personalization metrics - certona · personalization metrics ... or enterprise platform. in fact,...

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Contact us: Todd Scholl (858) 369-3888 [email protected] Personalization Metrics Introduction Today’s ecommerce world is nothing short of overcrowded. Customers literally have hundreds of choices when it comes to one single product, and loyalty to one online resource isn’t typical behavior online. With overwhelming choices, it’s easy for customers to become frustrated as targeted recommendations often solve this problem for customers, leading them to the right products on your site again and again. Personalization has been an integral part of ecommerce for years. As retailers attempted to provide a personalized shopping experience for their customers, recommendation engines helped automate the task of manual merchandising. By automatically recommending products to customers, retailers were able to create an online experience that not only gave customers the level of individualization for which they were looking, but also helped spur additional purchases. personalization solutions. By serving up product recommendations and promotional in customer loyalty and retention, while at the same time drive more sales and conversions. Few successful ecommerce sites are without a recommendation engine, whether serviced in-house, via a third-party solution vendor, or enterprise platform. In fact, in today’s online marketplace, the question is no longer whether an ecommerce site has personalized recommendations. It has instead matured to where and how they are being used. In general, recommendations help your loyal and repeat customers by delivering a personalized shopping experience and providing relevant suggestions for similar and/or alternative products, value-added accessories, cost savings and much more – based on one’s needs and behavior in real-time. But recommendations also assist new visitors by facilitating discovery and availability of your company’s product catalog and engaging them the moment they arrive to your she buys. The real question for most retailers, though, is how to measure the success of these recommendations. In a time where every single click a customer makes is converted into measurable data, gaining a clearer picture of the key performance indicators (KPIs) involved in measuring personalization success is an absolute must. Personalization Metrics

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Page 1: Personalization Metrics - Certona · Personalization Metrics ... or enterprise platform. In fact, ... Personalization has been an integral part of ecommerce for years

Contact us: Todd Scholl(858) [email protected]

Personalization Metrics

Introduction

Today’s ecommerce world is nothing short of overcrowded. Customers literally have hundreds of choices when it comes to one single product, and loyalty to one online resource isn’t typical behavior online. With overwhelming choices, it’s easy for customers to become frustrated as

targeted recommendations often solve this problem for customers, leading them to the right products on your site again and again.

Personalization has been an integral part of ecommerce for years. As retailers attempted to provide a personalized shopping experience for their customers, recommendation engines helped automate the task of manual merchandising. By automatically recommending products to customers, retailers were able to create an online experience that not only gave customers the level of individualization for which they were looking, but also helped spur additional purchases.

personalization solutions. By serving up product recommendations and promotional

in customer loyalty and retention, while at the same time drive more sales and conversions. Few successful ecommerce sites are without a recommendation engine, whether serviced in-house, via a third-party solution vendor, or enterprise platform.

In fact, in today’s online marketplace, the question is no longer whether an ecommerce site has personalized recommendations. It has instead matured to where and how they are being used. In general, recommendations help your loyal and repeat customers by delivering a personalized shopping experience and providing relevant suggestions for similar and/or alternative products, value-added accessories, cost savings and much more – based on one’s needs and behavior in real-time. But recommendations also assist new visitors by facilitating discovery and availability of your company’s product catalog and engaging them the moment they arrive to your

she buys.

The real question for most retailers, though, is how to measure the success of these recommendations. In a time where every single click a customer makes is converted into measurable data, gaining a clearer picture of the key performance indicators (KPIs) involved in measuring personalization success is an absolute must.

Personalization Metrics

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Table of Contents

Introduction..................................................... 3

Segmentation.................................................. 4

Demand............................................................. 5

AOV...................................................................... 6

Conversion Rates............................................ 8

Abandoned Carts......................................... 10

Non-Responders.......................................... 12

Wrap Up........................................................... 14

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Personalization Metrics

Introduction

Today’s ecommerce world is nothing short of overcrowded. Customers literally have hundreds of choices when it comes to one single product, and loyalty to one online resource isn’t typical behavior online. With overwhelming choices, it’s easy for customers to become frustrated as they try to find the right product at the right price. But personalized, targeted recommendations often solve this problem for customers, leading them to the right products on your site again and again.

