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Top Growth Marketing Trends for 2017

With the new year upon us, marketers scramble to put new plans into action while also identifying where we should place more of our efforts and resources. We ask ourselves where can I get the most “bang for the buck” for our organizations. We are looking to grow our current user base while also retaining more of our hard acquired customers.

Publications and journalists, including Forbes, MarketingLand, and others, have surveyed marketers about what they think the biggest needle movers in marketing are going to be in 2017. Of these surveys and conversations, here are the top 6 Growth Marketing Trends for 2017 backed by industry leading CMO’s who are betting big on them. For Growth Marketers, keeping users engaged will be a driving focus for 2017 as a stronger focus on retention becomes a guiding theme with unified, delightful customer experiences powered by powerful technologies.

1. Big Data (in real time and actionized)

Steve Fund, CMO of Intel said, “data is becoming the currency of marketing, and marketers will now have access to more data than ever…marketers will be able to use data to create more personalized and targeted products, messages, and customer engagements than ever before.”

Number 1 on the list = Big Data. (I know, Big data may seem like “yesterday’s news”, but bear with us) For this article we define big data to include real-time data, customer insight, and big data marketing applications. Big data tops our growth marketing trends because the volume of data coming in from consumers continues to increase year after year (be it from explicitly shared data to tracked behavior data), resulting in a rise in the importance of data scientists and data platforms. Data is the building block, the foundation, of customer identity and behavior that powers machine learning models and various marketing applications. As growth marketers, we’re looking to actionize this data faster and faster. Insights are great, however, we need ways to slice and dice the data manually and automatically to really affect our bottom line. In 2017, we’ll see more of the power of this data being wielded by marketers quickly and more easily than ever to create better experiences for every individual…and to build predictive models.

 

2. Artificial Intelligence & Predictive Analytics (for richer individual experiences)

Karen Walker, senior VP and CMO at Cisco elaborated “The key in 2017 will be transforming and analyzing data in order to derive contextual conclusions about our customers. What are they interested in? What needs can we anticipate? From consideration, to purchase, to renewal–it’s about delivering a richer, meaningful, individualized experience that helps our customers make faster, more educated decisions and deepens customer loyalty.”

AI has become incredibly powerful and accessible with the influx of more data over the years and the rise of more sophisticated technology. 2017 will see AI cement itself in the growth marketing space. The ability of machine learning to predict trends in customers and make complex recommendations across product/content categories has given many retailers the edge over the rest. For media companies, machines will curate the content to include in newsletters, onsite, and in app, transforming generic content feeds into timely, personalized content “magazines”.

To get this next level of understanding, growth marketers use predictive analytics to make sense of the scores of customer data to create actionable workflows and customer journeys that allow for the non-linear customer journeys that growth marketers are trying to tackle with legacy automation.

 

3. Marketing Automation (becomes smarter)

The CMO of Ally, Andrea Riley, said that their attention will be on “understanding, at a customer-level, personas, needs, wants and preferences and delivering them in a meaningful way through the channels they prefer to be communicated in.”

Marketing automation includes CRM, behavioral email marketing, web personalization, multi-channel messaging, triggers, and more. This encompasses a large portion of the marketing landscape and why it’s in our top 3. Seeing from this research on the State of Marketing Automation there is a lot of room for improvement in this area. Marketing automation isn’t going anywhere, instead, it is becoming smarter and flexible to incorporate real time triggers, catalog updates, and predictive analytics that drive users through funnels and increase user retention.

In the chart below, 2 out of 3 marketers state they aren’t even using their marketing automation platform to its potential (intermediate level and below). It’s time to review your use of your marketing automation technology to see if (1) there is any more juice you can get from it and (2) if it’s time to think about a change. A tool that isn’t being used has no value to the organization if it remains “rusting away” in the toolbox. (For the 18% who don’t use any marketing automation, it’s time to step up.)

Image from Scott Brinker as he talks about the changing roles of Marketing Automation

In all transparency, this is where Blueshift’s Programmatic CRM shines the brightest. Our technology enables growth marketers to become more customer-centric and leverage real-time behavioral data to reach every customer on an individual level throughout all marketing channels. Bringing Programmatic CRM into your marketing stack takes you to the highest level of sophistication with your “marketing automation” enabling you to automate the delivery of consistent and delightful user experiences on every channel with true scalability and greater results.

