Blueshift and Redmart Win MARKies Award for Best Use of Programmatic

Blueshift Disrupts Programmatic Technology with MARKies Award Win

Last night, Blueshift disrupted the programmatic technology space by winning a coveted MARKies Award for “Best Use of Programmatic” with their client RedMart, Singapore’s leading online grocer. The 11th Annual MARKies Awards were held in Singapore to honor work by top marketers, agencies, and brands across over two dozen categories. Winners of the MARKies set the benchmark for the industry and are recognized as top performers in their field.

“This is a win for the whole team at RedMart and Blueshift. In the last few years, the word “programmatic” has come to be associated solely with Advertising. Blueshift is disrupting that thought process in the industry, by demonstrating that programmatic techniques can be applied to CRM as well.” ~ Dhruv Shanker, VP – APAC | Blueshift

From Left to Right: Dhruv Shanker (APAC VP of Sales, Blueshift), Bi Ying Wong (Customer Engagement Marketing Manager, RedMart), and Penny Cox (VP of Commercial & Marketing, RedMart)

From Left to Right: Dhruv Shanker (VP – APAC, Blueshift), Bi Ying Wong (Customer Engagement Marketing Manager, RedMart), and Penny Cox (VP of Commercial & Marketing, RedMart)

The category “Best Use of Programmatic” is a new category for the awards. Programmatic is a term typically used to refer to advertising technology that strips out the manual placing of bids and targeting and makes it more automated through sophisticated software/platforms. Blueshift disrupts the marketing industry itself with the introduction of their Programmatic CRM built for forward-thinking brands, like RedMart, ready to harness behavior-based marketing coupled with powerful AI. With the recognition that Blueshift has received by winning a MARKies Award in Programmatic, it is evident that marketers are ready to take the next step with their CRM to overcome the current limitations of scaling behavior-based cross-channel personalization.

“Blueshift’s Programmatic CRM has helped us drive targeted lifecycle marketing, and dramatically improve our re-engagement rates. With Blueshift, we are now able to launch personalized campaigns on email & mobile app push notifications, and drive a consistent message across different marketing channels.”
~ Penny Cox, VP Marketing | RedMart

RedMart’s lean, forward-thinking marketing team used Blueshift’s Programmatic CRM platform to gain a 3x lift in purchases with personalized multi-channel lifecycle marketing.

Read the case study here>>


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Thousands of entries were submitted to the judges, but only a handful were actually recognized for excellence by winning. Within the “Best Use of Programmatic” category, dozens of advertising, technology, and automation companies entered. Blueshift and RedMart competed against Amnet (Microsoft), Publicis (Scoot Airlines), and Vizeum (IKEA) as finalists.

5 Must-Have Campaigns for Media and Publishing Marketers to Drive Growth

In this series of blogs I go 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 first industry deep dive is taking a look at the media and publishing industry.

Weekly digest email

Weekly Digest by grow by Acorns

The media and publishing industry is very content heavy and always changing. This high volume of time sensitive content gives growth marketers an opportunity to serve users fresh 1:1 content in many creative ways. With new content being added continuously and hundreds of thousands of people interacting with their content every hour, growth marketers have a huge amount of data at their disposal for engaging their readers and keep them coming back everyday. Below are the 5 top programs growth marketers in media and publishing industry use to drive higher customer engagement.

Personalized weekly digest: Recommend the best content of the week based on a user’s browsing history and attributes like location to all active users on a weekly basis.

 

Developing story alert

Developing Story Alert by Huffington Post

 

 

Developing story alerts: For news publications, updates to stories that users have previously expressed an interest or interacted with. This can be indicated by their browsing history or previous searches. Because of the news being time sensitive, the update should be sent within 15 min of the update.

 

Category affinity

Category Affinity Email by Flipboard

Category Affinity: Many media companies ask users about what topics they are interested in and want to see more of. Users can also show their affinity with a strong browsing and buying preference for a few categories. Recommend trending content from the categories preferred by the user on a weekly recurring basis.

 

Subscription Upsell: Convert freemium users to paid subscribers with relevant offers who have shown high purchase intent based on predictive scores. The cadence can be 1, 7, and 30 days after customer’s behavior indicates that they have a high intent.

 

Trending Content: Push out messages to highly active users recommending content that’s trending now in terms of views as soon as some new content becomes trending or newsworthy.

 


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 Growth Marketers Part 2 – User Retention

Read Part 1 and Part 3 of our series “Lifecycle Stages for the Growth Marketer”.


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 a a circular cycle because the goal of customer retention is to get them to move through the cycle again and again.

Once you have the customer, it’s time to keep the customer. For Growth Marketers, much of their time must be focused on this area, otherwise you risk churning higher than normal amounts of users. (what is “normal” depends on your industry and business model.) In this stage, the focus is on Retention.

 

Enter Retention Campaigns

The second stage of the customer lifecycle is retaining users you already activated with targeted content in the form of reminders or recommendations to reduce churn. Retention is a more effective way of growing revenue because companies aren’t stuck attracting, educating, convincing, and converting potential customers. Retention is also a more sustainable business model for sustained growth because you are marketing to customer who have already expressed an interest in the product and engaged with the brand. In studies by Bain & Company, increasing customer retention by 5% can result in an increase in profits of 25% – 95%, and the likelihood of converting an existing customer into a repeat customer is 60% – 70%.

