Link juice is a non-technical SEO term used to reference the SEO value of a hyperlink to a particular website or webpage. According to Google, a multitude of quality hyperlinks (or just “links”) are one of the most important factors for gaining top rankings in the Google search engine. The term “link juice” is SEO industry jargon. It’s often talked about in relation to link building efforts such as guest posting, blogger outreach, linkbait and broken link building. How Does Link Juice Work? Link juice, link authority, and backlink authority are all different words that mean essentially the same thing….
Jekyll is gaining popularity as a lightweight alternative to WordPress. It often gets pigeonholed as a tool developers use to build their personal blog. That’s just the tip of the iceberg — it’s capable of so much more!
In this article, we’ll take on the role of a web developer building a website for a fictional law firm. WordPress is an obvious choice for a website like this, but is it the only tool we should consider? Let’s look at a completely different way of building a website, using Jekyll.
Recently, we got our entire team to actively research and contribute ideas for optimization on our website and ran multiple tests. This post is a narrative of what we did after.
Who Is This Post for?
This post will help SaaS growth-hackers, marketers, and optimization experts to predict the business value from a test.
The aim of this post is to not only share the tests we ran on our website, but also introduce a revenue-based framework that predicts the business impact of an A/B test and prioritizing on the basis of it.
Need for a Model
After we propelled our team to suggest ideas for testing, we had more than 30 hypotheses looking at us, but no distinct way of knowing which of these to take up first. Of course, there is a range of prioritizing frameworks available, but we particularly wanted to look at the ones that would directly impact our revenue.
This framework helped us project the potential impact on the revenue from each test. Here’s what we did: Step 1
We decided to identify high-impact pages and winnow the pages that were not as important for our business, that is, pages where no goal conversions take place. We looked at Google Analytics for pages with the:
Highest Amount of Traffic (We used “New Users” to nullify visits by existing customers.)
Highest Number of Goal Conversions (Goal conversion, which contributes to your overall business goal, is the main goal for your website. In our case, this meant all qualified lead-generating forms. A free trial or request a demo qualifies a visitor as a lead with a genuine interest in our product; or, as the industry popularly refers to it, a Marketing Qualified Lead.)
This gave us a list of pages which were high-value in terms of, either traffic generation or last touch before conversions.
We identified the following key pages:
Blog pages (All)
Our main objective was to project an estimated increase in the revenue due to a particular test. If your test increases the conversion rate by say 20%, what would this mean for your business and, in turn, the revenue?
This is how our marketing funnel looked like:
Note: You should use data from the recent 3–6 months, and the average (mean) of each step. This is to accurately reflect what to expect from your testing and be relevant to your business.
For each of the “Key Pages” we identified in the first step, we also dug out the corresponding numbers at each funnel stage. We’ve explained each stage of the funnel and how it is calculated:
a) Key Page Traffic: The total number of pageviews per Key Page (new users in our case). You can find the data in Google Analytics.
b) Total Conversions: The total number of leads generated from each particular page. If there is an additional qualification your company follows, source this data from your preferred CRM or Marketing Automation software. For example, at VWO, we use Clearbit to qualify our leads in Salesforce.
c) Opportunities:The total number of opportunities generated for your sales team. This data will be available in your CRM; make sure to count qualified opportunities only.
d) Customers: The total number of customers created in a month.
e) MRR (New):Or monthly recurring revenue, means revenue booked on a monthly basis; you can use this to estimate annual recurring revenue, or ARR, as well.
Now that we had all the numbers needed in our arsenal, I decided to calculate some more internal benchmarks. This gave us the performance of our marketing and/or sales funnel.
We computed the conversion rate of a particular page, using the following formula: Existing conversion rate = (Total Conversions Key Page Traffic); this is represented as %
The conversion of your leads into opportunities: (Opportunities ÷ Total conversions) × 100, represented as %
The conversion rate of opportunities into customers: (Customers ÷ Opportunities) × 100, represented as %
The average revenue per user or ARPU: Total MRR ÷ Total number of paying customers
Now all you have to do is to impute these numbers in this template. The model uses all of that data and projects how much revenue increase or decrease you can estimate based on your test results. This estimate can give you a good idea of where to begin or prioritize your testing.
