Conversion Guide Hubspot.pdf

THE ADVANCED GUIDE TO

Scaling a Conversion Optimization Program

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Table of Contents

Introduction

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Chapter 1 Creating and promoting a culture of experimentation

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Chapter 2 Generating high-impact, data-driven hypotheses repeatedly

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Chapter 3 Building a long-term experimentation roadmap

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Chapter 4 Setting program goals and measuring progress

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Chapter 5 Ensuring continuous learning and inculcating improvements

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Chapter 6 Growing a winning optimization team

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Wrapping Up

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About VWO and HubSpot

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“Our success at Amazon is a function of how many experiments we do per year, per month, per week, per day.” Jeff Bezos CEO, Amazon

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Introduction It’s 2019, and conversion optimization is no longer a buzzword. If you’re a marketer, product manager, website owner, or in anyway responsible for improving conversions on a digital property, it’s a lever you can’t do without. A successful conversion optimization program requires a strategic, methodical approach to develop it. In this guide, we will focus on how you can work towards scaling your existing conversion optimization program to ensure the same.

If you’d like to take a step back and get started from scratch, we’ve got you covered. In the previous edition of this eBook, VWO and HubSpot got together to create The Complete DIY Guide To Improving Conversions In 60 Days, a time-bound resource which aims to enable budding optimizers build an end-to-end conversion optimization program. Let’s get started!

This eBook is divided into 6 chapters. Every chapter will walk you through one indispensable element of scaling a conversion optimization program.

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Why adopt a culture of experimentation? CHAPTER 1

Creating and promoting a culture of experimentation

How is it that some companies can consistently move faster than others and deliver delightful experiences? Why are some organizations able to innovate more quickly to deliver those experiences all along the customer’s journey? What sets today’s most innovative companies apart from their peers isn’t some kind of magic spell; instead, these companies have invested in creating and promoting a culture of experimentation, and made optimization at scale a core business practice. Having adopted this approach has helped businesses like Amazon, Netflix and Google to empower different teams and functions to collaborate on improving the customer experience.

A culture of experimentation is geared toward incremental, consistent improvements coordinated across all business functions to meet executive goals.

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How to build a culture of experimentation? Rome wasn’t built in a day, the same is true for the effort required in fostering a culture and mindset of experimentation within a company. Here’s how you get started:

Get stakeholders buy-in, establish basic principles Like a waterfall, culture too flows from the top to the bottom. To inculcate a healthy culture of experimenting and optimizing, it is imperative that the top management complies. If the top management is on the same page, it’ll realize the need for optimization and allocate sufficient budget for it. This will make it easier to execute any optimization program. The idea of optimization can be sold to the top management by highlighting its key benefits. You can show how the returns on efforts outweigh the investment, and strengthen your pitch.

Get the entire team involved to build a mindset While the top management provides resources for an optimization program, the success of the program also depends on the understanding and acceptance of the approach by the entire team. Get everyone involved and teach them enough so they can contribute. You can hold information sessions or talks to review the basics of optimization.

“If you want to incorporate a culture of optimization, you need to allow and encourage anyone to challenge or at least question the data or ask how you could test an idea. But when someone says or asks something, be sure to ask them to back up their claim with data. They need to be able to validate any of their claims or thoughts, or at least give reasons for them.” Michael Heiligenstein, Director of SEO at Fit Small Business

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Become data-driven first, intuition-driven never Before you begin optimizing your business processes, it is imperative to take stock of the current standing. You need to assess how you are placed currently and set benchmarks for improvements accordingly. A good optimization program would be rooted in in-depth data and research of what we are optimizing and for which end goals. When your employees start questioning subjectivity and validate their decisions with both data and insight, it ultimately makes your organization a more efficient one. According to Forrester Research, Insights-driven businesses are growing by more than 30% annually and are on track to earn $1.8 trillion by 2021.

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While these three points outline the essence of how you can build a promote a culture of experimentation and optimization, you need to come up with ways unique to your own organization. For inspiration on the same, this post is a good place to start.

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CHAPTER 2

Generating high-impact, data-driven hypotheses repeatedly

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Coming up with an idea that you want to test isn’t tough. Coming up with one that you should test can be.

What winning companies optimize for

Acquisition Metrics

More often than not, optimizers base their testing on intuitions and best practices, eventually yielding unfavorable results.

Behavior Metrics

Outcome Metrics Source

What siloed teams actually end up optimizing for beacuse of bad org/incetitive structures

Some others tend to adopt a myopic approach by keeping only a single aspect of the marketing funnel (acquisition/ behavior/outcome) in mind, without sight on the long-term goals. In order to scale your conversion optimization program, it is important to structure it as a methodical process.