 

4. Content (becomes more personal)

The CMO of Grubhub said that moving forward companies will have to “establish a lasting and meaningful connection with consumers” to stay on top of mind and build loyalty among their customers.

Content has evolved from simple text on a website to richer, more visual content such as videos, slides, infographics, etc. Now we are witnessing the next form of content marketing that involves using personalized marketing technology to deliver the right content to the right person with interactive content on responsive web pages, emails, apps, and more. Often, marketers create mountains of content that quickly gets buried, even when it is still relevant or simply worth-the-read –  the problem has always been getting that content to the right consumer at the right time.

Image from Scott Brinker as he talks about the 4th Wave of Content Marketing

Personalization requires data about the prospect in order to make educated guesses about their interest and feed them relevant content. More and more companies are taking a strategic approach to their content creation and distribution along with closely measuring content marketing ROI to make sure they are moving in the right direction. As Growth marketers, we use content everywhere. Making it more personal through recommendations and affinities creates that delightful user experience that creates loyalty and trust that inhibits churn.

 

5. Mobile Marketing (driving greater engagement and retention)

The CMO of Keds, Emily Culp mentioned that she is “constantly evaluating our UI and ensuring that we are delivering not only a brand rich experience but also one that is streamlined enabling a clear path to purchase.”

Research from last year shows that conversion rates are significantly lower on smartphones. It’s no wonder that we see great potential still to make a big impact with mobile marketing by making the user experience less “SPAMMy” and more conversational, more relative, more personalized. When I say mobile, I mean more than just smartphones. Many devices can be involved in this process so treating it as part of a multi-platform or multichannel strategy is more appropriate. I see mobile as being a complementary channel to the overall marketing strategy. Part of the mantra of “being where your customer is” involves reaching out to customers where they are, rather than waiting for them to come back to you. That means being on their tablet, or phone, or laptop with a consistent message that adds value and delivers a personalized experience.

Bring consumers back to your branded properties (including apps and websites) with rich push notifications that include deep lining into your app, images, and even carousels of suggested products/articles from them to read. And don’t forget about SMS messages. The goal is to drive them to engagement and the desired action. Mobile will keep your users and customers engaged and drive higher retention rates when approached intelligently. Mobile optimized sites are now table-stakes. Designing for a mobile-first experience is now expected.

 

 

6. Social Media Marketing

Obviously, social media marketing still has significant weight when talking to marketers but be mindful of what platforms you choose to play in. Statistics show the reduced popularity of some social networks in some countries, i.e. Twitter and Facebook are in decline or plateauing in many western countries while Snapchat, Instagram and Pinterest are still growing in usage. Being particular about your spend and the ROI you get is more important now than ever as many platforms now require a “pay to play” business model to get the reach needed to have an impact. Your customer experience extends across all channels and must drive greater adoption and retention.

 

Conclusion:

So, how does a Growth Marketer begin to make sense of these trends?

In short – stay customer focused. Remove channel barriers. Remove fragmented data sets. Leverage technology anywhere you can to drive results quicker and at scale.

References:
  1. http://www.forbes.com/sites/jenniferrooney/2016/12/19/heres-what-will-command-cmos-attention-in-2017/#7d0470b59758
  2. http://www.smartinsights.com/managing-digital-marketing/marketing-innovation/digital-marketing-trends-2016-2017/
  3. http://marketingland.com/4th-wave-content-marketing-marketing-apps-84108
Listening to your users: Inferring Affinities and Interests based on actual time spent vs clicks or pageloads

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.

Blueshift helps you prevent personlization pitfalls personlization fails - avoid sending ugly emails

Avoid Personalization Pitfall # 5: Ugly Personalization!

In this series, we cover the common pitfalls all marketers face at some point when scaling personalization in their triggered marketing. From emails to mobile push notifications to SMS to display retargeting, the common platforms used today to market across channels begin to lose efficacy when organizations try to personalize their communications to an ever more complex and growing customer base.