Step 4 (Optional)
This is where it may get tricky. At VWO, we sell both Enterprise plans and Standard plans. So to be fair, we must estimate each cohort with separate data and individual conversion rates. For example, Opportunity creation % for an Enterprise plan may be lower, but a Standard plan is easier to convert. You may want to decide what type of plan do you want to focus on. We, for instance, used website traffic and Alexa rank as the benchmark for lead qualification. We attributed more value to the leads that came in through key pages and prioritized them. This led us to the next step, which is the qualification rate of the said lead of high value. This rate may be in the range 30–50%, depending on your definition. It was interesting to note that each page had a different qualification rate. For example, we get better quality leads from our Request a demo page than we do from our free trial or blog post page.
After we had the model in place, we played around with the increase or decrease in our conversion rates. This was to identify what would be our best optimization opportunities?
The free trial pages and the home page were among the high-priority pages, in terms of the impact of revenue. (Unfortunately, I can’t share the exact numbers with you.) We first looked at the hypotheses on the free trial page:
Test 1 – Free Trial Page
Our hypothesis was “Illustrating VWO features and social proof on the free trial page will compel users to sign up for the free trial.” Here is a screenshot of what it looks like in VWO.
Bonus tip: VWO has recently launched a new capability called PLAN that lets you manage and prioritize your testing hypotheses. To learn more about this capability, visit the VWO evolution page.
This is what the control looked like:
Our heatmap data also showed a lot of users clicking the features page after accessing the free trial page.
Screenshot of heatmap data:
We created a variation which included the features we offer to solve this issue. Here’s a screenshot of the same.
We came up with a more targeted copy and changed the existing CTA to Request A Demo. Here is what the variation looked like:
We also wanted to change our positioning due to our foray into Conversion Optimization. The results from this test were that our variation beat the control and had more than 31% improvement in the conversion rate. Based on the first example, we have already implemented the new free-trial page as our main free-trial page now.Based on the second test, we updated our current home page. All in all, this model helped us correctly predict the best optimization opportunities, make our testing better, and more strategically aligned to business goals. Let me know your experience with this model and how you go about testing. Would love to hear your feedback on this!
As we look deep into 2017, one of the questions on every web developer’s mind ought to be, “What trend will define the web in 2017?” Just three years ago, we were talking about the “Year of Responsive Web Design”, and we’ve all seen how the stakes were raised when Google announced Mobilegeddon (21 April 2015) and started to boost the rankings of mobile-friendly websites in mobile search results.
Today, as our findings indicate, responsive web design is the norm, with 7 out of 10 mobile-optimized websites being responsive, up from 5 last year, which begs the questions: What’s next? Where is it all heading? We solved the screen-size issue and had a great run for a few years — now what?
What exactly are the benefits of a content hub strategy? Well, first of all, when done correctly, a content hub will capture a significant volume of traffic. And that’s what most online businesses want, right?
We have recently introduced several clients to the concept of a content hub and would like to share our experience in this article. The clients are high-quality portals filled with targeted, valuable and often evergreen articles that users can return to time and again.
“Google Penalty.” Those two words are all it takes to make all SEOs (search engine optimizers) and internet marketers cringe. But honestly, you shouldn’t really fear these words. There is an easy recipe to follow so that you’ll never have to worry about it: Make Sure Your On-Page SEO is Tip-Top – Many websites suffer from poor on-page SEO issues. Have an SEO professional audit your website and implement their recommendations. This means get your title tags in order, use meta descriptions, and stay away from keyword cannibalization. Create Useful Content – I purposely stayed away from using the word…
Hotjar’s content experiment with overlays is turning website visitors into new customers. Here’s how.
If you Google “Content is king,” here’s what you’ll find: More than 37 million Google results that justify how important content is online.
It’s a tired phrase, but it’s true. At Unbounce, for instance, our blog has been invaluable in growing our digital footprint and our business.
Every once in a while, you hear a story about someone who uses content to earn new customers and new revenue. And, they make it seem pretty easy (like “Why didn’t I think of that?”).
Well, Nick Heim, the Director of Inbound Marketing at Hotjar, has done just that. He offered website visitors an ebook at just the right time and in just the right way by using an overlay.
Overlays are modal lightboxes that launch within a webpage and focus attention on a single offer. Still fuzzy on what an overlay is? Click here.
Overlays, a type of Unbounce Convertables, allow you to show relevant offers to specific users at the perfect time, making them less likely to leave your website without converting.
By implementing a Convertable into his campaign, Nick isn’t just bringing in new leads, he’s actually turning website visitors into paying Hotjar users. So how’s he doing it?
Let’s start from the beginning
The TL;DR? Hotjar implemented a new Convertable on their pricing page, which resulted in new signups. The overlay offered visitors an ebook, The Hotjar Action Plan, in exchange for their first name and email address.
