Companies looking to remain competitive must now find ways to address consumers as unique individuals with highly specific, personal preferences. This is the essence of data-driven marketing. By gathering rich, relevant data on consumer behavior and demographics, businesses can target their leads and customers on a far more personal level, optimizing their engagement rates while […]
Companies looking to remain competitive must now find ways to address consumers as unique individuals with highly specific, personal preferences. This is the essence of data-driven marketing.
By gathering rich, relevant data on consumer behavior and demographics, businesses can target their leads and customers on a far more personal level, optimizing their engagement rates while ensuring a positive brand experience.
But delivering on this data-driven expectation can present a number of challenges – particularly for digital agencies, whose clients are throwing unprecedented amounts of data in their direction.
In an effort to find out how agencies are overcoming some of these obstacles, SharpSpring partnered with Ascend2 to field the Data-Driven Marketing Trends Survey. This paper draws on those results to offer an in-depth view of the challenges involved in successful data-driven marketing as well as the many ways in which agencies are helping their clients stay ahead of the curve.
Today, marketers have more opportunity to connect with an audience than ever. However, the challenges involved make it harder to create those connections. Consumers are bombarded with marketing messages everywhere they turn. So, assume that every minu…
Today, marketers have more opportunity to connect with an audience than ever. However, the challenges involved make it harder to create those connections. Consumers are bombarded with marketing messages everywhere they turn. So, assume that every minute of the day, they are distracted, overwhelmed, and overloaded. To be more specific, as you are reading this, 150,000 emails were sent, 3.3 million posts statuses were uploaded on Facebook, and 500 hours of content was uploaded to YouTube in the last 60 seconds.
“Monetate is a Leader. It is recognized as an easy-to-use yet powerful tool for advanced data-driven personalization and optimization. Monetate Personalization Engine applies an intuitive framework and UI to customize marketing and digital commerce experiences, aligning content to intent, with less reliance on developers for testing and editing.” Gartner Magic Quadrant for Personalization Engines (2019)…
“Monetate is a Leader. It is recognized as an easy-to-use yet powerful tool for advanced data-driven personalization and optimization. Monetate Personalization Engine applies an intuitive framework and UI to customize marketing and digital commerce experiences, aligning content to intent, with less reliance on developers for testing and editing.” Gartner Magic Quadrant for Personalization Engines (2019)
With the news that Monetate was recognized as a Leader for the second time in the second-ever Gartner Magic Quadrant for Personalization Engines, I’d like to thank our customers for innovating with us. With their invaluable support and our team’s dedication to their success, we are proud to have achieved this level of recognition.
The Monetate strengths highlighted in the Gartner research — addressing the cold start problem, making marketers less reliant on developers for testing and editing, targeting, and CX personalization — validate our commitment to delivering exceptional value to customers, both present and future. Our core mission is to help brands grow more of their customers into their best customers, and that’s why we’re investing heavily across Monetate’s executive leadership, engineering, services, and product teams.
Introducing our new leadership team and expanded vision
To continue delivering outstanding value to our customers, I am gratified to have hired Jonathan Bartlett, Chief Product Officer, to our formidable leadership team. Jonathan will accelerate our
ambitious business and product strategy. Monetate, the personalization pioneer, has now assembled a highly experienced leadership team that will drive us into our next decade of growth.
Our achievements in the first half of 2019 are very exciting: building a world-class executive team, being recognized as a Magic Quadrant Leader, kickstarting record talent growth, and, most importantly, embarking — together, with our customers — on an innovative product roadmap that I’m excited to preview publicly here.
What’s next for Monetate? It’s never been done before.
First, let me reaffirm my promise to Monetate customers: when I joined as CEO last year, I wrote about my commitment to being the best technology partner you have. As part of that commitment to delivering value and innovation, we’re investing in building the world’s first programmatic Personalization Exchange.
The Monetate Personalization Exchange is the missing piece brands need to deliver more quality experiences, more often, across all their properties. Monetate is building this network for the exchange of data, content, and value — connecting brands to data and content they need to finally deliver hyper-personalized customer experiences. The Personalization Exchange will also empower marketers to deliver more relevant onsite experiences after a paid media conversion, ultimately increasing yield on ad spend and decreasing customer acquisition costs.
This is an exciting time to work with personalization technology, and it’s a privilege to be part of this company’s relentless commitment to customer success and innovation.
Click below for a complimentary copy of the 2019 Gartner Magic Quadrant for Personalization Engines.
Once a primary differentiator, reliable customer service has now become a mandatory commodity. With rising consumer expectations and automated technologies, experience has replaced this long-heralded advantage.
Brands positioned with a customer-first, always-on experience optimization approach and those who build for personalization are poised to be market leaders. Becoming an experience-focus brand has been painted as more difficult than it is. The answers and truth are right in front of us. Your consumers have those answers, you just need to ask – and pay attention.