Personalization Can Get Ugly



Watch this video to learn more about this subject from Brian Monahan, former CMO of Walmart.com 


Please, stop sending ugly emails…especially if you are going through the trouble of personlizing them. (Strike that, just don’t send ugly emails.)

Marketers using legacy systems often find that they are unable to combine “automation” with “creative” in these systems. As a result, some of the automated messages delivered by these legacy systems look ugly & “too automated” instead of personalized and delightful.

The inconsistency originates from using systems that are so complicated that the marketers have to pull in the IT and design team to execute a certain responsive ad or email and the creativity of the marketer is left behind. The customer should have a visually consistent experience as they move from one channel to another. Be it your website, app, push notification, or email, the same unique look should come across in every touch point.

Simple, Clean Designs Delight

In our experience with billions of emails and hundreds of email designs it is evident that the cleaner, simpler, and more seamless layouts get the highest CTRs and conversion rates. The goal of reaching out to customers is to delight them with a message that will bring them back to your site rather than drive them away with ugly looking emails or push notifications.

Here is an example of an email with a poor personalization design:

screen-shot-2016-10-10-at-11-25-20-am

This is a welcome email for signing up with Sheplers website. First thing you notice is that you cannot tell what they sell from this email. There is no mention of my name to make this personal. There are no images of products that catch your eye or a call to action. Overall this email does not provide much value to the customer.

Here is an example of a nicely designed, personalized email:

screen-shot-2016-10-10-at-11-34-15-am

This birthday email from LaserAway is a good way to bring back customers to your store or just staying on top of mind. There are exclusive offers and discounts to take advantage of specifically for the birthday week. There is an urgency and promotion that customers can act on.

When designing your emails, ask yourself if it is something YOU would like to receive. Or ask your team mates, friends, or your mom. Just please, don’t design ugly personalized emails.


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books-student-study-education

Driving Student Retention for eLearning and MOOCs – Part 1

In this 2-part series, we address core common issues that marketers face in the eLearning/Massively Open Online Course (MOOC) market. Below is Part 1 of the series where we cover the issues with 1st-time enrollees and then repeat enrollment (taking more courses) at a high level. In Part 2 we will dive deeper into these, as well as a few more areas that are often forgotten (HINT: Do your students feel comfortable using your platform?) This is not an extensive list nor the full extent of our research, it is meant as a starting point.


 

Whether you call it student churn, student retention, student attrition, or a number of other terms, one of the primary issues for eLearning companies like Udacity, Coursera, EdX, and dozen of others is keeping students – more importantly, keeping students coming back. The stark reality is that up to 90% of students who enroll in an online course simply don’t complete the course, and that number only gets slightly better when students have actually paid for the course.

A University in Everyone’s Pocket

With today’s perpetually connected consumer, every person now walks around with an entire university in their pocket where an aspiring archaeologist can learn about the history of Egypt or a developer can now get a nano-degree in autonomous cars. But the beauty and the pain arises quickly when one is perpetually connected: there are so many choices, so many apps and emails and messages distracting us, and finally, an expectation of personalization and individual relevance within each choice and through every communication. The end result becomes not just an ADD-like attention span, at a more basic level, people just get busy and lose track of what they have signed up for. (And like mentioned earlier, just because someone has paid, doesn’t necessarily mean someone will stay committed.)

With this rise of a greater number Massively Open Online Courses (MOOCs) and their evolution into delivering “for credit” or certificates of completion, potential students have more choices. Students must feel they are being respected as individuals with their own education needs. They expect personalize, relevant communications at all times throughout their user experience both on and off the platform.

NOTE: One of the issues for eLearning are people who just “kick the tires” to find out what the content, site, or app is all about. In Part 2 of this series we will highlight a key strategy to better identify who those students are.


So how does an eLearning organization keep the students coming back?

How do you convert the “one-and-done”/fleeting students to a more casual student and then to the “life-long learner” that makes a habit out of taking classes and paying for courses, degrees, and certificates?