The overlay converted 408 visitors in the first three weeks, 75% of which were not existing Hotjar customers.
Once a visitor converted on the overlay they received an email from Hotjar right away. Non-customers received an email with the ebook as a PDF, along with an offer to try out Hotjar for an extended period of time.
For non-users, we sent them a quick instant thank you email followup that contained the asset and offered a 30 day trial of the Hotjar Business Plan. This is double the trial length a new user would usually receive by signing up through our site.
Here’s what the actual email looks like:
Hotjar makes good use of the email they sent to preexisting customers, too. That variation contains the ebook as well as a simple question about what type of content they’d like to see — allowing Hotjar to continue delivering value to their customers. #winwin
The overlay strategy
The overlay Nick built was set to appear only to first-time visitors who are exiting the Hotjar pricing page.
According to Nick,
This was more of a visitor experience decision than anything. We didn’t want to come off as badgering visitors in the research phase [of the buying process].
Setting trigger rules in the Unbounce builder.
So, did it work?
“Absolutely, we’re getting 60-70 new users per month as a result of the Convertable,” said Nick.
From the overlay, about 3% of page visitors convert on the page.
Of those that converted on the overlay, 75% were not current Hotjar customers and about 19% of the non-users who received their follow-up email with the PDF have become new Hotjar customers.
Already an Unbounce customer? Log into Unbounce and start using Convertables today at no extra cost.
Experimenting the Hotjar Way
Nick explained that his team at Hotjar hadn’t implemented overlays into their lead gen strategy before using the Unbounce Convertable; “this was a total experiment. We wanted to be able to nurture the new leads coming from different channels and bring them back.”
Nick pointed out that, “these things [overlays] can be used really wrong. You need to be careful and consider the human on the other end. Think about the entire process.”
For their experiment, Nick said, “[we didn’t have] hard goals, but we wanted to prove whether there was a case for using overlays.” Nick pointed out that it can be difficult to measure the negative effects of user experience — especially without a baseline to measure your results against.
“We wanted to see if the risk was worth the reward. We did get the quantitative results — which for us, measure better than industry standards.”
Hotjar’s Golden Rules for Using Overlays
Through this trial experience, Nick and his team at Hotjar established some general guidelines for using overlays. Nick shared his golden rule for delighting visitors with overlays (opposed to pestering them).
Start by asking yourself these questions:
First, is it appropriate to use an overlay in this part of the user journey?
If the answer is yes, ask yourself “What’s the least annoying way to accomplish that?” If the answer is no, don’t use it.
Second, “Does it solve the problem [website visitors] are looking to solve?” Nick emphasized that the offer on the overlay needs to align to the problem that people are trying to solve.
Finally, how do you know if you’re offering the right thing? Nick says, “Ask people! This is an awesome way to improve your content.”
Should you use Convertables?
Overlays give us marketers an opportunity to present the right people with the right offer at the right time. Of course, they can also be used to do the opposite, and, as Nick says, “you don’t want to leave someone with a bad taste in their mouth,”
Like any good data-driven marketer, you’re going to want to take it for a test drive. Like Hotjar, try experimenting with overlays to decide they’re a good fit. At the end of the day, it’s your customers and your brand that will decide if overlays work in your marketing strategy.
Do you remember the Groundhog Day movie? You know… the one where Bill Murray’s character repeats the same day over and over again, every day. He had to break the pattern by convincing someone to fall in love with him, or something like that.
What an odd storyline.
Yet today, it’s reminding me of a pattern in marketing. Marketing topics seem to be pulled by an unstoppable force through fad cycles of hype, over-promise, disappointment, and decline – usually driven by some new technology.
I’ve watched so many fad buzzwords come and go, it’s dizzying. Remember Customer Relationship Marketing? Integrated Marketing? Mobile First? Omnichannel?
A few short years ago, everyone was talking about social media as the only topic that mattered. Multivariate testing was sexy for about five minutes.
Invariably, similar patterns of mistakes appear within each cycle.
Tool vendors proliferate on trade show floors, riding the wave and selling a tool that checks the box of the current fad. Marketers invest time, energy, and budget hoping for a magic bullet without a strategy.
But, without a strategy, even the best tools can fail to deliver the promised results.
Now, everyone is swooning for Personalization. And, so they should! It can deliver powerful results.
PDF Bonus: Personalization Roadmap
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From simple message segmentation to programmatic ad buying and individual-level website customization, the combination of big data and technology is transforming the possibilities of personalization.
