There’s a scene in the movie “The Wolf of Wall Street” where Jordan Belfort, played by Leonardo DiCaprio, asks his friend to sell him a pen. In the movie, the friend answers with something about supply and demand.
The real Jordan Belfort, who the movie is based on, says the actual answer is different.
“The real answer is, before I’m even going to sell a pen to anybody, I need to know about the person, I want to know what their needs are, what kind of pens do they use, do they use a pen?”
This applies to any product. If you want people to visit your ecommerce store and buy from you, you need to know about them. You need to know their needs, goals, motivations and objections to buying your product. In short, you need to know your customer personas.
What is a customer persona?
Let’s clear a few things up first. A customer persona isn’t an actual person, and shouldn’t be based off of any one single customer. It’s a model that represents your customers, or a group of customers. The model may even look like and sound like a real person. If you have multiple customer segments, you can create a corresponding persona and assign fictional names to each.
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Secondly, while this model might be fictional, it’s definitely not derived from fiction. The model is constructed from real and complete data about your customers. It does not contain any conjecture or information about who you want your customers to be.
Typically, apart from a name, a persona contains information about customer demographics, such as age range, gender, employment, income and location. It also captures their goals, needs or wants, challenges and objections as related to your business.
You might want to add information about your customers’ online behaviors, like the sites they visit, their social network activity, how they found your site, and what they look for when buying online.
Finally, every aspect of your customer persona should have a purpose. For example, collecting information about their eating habits is useless unless your product is somehow related to food.
The point of the buyer persona is to give you an insight into the goals, motivations and behaviors of your customers. Each aspect of the persona should contribute to that in some way.
Here’s an example from Goodbye Crutches. It’s not very detailed, but it has essential information needed for them to market their products to this customer type:
Goodbye Crutches now knows it has to focus on online marketing and concentrate on spreading useful information and resources that will ease customers’ fears. They can’t directly push products onto consumers. They need to help them first before making the sale.
Developing Your Customer Personas: Qualitative Data
Most of the data you collect for your customer personas will have to come from qualitative research. That means communicating with your customers in some way and asking them questions about their goals and needs.
There are many ways you can do this:
Try to get customers to sit with you in the same room or video chat with you. This way you’ll also pick up on visual cues when your customers answer your questions. If in-person interviews aren’t possible, a phone call is a good alternative.
The best thing about a one-on-one interview is that you can change it if you need to. It becomes dynamic, like a conversation. So instead of asking questions that customers have already answered, you can dig deeper and find out more.
You can also cover a lot in one sitting because talking is the fastest mode of communication. Focus on subjective and open-ended questions, like finding out what drives them, what their objections to buying are, and how you can help them.
You can send questionnaires and surveys to customers after they subscribe. While not as effective as one-on-ones, it’s more convenient. It allows customers to reply when they have some free time.
You can also survey subscribers based on their buyer journey. For example, you might want to ask subscribers who haven’t bought anything what they are looking for and what’s holding them back from buying. For customers who just bought something, you can ask them why they bought from you and how your products satisfy their needs.
The problem with email is that people tend to ignore them until later, and most often they end up forgetting. To ensure that customers answer your surveys, you can do two things.
First, give them an incentive to answer your survey. Discounts and coupons are popular choices because they help customers save, and also make them shop with you again to take advantage of the deal.
Second, try not to make the survey too long. If customers see a long list of questions, they assume it’ll take a long time to answer and they’ll ignore it. Keep it short and don’t add too many open-ended questions.
On-site surveys, when done correctly, can provide some extremely relevant insights. They are presented to customers while they are browsing your site, so the timing is perfect.
You can trigger these surveys based on the page customers are looking at, or a number of other events. You can even trigger them as customers are leaving your site and ask them whether they found what they were looking for.
Because of their nature, pop-up surveys are best suited to multiple-choice questions. There’s not much real estate to play with if you pose open-ended questions.
Again, to incentivize visitors to answer your surveys, give them an on-the-spot discount.
Try to collect as many data points as you can. Remember, a persona doesn’t represent just one customer. After a certain point, you’ll find a pattern in your qualitative data.