The process involves conducting thorough research, asking the right questions, digging for answers in the problem areas, running smart tests, and eventually deriving valuable results. According to an Econsultancy report on CRO, companies with a structured approach to improving conversions were twice as likely to see a large increase in bottom line.

For testing to make any sense (and therefore for the result to have any value), you need to first learn how to continually generate high-impact, data-driven hypotheses repeatedly.

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Formulating hypotheses using observations

Observing Data You may have all the data lurking in your records, but all that data has to be distilled into a logical hypothesis. This is explained by the graph below.

Instead of randomly testing ideas that you ‘feel’ are good, the focus should be on building a solid hypothesis that maximizes chances for winning. Theoretically, there are two approaches to building a hypothesis. inductive approach — i.e. begin with brainstorming a set of ideas and then look at the data to validate those ideas and form a hypothesis. deductive approach of looking at patterns in your observations first, and then deducing a hypothesis for testing. Either way, the most crucial part in forming a strong hypothesis is the research that goes behind it.

Wisdom Knowledge Information

Data

Ability to use the knowledge to understand the reasons in larger contexts

Contextualized information

Data with analyzed relationships and connections

Simple and objective

Understanding

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Evidently, putting data into context with a certain level of understanding is the key. The “simple objective facts” or ideas could be transformed into a well-structured hypothesis by understanding your website analytics and aligning it with your business objectives.

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Unclear UI No one sees them Why...

Low Contrast Never Announced

Can’t show to boss People don’t see the value No one uses Reports Why...

Why...

Too much work involved Easier to do in Excel

No Data Visuals The UI is too confusing Why...

Don’t understand “Merge” Too many Dropdown

Analyzing your Website Analytics Your analytics data is your first port of call while formulating a hypothesis. With the wealth of data that gets tracked, you could get answers to the most obvious questions related to the current situation of your website. Website analytics tools like Google Analytics and Kissmetrics can show you quantitative data on how visitors navigate your website on a site architecture level. For any observation that you come across from analyzing data, ask yourself enough number of “why’s” to form a solid hypothesis.

Needs more testing Too many bugs in them Why...

Bad Browser support Response Time Poor

Source

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Observing Behaviors Now that you’ve gained an understanding of what visitors are doing on your website, you would next need to know why they’re doing it. Below are the two practices that could help you identify and eliminate the problem: Heuristic Analysis This is when usability experts review your website to identify any common usability/design issues. Each review is based on a set of usability best practice principles and/or design consistencies. One of the most popular heuristic analysis frameworks is defined by Jakob Nielsen.

Ten Usability Heuristics by Jakob Nielsen

Visibility of system status Give the users appropriate feedback about what is going on. User control and freedom Support undo, redo and exit points to help users leave an unwanted state caused by mistakes.

Aesthetic and minimalist design Don’t show irrelevant or rarely needed information since every extra element diminishes the relevance of the others. Flexibility and efficiency of use Make the system efficient for different experience levels through shortcuts, advanced tools and frequent actions. Help and documentation Make necessary help and documentation easy to find and search, focused.

Match between system and the real world Use the real-world words, concepts and conventions familiar to the users in a natural and logical order. Error prevention Prevent problems from occurring: eliminate error-prone conditions or check for them before users commit to the action. Consistency and standards Follow platform conventions through consistent words, situations and actions.

Recognition rather than recall Make objects, actions, and options visible at the appropriate time to minimize users’ memory load and facilitate decisions.

Help users recognize, diagnose, and recover from errors Express error messages in plain language (no codes) to indicate the problem and suggest solutions. 12

Visitor Behavior Analysis Next, examining the behavior of current visitors could help you identify the specific details of the most pressing issue with your conversion process. Visitor Behavior Analytics tools like Heatmaps, Clickmaps, Mouse recording, etc. could tell which part of the page in specific they spent most time on (or ignored completely).

Here’s an example: quantitative analytics can tell you that visitors are exiting your product page at an alarmingly high rate. Visitor recordings go a step further to actually show recorded sessions of your visitors on the product page, helping you visualize where the users spend most of the time on the page, areas they get stuck on, information they don’t seem to locate,and so on.

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Observing Opinions Your analysis (web analysis or visitor behavior analysis) could run the risk of narrative fallacy or confirmation bias while forming a formalized hypothesis. That is where collecting real-time quantitative and qualitative data via customer surveys could help. Surveys primarily come in two forms: On-site Surveys On-site surveys enable you to receive feedback from your users via a popup or a layer that the visitor is prompted to fill up. It is a great mechanism to find out more about your actual users and validate your hypothesis. You can gather information about the interests, attitudes or preferences, straight from the horse’s mouth.

