20171205 - How recommendations help you stay relevant in content overload

How Recommendations Help You Stay Relevant in the Era of Content Overload

One thing we can guarantee about the future: we’re never going to run out of content.

Take TV for example. Where once we had a handful of channels broadcasting one program at a time, we now have multiple streaming platforms, countless cable channels, on demand, and DVRs.

Or music: You’re not limited by your carefully curated CD collection anymore. You can choose from almost any song ever recorded on Spotify.

For the content consumer, it’s an embarrassment of riches. For businesses that rely on advertising or subscription revenue, it’s a challenge.

Attention spans are shrinking. With endless options, consumers will move on in matters of seconds if what they see or hear doesn’t capture their interest.

To stay relevant in the media industry — bringing targeted audiences, charging top dollar for your ads and maintaining a healthy growing subscriber base — your content needs to be relevant.

And, of course, every consumer’s tastes are different. The key to relevance is personalization recommendations.

For example, after revamping its mobile website to deliver a personalized, Facebook-like experience, USA Today saw a 75-percent increase in time spent per article.

Recommendation Models Used By Successful Advertising and Subscription Businesses

As content executive Paul Lentz points out, publishers have been using data to target specific content at specific audiences since the print era.

In today’s digital era, a few successful media companies have developed recommendation techniques to engage and retain users with almost supernatural precision.

  • After experimenting with content-based and collaborative filtering, the New York Times settled on a best-of-both-worlds approach that models the content and adjusts it according to viewing signals from readers, models reader preferences, and uses the resulting data to make recommendations.
  • Netflix’s recommendation engine divides users up into “a couple thousand” taste groups. Netflix claims the engine is worth $1 billion a year and is responsible for more than 80 percent of the shows users choose.
  • Spotify’s Discover Weekly playlists have become a favorite feature among users for introducing them to new songs and reminding them of old favorites. The “magic” of the algorithm, the man behind the playlist says, comes from comparing your listening habits to those with similar taste and “filling in the blanks.”

What does each of these approaches have in common? Each media company leveraged a massive database of user data to make comparisons among users, identify trends in their preferences, and anticipate their behavior.

You can do the same with Blueshift’s AI-powered marketing platform. Blueshift can help you

Learn how to configure recommendations in a single click or bring your own algorithms to BlueShift Personalization Studio.

20171205 - How Data AI and Automation Can Drive On-Demand Growth

How Data, AI, and Automation Can Drive On-Demand Growth

In the previous post, we outlined 5 tips for success in the on-demand economy. What each of these five tips comes down to is understanding what your users and service providers need, when they need it, and responding quickly and fluidly. In other words, anticipating what they want when they want it, and being there when they’re ready. This is how you build a community of enthusiastic, passionate users and service providers, eager to tell others about your brand.

How can you do all this? With the power of data.

We like to say that on-demand marketplaces are data-driven businesses. Every on-demand platform uses data in some way, and the successful companies are data-intensive operations.

But it’s not enough just to have the data about your customers and service providers. You need to derive useful insights from the data to provide immediate results and delight customers in real time. But with so much data coming in so quickly, our human brains can’t keep up. Artificial intelligence (AI) can.

Here are few ways successful on-demand service companies can use data and artificial intelligence to drive growth.

    1. Personalized onboarding. In the delivery business, once users sign onto a platform, 80 percent never leave. Artificial intelligence can pour through your customer data to personalize the onboarding process, ensuring customers feel cared-for even before they use your service.
    2. Personalized triggers and notifications. What users love about on-demand services is that they can find and track service providers effortlessly. AI platforms can automatically update customers and suppliers on delivery times, offer personalized choices based on where they are in their user journeys.
    3. Repeat transactions. Users who use the service again and again are the holy grail for on-demand businesses. With artificial intelligence, you can automatically target upsell and cross-sell opportunities for both buyers and service providers based on data. You can also segment them based on who is likely to re-engage or likely to churn, so you can target them with a tailored strategy.
    4. Curated content and offerings. Consumers demand relevant content, tailored to their interests and delivered to their device of choice. AI helps you personalize newsletters, web and in-app content, and recommendations based on user interests and affinities calculated from historic and up-to-the moment behavior
  1. Location-based personalization. As we said, most on-demand businesses start locally. With AI, you can segment your users based on where they are — but even more importantly, the context of why they’re there. You can provide location-specific offerings and recommendations across any channel, including the web, your mobile app, and SMS.

Explore How AI Can Transform Your On-Demand Service

On-demand businesses grow when they use data to understand the needs of their customers and service providers and connect them seamlessly. With a 360-degree view of your users and real-time insights powered by artificial intelligence, Blueshift can take the data crunching off of your hands so you can focus on strategy, growth, and retention.

To learn more about how Blueshift helps on-demand businesses grow, click here.

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.