PROBLEM #1: 1st COURSE COMPLETION

Getting students to complete their first course is half the battle. Often, these students are acquired with marketing spend. And, like mobile apps and games, the “novelty” or “excitement” of an online course and learning something new wains and that dreaded retention cliff proves time and time again less than 10% of students will complete their first course. And if they don’t complete their first course, they have a statistically low chance of enrolling in another, or paying for another course or certificate.

NOTE: While this problem primarily addresses getting students to complete the first course, the same strategy and engagement tactics can be used for every subsequent course.

SOLUTION:

Build a welcome/on-boarding series that leverages multiple channels. Simply relying on emails is a sure fire recipe for failure, especially when an eLearning destination has a mobile app. Be where your students are and where they engage. If they are in the App, trigger in-app messages based on their behavior, bring them back to the course. More importantly, when they are outside the app, leverage mobile push notifications that are tailored to the exact course they are taking. Make it personalized and relevant to them. (DO NOT simply blast them with a message that says “You’re course is waiting.”).

*  Send an onboard/welcome campaign

*  Use “reminders” to gently nudge them to complete the exact course and level they are in

*  Use multiple channels like mobile, email, in-app, even SMS

*  Personalize every communication to make it relevant and resonate with the student as an individual

 

PROBLEM #2: REPEAT ENROLLMENT:

Getting students to sign up for more courses must be an organization’s highest goal to get the highest LifeTime Value (LTV) from each student. This focus helps move students from the “one-and-done” learner to the more occasional or even life-long student. If an organization runs on the freemium model, then re-enrollment is paramount to generating revenue. If an organization is pure-play paid (either as a subscription or via certificates), then getting students to re-enroll and take more courses adds that additional revenue that drives higher LTV and helps identify behaviors and attributes that can feed into the acquisition strategy.

The feeling of accomplishment from completing a course is double edged. In one respect, they feel accomplished and satisfied. They completed a course. Either they feel empowered and, hopefully, want to learn more, or they feel satisfied, and, sadly, simply move on to another activity or interest outside of the learning environment. What would you do?

SOLUTION:

Capture that feeling of accomplishment and feed them more courses. IMMEDIATELY.

Build on the excitement with timely messages triggered on the completion or even near completion of that course. And don’t hesitate. Leveraging real-time triggers makes sure that messages are sent at the right time. At the bare minimum, prompt them to sign up for more courses, perhaps with incentives or discounts. BUT, if you really want to drive retention, the simple step is to offer them courses they may like, based on browsing behavior, the subject matter of the course they just completed, or use collaborative filtering to recommend courses that others have taken who have taken this course. Tracking real-time behavior is critical.

The advanced step: (HINT: you should be doing this to stay relevant and competitive) Leverage a full understanding of how they engaged with that course (length of course, timing, subject matter, test scores) coupled with a full history of ALL of their courses taken and browsed and engaged to deliver a truly personalized set of recommendations. Be student-centric by building out profiles of every student that not only tracks static attributes like geography, gender, email address, etc, but also keeps a full transaction and engagement history of courses and even the messages you’ve sent.

Then, use multiple communication channels to message them: emails, mobile push, SMS, In-App, On-Site, even leverage Facebook Re-targeting to get them to come back with very specific recommendations. (Yes, even your advertising should be personally relevant to re-engage your students.)

*  Timing is of the essence, act quickly to re-engage students.

*  Recommend courses that others like them have taken or that they have browsed.

*  Take it a step further and build out predictive recommendations based on their full engagement history.

*  Use every channel at your disposal to re-engage them personally, even Facebook.

NEXT STEPS:

Ask yourself:

  • What are you doing today to address these issues?
  • What data are you missing to start acting on these tactics?
  • Do you have control of the data?
  • Do you have real-time data that enables fast segmentation to engage students with up-to-the-moment messaging?

In Part 2, we will address HOW to get there and uncover a few more obstacles common with student retention for eLearning and MOOCs. In addition, we’ll show you how we help Udacity drive increased student retention through these very strategies.