Personalization has been an integral part of ecommerce for years. As retailers attempted to provide a personalized shopping experience for their customers, recommendation engines helped automate the task of manual merchandising. By automatically recommending products to customers, retailers were able to create an online experience that not only gave customers the level of individualization for which they were looking, but also helped spur additional purchases.

Today, there can be little doubt about the true benefits of these powerful personalization solutions. By serving up product recommendations and promotional offers that are unique to an individual customer, online retailers can achieve increases in customer loyalty and retention, while at the same time drive more sales and conversions. Few successful ecommerce sites are without a recommendation engine, whether serviced in-house, via a third-party solution vendor, or enterprise platform.

In fact, in today’s online marketplace, the question is no longer whether an ecommerce site has personalized recommendations. It has instead matured to where and how they are being used. In general, recommendations help your loyal and repeat customers by delivering a personalized shopping experience and providing relevant suggestions for similar and/or alternative products, value-added accessories, cost savings and much more – based on one’s needs and behavior in real-time. But recommendations also assist new visitors by facilitating discovery and availability of your company’s product catalog and engaging them the moment they arrive to your site. Their primary effect is focused on what a shopper buys, not necessarily if he or she buys.

The real question for most retailers, though, is how to measure the success of these recommendations. In a time where every single click a customer makes is converted into measurable data, gaining a clearer picture of the key performance indicators (KPIs) involved in measuring personalization success is an absolute must.

The Definitive Guide to Measuring Success

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The Purpose Behind Measurement

At Certona, we power the personalization solutions for many of world’s largest online retailers. Since our platform launched in 2005, we have been accumulating extensive historical data on how customers purchase and interact with our clients’ recommendations. We consult our partner clients on the best practices for measuring the effectiveness of recommendations and educate them on avoiding the pitfalls of misinterpreting data to ensure personalization remains as effective as possible.

To assist those responsible for ensuring the customer experience remains positive while sales remain high, the personalization team at Certona has developed an industry wide reference guide. By helping to define the KPIs involved in successful transactions, a deeper understanding of concepts such as demand, non-responders, average order values, response rates, and conversion rates can be attained to truly categorize achievements at every level. Every month we’ll discuss specific KPIs, what they mean, and how to use them in your business plans.

You Can’t Improve If You Don’t Know What Or How To Measure

In the world of ecommerce, there are a variety of different options when it comes to metrics. Without a good framework, accurate interpretations of these metrics can be challenging. Today’s merchandising, online marketing, and ecommerce managers have complex jobs from optimizing their customer experience and site design to maximizing sales and managing ROI of their technology partners – all without disrupting the shopping funnel. Like any business investment, they are accountable for measuring the return of their personalization efforts. With so much at stake, where is the best place to start?

Before you can define what metrics work best with regard to personalization engines, you must understand one key factor. Today’s methods for benchmarking are often hampered with inconsistent tracking and reporting metrics that are out of context with regard to the segment group targeted, the date range in which the purchase is occurring, and the channel and vertical in which the personalized recommendations are used. Clearing up these inconsistencies is the only way to truly measure the effectiveness of your personalization efforts.

Part 1: Segmentation Defined

Who Are You Targeting? Carefully defining the target of your personalization efforts is essential. To accurately measure how recommendations perform in a given vertical or channel—whether it be web, email, mobile, contact center, or in store—online and multi-channel retailers must first distinguish between customers who interact with the recommendations and those who don’t, called responders and non-responders respectively. Responders are typically your best customers. They spend more time on your site, convert more often, spend more, and have a higher propensity to return than non-responders. As a result, they have distinct conversion behavior that must not be mixed in and confused with the conversion behavior across all shoppers in a channel.

Also important to consider is which responders are relevant to your underlying business goals. Are your personalization efforts directed at shoppers of a particular category, brand, or location? This is important, for example, if you’re trying to boost sales on the West coast,

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but you’re mainly converting East coast shoppers. Staying mindful of who you are targeting will keep your success in perspective.

When Are You Targeting?Date range is just as important as the segment group. Seasonal trends are certainly a factor, but other date concerns can be, too. For example, if you’re running a particular sale, you may find different results. New campaigns, whether marketed to email audiences, television viewers, social networking contacts, or the general public through direct mail, can all have an effect on sales and online behavior.

