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Anonymous Users: Tactics for Knowing Your ‘Unknown’ Site Visitors

By Hugo Vint

In today’s e-commerce, online betting, and travel sectors (among others), personalised messaging and targeted offers are key to driving retail outcomes. In retail, for example, an entire 40 percent of consumers buy more from brands who personalise the shopping experience across channels.

But “personalising the shopping experience across channels” requires knowing the person behind the shopping experience.

However, too few of a retailer’s site visitors convert into known customers. A brand’s “anonymous” visitors – the individuals who visit a desktop or mobile site (or mobile app) without supplying any identifying information, or who browse as “guests” – make up between 57 percent and 98 percent of its audience.

How can a brand get to know these ‘anonymous’ users? While a portion will always remain elusive, digital messaging – and more specifically, browser push messaging – can help businesses turn a larger percentage of their anonymous users into known, engaged customers.

Armed with data on an ‘anonymous’ customer’s interactions with a brand across all of its digital touchpoints (including desktop website and mobile site or app), a brand’s marketing team can use that individual user’s preferences and behaviours to identify the right time to start opting in customers to receive browser push messaging..

Browser push involves a ‘soft opt-in’ approach that is less aggressive than the typical call-to-action signup,techniques currently being used making it a smart avenue for reaching those consumers who are less than eager to supply their personal identifying information. Here are a few tactics for using customer data to opt-in customers to browser .

Seek Their Permission: Asking an anonymous visitor if they would like to receive information, through a pop-up screen, banner, or other digital interface, before loading the ‘yes’ or ‘no’ native browser opt-in request can lead to higher browser opt-in rates.

Deploy a personalized message: If the anonymous visitor is searching for a specific item, or browsing certain categories then he or she is more likely to respond favourably to an opt-in request when asked if he or she would like to receive further information about that particular product or category.

Get them to reveal more over time: Once opted in to receive browser push messaging, brands can invite the anonymous users (gently, of course) to further engage with the brand – whether by asking them to sign up for an email newsletter, download an app, or register for a loyalty program.

Convert faster thanks to personalisation: Once the customer is engaged, the brand can deliver a more individualised messaging experience based on how the customers interact with messages on an individual level which in turn captures, data-driven preferences about timing, average order value, session lengths and more.

Filed Under: Tags: , , , With: digital marketing, Marketing Techniques, Mobile Marketing

New Tactics for Driving Native Content Distribution & Engagement Outcomes for Publishers

By Brendan O'Kane

To replace the revenue they used to get from premium-rate print advertising, many digital publishers are investing in the outgrowth of sponsored/branded content – aka “native advertising.”

Native content – such as articles, videos, infographics, or other items created or co-created by advertisers – is not only a smart, partnership-driven approach to earning ad revenue, it also provides a high-value way for advertisers to share their messages without making the direct sell. Mindful of that soft-sell appeal, however, publishers have to take a more nuanced approach to pushing native content at readers (without allowing that push to feel like a direct sell, either).

If they do that successfully, however, publishers can boost the value their native ads deliver to advertisers by showing they drive substantive levels of readership, clicks, or conversions. Here are three messaging-based tactics for publishers to try.

Utilizing a Rich Inbox. Rich inboxes are ‘email-like’ inboxes, within a mobile browser or desktop browser, that are available to all users – with no user “opt-in” involved. Because they’re on every user’s interface, rich inboxes are a high-value avenue for driving interactions with users who are already active on a publisher’s site (i.e., right website where they will be most prepared to engage with native content). By segmenting native content dissemination based on the interests of the visitor, publishers can deliver significantly higher levels of reach for advertisers.

Incorporating Browser Push. With browser push, publishers can send messages to opted-in users as notifications in the desktop or mobile browser, rather than just inside their site or app – giving publishers the ability to engage readers even when they’re not on their platforms. Publishers can place native content headers or calls-to-action into browser push notifications to drive measurable, attributable increases in engagement with sponsored content.

Sophisticated Segmentation. With an intelligent messaging toolset, publishers can segment their user groups in a more targeted way – enabling users to receive native content based on criteria such as the depth of engagement with specific content categories. That not only increases the potential for individual end users to interact with the material, it also provides greater insight into which messages created the most clicks, or which ads had the longest read times. That intelligence helps sales teams price their premium content more accurately and helps marketers better measure (and improve) the value of their messaging campaigns.

Filed Under: Tags: , , , , With: Engagement, Mobile Marketing, Personalization

How Machine Learning Unlocks Marketing Performance on the OtherLevels Platform

By Jacek Serafinski

At OtherLevels, our ultimate goal is to help our clients broaden their ‘customer reach’ by engaging customers across multiple communication channels through second-generation digital messaging, making sure the reach and outcome of every marketing campaign launched through meets their expectations.

In most cases, there are many different drivers behind the success of a given campaign. Finding and understanding those drivers is key to meeting expectations and improving our clients’ outcomes – and the more data we collect, the more clear and accurate our analysis of the relationship between our clients and their customers can be.

Through data-driven intelligence, we can see how fluid and dynamic these brand-consumer relationships really are across various devices, channels, message types, and more. The fact that we expose users to second-generation digital messaging – reaching far beyond the standard email campaigning to leverage push notifications through browser, in-app messages or rich inbox communication – also impacts the dynamic interactions we see in our customers’ data.

Armed with that data, OtherLevels serves clients through the power of machine learning. In recent years, machine learning has proven its usefulness in business areas where the outcomes of the decisions of users in the past can be leveraged to support decisions in the future: Usage of automated machine learning models and algorithms allows for the discovery of patterns and interconnections between certain parameters of the decision and the outcome.

