How to create a seamless cross-channel customer journey with call tracking

When consumers jump from online to the phone, it can be a frustrating experience. But it doesn’t have to be.

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Have you ever started the purchase process online for a complex product like a mortgage or healthcare then had to call the company to get questions answered?

Usually, it goes like this: Fill out forms online then get stuck. Call, then repeat everything you put in the forms. Get transferred, repeat everything again. Find out you got transferred to the wrong rep, throw your phone across the room, pour a glass of wine and buy something nice for yourself on Etsy instead of doing grown-up things. 

While it may seem this like this is done to intentionally torment you, the cause is usually an inability to pass data from online to offline realms. Here’s how you can create a seamless online-to-offline experience for your customers. 

How call tracking platforms can help

Buying journeys are increasingly digital, but over-rotating to online self-service can be a major source of frustration for consumers who need help sorting out a complicated purchase. Many times, they are going to want to pick up the phone to talk to a person.

In fact, Invoca research conducted by the Harris Poll found that in considered purchase categories like healthcare and home improvement services, over a quarter of consumers prefer to complete transactions over the phone. 

The danger comes when you play bury-the-phone-number to force people into a digital-only transaction — when a company only has automated communications and no option for human interaction, more than half of consumers (52%) feel frustrated and nearly one in five actually (18%) feel angry. That’s probably not the experience you are looking for. 

When companies do encourage consumers shopping or researching online to call, they can run into different issues and new ways to frustrate them. When a customer goes from clicking your ad, hitting your website, to calling your business, that often creates a data gap with two primary effects:

  1. The call center has no context for the call, making it more difficult to provide exceptional service.
  2. Marketing loses track of the transaction and has no data to optimize the customer journey. 

This is where you need a call tracking and conversation analytics platform to bridge the gap. It’s a critical piece of the martech stack for any company that makes sales, sets appointments, or gives quotes over the phone. Call tracking and conversation analytics platforms can not only analyze what’s happening on the phone to classify calls and identify conversions, but they also track the digital journey that leads up to a call so marketers can get both attribution data and customer journey insights that allow them to optimize cross-channel buying experiences.

Here are just a few ways you can use call tracking platforms to create a seamless cross-channel customer journey.

Route calls to the right place the first time

If a potential customer finds your company online and they are calling to make a purchase, you don’t want to route the call to a customer service rep. This not only wastes the customer’s time, but it also burns up valuable call center resources getting them to the right place. You can improve call conversion rates and ensure the best possible experience by getting your callers to the right destination quickly.

There are three common methods of routing calls with a call tracking platform that can help accomplish this. You may end up using one or all of these, depending on your level of routing sophistication and customer needs. 

Routing with call treatments

Call treatments are one of the simplest methods of call routing and it can be accomplished with a call tracking platform or in your telephony system. You can route by asking a caller to respond to a question using key presses, usually something like, ‘for sales, press one. For customer support, press two’. 

Location-based routing

If your business has multiple locations, you can also route calls based on the location of the caller. This can be accomplished via the callers’ area code using your telephony tools, but this poses a risk of improper routing since people frequently keep out-of-area phone mobile numbers long after they have moved.

Using a call tracking platform, however, you can present each caller with a unique local number (based on their IP address, not their phone’s area code) on your website or search results to make sure they get to the right location. Some call tracking platforms can even use tag-based tools that will automatically identify and replace all of your phone numbers on a given web page so you don’t have to do it manually. While online users are all presented with unique phone numbers for tracking purposes, they are still routed to your desired existing phone numbers. 

Route calls with combined data sources

The most advanced flavor of call routing uses a combination of digital data captured by a call tracking platform, third-party demographic data, and/or your own first-party data that lives in your CRM or other internal sources. Invoca’s call tracking platform accomplishes this through three features in the platform called custom data, enhanced caller profiles, and lookup tables.

Custom data is the umbrella name for any data captured by Invoca that fall outside of standard UTM parameters or required integration IDs. Custom data fields are customizable to your business and typically include information like customer IDs, product SKUs, and shopping cart cookies.

Enhanced caller profile data is third-party demographic data matched to the caller. Examples of this include age, home location, and homeowner status. Lookup tables enable you to upload first-party offline data using a match-value captured by an Invoca custom data field. By tapping into these rich sets of data, you can dynamically route callers to the best destination, eliminating call transfers and key presses often associated with calls to businesses.