What Are You Targeting?Recommending products is the bread and butter of today’s personalization solutions. However, virtually every aspect of the shopping experience can be personalized. The metrics used to evaluate personalized banners, video content, or landing pages are not always appropriate for measuring the effect of product recommendations.

Where Are You Targeting?This is more than simply the channel factor. You must consider where the recommendations are viewed—and there are a couple of different influences at play. Naturally, many recommendations are placed directly on the site. They can appear on the home page, within the category the customer is shopping, the product detail page, your cart, or at checkout. Additionally, recommendation engines can make suggestions through other channels including email, mobile, and during an in-store purchase. All of these locations may change the way your customer is shopping, and, as a result, your bottom line.

A single metric can vary widely across responder/non-responder, date range, content, and channel categories. Understanding what influences these metrics can make a difference in the interpretation of the metric and ultimately how you execute using that intelligence.

By defining the focus of your personalization efforts, you align it with your underlying business goals.

Part 2: Defining Demand

Once you gain a deeper knowledge of how to define the segmented groups involved, you can move on to looking more carefully at the demand for your product line and how it fits in with your efforts as a whole. If it’s not increasing, you’re probably wasting your time.

Demand is essentially revenue, and it comes in many forms – channel, segmentation, the type of shopper, top sellers, and profit margin, just to name a few. In fact, it’s the one metric that can be sliced and diced a thousand different ways. To increase your revenue, you first have to define which demand slice is the focus. When you’re thinking in terms of personalization, it’s all about responder and recommendation demand.

Responders are shoppers who respond to or click and buy recommendations. Responders are better shoppers because they convert more frequently, spend more money on average and buy more items per order than anyone else on the site. Within the revenue you get from responders, you can separate out the recommendation revenue to better understand the effectiveness of your personalization strategies and its impact on the demand.

Increasing shopper engagement or creating a better shopping experience will increase demand. Recommendations are the single best way to motivate your responders to

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increase engagement levels. Recommendations can do a number of things including help shoppers recall a forgotten shopping cart, present the items they most want at the “front” of their shopping experience, or highlight a variety of items that are similar in nature. The shopper responds by purchasing more items with greater frequency. This results in higher average order values, or AOV , increased conversion rates, and greater responder and recommendation revenues. Demand increases, and hopefully, so do profits.

Testing Demand On Your Site

Personalization strategies are used to increase response. The results leave a great trail of business intelligence to analyze. With any recommendation system, you must test a number of different strategies to measure shoppers’ response to your efforts.

How these strategies succeed is measured by responder demand, recommendation contribution, and AOV. Here are a few of the aspects you may want to analyze.

• Which strategies performed better against your baseline?

• Does a combination of strategies work more effectively than a single strategy?

• Is your margin staying steady or increasing?

• Are more shoppers responding to the strategy than non-responders?

• What were your measurements before/after your personalization efforts or after changes to your personalization strategies?

• Which strategies can you leave behind to focus on those that are delivering more recommendation demand, more responder demand, and higher AOV?

Remember that you want to increase engagement; your goal is to make it easy for the shopper to engage with your product line, buy more, and return more frequently.

Measuring Success

Measuring the direct effect of change to a business rule or overall merchandising strategy is an important factor to determining success. The ability to define your recommendation demand in concrete numbers is THE metric to measure success from testing and optimizing personalization strategies. Without it, you may be fruitlessly wasting your personalization efforts.

Part 3: AOV

There are many ways to boost profits. For retailers average order value is considered a key performance indicator and an essential part of understanding how to generate new revenue.

Average Order Value Defined

Also called AOV, your average order value is basically the average amount spent for a single checkout purchase on your site for a particular customer segment or group. For example, you might want to investigate the AOV for new customers compared to returning or loyal customers to see how they differ or for those who purchase a particular type of product.

As you begin to explore your AOV for certain subsections of your shoppers, it’s important to note that not all personalization software vendors calculate this metric in the same manner. The idea of boosting your AOV is a huge selling point for vendors, but there are

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many different ways to come up with what seem to be solid numbers. While most of them are very good, some are unique, so it’s important that you take a careful look at what’s behind those numbers before moving forward with optimizing AOV.