But the rise of personalization tools and popularity has meant the rise of marketers doing personalization the wrong way. I’ve lost track of the number of times we’ve seen:
Ad hoc implementation of off-the-shelf features without understanding what need they are solving.
Poor personalization insights with little data analysis and framework thinking driving the implementation.
Lack of rigorous process to hypothesize, test, and validate personalization ideas.
Lack of resources to sustain the many additional marketing messages that must be created to support multiple, personalized target segments.
That’s why, in collaboration with our partners at Optimizely, we have created a roadmap for creating the most effective personalization strategy:
Step 1: Defining personalization
Step 2: Is a personalization strategy right for you?
Step 3: Personalization ideation
Step 4: Personalization prioritization
Step 1: Defining personalization
Personalization and segmentation are often used interchangeably, and are arguably similar. Both use information gathered about the marketing prospect to customize their experience.
While segmentation attempts to bucket prospects into similar aggregate groups, personalization represents the ultimate goal of customizing the person’s experience to their individual needs and desires based on in-depth information and insights about them.
You can think of them as points along a spectrum of customized messaging.
You’ve got the old mass marketing approach on one end, and the hyper-personalized, 1:1, marketer-to-customer nirvana on the other end. Segmentation lies somewhere in the middle. We’ve been doing it for decades, but now we have the technology to go deeper, to be more granular.
Every marketer wants to provide the perfect message for each customer — that’s the ultimate goal of personalization.
The problem personalization solves
Personalization solves the problem of Relevance (one of 6 conversion factors in the LIFT Model®). If you can increase the Relevance of your value proposition to your visitor, by speaking their language, matching their expectations, and addressing their unique fears, needs and desires, you will see an increase in conversions.
Let me show you an example.
Secret Escapes is a flash-sale luxury travel company. The company had high click-through rates on their search ads and directed all of this traffic to a single landing page.
The ad copy read:
Save up to 70% on Spa Breaks. Register for free with your email.”
But, the landing page didn’t reflect the ad copy. When visitors landed on the page, they saw this:
Not super relevant to visitors’ search intent, right? There’s no mention of the keyword “spa” or imagery of a spa experience. Fun fact: When we are searching for something, our brains rely less on detailed understanding of the content, and more on pattern matching, or a scent trail.
In an attempt to convert more paid traffic, Secret Escapes tested two variations, meant to match visitor intent with expectations.
By simply maintaining the scent trail, and including language around “spa breaks” in the signup form, Secret Escapes was able to increase sign-ups by 32%. They were able to make the landing page experience sticky for this target audience segment, by improving Relevance.
Step 2: Is a personalization strategy right for me?
Pause. Before you dig any deeper into personalization, you should determine whether or not it is the right strategy for your company, right now.
Here are 3 questions that will help you determine your personalization maturity and eligibility.
Do I have enough data about my customers?
Personalization is not a business practice for companies with no idea of how they want to segment, but for businesses that are ready to capitalize on their segments.
For companies getting started with personalization, we recommend that you at least have fundamental audience segments in place. These might be larger cohorts at first, focused on visitor location, visitor device use, single visitor behaviors, or visitors coming from an ad campaign.
Do you have a team in place that can manage a personalization strategy?
Do you have a personalization tool that supports your strategy?
Do you have an A/B testing team that can validate your personalization approach?
Do you have resources to maintain updates to the segments that will multiply as you increase your message granularity?
Personalization requires dedicated resources and effort to sustain all of your segments and personalized variations. To create a truly effective personalization strategy, you will need to proceduralize personalization as its own workstream and implement an ongoing process.
Which leads us to question three…
Do I have a process for validating my personalization ideas?
Personalization is a hypothesis until it is tested. Your assumptions about your best audience segments, and the best messaging for those segments, are assumptions until they have been validated.
Personalization requires the same inputs and workflow as testing; sound technical implementation, research-driven ideation, a clear methodology for translating concepts into test hypotheses, and tight technical execution. In this sense, personalization is really just an extension of A/B testing and normal optimization activities. What’s more, successful personalization campaigns are the result of testing and iteration.
– Hudson Arnold
Great personalization strategy is about having a rigorous process that allows for 1) gathering insights about your customers, and then 2) validating those insights. You need a structured process to understand which insights are valid for your target audience and create growth for your business.
WiderFunnel’s Infinity Optimization Process™ represents these two mindsets. It is a proven process that has been refined over many years and thousands of tests. As you build your personalization strategy, you can adopt parts or all of this process.