A Real-Life Example
When Whirlpool started creating customer personas, they found four main personas and four secondary personas. The four main personas were purchasers under duress (someone who needed a refrigerator immediately), planned remodelers, new owners and owners with repair.
When conducting their interviews, they tried to understand the mindset that each customer had when visiting their website. It was important to find out what triggered them to visit the Whirlpool site and what kind of solution they were looking for.
All this information was included when they created the personas, along with ages, names, occupations and incomes. They also added pictures of real people to help their marketing visualize the personas.
You’ll notice that Whirlpool also used website statistics to help them with their persona creation. Let’s see why this is important.
Developing Your Customer Personas: Quantitative Data
While you can create customer personas solely through qualitative research, adding some quantitative data will help you strengthen them. After all, data doesn’t lie!
Another advantage is that you can collect way more information with quantitative data than you can with qualitative. While many customers may ignore your survey requests, a good analytics solution can help you track online behaviors of every single customer.
Your analytics solution will also help you close the loop with your personas. Qualitative data helps you create the personas, but quantitative data helps you make use of them. We’ll come to that in the next section, but for now here are some metrics to track:
Your analytics app can give you a much better view of customer demographics because of the number of data points it collects. Find out where your customers are coming from, what device or browser they use, and when they typically browse your store.
This means you don’t need to waste time asking customers where they are when you interview them. Use that time to find out the things that your analytics software can’t.
Your acquisition channels will help you understand why customers came to your site in the first place. Were they actively searching on Google or did they come from an ad? Did they see a review of your product online or did a friend refer them?
If they came via a search engine, you can also track the keywords they used to find you. This will give you an insight into what your customers value. Bargain hunters will use words like “cheap” while those who care about performance will use “best”.
During your qualitative research you might have noticed a pattern in the phrases and terms your customers use. Tying that in with your search engine data will show you which terms are most significant.
The customer journey through your site is important too. What pages do they visit and how long do they spend on each page? How your customers navigate your site tells a story and helps you differentiate between segments.
The segment that visits your “About” and “FAQ” pages are probably very early in the buyer cycle. They want to get to know your company better and aren’t ready to buy from you just yet.
The segment visiting your blog are now considering your products and want to find more information. They are a little further along the cycle, but still not quite ready to buy.
Finally, the group spending a lot of time on your product pages is probably ready to buy. Something might be holding them back, so look through your qualitative data to find out what it is.
The flow will also tell you where customers are leaving your site. Are they dropping out at the product page or another page? This can give you insights into how your customers behave at key conversion points.
Customer Lifetime Value
You should be tracking customer lifetime value already. It’s one of your most important metrics and can help you identify which are your most profitable personas. These are the people you want to spend more time crafting offers for, as they have a higher likelihood of converting.
Putting Your Customer Personas to Work
Now that you’re created your customer personas and boosted them with quantitative data, it’s time to actually put them to some use. At the end of the day, you want to increase conversion rates and make more sales.
Here are some applications of customer personas:
You can now craft a user experience on your site that speaks directly to each persona. No more generic content that makes customers feel like you don’t have the answers they’re looking for.
We’ve already seen how Whirlpool created their personas, so now it’s time to look at how they used them. Their problem was that their website was too generic, so they decided to redesign around their personas.
They created functionalities that catered to each persona. The duress purchaser needs to buy something quickly, so they implemented a filtering feature to help customers identify the best product for them in as little time as possible.
The remodeler, on the other hand, likes to take time in making a decision, so they created a showroom feature that allows customers to explore products at length. They also added a feature that allows remodelers to email products to other people involved in the remodeling.
The new redesign increased page-views by 42% and decreased bounce rates by 10%.
When it comes to ecommerce conversion rates, product copy is one of the most important factors. It’s your sales pitch, the last thing a customer reads before clicking the “Buy Now” button.
While creating their personas, Leo Schachter Diamonds focused on the motivations and psychology behind their customers’ purchases. This helped them created targeted copy that answered questions their personas had.
This strategy helped them pre-empt any objections customers had to buying diamonds from them. Before this strategy was implemented, only 0.86% of all site visitors would go on to find a jeweler who sold Leo Diamonds. Now, because the copy solves their needs, 54% of them go on to find jewelers. That’s an increase of 5,500% in conversion rates!