Unify your online and offline data sources

To avoid data gaps that can cause a fragmented buying journey, you need to unify your online and offline data sources. Easier said than done, right? This isn’t always a simple task, but call tracking platforms that are integrated with other data sources and martech platforms can help you accomplish this. 

Call tracking platforms enable marketers to tie consumers’ digital journey data to phone calls using online data collection and trackable phone numbers. By unifying this information in the platform, you can analyze digital and call data in one place. Many marketers who use call tracking also use integrations with their analytics platforms like Google Analytics and Adobe Experience Cloud to analyze, unify, and take action on data in one place.

Using the Invoca platform as an example, here’s how the data is captured and what it means for you. In the call report, you can see all of your inbound calls and call volume trends at a glance. Clicking on a specific call brings up the call details where you can see a unified view of all digital and offline data associated with that individual call. You’ll see information about the call itself like key presses in the IVR system, call duration, and the full recording of the call. This data is valuable to help segment your calls, such as sales versus support calls, and to understand your standard call metrics.

You’ll also get detailed information specific to each caller like their name, caller ID, and demographic information such as age and home address. You will also get customer journey data like ad exposure and webpage visitation. You can think of this as cookie or campaign data. For example, you can see exactly which paid search campaign and keyword led to a call. By tying the digital campaigns to the offline call action, you can now understand which campaigns are driving valuable phone calls. 

Lastly, Invoca is able to analyze conversations and identify call outcomes in real time. Outcomes could include actions such as submitting an application or purchasing a product. 

By using a call tracking platform to route your calls and unify online and offline into rich call profiles, you can get actionable insights to help you make more informed marketing decisions that can help create a friction-free multi-channel buying experience. 

Learn more ways to create a seamless cross-channel customer journey in the Call Tracking Study Guide for Marketers.

The post How to create a seamless cross-channel customer journey with call tracking appeared first on Marketing Land.

eCommerce Personalization Strategies Using Google Analytics and Other Free Tools

Here we outline our method to personalize eCommerce sites using just Google Analytics and simple cookies. No elaborate third party tools required.

Enterprise personalization software is typically complicated, expensive, and possibly overkill for a majority of eCommerce companies. In our experience, this leads many to avoid or delay implementing personalized experiences on their sites, which likely leaves revenue — and a better conversion rate — on the table. 

So, to help more eCommerce brands implement personalized experiences that can increase conversions, we’ve begun developing a new process for personalizing eCommerce websites that solely uses Google Analytics and simple Javascript cookies. As a result, our method can work regardless of eCommerce platforms, marketing automation platforms, or in addition to any personalization technology already in place. 

This process gives eCommerce brands the flexibility to “do” personalization on their own terms, from the spectrum of light personalization (a few custom experiences in specific situations) to as much personalization as they want (many customizations for large portions of their audience), all without bloated, expensive personalization platforms with inscrutable artificial intelligence or machine learning algorithms. 

We’ve just begun developing this process at Inflow — we are actively applying it to client sites as we write this — but we’ve used these core concepts for a long time. Namely, our process is based on how we implement A/B tests for clients that aren’t using a 3rd party A/B testing tool.

In this article, we detail each step of our eCommerce site personalization process, using various eCommerce personalization examples throughout. 

Note: If you manage an eCommerce business and want to know how this process could help your website personalization efforts, you can learn more and talk to our CRO team here

Overview of Our eCommerce Personalization Process

Any eCommerce personalization process really just needs to do 2 things: 

  1. Bucket online shoppers into specific personas 
  2. Show custom (personalized) experiences to each persona. 

Ours is no different, but let’s use some examples to get a sense of how this works.

First, a clothing store may want to know if an anonymous user is a man or woman. The user could (a) indicate their persona through their site interactions which (b) helps the store display products that they would be interested in. 

Or, in a more subtle example, a yoga store would benefit from knowing whether a user is a beginner or an advanced yoga instructor. The bucketing of users (step a) in this case, is not as obvious as the man or a woman example above, but if they were able to do so, they could offer a personalized shopping experience (step b) for each group; showing beginner friendly items to one group and bulk discounts on commonly used items for instructors to the other group.

Our specific process for doing this breaks down into a 4 steps that we explain in turn below:

  1. Brainstorm and Bucket Personas
  2. Determine Characteristics and Site Behaviors
  3. Analyze Data
  4. Personalize the Website

Steps 1 – 3 are dedicated to the first goal: Bucket users into personas, which is the foundation of this process, while Step 4 is to show users a personalized experience depending on which bucket they are in (this is the easier, final step). 

