

App benefits: The GPS in phones is a great feature to get creative with. Anything from store proximity offers, to digital loyalty cards showing up on your screen when you are near a store can be a great incentive for customers to make a purchase. Giving special discounts i.e. “Mobile Offers” in the Sephora app is a great incentive for customers to convert.

Personalized push notification

Personalized push notification

Meaningful Engagement: Sending your customers personal and relevant content in your text and push notifications makes a meaningful connection with your customers rather than the generic messaging they have grown immune to. Not only can you use their name, but also other attribution like items left in cart, geo targeted promotions or next best product based on their last purchase. Engaging your app audience outside of the shopping experience is also important to maintain customer relationship. A company that does a great job at this is Starbucks, which sends its app holders “song of the week” and “app of the week” every week and the occasional special offers from the store.

Rich push notification

Rich push notification

Express Line for Mobile: Quick checkout options from PayPal, Apple Pay, Visa, etc. can greatly reduce the checkout time with a mobile device. Including a fast payment method will become a norm in the near future as smartphone users grow year after year.

Social Connectivity: Prompt users to share out on social networks and drive customer engagement with campaigns that let users share content on your app or drive a conversation around a certain product or event. As you drive engagement you also create a loyal customer base that keeps coming back, similar to our example of Sephora.

The mobile market has marked a major shift in the commerce market and is worth paying attention to because this vertical is only going to increase in the coming years, as will the market share for mobile revenue.

 

5 Tips to Reignite Referral Marketing

Referral marketing, or also known as word-of-mouth marketing, happens organically by enthusiastic or satisfied customers, but it can also be influenced by companies with the right strategy. You would naturally trust a friend’s opinion more than a basic ad you see on the web. And statistics also say that “53% of users who clicked through a link shared by a friend of Facebook went on to make a purchase” (SociableLabs). So if you have been on this bandwagon for a while or just starting to realize its power, here are a few tips to boost your referral program.

  1. Find users who have experienced a high-five moment: From the time a customer makes a purchase to the time they are excited to use it and eventually forget about it, there is a small window of opportunity when you can high-five a customer and influence them to refer your product to their friends. Its important to ask and ask at the peak of their enthusiasm, because it could be the difference between sharing your product with 20 friends and ignoring your request. Blueshift’s segmentation engine and API makes it easy to find customers who share out to friends from your website and reach out to them with a personalized message.
  2. Think about app referrals: Mobile browsing has now overtaken desktop users and mobile marketing can no longer be ignored. Rethink your app referral strategy to make it flow better for the end user. Companies like https://branch.io/ help you track referrals from apps to check your progress and the impact it is making on your sales number. Testing your efforts and measuring the outcome continuously will give you a good idea of your success rate and what steps can be taken to improve your numbers.
  3. Visibility is everything: Make sure your referral option is very visible when you do ask for it. Not only that, it can be included on a product page, or in the signature of a transaction email. It can even be as simple as a check box on the checkout page. The more visible you make it, the more likely a customer is to use it.
  4. Easy peasy: Make sure your referral option is easy to share across the main social networks and email so your customer doesn’t have to jump through hoops to tell their friends about your product. Also create some attractive looking messaging around your product or brand so it makes you look your best to first time customers.
  5. Make an offer: Offering incentives for referrals is a sure way of getting a customers attention and keeping their business. A time sensitive offer creates a sense of urgency for the customer to act now rather than later. Store credit or a discount on a purchase attracts them to share your product and keeps them coming back to your store to redeem their rewards. Either way its a win win situation for your company.

    Naturebox referral

    Example of a time sensitive offer by NatureBox that doesn’t cost your customer anything, it is time sensitive, and makes them look good in front of friends.

There is no one recipe that works for every kind of business. It is an ongoing interaction with your customers that has to be monitored and adjusted with time. Changing up your content and offers from time to time keeps things interesting in the relationship, and keeps your customers engaged.

 

The Case For Predictive Segmentation – Part 1 of 2

Philip Kotler Segmentation Quote

Retention & Growth marketers are often interested in taking action on a segmented base of users. Classic segmentation methods include

  • Lifecycle based segments: new, active, lapsed etc.
  • Behavioral segments based on user behavior on the website/app
  • Demographic: Age, gender, location, household income, education based
  • Traffic source based
  • First purchase product/category etc.

Given all these ways of segmenting, how should any marketer approach segmented marketing, for the purpose of improving their core retention metrics? Some of the metrics CRM or growth marketers might be interested in improving through a segmented marketing strategy may be around activation rates, repeat purchase rates, or churn/retention rates.