Now that you know your customers’ needs and requirements, you can create blog posts and other content that is relevant to them. This will attract more qualified leads with a higher chance of converting to customers.
After Skytap identified their personas, they were able to create targeted blog posts that impressed visitors. The relevancy of their content increased their organic search traffic by 55% and converted 124% more visitors into leads.
Collegis Education repurposed their content to create a targeted email campaign for subscribers. They segmented subscribers according to persona and sent them high-level content to help them solve their pain points. This resulted in an increase of 28% in open rates and 7% in conversion rates.
Your personas give you an idea of the language your customers use and the websites they frequent. Use this to optimize your PPC campaigns.
In Google AdWords, use the same keywords that customers use to make your ads more relevant. Your ad click-through rates will improve.
You can place your banner ads on websites that your customers visit. Again, use language that they identify with to increase your click-through rates.
Apple typically creates ads geared towards consumers, but as their products started gaining traction in businesses they wanted to include this new persona as well. In this iPad 2 ad, you can see them highlighting applications to view the stock market and make presentations.
A Tool to Understand Your Customers
If you find that consumers are not responding to your content or your offers, and that your website conversion rates are low, it means you don’t understand your customers. Use the customer persona as a tool to find out what their needs are and how you can better address them.
Have you started creating your customer personas? How many do you have?
Good data analysis is the search for cause: attempting to uncover why something happened. Traffic to the website is low—why? Our email click through rate is improving—is it because we recently redesigned our email template, or because we’re focusing on more direct calls to action? The best way to find these answers is to rely on the same approach that scientists have used for centuries—experimentation.
As technologist Scott Brinker advises: “Experimentation is the gold standard of causation.” A thoughtfully crafted experiment allows you to zero in on the variables that influence your data. Instead of retroactively analyzing your data, you isolate your assumption and design an experiment that will allow you to test it. These tests start with a hypothesis.
State your hypothesis
A hypothesis is a predictive statement, not an open-ended question. A good A/B testing ahypothesis will invite you, through research, to identify a potential solution. Let’s look an example of an experiment that RJMetrics ran on their website.
Data-driven tips and how-to’s that help your business go from 0 to 60.
In a pricing page experiment, RJMetrics’ hypothesis was informed by qualitative data on how visitors were interacting with the web page. They used Crazy Egg to produce a heat map that showed high and low-activity parts of the page:
Stephanie Liu, front-end developer at RJMetrics and Optimizely’s Testing Hero of the Year, crafted the following hypothesis:
My hypothesis was that moving the button into the white hot scroll map area would cause the design to have a higher conversion rate as compared to the original pricing page. More people would pay attention to the button simply because their eyes would be lingering there longer.
Here’s her original version:
Here’s her variation:
Stephanie’s experiment proved her hypothesis to be correct, and her improved pricing page resulted in a 310% improvement in conversions on the pricing page—a staggering win, due to diligent use of data and a well-formed hypothesis.
The Inspectable Elements of a Hypothesis
Let’s boil down a hypothesis to its individual components. Data fits into the hypothesis framework in a number of areas.
“If _____[Variable] _____, then _____[Result]_____ [Rationale].”
The Variable: A website element that can be modified, added, or taken away to produce a desired outcome.
Use data to isolate a variable on your website that will have an impact on your performance goals. Will you test a call to action, visual media, messaging, forms, or other functionality? Website analytics can help to zero in on low-performing pages in your website funnels.
Result: The predicted outcome. (More email sign-ups, clicks on a call to action, or another KPI or metric you are trying to affect.)
Use data here to determine what you’re hoping to accomplish. How large is the improvement that you’re hoping for? What is your baseline that you’ll measure against? How much traffic will you need to run an A/B test?
Rationale: Demonstrate that you have informed your hypothesis with research: what do you know about your visitors from your qualitative and quantitative research that indicates your hypothesis is correct?
Use data here to inform your prediction: quantitative insights can be very helpful in formulating the “why.” Your understanding of your customer’s intent and frustration can be enhanced with an array of tools like surveys, heat maps (as seen above), and user testing to determine how visitors interact with your website or product.