Step 1: Brainstorm and Group Personas Together

The first step is to brainstorm who your users are and bucket them into personas. This step lays the foundation for the entire personalization process because it determines the types of potential customers we’re going to personalize for.

It’s important to note that we aren’t tracking anything here, we are just making a hypothesis about who our users are based on their actions. By the end of this step we want to have solid hypotheses of what user personas could benefit from a custom site experience.

To use a specific example, one client we have, Mountain House, sells freeze dried food. They’ve noticed over time that two different types of customers (personas) use their products: preppers and backpackers. 

A view of the landing page for Mountain House.

One of our clients, Mountainhouse.com, who sells freeze dried foods.

Preppers, or survivalists, are buying goods and food for potential emergency scenarios. Backpackers, of course, are buying goods and food for backpacking and camping trips. 

They use similar products but have vastly different needs and goals. But we may hypothesize that if we knew whether a given user was a prepper or a backpacker, we could increase the site’s conversion rate by showing a custom experience, and more relevant products, to each. For example, buying in bulk could be good for preppers while backpackers will want to buy individual items, perhaps in more variety. 

The details of your personas will depend on your particular store.

A photo showing the eCommerce personalization that Mountain House uses for their advertising.

Two types of promo graphics on Mountain House’s site at the time of writing. (Top) Imagery and copy clearly focused on preparedness. (Bottom) Images on the packaging of backpackers and campers.

Once we finish creating our personas, the next step will be to think about the site behaviors that can indicate to us which persona an anonymous user belongs to. This will allow us to eventually cookie each user into a persona so we can later show them the proper personalized experience.

Step 2: Determine Characteristics and Site Behaviors That Indicate Which Persona a User May Belong To

Once we have the buckets from Step 1 we need to figure out what actions or behaviors can help us determine which persona new, anonymous, users belong to. 

Some examples of these actions include:

  • Viewing a category
  • Purchasing a product
  • Entering information into the website (i.e. searching for something)
  • Clicking on the navigation menu
  • Engaging with a module that helps narrow products
  • Subscribing to an email list to receive relevant content

We’ll start by finding some obvious actions. Continuing on with our hypothetical clothing eCommerce store from above, viewing men’s jeans or bras for example could be a clear and obvious site behavior that indicates which persona (men or women) an anonymous user belongs to. 

In some situations, finding a difference between personas is more difficult. Consider the yoga store example from earlier where we’re trying to differentiate between beginners and instructors. If there aren’t products or categories that clearly differentiate between the personas (both may buy yoga mats and clothing for example), one option is to use personalized content to help us differentiate. 

Two variable eCommerce personalization blog examples on Yoga Outlet.

Two content pieces on Yogaoutlet.com’s blog. The left, could apply to any persona, however the right likely applies to beginners or non-instructors. In contrast, a blog post on managing or growing a yoga studio, for example, would clearly appeal to instructors.

If a new, anonymous user site visitor signs up to receive basic yoga instructions, for instance, we could safely assume they belong in the beginner bucket. Likewise, you might have, or be able to write, content targeted specifically at instructors or more experienced yogis that will help you identify and personalize those user experiences. 

We have even been able to bucket an anonymous user by promising future personalized content in an email opt-in form. The act of offering this content can often convince a user to indicate their persona by allowing them to choose to receive relevant content.

Step 3: Track and Analyze Data to Confirm Actions That Will Bucket Users Into Personas

In Step 3, we’ll track and analyze the customer data we’ve been gathering and use it to figure out what actions are strongly indicative of an anonymous user’s persona. We’ll use these to actually personalize our site customer experience later on. 

How to Setup Custom Dimensions to Track Site Actions

In Google Analytics (GA), we can track user actions using custom dimensions. A dimension in GA is a characteristic of a user, session, or even hit (page view or event). Typical dimensions in GA include Page, Source/Medium, Device, etc. and are sent to GA as part of any page view or event. 

Some common dimensions in Google Analytics.

Custom dimensions are similarly not sent on their own, but must be sent as a parameter of another event or page view. For instance, if you wanted to log that someone viewed Men’s products in a custom dimension, you would have to either add the dimension to the page view of men’s product pages, or add an event to any clicks leading to men’s pages that would include the custom dimension. 