To maximise client outcomes, OtherLevels uses machine learning based on users’ interaction with multiple channels – taking into account various parameters such as time of message sent, channel per customer, average response time and many more – to drive predictive analytics: Pre-calculated prediction models that allow us to support marketers’ decisions about the specifics of campaigns in near real-time during the selection of specific parameters.

Essentially, the predictive models allow us to learn behavioural patterns in users’ interaction with marketing campaigns while the usage of machine learning techniques allows the models to maintain high accuracy over time as behaviours change.

These predictions are very subjective to individual characteristics of the audience of our customers – which, of course, are fluid and can vary across our different clients. But as we collect and amass more data, the quality of that data – and its power in predictive analytics –amasses as well, helping us support the definition of our clients’ campaigns with efficiency in order to ultimately minimise marketers’ workload.

As early adopters of second-generation digital messaging, our clients are among the first to realize the usefulness, value, and impact of new marketing communications channels before they become mainstream. At OtherLevels, we’re thrilled to be helping our clients unlock stronger marketing outcomes through the power of machine learning-backed messaging.

Filed Under: Tags: , , , With: Engagement, Marketing Techniques

Using Machine Learning to Drive Stronger Marketing Outcomes

By Brendan O'Kane

Much like artificial intelligence, “machine learning” is a tech-industry hot topic seeing increased application in B2B and B2C marketing. Though a complex concept, machine learning is largely about using technology to analyze data for patterns, then automatically create and deploy rules (based on those patterns) to solve problems or improve processes.

For marketers looking to power more impactful outcomes for their companies’ bottom lines, machine learning is key asset to look for from a digital messaging vendor. With the right partner, marketers can use machine learning functionality to take their campaign efforts a step beyond personalization to the “holy trinity” of individualization: The right message, on the right time, on the right channel.

Pattern Recognition Creates Personalization Power

Machine learning provides value beginning with pattern recognition. When dealing with massive volumes of users (both known and anonymous), it’s impossible for marketers to compile and process such huge amounts of data coming from multiple sources – ranging from apps and mobile websites to purchase behavior and offer redemption.

As such, it’s historically been a challenge for marketers to predict what campaign strategies will be most effective for each individual customer. Yet as personalization has grown increasingly important to the success of email marketing and other digital messaging strategies, machine learning has risen up to help them analyze historical campaign data to deliver highly targeted marketing offers.

In addition, machine learning helps marketers in other key areas, including:

Customer segmentation – By finding patterns in large pools of data, machine learning can help marketers segment their messaging campaigns down to the smallest, most homogenous groups of customers displaying similar behaviors and preferences.

Channel selection – Rather than blast all customers via email or another single channel, machine learning can automatically enable marketers to target users on the channel the individual end customer is most likely to engage with (based on past behavior).

Customer churn prediction – Machine learning helps prevent churn by recognizing the drop-off habits of customers who churned in the past, thus deploying rules to lessen the likelihood of brands losing those users who are at a high risk of churning.

Message timing – Timeliness is an underappreciated key to marketing effectiveness.

Customer lifetime value forecasting – Machine learning can help marketers predict and capitalize on the customer lifetime value (LTV) of existing customers to cultivate higher conversions from brands’ most valuable customers over time.

With a digital messaging strategy powered by machine learning, companies across many sectors including igaming, wagering, travel, and hospitality can see much stronger retention and conversion rates that drive meaningful outcomes on their bottom-line performance.

Contact OtherLevels today to discuss how we can drive stronger digital marketing outcomes for your brand.

Filed Under: Tags: , , , With: Marketing Techniques, Personalization

Mobile Commerce: Capitalizing on the ‘Multi-Device Path to Purchase’

By Brendan O'Kane

With consumers now spending an average of 174 minutes per day on their mobile phones, one BI Intelligence report projects that by 2020, mobile commerce will make up 45 percent of total e-commerce – totaling $284 billion in sales.

Yet that big number is a huge three times more than what’s projected for 2016: BI Intelligence predicts mobile commerce will hit just 20.6 percent of overall e-commerce, or $79 billion, this year.

With so much mobile activity in play, why isn’t “m-commerce” moving faster toward 2020-level totals? Because many mobile users take a multi-device path to purchase: 1 in 3 shoppers does research on a mobile before buying on desktop or offline (1 in 2 among shoppers ages 18-34). Why is this happening?

A common reason: Dissatisfaction with the mobile shopping – or mobile buying – experience. According to the results of a Facebook study, the top reasons people cited for buying on their desktop or laptop versus their smartphone or tablet included:

It’s easier to see all the available products on desktop/laptop (54 percent);

It’s difficult on a smartphone/tablet to compare products/retailers and get all of the information I need (26 percent); and

Entering personal data (address, credit card number, etc.) is not very user friendly on mobile devices (26 percent).

Addressing these issues is a challenge for retailers. After all, it’s unlikely consumers will actually triple the amount of time they spend on their phones between now and then.

Ultimately, while e-commerce is increasingly a mobile-first ecosystem, it’s still very far from a mobile-only ecosystem. To maximize outcomes in today’s multi-device landscape, marketers should embrace smart digital messaging strategies that help them reach customers right where they are on the “multi-device path” in a given moment. With an intelligent messaging toolset, brands can automatically target consumers on the exact channel each individual end user is most likely to engage with, given their existing interaction habits and preferences.

With targeted offers and high-impact, data-driven messaging tactics, brands will see their audience making more purchases across a range of devices and channels. To learn more, contact us.

 

Filed Under: Tags: , , With: digital marketing, Engagement, Marketing Techniques, Mobile Marketing, Personalization