There are two critical phases to an effective personalization strategy: Explore and Validate. Explore uses an expansive mindset to consider all of your data, and all of your potential personalization ideas. Validate is a structured process of A/B testing that uses a reductive mindset to refine and select only those ideas that produce value.
Without a process in place to prove your personalization hypotheses, you will end up wasting time and resources sending the wrong messages to the wrong audience segments.
Personalization without validation is simply guesswork.
Step 3: Personalization ideation
If you have answered “Yes” to those three questions, you are ready to do personalization: You are confident in your audience segments, you have dedicated resources, perhaps you’re already doing basic personalization. Now, it’s time to build your personalization strategy by gathering insights from your data.
One of the questions we hear most often when it comes to personalization is, “How do I get ideas for customized messaging that will work?” This is the biggest area of ongoing work and your biggest opportunity for business improvement from personalization.
The quality of your insights about your customers directly impacts the quality of your personalization results.
Here are the 3 types of personalization insights to explore:
You can mix and match these types within your program. We have plenty of examples of how. Let’s look at a few now.
1) Deductive research and personalization insights
Are there general theories that apply to your particular business situation?
Psychological principles? UX principles? General patterns in your data? ‘Best’ practices?
Deductive personalization starts with your assumptions about how your customers will respond to certain messaging based on existing theories…but it doesn’t end there. With deductive research, you should always feed your ideas into experiments that either validate or disprove your personalization approach.
Let’s look at an example:
Heifer International is a charity organization that we have been working with to increase their donations and their average donation value per visitor.
In one experiment, we decided to test a psychological principle called the “rule of consistency”. This principle states that people want to be consistent in all areas of life; once someone takes an action, no matter how small, they strive to make future behavior match that past behavior.
We asked visitors to the Heifer website to identify themselves as a donor type when they land on the site, to trigger this need to remain consistent.
Notice there’s no option to select “I’m not a donor.” We were testing what would happen when people self-identified as donors.
The results were fascinating. This segmenting pop up increased donations by nearly 2%, increased the average donation value per visitor by 3%, and increased the revenue per visitor by more than 5%.
There’s more. In looking at the data, we saw that just 14% of visitors selected one of the donation identifications. But, that 14% was actually 68% of Heifer’s donors: The 14% who responded represent a huge percentage of Heifer’s most valuable audience.
Now, Heifer can change the experience for visitors who identify as a type of donor and use that as one piece of data to personalize their experience. Currently, we’re testing which messages will maximize donations even further within each segment.
2) Inductive research and personalization insights
Are there segments within your data and test results that you can analyze to gather personalization insights?
If you are already optimizing your site, you may have seen segments naturally emerge through A/B testing. A focused intention to find these insights is called inductive research.
Inductive personalization is driven by insights from your existing A/B test data. As you test, you discover insights that point you toward generalizable personalization hypotheses.
Here’s an example from one of WiderFunnel’s e-commerce clients that manufactures and sells weather technology products. This company’s original product page was very cluttered, and we decided to test it against a variation that emphasized visual clarity.
Surprisingly, the clear variation lost to the original, decreasing order completions by -6.8%. WiderFunnel Strategists were initially perplexed by the result, but they didn’t rest until they had uncovered a potential insight in the data.
They found that visitors to the original page saw more pages per session, while visitors to the variation spent a 7.4% higher average time on page. This could imply that shoppers on the original page were browsing more, while shoppers on our variation spent more time on fewer pages.
Research published by the NN Group describes teen-targeted websites, suggesting that younger users enjoy searching and are impatient, while older users enjoy searching but are also much more patient when browsing.
With this research in mind, the Strategists dug in further and found that the clear variation actually won for older users to this client’s site, increasing transactions by +24%. But it lost among younger users, decreasing transactions by -38%.
So, what’s the takeaway? For this client, there are potentially new ways of customizing the shopping experience for different age segments, such as:
Reducing distractions and adding clarity for older visitors
Providing multiple products in multiple tabs for younger visitors
This client can use these insights to inform their age-group segmentation efforts across their site.
Ask your prospects to tell you about themselves. Then, test the best marketing approach for each segment.
Customer self-selected personalization is potentially the easiest strategy to conceptualize and implement. You are asking your users to self-identify, and segment themselves. This triggers specific messaging based on how they self-identified. And then you can test the best approach for each of those segments.
Here’s an example to help you visualize what I mean.