Strengthening your Hypothesis
Not all hypotheses are created equal. To ensure that your hypothesis is well-composed and actionable, use a few of the following tips. Here are some examples of strong and weak hypotheses:
| Strong Hypothesis
|| Weak Hypothesis
| “If the call-to-action text is changed to “Complete My Order,” the conversion rates in the checkout will increase, because the copy is more specific and personalized.”
This hypothesis is strong because of its specific variable to modify (CTA text) and rationale, which indicates an understanding of the audience for the page.
|| “If the call-to-action is shorter, the conversion rate will increase.”
This hypothesis is weak because it is very general, and does not include a rationale for why the proposed change would produce an improvement. What would be learned if this hypothesie
| “If the navigation is removed from checkout pages, the conversion rate on each step will increase because our website analytics shows portions of our traffic drop out of the funnel by clicking on these links.”This hypothesis is strong because it is supported by website analytics data that highlight a high-impact opportunity for streamlining the checkout process.
|| “If the checkout funnel is shortened to fewer pages, the checkout completion rate will increase.”
This hypothesis is weak because it is based on the assumption that a shorter process is better, but does not include any qualitative or quantitative data to support the prediction.
A strong hypothesis is:
Testable. Can you take action on the statement and test it? Keep your predictions within the scope of what can be acted upon. Avoid pulling multiple variables into the statement—a more complex hypothesis makes causation more difficult to detect. For instance, don’t change copy on multiple parts of a landing page simultaneously.
A learning opportunity, regardless of outcome. Not every experiment produces an increase in performance, even with a strong hypothesis. Everything you learn through testing is a win, even if all it does is inform future hypotheses.
That brings us to our next tips for using hypotheses:
Hypothesize for every outcome. One of our solutions partners, Blue Acorn, mentioned a hypothesis best practice that we think is fantastic. To ensure that every experiment is a learning opportunity, think one step ahead of your experiment. What would you learn if your hypothesis is proven correct or incorrect in the case of a variation winning, losing, or a draw?
Build data into your rationale. You should never be testing just for the sake of testing. Every visitor to your website is a learning opportunity, this is a valuable resource that shouldn’t be wasted. RJMetrics recently wrote a tutorial on how to use data to choose and prioritize your tests, you can check it on the Optimizely blog.
Map your experiment outcomes to a high-level goal. If you’re doing a good job choosing tests based on data and prioritizing them for impact, then this step should be easy. You want to make sure that the experiment will produce a meaningful result that helps grow your business. What are your company-wide goals and KPIs? Increasing order value, building a revenue stream from existing customers, or building your brand on social media? If your experiments and hypotheses are oriented towards improving these metrics, you’ll be able to focus your team on delving into your data and building out many strong experiments.
Document your hypotheses. Many website optimization experts document all of the experiments they run on their websites and products. This habit helps to ensure that historical hypotheses serve as a reference for future experiments, and provide a forum for documenting and sharing the context for all tests, past, present, and future.
Now, Build Your Own
A hypothesis is a requirement for anyone running A/B tests and experiments on their website. When you build your own hypotheses, remember to:
- Clearly define the problem you’re trying to solve, or metric you’re looking to improve
- Bring quantitative and qualitative data into the hypothesis
- Test the hypothesis to strengthen and ensure it is actionable
- Look at every experiment as a learning opportunity
If you need some extra help check out our ebook, Building your Company’s Data DNA for more tips on how to build data-driven hypotheses.
Getting found in the iOS app store is a challenge, with more than one million active apps vying for users’ attention. App publishers and developers have a number of obvious marketing tools at their disposal, like advertising and pay-per download, to get more people to notice their mobile apps. But these are costly and not for everyone.
Beyond the obvious advertising tools, the iOS app store has another, often overlooked, way to promote discovery: app price changes. When a publisher or developer lowers the price of a paid app it gets added to Apple and third party RSS feeds that are distributed to thousands of sites and twitter feeds focused only on promoting apps that have gone on sale or have recently become free.
How it works
This marketing tool, more akin to merchandising, requires little to no budget but, according to our analysis of all iOS apps during most of 2013, it has a significant impact on positioning in Apple’s Top Paid and Top Grossing ranks. This directly translates into better visibility, downloads and revenue.