It is also essential to note that, like other metrics, you can’t look at your AOV in isolation. You have to consider it along with the conversion rate. Your conversion rate will generally have an inverse trade-off to AOV. If you move to boost conversion rates, your AOV may decrease. If you move to boost AOV, it’s likely your conversion rates will remain flat or go down. Even with this trade-off, though, looking at both of them together can give you yet another important metric to consider, the revenue per visit, or RPV. Multiply the conversion rate by the AOV and you get the RPV, an important tool in the quest to better understand your customers and how they interact with your site as a whole. For example, giving up a little conversion rate can result in a bigger gain in AOV, overall boosting RPV.

The best way to increase your average order value without demeaning the other metrics you’ve been carefully monitoring is to take a balanced approach to the entire measurement. It’s one strategy you certainly want to consider, but understanding that you need to increase your AOV and increase your conversion rate while simultaneously understanding that you can increase both is important. Developing that strategy begins with finding your company’s sweet spot.

The Sweet Spot

The sweet spot is the combination of conversion rate (CR) and AOV that maximizes your businesses RPV. Sometimes you will improve your business by decreasing AOV and increasing conversion rate, and others it’s the opposite.

Almost every industry has a sweet spot. In high fashion apparel, for example, consumers aren’t as price sensitive and therefore are still likely to purchase even when price increases on a desired product. On the other hand, for electronics, consumers tend to shop for the best price more frequently and therefore are generally sensitive to price fluctuations. For these electronic products, attempting to increase AOV by increasing prices, even slightly, could significantly decrease your conversion rate. Knowing where your business is on that price sensitivity curve is key to setting your AOV goals and maximizing RVP.

ElectronicsApparel

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Properly optimized recommendations can help you move along the curve within your industry, improving your profits as you move forward. Remember to consider the weight for your margins, and that can depend extensively on your business. A good recommendation engine will allow you to maximize those margins and help you tweak that sweet spot to help increase your AOV.

Fine Tuning

As you begin to see exactly where you can increase your company’s AOV, you’ll need to take a careful look at your site to see what you can improve. Different products and verticals have different abilities to influence AOV. Consult with industry thought leaders and peers to identify your vertical’s differences and apply what matters most to your business.

Research into your product lines can help you better develop the right strategy to increase your AOV. For example, you may need to sell more with your accessories line to help increase shopping cart totals. Or you could simply emphasize cross selling to help. You could also upsell with some substitutions, allowing for a much higher price tag in the end. Looking at the vertical, your product catalog, and your segmentation or target audience will help you decide on the right strategy for your company. A good recommendation engine will help you not only decide which strategy is best, but it will also employ that strategy across your site. Remember it’s not just one strategy. It’s a collection of right strategies for every location on your site.

Testing 1, 2, 3

As with any metric, testing your results is the only way to continually improve them, and initially, you’ll have to continually test changes to your merchandising rules and analyze results to decide what is and is not working.

If your overall goal is to try to optimize your AOV, make sure it is not at the expense of overall profits. Remember the potential trade-off between AOV and conversion rate, so if your efforts begin to affect your business adversely, measuring its impact and allowing you to try something else should be fairly easy with the right recommendation engine.

Part 4 Defining Conversion Rates

Website conversion rates are one of the hottest topics in the ecommerce industry, and with good reason. A move to increase your conversion rate from one to two percent means doubling your profits, and there are few e-tailers in today’s economy that wouldn’t jump at that chance. While it may seem deceptively simple if you look at online blogs devoted to tracking and increasing conversion rates, the reality is this metric isn’t quite as straightforward as many would have you believe. In fact, it’s easily one of the most complicated metrics to consider.

It may help to start with some definition in the matter. According to Forrester Research, the average conversion rate for ecommerce stores is 2.9%. That number alone tells us quite a bit about consumer buying habits, as does the fact that 97% of shoppers aren’t making those conversions as most e-tailers would hope.

If you want to begin really understanding and optimizing your own conversion rates, the key is to determine the different conversion rates that apply. This is critical if you’re thinking in terms of personalization. You have to consider not only the site-wide rates, but also those consumers who respond to your recommendations as well as those who

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don’t. Conversion rate by location of the recommendation versus the conversion rate by the type of consumer will give you even further insights. If you have recommendations on your search page, you may see a very different rate than if on the cart. Likewise, there is a different rate among consumers who respond to your suggestions than those who don’t.