In general, there could be three things that you want to think about when planning to use on-site surveys: Why ask the question: Clearly outline the end goal you’re conducting the survey for. For instance, whether you want to feedback on website design/content/ relevance, etc. When to ask the question: Asking the right question at the right time is important. You could look at your average time on site and/or page view metrics and ask questions to visitors who have engaged enough with your website/page (for qualification reasons). Which questions to ask: Which questions to ask depends heavily on your end goals. If you’re doing a voice-of-customer research to gain insights on copy/ design, open-ended questions are gold. If you’re trying to quantify customer experience, measuring Net Promoter Score (NPS) could do the trick. Here are some additional tips that would make your website surveys shine, and a guide to surveys to get you going.

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CHAPTER 3

Building a long-term experimentation roadmap

Building a strong hypothesis is crucial for testing—no doubt. But what should you do when you have a backlog of potential hypotheses to test? How should you go about deciding which one to test first? Often, organizations do not have a structured approach to conversion optimization and arbitrarily pick out a hypothesis from the pool of options they have. However, organizations following a structured approach to it realize the need for a robust prioritization framework. Let’s first look at why scaling your optimization program needs a prioritization framework in the first place.

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What’s the importance of a prioritization framework? Analogous to a calendar, an experimentation roadmap provides a clear direction to your optimization program and prevents you from doing aimless testing. This framework enables you to maintain a dedicated schedule for all your CRO activities. Michal Parizek, Senior eCommerce & Optimization Specialist at Avast, in his interview with VWO, points out the importance of keeping a testing calendar:

“A test calendar helps to keep focus on important tests being launched on time. It is also vital for resource planning and for bringing all stakeholders in a loop. We usually do a quarterly overview of what tests we’d like to run and then we specify and add details on a monthly basis.”

1 2 3 Prioritization helps you answer,

“here’s what we’ll test, in this order, and here’s why.”

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Setting up your prioritization framework You would find a plethora of frameworks for prioritizing your testing hypothesis. Some of these are listed below: Widderfunnel’s PIE framework for Prioritization: A prioritization model by Chris Goward that fosters scoring (on a scale of 10) on three factors: Potential, Importance, and Ease. Bryan Eisenberg’s T.I.R Model: Prioritization calculated by multiplying the individual scores of Time, Impact, and Resources. Monetate Model: Prioritization based on the level of difficulty of the test, creative effort required, and potential of the test. Hotwire’s Points Model: Binary scoring on various questions such as those based on reach, lift, strategic fit, and design. So which of these should you decide to apply to your conversion optimization plan? On what factors would you base your prioritization framework?

Karl E. Wiegers, in his paper on Prioritizing Requirements states this simple rule for prioritization:

“When setting priorities, you need to balance the business benefit that each function provides against its cost and any implications it has for the product’s architectural foundation future evolution.”

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Determining the Potential Impact Depending on the research you have put in to build your hypotheses, you can estimate the impact that it might yield. The more insight-driven your hypothesis is, the more confidence you can have on its success. For example, your heatmap analysis could indicate that a large chunk of your visitors are not able to locate your CTA, making you confident that changes made to it will yield positive results. Some of the other factors to estimate the impact of the test are:

Business Objectives

Website goals

Key Performance Indicators

Target metrics

Alignment with Business Objectives For each hypothesis you want to test, there would be a target metric that you will track. It is imperative that this key metric is in alignment with your business objectives. ConversionXL explains the linear correlation between the business objectives and target metrics here:

Prioritization eventually becomes a matter of answering these questions:

Which business goals are we trying to improve at this moment?

Which features or pages are closely associated with the business goal?

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Location of the Test The page on which the test is performed is also critical to understand its potential. You can look for: Most critical pages In terms of the business goals they are seeking to fulfill. For instance, a pricing page would be more critical to a SaaS business than say the “About” page.

Most visited pages Pages with high traffic volume are more likely to yield quicker results. The graph below indicates how higher the traffic, the lesser time it takes for the test to reach a conclusive result.

Pages with expensive visits When choosing between two pages with similar traffic and conversion rates, pick the one with a higher cost of traffic (through paid advertisements) for a better split-testing ROI.