User retention gives growth marketers a lot of opportunity to deliver targeted content through many channels and in many forms. They can impact retention by creating delightful customer experiences through all their marketing channels on a 1:1 level using powerful reminders and recommendations. Lets dive deeper into what these reminders and recommendations can look like for growth marketers.

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Here is an example of 1:1 content recommendations in an email sent by a Blueshift customer

Reminders: 

  • Status in the Product: This type of reminder can be related to any incomplete activity in their account (e.g. “complete your profile” or “turn on push notifications”).
  • Weekly Activity Digests: Recurring personalized emails are a great way to keep active users engaged and staying on top of mind. For retailers this could mean sending a weekly email of new and trending items in their “Liked” categories or for media companies it can be trending content in the topics users are interested in.
  • Abandoner Re-Targeting: These reminders can be related to user activity such as browsed items or wish-listed products. For content businesses this can take the form of recommended content related to last viewed article or video.

 

Recommendations:

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Here’s an example of a catalog update message sent as a rich mobile push. All messages MUST be personalized!

  • Recommendations based on the customer’s Interaction Graph: The way users interact with your catalogue of products or content makes up their persona. This information is great for recommendations based on graphs created by users and other users. For example Twitter email notifications that give you suggestions on who to follow uses this same logic. The same idea can be used by retailers by leveraging data about people and products they have interacted with.
  • Recommendations based on affinity: Retail/E-commerce & media companies have large product catalogs or content. They have an even bigger data set of all the interactions users have with their catalog. This data can provide insights into preferences of users to certain categories, brands, authors, artists, price-points and more. The key to detecting user affinities is to not only look at individual user’s behavior, but also to normalize the behavior relative to other users. Growth marketers use these affinities to tailor marketing messages to every user on every channel, driving 3-10X higher response rates.
  • Recommendations based on change/updates in the catalog or app: Changes in your catalog of products or content, e.g. new arrivals in relevant categories, price drops on items that the user engaged with the website and app. These triggers are especially good for mobile push notifications since they are “newsworthy”.

Read Part 1 and Part 3 of our series “Lifecycle Stages for the Growth Marketer”.


 

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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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.

 

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.


 

What is Programmatic CRM? [Infographic]

Programmatic CRM is a technology that enables marketers to be customer-centric and leverage real-time behavioral data to reach every customer on an individual level throughout all of your marketing channels. Bringing Programmatic CRM into your marketing stack enables marketers to finally automate the delivery of consistent and delightful user experiences on every channel with true scalability and greater results.

Reach the perpetually connected consumer across all channels at the moment they are most inclined to engage with you brand.

The building blocks of Programmatic CRM

Programmatic CRM is built up a number of key components that work together to Engage with Segments-of-One:

  • Real-Time Triggers to engage customers based on their actions
  • Cross-Channel Reach to be customer-centric, not channel-centric
  • Personalized Recommendations to tailor recommendations to user behavior
  • Dynamic Audiences for segments that update with every customer interaction
  • Measurability to deliver end-to-end reporting on engagements and conversions

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Isn’t it time you stop marketing to stale databases built of attributes and demographics?

 

Market to Verbs, not Nouns

Marketers have always believed in targeted marketing. In the past, targeting has meant building a database of customers and their attributes, especially demographic attributes like first & last name, gender, location, and more. In this notion of database marketing, the databases describe nouns, like customers and products, and attributes of these nouns.

I wrote an article today in CMS Wire on how marketers should market to verbs, not nouns. Today’s leading edge marketers are finding that targeting based on nouns is outdated in the world of “Perpetually Connected Customers”, who are accessing information every second on web & mobile. The Perpetually Connected Customer’s actions & behavior are an indicator of their needs and wants desires, and marketers are confirming something that we have always suspected: that customers are multi-dimensional, and behave differently at different times.

2016-13-september-constant-motion

Why are verbs more important than nouns in targeted & personalized marketing? And why is their importance increasing over time? The two primary drivers are related to how customers have changed over the recent years, and how media has changed:

  • Perpetuals are not the same consumer from moment to moment: Perpetuals’ willingness to consume, changes depending on what they are doing. When describing people or customers, you could choose to describe them using static attributes like location or gender, that don’t change over time. However, people are multi-dimensional, and their interests and desires shift over time. Understanding the customer’s stream of actions is the only way to react to the changing desires of the perpetually connected customer.
  • The death of mass-media and the drive towards 1:1 personalization: Customer attention spans are shifting away from mass-targeted media (like broadcast TV) towards truly personal mediums where people consume content on their own terms. Correspondingly, marketers and advertisers need to shift their framework away from describing people in ways that are hangovers from the mass-media world –using attributes like gender, location, education etc. Instead, marketers need to concentrate on understanding the set of actions that truly set every individual apart, as no two customers rarely ever follow the same sequence of interactions with the same items.

To read more, head over to CMS Wire.