In working with more than 30 brands on their experience strategies, I’ve found four critical steps to helping brands successfully migrate to become customer experience leaders in their market. The simple formula is to identify, measure, build and test.
Identify audiences and journeys
Identify your audience
Let’s start with an exercise. Suppose money is no object, and you get to pick out a new vehicle. Take a moment to picture what you’d like to buy. Now that you have that vehicle in mind, let’s assume that this is the vehicle everyone else wants. It seems ridiculous that the vehicle you want is assumed to be the vehicle everyone else would want. But, how often do you create experiences using that same assumption? As you design an experience, you need to have an audience in mind, but oftentimes, experiences are developed in a vacuum without consumer feedback. In our current environment audience strategy and experiences should never be developed without some type of consumer insight.
Here are a few questions to help you get started in assessing your audience(s).
Who is my current audience?
What data sources do I have available to me (research, analytics, databases, etc.)?
What do they prefer? What are their motivations?
Who is/not responding?
Do my loyal customers look different than everyone else? What type of data and insights am I missing?
Identify audience journeys
I often think of the journey as the foundation. The good news about building out an audience journey is that there are a lot of good approaches. I do not believe there is one single source of truth to creating an audience journey. The important thing is that you create one. If your budget, resources, and time only allow for a whiteboard brainstorm session, then do it. If you have behavioral data at your fingertips and can look at connected event stream data by specific channels and by individual, then do it. If you have the ability to conduct primary research, please do it.
After building a journey, the first mistake I see is that too many brands try to tackle fixing all of the possible interactions they’ve discovered. Prioritization becomes key; if you are able to gather consumer-driven insights to measure and help you prioritize experiences, then that should be your next step.
How do they behave? How do they buy? What are the most common paths to purchase? What are all of the possible interactions?
Beginning to think from the consumer’s perspective is the right first step, but it is far more effective to actually measure experiences from their direct interactions. Always-on customer-listening engines have been around for decades. Today’s new wave of measurement is more effective but needs to be further elevated. The Customer Effort Score (CES) has come to the forefront of this movement but is lacking in three critical components: measuring multiple interactions, measuring importance, and measuring revenue. This four-dimensional approach has the power to begin moving the needle.
The measurement of ease to work with a brand across interactions, prioritized within the journey, allows brands to identify the most critical points within the consumer experience. This enables brands to find quick wins to remove as much friction as possible. In the example provided in the image above, one would initially think that “compare plans” and “cancel subscription” should be the areas of focus, but a closer look at importance guides you to prioritize “compare plans” to have the greatest impact.
What are their significant phases of interaction in their journey? Which interactions are the most important? What interactions are in desperate need of help? What is the revenue associated with each interaction?
With a foundational and an architectural assessment, you’ll be poised to build best-in class experiences based on consumer insights. Along the way, an audit of data and technology will become critical to supporting the automation of personalized, people-based experiences. The alignment of key stakeholders across the organization will be another critical component to driving change, which is why a data-driven approach to prioritization from the consumer’s perspective is needed for the potential political battles you’ll be up against.
Another supporting point for your internal journey will be the results from prioritized quick wins. A four-dimensional prioritization of experiences allows the brand to hit the ground running, making immediate improvements to prove out the work, while also laying out critical interactions that may take more significant efforts to improve for long-term planning.
Who are the key stakeholders (detractors/supporters)? What quick wins are we going to tackle? What is our long-term experience roadmap? What technologies/data do I need?
Another shift in the market over the years has continued in the same vein of always-on, quick-win optimization. Take, for example, website redesigns, as depicted in the image above. Traditional methods would call for significant redesigns every couple of years, requiring weighty amounts of time and money, with gaps and subpar experiences in between. There is a better way. If you are truly interested in meeting consumer expectations you’ll not only be measuring and tracking those experiences on an ongoing basis, but you’ll be consistently making updates to improve them.
What approach are we using today? What tools do I need to conduct testing? What should we test first? Who (internal and/or consumers) should I gather feedback from?
I believe Dentsu Aegis Network Americas CEO Nick Brien sums it up best when he says, “There’s been a fundamental shift in the balance of power. When I started in marketing, I lived in a brand-led world – you changed consumer behavior. But now we live in a consumer-led world. It’s about changing your brand behavior, it is about personalization, it is about relevance, it is about engagement.”
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Did you know that we just released a brand-new Amazon Video Series to help you get started on the Amazon ad platform? The series shares best practices that our experts have fine-tuned over years of driving results for brands on Amazon, with each episod…
Did you know that we just released a brand-new Amazon Video Series to help you get started on the Amazon ad platform? The series shares best practices that our experts have fine-tuned over years of driving results for brands on Amazon, with each episode lasting only one, easy-to-watch minute. You can find the entire series here.