 

keep-calm-and-ai-on-with-blueshift2

Practical AI for Growth Marketers

A.I has had a media resurgence in the recent past, thanks to the incessant coverage in every outlet and overblown hype for and against what it all means. Underneath the hyperbole there are real breakthroughs but also many challenges and practical considerations in using these innovations. This post by Crowdflower, a crowdsourcing platform used by many for improving the RoI of A.I projects puts it well when they say “A.I is a pragmatic technology that can be applied to solving today’s problems but you need to understand the limiting beliefs of A.I, and replace myths with truths”.

Growth marketers at B2C organizations specifically face formidable challenges in using A.I or machine learning in their day to day efforts. Data at their disposal spans many sources, updating via real time streams and likely runs into petabytes in size. Here are few practical considerations in realizing good RoI from your A.I project investments.

 

Simple vs Diverse data formats:

Today’s customers are tethered to their devices 24/7 and switch between them seamlessly. Advances in Big Data technologies like Hadoop have made it easy to capture raw data in diverse formats and store them across several different data stores usually called data lakes spanning SQL systems, NoSQL systems, flat files and excel sheets. As a growth marketer this is the raw gold mine you are working with and you should prioritize data capture in any format over shoehorning it to a particular data store or schema. A.I tools that you invest in should adapt to this mix of structured and unstructured data.

 

Real Time vs Batch mode:

The half life of consumer intent is getting shorter with each passing year, and customers expect “on-demand” experiences that are contextually relevant and personalized to them across every device. Growth marketers should prioritize simpler AI algorithms and processes that can adapt well to real time data than more complex batch mode solutions that may need several hours or days to execute. Pay close attention to training time it takes to build and deploy A.I models and how fast can they incorporate new data.

 

Complete vs Sparse data:

While it’s ideal to have every attribute and preference known about all users, in reality you will end up with incomplete or partially known data fields despite your best efforts. B2C growth marketers in particular should expect this from day one and invest in tools and solutions that adapt well to incomplete data. Take for example a user location, there may be a mix of user given location data, with device lat/long, ip to geo, inferences from content viewed or searches done and more. As a growth marketer you should prefer A.I tools that can adapt well to the mix of all this data and output best effort answers for widest user base than on few users with complete and clean data.

 

Size of training data:

Most A.I algorithms expect training data to be fed to them and the size and availability of training data is big obstacle to overcome to use them effectively. Certain class of A.I algorithms like Boosted Random Forests are better at adapting to the size of training data than Convolutional Neural Networks aka Deep Learning. Growth marketers should prefer those algorithms that can work with limited training data and have in-built sampling techniques to deal with disproportionate class sizes.

 

Black box vs Explainable Models:

A.I algorithms come in many forms, from easy to understand decision trees to black box complex ones like Deep Boltzman machines. Navigating the black boxes can be tricky, what works today cannot be said of tomorrow and need very careful tuning to yield short term results. Growth marketers should prefer AI algorithms that explain their outputs, and helps marketer understand various factors and weights given to them in realizing that output. Tools that iterate quickly and incorporate domain specific knowledge much more easily are likely to work better in the long term than hyper optimized black boxes with enticing short term yields.

When it comes to the nitty gritty of it all remember that A.I is no magic bullet but a practical tool to achieving your custom goals.

Keep calm and A.I on.

Urban Ladder Largest Home Furnishings Furnitire Home Decor Online Shopping uses Blueshift to Engage Online Furniture shoppers

4x Conversion Lift: Urban Ladder Finds The Secret Sauce to Reach Online Furniture Shoppers

download-the-urban-ladder-case-study-by-blueshift2


Urban Ladder is a leading online furniture and home decor company that provides a curated shopping destination for your home. Their modern designs and uniquely styled products attract millions of customers and has propelled them to be the #1 source for furniture in India. With millions of customers coming to their site via multiple channels and interacting with their catalogue of over 4,000 products across 50 different categories, they found it hard to market to all their customers while staying true to their promise of a personalized experience.


“With Blueshift, we have launched very personalized triggered campaigns on email & mobile app push notifications. We are seeing significant improvements in conversion rates on these marketing campaigns which are highly targeted and relevant for the users.”