Furthermore, you also need to consider the channel involved. Those who respond online will always have a different conversion rate than those who respond by mobile device, email, your contact center, or other channels you’re using to reach consumers.

What Influences Conversion Rates?

To increase your conversion rate, naturally you have to understand what influences it, and there are several factors at play here.

First, it’s important to understand that the process flow, also called the purchase funnel, can change the way customers respond. Use the wise adage: Keep it simple for customers to add products to their carts. Use an add to cart button so the visitor remains on the page being viewed. Present a view bag overlay that allows the shopper to continue shopping or go straight to cart to complete. Simple distractions can interrupt the process, and that could spell trouble for your conversion rates overall. In order to better understand how process flow is impacting your conversion rates, you need to breakdown and measure the process within mini conversions from step to step. This is usually referred to as click-thru rates. Analyzing your purchase funnel in this fashion will aid you to identify potential issues with your conversion rates.

Page location also affects your conversion rates as does the number of recommendations you offer. If consumers don’t notice your recommendations because the call to action is poorly displayed or you’re offering far too many or too few recommendations, chances are your conversion rate will perform under par.

Additionally, information about the product can affect your conversion rate. Typically we are dealing with a limited amount of sales space. The images you post as well as the unique product detail information you provide could alter the way consumers engage with that item. Some of that will depend on your business category. For example, if an apparel site, think bright colors in photos to highlight the features of the product, large enough to see fabric, print, design, etc. In sports equipment, offer full images and exposure so as not to leave out any important functional features.

Finally, you have to factor in business rules and filters as well. Pushing a particular brand or price point, seasonal product turnover, tight inventory availability, and pressure on margins will all influence conversion rates. Weighing these business objectives against the effect to conversion rates should always be considered.

Developing Best Practices

In the world of recommendation software, increasing your conversion rate means you must have two primary goals in mind. Every product recommendation should provide two readily apparent options to the consumer. The first is that every recommendation should have a call to action. This could be a buy now button, add to cart, reserve, or whatever. Second, every recommendation should offer consumers the ability to learn more about a product if they are still unclear if that particular product is right for them. If your call to action doesn’t work, it is imperative that you keep that customer engaged and learning more about the product if you want them to convert at a later point in time.

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Optimizing Your Conversion Rate

As you work toward optimizing your conversion rate, remember this metric is only part of the story. Conversion rates are not created equal. Their importance depends on consumer price sensitivity, and how it relates to your niche and/or product categories.

The true metric for success is your revenue per visit, or RPV. You may recall this from part three of our series. You must develop a strategy that increases both your conversion rate and your RPV at the same time, and the only way to do that is to find your sweet spot.

A good personalization engine will help you optimize the purchase funnel using the best strategies to encourage conversion. This varies from company to company. Some retailers have a high average order value, or AOV, and a low conversion rate while others have a very low AOV and a very high conversion rate. It depends on the business objectives of the retailer. In some cases, increasing that conversion rate may decrease your AOV, which is why finding your own sweet spot is essential.

Part 5 Defining Cart Abandonment

Shopping cart abandonment remains a serious challenge to online marketers. On average, 7 out of 10 shopping carts are abandoned for most retailers. Industry publications report that the average site conversion rate is between 2% to 3%. Clearly, abandonment rates indicate lost opportunities, but this is not always bad, depending on the reason for not checking out.

According to a recent study by Forrester Research, “Understanding Shopping Cart Abandonment”, 88% of U.S. online customers abandon at least one shopping cart each year without completing a transaction. For a variety of reasons, customers get frustrated or distracted in the checkout process. Most refuse to checkout when they find the shipping fees and total costs do not fit their budget. While others are just not yet ready to purchase, these early stage shoppers might use their virtual shopping cart to compare products or as a wish list.

Why Shoppers Abandon?

Cart abandonment rates vary by product category, the type of shopper, gender, total basket worth, time period, and channel. In regards to personalization, there are many best practices for relevant and timely product recommendations before consumers abandon their carts. These recommendations should add value to the cart and not replace products or disrupt the purchase process. However, for the purpose of this chapter, we will discuss how personalization can address abandonment ‘after’ consumers have abandoned their shopping cart.