So, what is Enhanced Brand Content (EBC, also known as the A+ tool)? It is an under-utilized feature that provides your brand story to customers. The tool enables you to do this through additional rich images, comparison charts, and product and brand information to help describe your product features in a unique way. Amazon offers multiple Enhanced Brand Content templates to choose from, or you can create a custom template. These templates offer you various ways to structure your content. What works best is dependent upon your unique brand, product, and content.
Why is this important?
Enhanced Brand Content helps to build your brand and product story and gives you an introduction to your customers. By offering this, it educates prospective buyers on your product, so they become more educated and trusting of your brand. It ultimately helps with conversions.. As a seller, this is your chance to create differentiation and increase ROI of your advertising.
How to get started
To use Enhanced Brand Content, you must be either an approved vendor or a brand registered with Amazon. Once you are approved, you can add EBC to products that are part of your approved brand catalog. Go here to learn more about the Amazon Brand Registry process.
Want to learn more about getting started with Amazon? Check out our full Amazon Video Series here for insights and resources on Bidding, campaign structure and more to help you get started. Also, check out our Amazon Strategy Guide for more!
The following is an extract from the Conversion Rate Experts Company Manual, which all our team members go through when they join Conversion Rate Experts. We hope you find it useful. 現地現物 (pronounced “Genchi Genbutsu”) is Japanese for “Go and See.” The…
The following is an extract from the Conversion Rate Experts Company Manual, which all our team members go through when they join Conversion Rate Experts. We hope you find it useful. 現地現物 (pronounced “Genchi Genbutsu”) is Japanese for “Go and See.” The phrase, which was evangelized by Toyota’s Taiichi Ohno, is perhaps best understood to […]
As anyone who does business online knows, a high-quality website is critical to succeed. Getting a high-quality website, however, isn’t always easy. Whether you’re brick-and-mortar or internet-only, maintaining a strong web presence is essential …
As anyone who does business online knows, a high-quality website is critical to succeed. Getting a high-quality website, however, isn’t always easy. Whether you’re brick-and-mortar or internet-only, maintaining a strong web presence is essential to bringing in new customers. But if you don’t plan on building your website on your own, how do you know […]
How do CRO professionals run experiments in 2019? We analyzed 28,304 experiments, picked randomly from our Convert.com customers. This post shares some of our top observations and a few takeaways about: When CROs choose to stop tests; Which types of experiments are most popular; How often personalization is part of the experimentation process; How many […]
How do CRO professionals run experiments in 2019? We analyzed 28,304 experiments, picked randomly from our Convert.com customers.
This post shares some of our top observations and a few takeaways about:
When CROs choose to stop tests;
Which types of experiments are most popular;
How often personalization is part of the experimentation process;
How many goals CROs set for an experiment;
How costly “learning” from failed experiments can get.
1. One in five CRO experiments is significant, and agencies still get better results.
Only 20% of CRO experiments reach the 95% statistical significance mark. While there might not be anything magical about reaching 95% statistical significance, it’s still an important convention.
You could compare this finding with the one from Econsultancy’s 2018 optimization report in which more than two-thirds of respondents said that they saw a “clear and statistically significant winner” for 30% of their experiments. (Agency respondents, on the other hand, did better, finding clear winners in about 39% of their tests.)
Failing to reach statistical significance may result from two things—hypotheses that don’t pan out or, more troubling, stopping tests early. Almost half (47.2%) of respondents in the CXL 2018 State of Conversion Optimization report confessed to lacking a standard stopping point for A/B tests.
For those experiments that did achieve statistical significance, only 1 in 7.5 showed a lift of more than 10% in the conversion rate.
In-house teams did slightly worse than average: 1 out of every 7.63 experiments (13.1%) achieved a statistically significant conversion rate lift of at least 10%. Back in 2014, when we published an earlier version of our research on CXL, this figure was slightly higher, about 14%.
Agencies did slightly better: 15.84% of their experiments were significant with a lift of at least 10%. This number was much higher (33%) in our earlier research, although the sample size was significantly smaller (just 700 tests). Still, in both studies, agencies did better than in-house CRO teams. This year, they outperformed in-house teams by 21%.
(We didn’t find any significant difference between agencies and in-house customers when comparing their monthly testing volumes.)
2. A/B tests continue to be the most popular experiment.
A/B testing (using DOM manipulation and split URL) is still the go-to test for most optimizers, with A/B tests totaling 97.5% of all experiments on our platform. The average number of variations per A/B test was 2.45.
This trend isn’t new. A/B tests have always dominated. CXL’s test-type analysis over the years also shows this. Back in 2017, CXL’s report found that 90% of tests were A/B tests. In 2018, this figure increased by another 8%, reinforcing A/B testing as the near-universal experiment type.