Ashish Goel, CEO of Urban Ladder

 

Urban Ladder turns to Blueshift to help address its issues

Urban Ladder’s unique design at the heart of every marketing message

Urban Ladder’s unique design at the heart of every marketing message

Urban Ladder has a distinctive brand and look which had to come across in every channel they market across. With a web based store and a mobile app, they had a hard time tying in multiple data sources into a unified customer profile in real-time. They needed a robust recommendation engine for their 4,000+ product catalogue consistent with each person’s browsing and purchase behavior. Handle personalization to varied sales cycles, like furniture which tends to have long consideration cycles rather than home decor, which can be impulsive.

 


Blueshift’s Solution

urban-ladder-out-of-stock-notification-with-recommendations-of-other-products

Blueshift provided the ability to unify each user’s behavior data across mobile and email for a complete 360-degree view of the customer. It enabled Urban Ladder to deliver a consistent user experience across all channels that represented their brand along with powerful recommendations and simplified paths to purchase.

After a quick integration Urban Ladder was able to launch cross-channel triggered campaigns for welcome series, abandonment, post purchase, complete-the-look cross sells, and product recommendations based on user behavior in just a few days.

 

 

 

Urban Ladder using the “back in stock” product alert in their newsletters powered by Blueshift.

Urban Ladder using the “back in stock” product alert in their newsletters powered by Blueshift.


 

Conclusion

Urban Ladder Realizes a 4x lift in conversions and a rapid time to value outperforming all other vendors
Urban Ladder now delivers a delightful user experience across mobile & email by combining their in-house creative team and Blueshift technology. Using the 360-degree customer profile powered by Blueshift as the foundation of their customer data and utilizing deep segmentation capabilities of Blueshift, Urban Ladder has seen 4x higher conversion rates over previous tactics.

download-the-urban-ladder-case-study-by-blueshift2

Send messages when someone is most likely to engage and convert, not just open with blueshift

Send Time Optimization or Engage Time Optimization?

Marketers should adapt their send time to each user individually, and send campaigns closer to the times when they are more likely to engage in downstream activity.

As you might have read in our previous blog post “Re-Thinking Send Time Optimization in the age of the Always On Customer“, Blueshift focuses on “Engage Time Optimization” rather than what marketers traditionally call as “Send Time Optimization”. Since we’ve posted this article, we’ve elaborated a bit on the details of the development of that feature on Quora (When is the best time (day) to send out e-mails?). Through this post however, we would like share more of those insights, and advocate for focusing on optimizing downstream user engagement metrics rather than initial open rates.

The idea of “Send Time Optimization” is not new, and has been around for quite some time. One of the more recent reports on this was posted by MailChimp in 2014, but articles and discussions on this topic go back as far as 2009 and older. The data science team at Blueshift followed the hypothesis that if there is a specific hour of the day, or day of the week that an audience is more likely to engage, that should reflect in increased open (or even click) rates when messaged at different times.

Open Rates vs Click Rates

In order to observe this effect (or the absence of it), we analyzed over 2 billion messages that were sent through Blueshift. Some of the results are presented in the graphs below for one of our biggest clients.

Through the Lens of Open Rates

“irrespective of the segment that was targeted, the audience size and the send time, the open rate is the highest in the first two hours after the send”

We looked at the open rate (%, shown on the Y-axis) in the first 24 hours after the send was executed (in hours, shown on the X-axis).

open_rates

What you see are 18 email campaigns from one client over the period of one month (totaling over 20 million emails). On the top left, we see campaigns sent out on Monday, next, Tuesday, and so on – through Saturdays on the bottom right. There were no campaigns on Sunday for this client during this month. These campaigns were sent to audiences ranging from tens of thousands of users in specialized segments (e.g. highly engaged  customers) to large segments of 2–3M users. The send times varied from 5AM – 12PM (in parenthesis in the legend).

What you can see from this graph, is that even though the campaigns were sent out on different days of the week and at different hours, the initial response in term of open rates is very predictable for the first hours. The conclusion from these plots is that irrespective of the segment that was targeted, the audience size and the send time, the open rate is the highest in the first two hours after the send. Depending on the actual time of the send you can achieve a slightly higher open rate in the first hour, but you might loose more ‘area’ in the following hours, accumulating to more or less the same open rates after some hours.