450 400 350 300 250 200 150 100 50 0

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Determining the Potential Ease To estimate the effort required to test your hypothesis, consider the following factors:

Availability of Resources A full-fledged CRO program would require help from a designer, copywriter, developer and so on. By looking at the bandwidth and strengths of your team/members, you can estimate which particular test should be conducted first.

Cost of the Test The ease with which the test can be conducted is also dependent on the cost of conducting the test. The cost would typically include the cost of resources and tools used in the optimization process.

Duration of the Test To conclude, the potential ease of conducting the test can also be estimated by the time it would take to finish the test. You can calculate for how long should you conduct a test by using VWO test duration calculator.

A cumulative assessment of the two factors shared could give you a simple framework for prioritizing. For instance, you would prioritize tests with high impact and low effort more than the ones with say, low impact and high effort.

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CHAPTER 4

Setting program goals and measuring progress

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Successful conversion optimization programs and teams set goals for experimentation and then measure their performance for those goals over time. To perform well, it’s important to track both the volume and velocity of experiments you are running to understand if you are improving over time. Measuring the time it takes an experiment to go from an idea to deployment, and how many experiments that you have at each stage of the process - from ideation and development, to testing and analysis - can also yield valuable insights.

Step-by-step breakdown of goal setting Here are the four steps we feel essential when you’re trying to set goals and measure the progress for your conversion optimization program.

STEP 1

Write down your business goals Conversion Optimization should be based around business goals. It is like building a house. You can’t paint the walls until you have walls. You can’t put up walls until you lay the cornerstone. If you try to go out of order you’ll end up with one that couldn’t stand up to the stormy days. So start by looking at your overarching business goals. What do you want to achieve in the next one year? Two years? Five years?

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STEP 2

Cascade business goals into conversion goals Once you have established a solid foundation, you can start building the walls. Conversion rate optimization is all about your digital properties. So the most important question to ask yourself now is: how can my website or mobile app contribute to my business goals? Then, you can set specific goals that will have a measurable impact on our business goals.

STEP 4

Optimize to improve those metrics over time

STEP 3

With all the hard work you did in the previous steps will pay off now. Now that the foundation has been laid, you can start building on it.

Now it is time to make your goals measurable (and potentially break them down further). For each of your website goals, you need to have a metric or set of metrics to measure. These metrics will then be the measuring stick by which you choose to optimize and determine the success of our individual conversion optimization campaigns.

Each of your optimization tests should be directly relatable up the chain and back to your business goals so that you can track measurable success. Again, don’t create tests just for the sake of creating them. Make sure they have a purpose. Selecting and creating conversion optimization tests becomes incredibly simple and straightforward as long as you’ve got your foundation laid in advance. Conversion optimization is only useful if it is in service of a business goal, so make sure you aren’t getting ahead of yourself.

Assign metrics for each conversion goal

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Macro conversions and micro conversions Here’s another important thing to consider about your conversion optimization program: not all visitors to your site are at the same buying decision stage. Some are just looking to learn things, some are actively prospecting, and some others are ready to buy today. The fact that visitors will be at all different stages is why you need to track and optimize for conversions at all stages for every user.

Macro conversions, on the other hand, are actual sales conversions, like a checkout for an eCommerce business or making a call to your Sales team.

Are you looking for more free trial sign-ups, more average revenue per visitor, or top-of-the-funnel ebook downloads?

Micro conversions are low-involvement commitments from a visitor, like an ebook download or a newsletter subscription.

Enter macro conversions and micro conversions.

Micro

Macro

It may seem like macro conversions are all that matter. However, micro conversions are the baby steps that visitors start with to get to know your brand, eventually leading them to become customers. Good conversion optimization requires tracking both.

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CHAPTER 5

Ensuring continuous learning and inculcating improvements

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Developing sound data-driven hypotheses and conducting experiments can uncover surprising findings filled with useful insights. The potential business impact of these insights hinges onYour team’s ability to identify and interpret them. Whether your results are good, bad, or inconclusive, they are still results, and there is always something to learn.

Investigating and analyzing your outcomes Before you can take action you’ll need to evaluate the results of your experiment to determine whether tests were winning, losing, or inconclusive. During the investigation phase, start by comparing results to your original business goals we discussed in the previous chapter, and your hypotheses to bring the tests full circle. Refer to your campaigns’ overall results to get an idea of the average visitor’s or user’s behavior, then segment your results to focus on specific segments. This deep dive into audience segments can help you gain actionable insights and even uncover surprising conclusions.

