Build the most effective personalization strategy: A 4-step roadmap

It’s Groundhog Day again.

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 tactics seem to be pulled by an unstoppable force through fad cycles of hype, over-promise, disappointment, and decline – usually driven by 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.

(Side note: That’s why I’ve been advocating for years for marketers to start their optimization programs with a strategy in addition to the best tools.)

Now, everyone is swooning for Personalization. And, so they should! It can deliver powerful results.

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. In fact, in 2018, marketers are more unsatisfied with their personalization efforts than previous years.

This year, we found that marketers are more unsatisfied with their current efforts and are less confident in their ability to achieve successful personalization today. Only 12% of marketers are “very” or “extremely” satisfied in the level of personalization in their marketing efforts, while 38% are “moderately” satisfied.

– 2018 Trends in Personalization Survey Report from evergage

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 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

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:

“Spa Vacations
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.

(Note: some of the foundational research for this originated with Peter Pirolli at PARC as early as the 90’s.)

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. Optimizely refers to this strategy as “symmetric messaging”, meaning the expectations you set in an ad should match the experiences on your site.

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.”

– Hudson Arnold, Strategy Consultant, Optimizely

For companies that are exploring 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.

If you haven’t categorized your most important visitor segments, you should focus your energies on data clarity and segmentation first, before moving into personalization.

Do I have the resources to do personalization?

• 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 experimentation 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.

Conversion’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 experimentation 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.

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:

• Deductive research
• Inductive research
• Customer self-selected

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 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 Conversion’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%. Conversion 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:

1. Reducing distractions and adding clarity for older visitors
2. 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.

(Also, this is a great example of why one of Conversion’s five core values says “Grit – We don’t quit until we find an answer.”)

3) Customer self-selected personalization

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:

1. What are the best segments?
2. 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.

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 Conversion’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.

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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 experimentation inform future audience segments and personalized messaging, while insights derived from personalization experimentation inform future 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 fresh insights. **