Through the Lens of Click Rates

Naturally, the question comes to mind if there is any measurable effect when we look at clicks, which can be considered as a deeper form of engagement by the users that received the message:

click_rates

But as you can see from these second set of graphs where the Y-axis represents the click rate (%), we observed a very similar behavior: the actual response rate in terms of clicks does not significantly change when a campaign is sent at a different time.

We came to the same conclusion when repeating this experiment for opens and clicks for other clients in our dataset as well. After doing more in-depth analysis on our datasets, we observed that users that were targeted in email campaigns at certain times, showed engagement (e.g. visits to the website or app) at other times. Users prefer to engage deeply at certain hours of the day while casually browsing through out. Marketers should adapt their send time to each user individually, and send campaigns closer to the times when they are more likely to engage in downstream activity. You can find more info about this “Engage Time Optimization” in this post.

 

poor-historical-view-of-customer

Personalization Pitfall #4: Poor Historical View of the Customer

In this series, we cover the common pitfalls all marketers face at some point when scaling personalization in their triggered marketing. From emails to mobile push notifications to SMS to display retargeting, the common platforms used today to market across channels begin to lose efficacy when organizations try to personalize their communications to an ever more complex and growing customer base.

Poor Historical View of the Customer



Watch this video to learn more about this subject from Brian Monahan, former CMO of Walmart.com 


Lifecycle marketing is a highly engaging way companies can re-activate or re-engage old customers. Using past interaction and transactions online, companies surface relevant products and promotions through different channels to influence a purchase. Sounds simple enough right? On the contrary having a 360 degree view of your customers over a long period of time and in real-time is very tricky for most businesses and our pitfall number 4.

Out with the old…

An old approach to this strategy has been to remarket to customers based on each item they browsed without taking their historical behavior into consideration. If a customer is browsing patio chairs, hammocks, and outdoor umbrellas, they are probably looking to furnish their backyard. Offering them 5 options of patio chairs might not be the best way to influence a sale.

personalization-pitfal-4-poor-histrical-view-of-customers-pic-1

 

personalization-pitfal-4-poor-histrical-view-of-customers-pic-2

Overcome Amnesia of Your Customers

Your product recommendation engine has to be smart enough to suggest “next best products” or “complete-the-look products” or a product in the same category or brand. Only personalized, smart product placement and recommendations can work to win back customers in the highly competitive market of today.

The key to re-marketing the right way is to connect every piece of user behavior and past purchase in real-time with a deep knowledge of the company’s catalog. Using a holistic customer view, marketers can provide a hyper-personalized story relevant to each user’s context.


Subscribe Now to this series
To learn more about all the common personalization pitfalls covered in this series, watch this VentureBeat Webinar that provides real world examples and fixes you can start using now.


  • Each update sent directly to you with extra tips NOT included in the blog posts
  • Access to the VentureBeat Webinar with former head of marketing at Walmart.com
  • Receive an audit of your current triggered activities with a marketing consultant

BLueshift solves the message overload problem for marketers. Monitor the frequency of all of your messages across all channels to avoid annoying your customers

Personalization Pitfall 3: Customer Message Overload

In this series, we cover the common pitfalls all marketers face at some point when scaling personalization in their triggered marketing. From emails to mobile push notifications to SMS to display retargeting, the common platforms used today to market across channels begin to lose efficacy when organizations try to personalize their communications to an ever more complex and growing customer base.

Overcoming the Message Overload Pitfall


Watch this video to learn more about this subject from Brian Monahan, former CMO of Walmart.com 


When you build out your company’s personalized marketing landscape you soon find your volume of messages increasing exponentially. As you set up re-engagement campaigns along the customer journey, the volume of messages across all channels can quickly add up to 10 or 15 different messages. Of course, that doesn’t mean you send them all 10 of these messages in one day or even a week. Customers feel overwhelmed if their inbox is flooded with one particular company emailing them again and again. Message overload is a sure way of ending up in your customer’s spam folder or worse, unsubscribing from all your communications. This rapid deluge of communications to your customers is our pitfall #3.