Forrester’s study also asked approximately 3,000 consumers why they abandon shopping carts. Here are their top five reasons:

Source: Forrester May 2010. Note respondents were able to give multiple answers

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Staying in line with previous years, shipping costs remain the top reason shoppers abandon their carts. It is interesting to note that the top five reasons do not concern the checkout process; rather it has to do with the shopper not liking the final price or just being unready to buy. In other words, personal issues fuel cart abandonment.

How To Engage The Shopper With The Abandoned Cart Via Email Remarketing

The most effective best practice is to use email campaigns to entice shoppers to return to your site and complete a purchase. While this strategy requires resources, it is a proven way to re-engage with those who at one time put items in their cart. Just like in any email campaign, you need to consider the timing, content, frequency and yes, personalization.

Just as sending emails too soon (prior to 24 hours) or too late (more than 72 hours) can influence your shopper response significantly, so can the number of emails. In many cases, just one email or one offer may not be persuasive enough. A good remarketing campaign can segment those who abandoned their carts with the context of their visit to again present the abandoned product/s and additionally, offer up relevant recommendations for similar or alternative products based on the items that were abandoned.

When consumers read your email or return to your site, they must be able to pick up where they left off. Including recently viewed items and relevant recommendations makes the shopping experience faster and more satisfying to the returning shopper. These strategies help you complete the ‘lost’ sale by visually reminding shoppers of recently viewed items and/or the items they left in the cart. This experience gives the shopper the option to easily and more quickly purchase previously viewed items. Sophisticated retailers will use different strategies and business rules or even different email triggers depending on a shopper’s status as a returning or a new customer and other variables learned from the shopper’s profile and behavior.

But how do you measure the success of your email remarketing efforts? Have the abandoned cart emails generated additional revenue? Are these emails driving measureable AOV? Do these emails have measureable conversion rates different from transactional or promotion emails? Online retailers should view several metrics provided within their analytics and email service provider reports to help capture lost opportunities and optimize their campaigns.

Example: Capturing Missed Opportunity Assumption 1: Online retailer making $500K per month. Assumption 2: 3 out of 10 purchase; 7 abandon the cart.

With personalized retargeting, the business above converted an additional 1% of total visitors (33% additional customers), resulting in another $166,666.67 per month. This equates to an increase of $2 million a year.

Monthly Value

% Visitors

Monthly Value % Visitors

Cart Value

$1,666,667

10%

$1,666,667

10%

Transaction Value $500,000 3% $666,667 4%

Without Personalized Retargeting With Personalized Retargeting

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The Silver Lining To Cart Abandonment

Improving cart abandonment is a continuous effort, but with better understanding and a good personalization engine you can turn cart abandonment rates into positive conversions and improve your business’ bottom line.

According to a recent study by email service provider, Listrak, researchers found that close to 19% of Top 500 e-Tailers are recovering revenue by implementing shopping cart abandonment strategies. The same study also indicated “that the Second 500 are keeping up closely with the Top 500 with cart abandonment campaigns, an indication that this strategy is feasible and not just for the ‘big boys’.”

Integrating a good personalization platform into your email remarketing efforts is one of the smartest strategies your business can do to address cart abandonment. As a shopper yourself, wouldn’t you like to view personalized and relevant content with your favorite online shopping site?

Part 6 Non-Responders

Clickz.com identifies 10 different types, ranging from the “distracted shopper” to the “thrifty shopper”. In order to properly optimize your site with personalized recommendations, you must understand the types of shopper that use your site. This can be challenging because there are as many different types of people as there are varieties of shoppers.

Luckily for you, when it comes to the optimization of personalization, you only need to focus on two different types of site visitors — the non-responders and responders.

Non-Responders vs. Responders

What does it mean to be a non-responder? Simply put, a non-responder is a visitor that does not interact with any recommendations on your site. Logically, this means responders are those that do interact with site recommendations. Your goal in site personalization should include the conversion of non-responders to responders.

Why? What difference will it make? Let’s look at it from a numbers perspective. Non-responders generally make up around 80-90% of your traffic, leaving responders between 10-20%. Despite being significantly smaller in number, responders usually account for a larger portion of revenue.