Certainly, A/B tests are simpler to run; they also deliver results more quickly and work with smaller traffic volumes. Here’s a complete breakdown by test type:
North American optimizers ran 13.6 A/B experiments a month, while those from Western Europe averaged only 7.7. Using benchmarks from the the 2018 CXL report, that puts our customers in the top 30% for testing volume.
There were other cross-Atlantic differences: Western Europe runs more A/B tests with DOM manipulation; the United States and Canada run twice as many split-URL experiences.
3. Optimizers are setting multiple goals.
On average, optimizers set at least four goals (e.g. clicking a certain link, visiting a certain page, a form submit, etc.) for each experiment. This means they set up three secondary goals in addition to the primary conversion rate goal.
Additional “diagnostic” or secondary goals can increase learning from experiments, whether they’re winning or losing efforts. While the primary goal unmistakably declares the “wins,” the secondary metrics shine a light on how an experiment affected the target audience’s behavior. (Optimizely contends that successful experiments often track as many as eight goals to tell the full experiment story.)
We see this as a positive—customers are trying to gain deeper insights into how their changes impact user behavior across their websites.
The 2018 edition of Econsultancy’s optimization report, too, saw many CRO professionals setting multiple goals. In fact, about 90% of in-house respondents and 85% of agency respondents described secondary metrics as either “very important” or “important.” While sales and revenue were primary success metrics, common secondary metrics included things like bounce rate or “Contact Us” form completion rates.
The Econsultancy study also found that high performers (companies that secured an improvement of 6% or more in their primary success metric) were more likely to measure secondary metrics.
4. Personalization is used in less than 1% of experiments.
Personalization isn’t popular yet, despite its potential. Less than 1% of our research sample used personalization as a method for optimization, even though personalization is available at no added cost on all our plans.
Products like Intellimize, which recently closed $8 million in Series A funding, and Dynamic Yield, recently acquired by McDonald’s, are strong indicators of investors’ and corporate America’s big bet on personalization.
But as far as the CRO stack goes, personalization is still a tiny minority. A quick look at data from BuiltWith—across 362,367 websites using A/B testing and personalization tools—reinforces our findings:
We did find that U.S.-based users are using personalization six times more often than those from Western Europe. (Additionally, some 70% of those on our waitlist for an account-based marketing tool are from the United States, despite the product being GDPR-compliant.)
Personalization in the European market and elsewhere may rise as more intelligent A.I. optimization improves auto-segmentation in privacy-friendly ways.
Back in 2017, when Econsultancy surveyed CRO professionals, it found personalization to be the CRO method “least used but most planned.” Some 81% of respondents found implementing website personalization to be “very” or “quite” difficult. As several reports mentioned, the biggest difficulty for implementation of personalization was data sourcing.
Our findings on personalization diverged with a few other reports from the CRO space. Econsultancy’s survey of CRO executives (in-house and agency) reported that about 42% of in-house respondents used website personalization, as did 66% of agency respondents. Dynamic Yield’s 2019 Maturity Report reported that 44% of companies were using “basic” on-site personalization with “limited segmentation.”
When CXL surveyed CRO professionals for its 2017 report, 55% of respondents reported that they used some form of website personalization. In the latest CXL report, respondents scored personalization a 3.4 on a 1–5 scale regarding its “usefulness” as a CRO method.
5. Learnings from experiments without lifts aren’t free.
In our sample, “winning” experiments—defined as all statistically significant experiments that increased the conversion rate—produced an average conversion rate lift of 61%.
Experiments with no wins—just learnings—can negatively impact the conversion rate. Those experiments, on average, caused a 26% decrease in the conversion rate.
We all love to say that there’s no losing, only “learning,” but it’s important to acknowledge that even learnings from non-winning experiments come at a cost.
With roughly 2.45 variations per experiment, every experiment has around an 85% chance of decreasing the conversion rate during the testing period (by around 10% of the existing conversion rate).
Businesses need to archive and learn from all their experiments. According to the CXL report, about 80% of companies archive their results, and 36.6% use specific archiving tools. These are strong indicators that CROs are refueling their experimentation programs with learnings from past efforts.
But while tracking results and documenting learnings can improve a testing program in the long run, there’s urgency to learn from failed experiments and implement successes quickly.
There’s also a need to research and plan test ideas well so that experiments have a higher likelihood of success. The ResearchXL model is a great way to come up with data-backed test ideas that are more likely to win.
While our research helped us establish some industry benchmarks, a few of our findings hardly surprised us (for example, the popularity of A/B tests).
But what did surprise us was that so few customers use personalization. We expected more businesses to be progressive on that front since the feature is available in all our plans and doesn’t require massive traffic volumes. As noted earlier, better data management may make personalization easier for companies to execute.
Other than that, we view the setup of multiple goals as a positive sign—testers want to dig deeper into how their experiments perform to maximize learnings and, of course, wins.