Here are 2 steps for how you can use segmented marketing to drive higher response rate on any metric, say, repeat purchase rate:

  1. Use criteria that lead to a big spread in response rates in the steady state: Researchers on segmentation, have pointed out the need for segments to be  identifiable, substantial, accessible, stable, differentiable and actionable.  In the digital world, the tests on identifiable, accessible, and actionable are often easy to meet with segments. However, what really separates useful segments from the rest is the differentiability of the segment especially in response rates, their stability (i.e. whether the same criteria continue to map to differentiated responses over time), and whether the segments are substantial in size. In other words, you need to identify large segments of users whose response rates are substantially (3-10X) different from the average
  2. Test what happens to the steady state when you introduce a message or an offer: Once you have identified your segments in a steady state, you want to be able to test how you could increase the response rates further by introducing new variables like product features, offers, or content.

We will focus this first post in a 2-part series on the 1st of these challenges:large segments of users whose response rates are substantially (3-10X) different from the average. While this sounds simple enough, in practice, quickly figuring out the criteria that lead to a big spread in response rates could take some work. For example, in a typical setting, your gender based response rates might not look very dissimilar for repeat purchase: Let’s say your average repeat rate is 50%, and that men have a repeat purchase rate of 45% and women 55%. Now, there is clearly a difference in repeat purchase rates, but not enough for you to build compelling campaigns around it. At the other end of the spectrum, you might be able to identify outlier users, whose response rates are very high – but these outlier users may not meet either the stability test (e.g. will they continue buying the same way), or may not be a substantial enough segment.

This is where predictive segmentation & machine learning have a role to play. Machine learning can quickly figure out variables that are important, and combine them to come up with models that give you a 3-10X separation on large buckets of users. At Blueshift, we make this easy by making predictive segmentation scores available “on tap” for you to use.

Blueshift Predictive Repeat Purchase Score

Visualization of predictive score percentile with corresponding repeat purchase rates

For instance, in this graph, you can see how multiple variables have been combined to generate a repeat purchase score that gives you a nice spread in predicted response rates. In this example, users in the top 10 percentile of scores have a 90% repeat rate, whereas users in the bottom 10 percentile of scores have a repeat purchase rate of only 9%.

Now that you have identified a way to find segments of users who have a much higher response rates than the average in the steady state, how do you test the actions that will help you improve these metrics? Our second post in this series will look at that.

5 Essentials For E-commerce Push Notifications

Mobile commerce is growing much faster than ecommerce. Mobile apps are not only extending commerce beyond the desktop, but also enabling new e-commerce use cases like  on-demand services.

However, e-commerce apps tend to have much lower retention than other categories like messaging apps. According to a study, e-commerce apps only have a 13% retention after 1 month. Push notifications are often used for increasing user retention and re-engagement within apps. While push notifications can be extremely effective when done well, they can also be annoying when done wrong. Compared to emails, the bar for relevancy that a push notification has to meet is unusually high: firstly, there’s far less that you can say in a push notification; and secondly, unlike emails that can be perused later, push notifications interrupt the user with the expectation of an immediate response.

Relevant rich push notification from Groupon

Relevant rich push notification from Groupon

Here are our 5 rules for driving superior e-commerce retention through relevant push notifications:

  1. Personalize based on the user’s history: There are many ways of personalizing your push notification based on the user’s past behavior in your app and on your website. You could tailor the content to the categories they have shown affinity with in their browsing and buying history. You could time the push notifications to go out at the times when the user has interacted with the notification in the past. Reacting to the user’s implicit preferences in these ways can make the push notification highly engaging.
  2. Target the user in-context: Another dimension for mobile personalization is the user’s context right now, or their recent behavior with your app. Has the user been highly engaged with the app recently? Have they abandoned certain activities ? Has their search behavior shown them to be in market for certain items? Targeting based on such context, especially around location or recent activity, is highly effective.
  3. Surface real-time and time-sensitive content: Since push notifications encourage immediate action, time-sensitive content like new product launches or time-bound promotions do extremely well. Flash-sale sites do this really well by notifying users of new events.
  4. Engage users with rich content: Rich push notifications enable you to send multimedia or app content to your users. By providing more information about the notification upfront, rich push helps increase the relevance of messaging.
  5. Deep link to make it a smoother experience: Deep linking into relevant content within your app enables users to act on the push notification instantly, and drive increased conversions. Instead of opening the home-screen, users perceive the push notification to be more relevant when it lands them directly onto the offer or product promised by the push notification.