One of our clients is a Fortune 500 healthcare company — they use self-selected personalization to drive more relevant content and offers, in order to grow their community of subscribers.
This client had created segments that were focused on a particular health situation, that people could click on:
“Click on this button to get more information,”
“I have early stage disease,”
“I have late stage disease,”
“I manage the disease while I’m working,”
“I’m a physician treating the disease,” and,
“I work at a hospital treating the disease.”
These segments came from personas that this client had developed about their subscriber base.
Once a user self-identified, the offers and messaging that were featured on the page were adjusted accordingly. But, we wouldn’t want to assume the personalized messages were the best for each segment. You should test that!
In self-selected personalization, there are two major areas you should test. You want to find out:
What are the best segments?
What is the best messaging for each segment?
For this healthcare company, we didn’t simply assume that those 5 segments were the best segments, or that the messages and offers triggered were the best messages and offers. Instead, we tested both.
A series of A/B tests within their segmentation and personalization efforts resulted in a doubling of this company’s conversion rate.
Developing an audience strategy
Developing a personalization strategy requires an audience-centric approach. The companies that are succeeding at personalization are not picking segments ad hoc from Google Analytics or any given study, but are looking to their business fundamentals.
Once you believe you have identified the most important segments for your business, then you can begin to layer on more tactical segments. These might be qualified ‘personas’ that inform your content strategy, UX design, or analytical segments.
Step 4: Personalization prioritization
If this whole thing is starting to feel a little complex, don’t worry. It is complex, but that’s why we prioritize. Even with a high-functioning team and an advanced tool, it is impossible to personalize for all of your audience segments simultaneously. So, where do you start?
Optimizely uses a simple axis to conceptualize how to prioritize personalization hypotheses. You can use it to determine the quantity and the quality of the audiences you would like to target.
The x-axis refers to the size of your audience segment, while the y-axis refers to an obvious need to personalize to a group vs. the need for creative personalization.
For instance, the blue bubble in the upper left quadrant of the chart represents a company’s past purchasers. Many clients want to start personalizing here, saying, “We want to talk to people who have spent $500 on leather jackets in the last three months. We know exactly what we wanna show to them.”
But, while you might have a solid merchandising strategy or offer for that specific group, it represents a really, really, really small audience.
That is not to say you shouldn’t target this group, because there is an obvious need. But it needs to be weighed against how large that group is. Because you should be treating personalization like an experiment, you need to be sensitive to statistical significance.
The net impact of any personalization effort you use will only be as significant as the size of the segment, right? If you improve the conversion rate 1000% for 10 people, that is going to have a relatively small impact on your business.
Now, move right on the x-axis; here, you are working with larger segments. Even if the personalized messaging is less obvious (and might require more experimentation), your efforts may be more impactful.
Food for thought: Most companies we speak to don’t have a coherent geographical personalization strategy, but it’s a large way of grouping people and, therefore, may be worth exploring!
You may be more familiar with WiderFunnel’s PIE framework, which we use to prioritize our ideas.
How does Optimizely’s axis relate? It is a simplified way to think about personalization ideas to help you ideate quickly. Its two inputs, “Obvious Need” and “Audience Size” are essentially two inputs we would use to calculate a thorough PIE ranking of ideas.
The “Obvious Need” axis would influence the “Potential” ranking, and “Audience Size” would influence “Importance”. It may be helpful to consider the third PIE factor, “Ease”, if some segmentation data is more difficult to track or otherwise acquire, or if the maintenance cost of ongoing messaging is high.
To create the most effective personalization strategy for your business, you must remember what you already know. For some reason, when companies start personalization, the lessons they have learned about testing all of their assumptions are sometimes forgotten.
You probably have some great personalization ideas, but it is going to take iteration and experimentation to get them right.
A final note on personalization: Always think of it in the context of the bigger picture of marketing optimization.
Insights gained from A/B testing inform future audience segments and personalized messaging, while insights derived from personalization experimentation informs future A/B testing hypotheses. And on and on.
Don’t assume that insights gained during personalization testing are only valid for those segments. These wins may be overall wins.
The best practice when it comes to personalization is to take the insights you validate within your tests and use them to inform your hypotheses in your general optimization strategy.
** Note: This post was originally published on May 3, 2016 as “How to succeed at segmentation and personalization” but has been wholly updated to reflect new personalization frameworks, case studies, and insights from Optimizely. **
Still have questions about personalization? Ask ’em in the comments, or contact us to find out how WiderFunnel can help you create a personalization strategy that will work for your company.
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