For example, adding a new feature into your website experience may have a much greater on mobile visitors than desktop visitors. Asking some of the following questions can help you analyze your results across experiments more effectively: Do any specific segments behave differently from the overall audience? If so, what are the attributes and goals of this particular segment? Why do you think they responded to the test the way they did? Which variation did your most valuable visitors choose? Use the answers to these questions to make datadriven business decisions based on your results.

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What to do with your test results When a Winning Variation is Found Great! What you should do now is answer these questions, and proceed as follows:

What is the cost of deploying the change (engineering hours, design hours)? Does the expected increase in the revenue justify the cost involved?

YES

Talk to your engineering and design teams to get the change implemented. Analyze the test data to see if there are further opportunities to optimize. Use these learning outcomes to fuel further optimization efforts.

When a Losing Variation is Found When the variation loses, make sure you: Look at the research; ensure that the hypothesis isn’t faulty.

Go through relevant case studies! It could reveal new perspectives.

Analyze the test data; do segmentation to reveal further insights.

Reconstruct the hypothesis to accommodate new insights that were missed in the initial research.

Validate research data with surveys and visual analytics.

Go back to following your conversion optimization process.

NO

Hold on to deployment. Use post-test segmentation: can the hypothesis be refined for more impact? Reconstruct the hypothesis Use these learnings to fuel further optimization efforts

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Sharing and evangelizing learnings Once you have the data with you, promoting company-wide transparency is just as critical to a successful experimentation program. It widens the impact of your testing, promotes the culture you’ve worked so hard to develop, and evangelizes the importance of continuous iteration. Start by sharing results with your testing team, relevant stakeholders, and anyone involved in the ideation process. Make sure you include the purpose of each test, clearly define the details and hypothesis, provide details about the impact on revenue, and share lessons learned – regardless of the outcome. Don’t be afraid to get creative with your sharing routine. Distributing something as simple as a “Which Variation Won” poll that highlights the hypotheses and key learnings can increase engagement and get more employees invested in your experimentation program.

Don’t be afraid to get creative with your sharing routine.

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CHAPTER 6

Growing a winning optimization team

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The importance of building strong, efficient teams is not a new concept. Teamwork about creating a clear vision, aligning on objectives, and working as a unit to achieve success. As companies seek to grow and optimize their experimentation efforts building teams dedicated to testing ideas and concepts is the key to unlocking the path to relentless innovation. So, what do experimentation teams look like at the enterprise level?

What does an ideal team look like? Since Conversion Optimization is cross-functional, it involves a team of professionals from different departments and functions to coordinate effectively and make the most out of available resources. At any given time, an optimization team needs to collectively possess talent with all of the following skills: A Conversion Rate Optimization Manager is in charge of developing an end-to-end plan for your optimization program—enumerating and prioritizing essential activities. A Data Analyst monitors your website data and user behavior using relevant tools to uncover actionable insights. A Web Graphic Designer will help you design web pages with a focus on increasing conversions.

A Copywriter creates copy that will reduce customers’ anxieties, ease friction, and persuade visitors to take the desired action. A Web Developer is in charge of developing variations of webpages for A/B tests using optimized code. Going forth, you should focus on determining who will take care of which responsibilities for your optimization needs. Based on the bandwidth and budget at your disposal, you can either onboard key hires for these roles or identify talent from within your organisation and rope them in. An ideal case would be a dedicated professional possessing one of each skill. However, not every organization has the resources to dedicate five employees for it. For many small businesses, a single team member can also take care of multiple functions.

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Invest in ongoing education and growth opportunities The best organizations, that run very mature experimentation programs, tend to invest heavily in employee development. That means granting generous education stipends for conferences, books, courses, and internal trainings. Different companies can have different protocols as well. Airbnb, for example, sends everyone through data school when they start at the company. HubSpot gives a generous education allowance.

There are tons of great Conversion Optimization specific courses out there nowadays. Some programs you should make everyone in your team run through are:

Intermediate Google Analytics A/B testing mastery course Creating A Conversion Optimization Strategy: A HubSpot Academy Course CRO Certification Program

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Wrapping Up As this guide comes to an end, it would be beneficial for us to circle back and reflect on the most important aspect when it comes to scaling anything in a business: culture and mindset. Make sure you’re able to follow the basic tenets of the same shared in this eBook.

Learn more about conversion optimization in HubSpot Academy. LEARN MORE

Before you leave, be sure to pay this guide forward. Share it with your friends, colleagues, or anyone who you might think could gain value from it. What’s more, bookmark it for your own reference, and be sure to download a copy. Good luck, and happy optimizing!

If you’re looking to explore a platform for scaling your conversion optimization program, try out VWO free for 30-days. SIGN UP FOR FREE TRIAL

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