In fact, as can be seen in the graph below, compared to paid apps that never changed their prices, paid apps that made such changes (both increases and decreases) grew the average number of days they were ranked by 21% in Top Paid (+9 days) and 70% in Top Grossing (+16 days). These apps also improved their average rank by 20% in Top Paid (-45 positions) and 19% in Top Grossing (-46 positions). These improvements were not only for the most popular iOS apps, but also for less established new apps and poorly performing apps that have been around for a while.
The number of price changes, whether increases or decreases (including to $0) also matters. 1 or 2 changes during a year provides very limited improvements. But when changes are made once per month (12 total), improved rank and the number of days ranked healthily. Increase that number to 1 per week (52 total) or more and that’s when developers started to see the largest improvements to app ranks and thus downloads.
Applying this to your app
Here are the key rules that mobile app publishers and developers should follow when developing their price marketing strategy:
All paid apps should look to go on sale, on average, at least once per month. With the corresponding price increase, that makes 24 price changes per year. More experienced app developers and marketers can look to do more to maximize downloads, including intraday changes to target specific countries or types of users, but 1 per month is a good start for most apps.
Allow Settling Time
Price changes can take anywhere from 20 minutes to more than 15 hours to spread throughout iTunes’ storefronts (New Zealand is usually one of the first then it follows time zones to reach European storefronts and the US). In addition, it can take time for users to discover the new price, either directly or through a third party site like AppShopper. So unless you are looking to make multiple price changes a day, which can be rewarding but requires constant attention and/or the right tools, most publishers should let their app’s sale breathe for 48 to 72 hours.
Focus on Down Cycles
Given the cyclical nature of downloads and ranks, price changes should generally not be made when the app is experiencing a growth spurt. Instead, the price change should be timed with an app’s slowing downloads or sagging rank.
React to Competition
If your app is a soccer app at $2.99 and EA’s FIFA 2014 goes from $4.99 to $0.99, you need to react immediately, in order protect your positioning and sales. If this example does not directly apply to you, remember that competitors are not just direct competitors. They may also be apps ranked just above you in your genre or category, or those appearing before you in key searches on iTunes.
Varying the times, days of the week and the amounts of your price changes will avoid predictability that could be gamed by both competitors and users.
Every price change should be an opportunity to test a new price and new price steps. That may not always be possible if you are at $0.99 and going free. But even then you should be testing various target prices (the price you go to after a sale). Here are examples of variations in price changes:
- The price of your app is lowered to varying tiers in 1 or 2 steps (e.g. $3.99 -> $0.99 or $3.99 -> $0.99 -> $0.00)
- Then the price is increased in 1-3 steps (e.g. $0.99 -> $3.99, $0.99 -> $4.99 -> $3.99, $0.99 -> $1.99 -> $3.99)
Pricing changes are a simple, effective way to get your app in front of people. You can make these changes yourself, or if you’re looking for some extra assistance, talk to us. The Loadown can help you automate this exact type of optimization.
Always be testing should be the mantra of every ecommerce store. Incremental improvements on your homepage, product page, or click-through-rates have a snowball effect on your bottom line. Today’s guest post is from, Sean Ellis, CEO of Qualaroo and founder of GrowthHackers.com. Sean has held marketing leadership roles with companies including Dropbox, LogMeIn, Uproar, and Eventbrite. He literally wrote the guide to conversion rate optimization. Read on to hear what Sean has to teach you about optimizing conversion rates to find sustainable ecommerce growth.
Growing an ecommerce business is hard. But what if I told you that the answer to your growth challenges is right in front of you? Conversion rate optimization is critical for any business, but none more so than in ecommerce—where each conversion improvement results in immediate improvement in sales.
But CRO can also be a frustrating, fruitless practice, leading many ecommerce managers to abandon it in search of other opportunities for acquiring new visitors. In my experience, CRO is the most powerful lever you have to improve your ROI and overall site performance. It has the ability to turn unprofitable traffic into profit centers, and delivers sustainable growth that compounds itself over time.
We talk about repeat purchases a lot on this blog. We talk about it so much that today we’re thrilled to have Nima Patel from Lettuce on our blog to talk about it for us. From the company that makes order management fun, here are two strategies to get more repeat customers.