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For example, let’s say a site generates $200 million in sales from 50 million visitors. Responders will account for up to 25%, which, in this case, is $50 million. Non-responders on the other hand will make up that remaining $150 million. Let’s assume your responder percentage is very high at 20%. This means 10 million visitors are responders and 40 million non-responders. When broken down to an individual level, a single responder accounts for $5 versus $3.75 a visitor for non-responders or 33% more.

When comparing these two groups, this will often be the case. Responders, by their nature of being more engaged, have higher average order values, conversion rates, demand and revenue per visit, which means it is paramount to optimize personalization for this audience segment in order to maximize revenue. Imagine the kind of revenue bump your site could see if you could increase your responder percentage by just three percent. The key question now comes – how do you do this?

Turning A Non-Responder Into A Responder

There are a few things you can do to start increasing your responder numbers. First, let’s talk about trust, because, with trust comes engagement.

A visitor will choose to interact with your site only if they deem it trustworthy. Including recommendations on your site can factor into the overall trust level. You should leverage recommendations as a source of validation and expert opinion. If relevant, your visitors will trust your recommendation as helpful suggestions.

An appealing site design should also cause a visitor to have more trust in your site.

When considering site design from a personalization point of view, consider the number and location of your recommendations as they affect the overall look and feel of the site, which, as Dr. Liz Sillence found, is crucial when it comes to website trust.

Dr. Liz Sillence, a postgraduate psychology professor & member of PACT (Psychology and Communication Technologies), focuses on trust and online interactions, and is currently examining the influence of the online experience on behavior and decision making. According to one of her studies, design was the first filter with which participants deemed a site trustworthy or not. In fact, when a site was deemed untrustworthy, 83% of the comments were related to design factors such as unfavorable look and feel, poor navigation, color, and text size.

With this in mind, ask yourself a few questions. Where are your recommendation boxes? Do you have recommendations on your search page? What about on your no results page? Product display pages? Home page? The more visitors you reach and get exposed to recommendations, the more chances you have to engage them in the purchase funnel.

To be even more effective, you should be testing the different locations of the recommendation boxes. Which pages do you get the best results with? Does the box provide better results above the fold or at the top or right vertical? Do they work better when placed near calls to action?

These are the kinds of questions you should ask yourself when considering site design and your recommendation boxes. A higher number and optimized placement will naturally move the needle based on an increased opportunity to see a recommendation and convert. Also consider that one of the three factors that participants mentioned about why they decided to delve deeper into a site was the availability of interactive features.

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After site design, consider the type of visitor to the site. This will influence your preferred recommendation strategy.

What happens if the visitor is brand new to the site with no purchase or browse history? A smart personalization platform can serve up strategies based on keyword search context if referred from a search engine to provide the visitor with immediate, relevant recommendations based on the business rules you have pre-defined. Of course, the platform could also serve up Top Sellers, Best Rated or the like, depending on what performs best.

If the visitor is a return visitor, blending recently viewed items with top rated, most popular, co-viewed or other strategies will also help to move non-responders to becoming responders. It is a matter of letting the platform trigger the most relevant strategy based on the individual profile it is interacting with.

Learning From Non-Responders

Inevitably, you will continue to have non-responders, leaving you a great opportunity to learn from their behavior.

A good personalization platform is something that learns and factors in both what works and what does not. This makes the underlying technology twice as intelligent and efficient.

The information it collects for itself can also be provided to you. With proper attribution and reporting, a recommendation engine can give you relevant, actionable businesses intelligence that can be used to provide your visitors with improved site experiences.

More Responders, More Money, and A Better User Experience

Stated concisely, the higher your percentage of responders, the more revenue your site will generate.

Certona’s personalization platform is geared to help you convert non-responders quickly and effectively due to its ability to self-optimize after each interaction across any touch point while accurately tracking and measuring results.

Part 7 Wrap Up

With the right strategy and execution plan, the combination of personalization and digital marketing can be a powerful force for business. Yet, in order to reap the full rewards, it is crucial that you understand how success measures and key performance indicators (KPIs) are defined. Hopefully after reading this guide you have gained a clearer understanding of what certain KPIs mean for your retail company and how to accurately measure them against your business goals.

Series Summary

Gaining a clear understanding of your metrics is much like a photographer’s approach to capturing a perfect photo – only with the right lens adjustment and filters, can perfection be realized.

With personalization, this starts with segmenting your audience appropriately as you decide how, when and where to leverage your personalization strategies.