Don’t Be Annoying…

Message Overload across all channels is a personalization pitfall

Finding the balance between quality and quantity will save marketers from those dreaded mistakes of sending a customer too many messages in a day. But how do you make sure you aren’t sending to many messages across all your channels?

The way to achieve message zen is by smart segmentation of customers who fit a certain criteria based on their attributes and behavior on site. Behavior-based marketing resonates better than single trigger marketing because it tends to be more accurate rather than an in-the-moment action or even sloppy demographic focused bucketing. Grouping together customers who have shown similar behavior and sending a set of targeted messages that are personalized to their persona is a controlled way of using triggers on your site.

Think Beyond the Inbox…

“don’t simply focus on the amount of messages you send per channel, look at the aggregate of ALL of your messages sent through all of your channels”

Another way of working around the message overload problem is to build and monitor multi channel campaigns. Marketers constantly compete for inbox space along with numerous other brands. When’s the last time you looked at your inbox and didn’t feel like you were being yelled at by dozens of brands? A quick reminder to complete your purchase and checkout can easily be done via text message or push notification – abandoned cart campaigns are not simply just an email tactic. Dividing your messages across different channels can keep your brand name top of mind and limit annoying your customers. And remember, don’t simply focus on the amount of messages you send per channel, look at the aggregate of ALL of your messages sent through all of your channels. Otherwise, you still run the likely risk of annoying your customers with message overload.


Subscribe Now to this series
To learn more about all the common personalization pitfalls covered in this series, watch this VentureBeat Webinar that provides real world examples and fixes you can start using now.


  • Each update sent directly to you with extra tips NOT included in the blog posts
  • Access to the VentureBeat Webinar with former head of marketing at Walmart.com
  • Receive an audit of your current triggered activities with a marketing consultant

Blueshift challenges send time optimization with engage time optimization

Re-Thinking Send Time Optimization in the age of the Always On Customer

Many email service providers tout Send Time Optimization as an add-on feature and promise marketers that they can tailor their marketing campaigns to the exact time their customers are expected to open their emails. It’s tempting to take that at face value and think it’s a silver bullet to improving your customer engagement. Our internal research, after analyzing over a billion emails sent through the Blueshift platform over last year, has shown that in the age of smartphones and always on connectivity, the notion of “Send Time Optimization” needs some serious re-thinking.

Stop Optimizing to “Open Rates”

“look at full downstream activity and measure what windows of time their customers are more likely to follow through and complete specific goals”

Today’s perpetually connected customers are much more likely to have many more frequent bursts of activity around the clock than a recurring habit of opening their emails at a certain time of day or clicking onto sites or apps at specific hour. Then what does it mean to do “Send Time Optimization” for marketers? Instead of optimizing for immediate opens, marketers need to focus their attention and look at full downstream activity and measure what windows of time their customers are more likely to follow through and complete specific goals than when they open or click emails. The true measure of success should be specific conversion goals or sum total of time spent on your site or apps.

As a results-driven marketer ask yourself: “Would you rather have someone who opened a message, or someone who converted/made a purchase?”

Enter => Engagement Time Optimization

Blueshift’s recently released Engage Time Optimization computes windows of time for each user where they are more likely to engage fully, rather than optimizing for immediate opens or clicks. We look at the sum total of time spent by each customer over a long period of time and rank each hour in the day based on time spent and how deep in the conversion funnel they got to. You can access “hour affinity” for each user through the segments panel under “User Affinity” tab inside our application dashboard.

Re-Thinking Send Time Optimization in the age of the Always On Customer - look at engage time optimization to optimize your campaign sends to further down the purchase funnel

 

You can use these “hour affinities” like any other user affinity attributes during the segment creation and tailor campaigns to specific audiences. For example you can create segments of users who prefer “morning” hours by picking 5am to 8am or those who prefer “evening” hours by picking 5pm to 8pm or any other combination. We believe this offers a powerful alternative to traditional “Send Time Optimization” feature by tailoring the campaigns to the customers based on their full funnel behavior than on immediate opens or clicks.

 


If you’d like to see a demo or request more information on Engagement Time Optimization, contact us via our site or email us at hello@getblueshift.com.