By now you’ve heard that repeat customers are an incredibly valuable segment for any ecommerce business and should not be ignored. Although that’s easy to understand at a high level, getting your customer base to buy more often isn’t so simple to implement. Monetary and points based rewards programs are standard, but what if we told you today that there were ways to earn deep customer loyalty without having to resort to generic financial incentives?
It’s definitely possible.
The post below was submitted to us by nomorerack, a fast-growing online shopping destination with an avid team of RJMetrics users. To see what RJMetrics can do for you, get started with our 30 day free trial today.
At nomorerack.com, our goal is to be the go-to online shopping destination for those who want quality brand name apparel and accessories for up to 90% off retail. A key to achieving that goal is having a deep understanding of our customers’ behavior.
In this post, we outline our methods for maintaining a consistent, deep understanding of our customer base that evolves with our data.
Quest for Customer Insights
Our long-term success is strongly dependent on client satisfaction. We’re focused on making sure that our customers keep coming back, refer their friends and help our community grow.
To better understand our customer base, we wanted to use important metrics like revenue per user (RPU), time between purchases, and cohort analysis. It was critical to us that we be able to access these metrics on-the-fly as our data changed and segment them by things like acquisition source. Understanding the returns we see from different channels is critical because it tells us which avenues are most effective and where we should be directing our resources.
To address these needs, we went looking for an analytical tool that allows non-technical team members to pull frequently updated reports and run queries via a simple user interface. It was also important that we get up and running as quickly as possible. We looked to the cloud.
Cloud Business Intelligence
A quick search led us to RJMetrics, which provides hosted data analytics software. We reached out to them and signed up for a free 30 day trial in which we asked to measure those key metrics like RPU, lifetime revenue (LTV) and repeat purchase patterns.
Vishal Agarwal, our Director of Business Development, signed up for RJMetrics on a Friday and was running these critical reports by Tuesday of the following week. By Thursday, our whole team was trained on RJMetrics’ system. Within a week of signing up, we were already saving many hours that were previously spent on report generation and data exploration.
Another unexpected plus came as a result of RJMetrics’ experience in working with e-commerce companies like ours. RJMetrics has developed a suite of best-practices metrics that are readily available out-of-the-box. Through cohort analysis, we are able to group customers by their registration dates and analyze their subsequent purchases over time on a single chart. This exercise was brand new to our team and would have taken us hours to build in Excel.
We knew this subjectively but the cohort chart confirmed that we had an amazingly loyal customer base. Customers acquired in November 2010 have continued to spend the same amount with us month on month, right till date. This was extremely encouraging evidence that our customers love our products and are far more valuable than just the amount of their first purchases.
While we were very focused on acquiring new subscribers, what was very surprising that 70% of our revenue always came from existing customers.
RJMetrics also helped us optimize marketing dollars. Their “repeat purchase probability” and “average time between purchases” metrics helped us in planning email triggers and targeting specific audiences within our user base. We also learned that only 5% any given day’s sales came from users who registered on the same day, which encouraged us to place increased focus on converting new users.
To share these metrics internally, we leveraged the “syndicated dashboards” feature in RJMetrics. This feature allows us to share common dashboards such as “sales,” “supplier” and “marketing,” with different teams internally. This way, management can clearly communicate with key teams through one set of metrics. No more exchanging multiple emails with messy excel spreadsheets and end of the day reports.
New Insights Every Day
Once we started digging into our data using RJMetrics, we realized that its scope is much wider than just calculating cohorts or LTVs. RJMetrics became a one stop shop for all of our data needs – from basic revenue reporting to the more complex analysis of ancillary data sets.
The beauty of RJMetrics is that it can incorporate any data that lives in our backend database. Every time we start tracking a new data field, RJMetrics can incorporate it into our hosted data warehouse, and we are able to start charting it in a matter of hours. For example, we just started analyzing customer surveys and not only are we able to analyze customer satisfaction and chances of repeat purchase, but we are also linking this data to its respective products and vendors. This allows us to measure company performance through suppliers, products and deal campaigns. In other words, our customers are now actively defining what we sell.
We chose to rely on a third-party service to enable the analysis of our backend data and we are thrilled with the results. Rather than re-invent the wheel, we left it to the experts at RJMetrics and have been able to reap the benefits extremely quickly.