Having done this, the various metrics that make up demand, or more specifically revenue, become more meaningful. But which ones are the most important in terms of personalization?

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When considering personalization from the standpoint of targeted, individualized product recommendations, which allow for increased cross sell and upsell opportunities, retailers should focus on average order value (AOV) and conversion rates. These metrics directly impact total sales, so keeping an eye on them can clue you into whether your product recommendation strategies are proving effective.

It is important to keep in mind that average order value and conversion rate are not independent of one another. For example, increasing product prices may drive up average order values, but could simultaneously cause a drop in your conversion rate. Depending on your business, you must tune and balance your recommendation strategies to help find the sweet spot where you see the most revenue per visit.

As AOV and conversion rates correlate to responder rate, the significance of responders should not be downplayed. Through their nature of being engaged, responders buy more and buy more often. This means that an increased responder rate equates to increased revenues. It stands to reason then, that one of your main objectives should consist of creating more responders.

External Influences

While understanding KPIs is essential to your personalization and optimization efforts, it is equally important to maintain a holistic view that includes external factors. Take seasonality, for example. Throughout the course of the year, depending on your industry, there are particular time spans that are going to affect total visitors, new visitors, non-responders, etc. Is the winter holiday season (especially days like Black Friday and Cyber Monday) going to cause a spike in traffic on your site? Will you see an increased number of non-responders? More abandoned carts? Boosted sales and average order values? The answer is more than likely, yes. But, these jumps should not necessarily elicit panic or elation. Take a step back and assess the numbers relative to the time of year (in this case the numbers will be inflated due to the influx of seasonal shoppers).

Seasonality, though, is not the only external factor affecting a company’s personalization efforts. Ultimately, any major change can impact personalization performance. Consider the following:

• Changes within your product catalogs, offerings, data feeds or otherwise can impair performance. Whether you lost a popular product, added 1,000 new SKUs, or changed manual product associations, your recommendations may be fundamentally different due to these changes.

• A change of your ecommerce provider can have an enormous impact on performance when you consider the level of modifications and effort required to overhaul a site, which includes testing to ensure data systems back up and reporting is accurate.

• Channel behavior can adversely affect many of your KPIs. Ask yourself if any new channels have gained traction recently and if you have taken strides to optimize differently for that particular channel.

• Site design changes will inherently cause user interaction to change. Take stock of what you have done (e.g. recommendation box relocations, navigation changes, cart overhaul) and analyze the data accordingly.

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As a best practice, both you and your personalization platform should expect change. The right personalization provider will help you anticipate, adapt and capitalize on these external fluctuating factors.

Leveraging Customer Data

With the advent of the smartphone (particularly the iPhone) and the introduction of tablets, consumers are now more connected than any other time in history. These devices allow consumers to access your content and brand at any given moment, anywhere across the world. This is the age of the omnichannel shopper.

A cornerstone practice for success in online retailing now is having a complete end to end view of your customers. This new age of the omnichannel shopper has brought a new set of challenges in completing that view. Consumers now expect retailers to have all of their information and preferences synchronized seamlessly to deliver the best experience possible no matter the device or their current location. The right platform can establish a common identifier per individual profile and share that profile among all possible touch points – web, mobile, email, contact center, social and in-store – thereby eliminating any possibility of irrelevant personalized content.

This unified profile information is the driving force for truly creating connected experiences. Having a clear 360 degree view of each individual will naturally allow for smarter more optimal personalization, which will help you achieve your business objectives.

Ensuring Long Term Success

The omnichannel shopper is here to stay and with the continuous and rapid development of innovative technology, shoppers will continue to gain more ways to interact with your brand. To succeed in this ever changing landscape, it is best to enlist the help of a solutions provider, like Certona, that is skilled in omnichannel personalization.

The goal should always be to incorporate all new sources of customer information and employ that data to create a truly personalized experience, regardless of channel or device. When data, metrics and KPIs are aligned across all sources, departments, and channels, your optimization decisions will be undoubtedly sounder. Only with the right partner can you meet the needs of today’s connected consumer, and compete for their loyalty and repeat business.

Thanks for reading!

The Certona Team

CERTONA9520 Towne Centre Dr., Suite 100San Diego, CA 92121certona.com