For more information on how this works, check out the GA Help Article on Custom Dimensions here

Once the dimension is in GA, you are then able to view reports based on whether someone has the dimension set or not, with separate line items for each value in the dimension.

For example, the image below shows a custom dimension with various “Product Color” values for a particular client. If we hypothesized that product color choice was indicative of a certain persona, we could use this data to help us bucket users into personas. 

Google Analytics Product Color Values for a client

So for example, in our men/women’s apparel store, clicking on a women’s category might trigger us to set a custom dimension identifying this user as a woman in the customer journey. 

In our yoga example, someone visiting an article on how to do beginner yoga poses might trigger us to set a dimension identifying this user as a novice, while someone visiting an article on mastering acroyoga moves might be pegged as an advanced user. 

Compare Stats from Different Custom Dimensions to Finalize the Criteria for Bucketing Users Into Personas

So what actions should we track that will hopefully indicate whether an anonymous user belongs to a given persona? 

This is a critical part of the process, and here’s how we typically do it. 

First, we think of some “anchor actions” that almost definitely indicate the user is in a given persona. These are the ones you’re sure of. For example, in our hypothetical apparel online store, it’d be something like adding women’s jeans to their shopping cart indicates they are interested in women’s apparel, and adding men’s jeans indicates they’re interested in men’s apparel.

But (and this is key) just having a few anchor actions is not enough because it’s very likely that a large fraction of your site traffic will never take that action. If you only have a small number of dimensions that lead to a user being bucketed into a persona but, say, 50% of your traffic never takes those actions, then your personalization effort just won’t apply to a large percentage of your traffic, which means all your effort in personalization may not be as impactful as it could be. 

So, second, you’ll want to liberally think of many other user actions that could also indicate a user should be bucketed into a given persona. Let’s call these “candidate actions”. These can be any of the action types we’ve been discussing so far: clicks on certain pages, adds items to cart, downloads a PDF, whatever you think could indicate a user belongs to a certain persona. Create custom dimensions for them in GA, as well. 

Third, let some time pass, ideally a few weeks to collect data in GA. 

Fourth, now compare how well the data from your candidate actions line up with your anchor actions. Basically you’ll assume the anchor actions are the source of truth for whether a user is in a specific persona. So each candidate action can be compared to the anchor actions to see, whether there’s overlap between users. 

Candidate Action + Anchor Action: Good Overlap vs Poor Overlap.

For example, let’s say you think downloads of certain content indicates a user is interested in women’s products. You’d compare and see, of the users who took this downloading action, what percentage also took the women’s anchor action vs. the men’s at various touchpoints with your site. If a decent fraction also took the men’s anchor action, your hypothesis that downloading this content indicated they were interested in women’s products is not likely to be true, so we’d discard this candidate action as not very telling of which persona a user belongs to. 

From a high level, our goal here is to pick a handful (3-5) of the most indicative and consistent criteria for accurate personalization without going into too much detail. Too many details can lead to conflicting signals and make personalization too complicated. At some point if we keep adding behaviors, we’ll either have to use an algorithm or manually choose a prioritization to follow. 

It’s important to note again that we still haven’t personalized anything on the site yet. Step 1, 2, and 3 are all about figuring out exactly who our personas are, how to bucket them, and what types of actions we can use to personalize our site and product recommendations.

Step 4: Personalize the Website

At this point we have clear buckets for our personas and we know what behaviors we can use to bucket a new user into each of them. Now we’re going to leverage the data we’ve gathered and finally set up the personalization for our site. 

This is the easy part! If you’ve done the first 3 steps well, a good marketer can easily identify tests/personalized recommendations that are likely to move the needle.

To do this, we’ll start building a list of hypothesis of custom experiences that we could show to the different personas that we think could increase their chances of converting.

  • Change homepage tiles to show products that appeal to one or the other
  • Prioritize navigation to show the categories most likely to convert or have a high AOV to one persona or the other
  • Change messaging and value proposition language in real-time on product pages around certain products (e.g. in our prepper vs. backpacker example, the same food could be positioned as “long lasting” (prepper) vs “light and delicious” (backpacker)
  • Show messaging around abandoned cart items on the homepage, in pop-ups or somewhere else on the site
  • Segment to a separate email newsletter to deliver more relevant email marketing campaigns to certain personas
  • Show a specific upsell on checkout, or somewhere else onsite so the right visitors see better related products (to increase average order value)

To actually execute on this, we’ll code simple Javascript cookies, which we deploy through Google Tag Manager, that essentially say: if a user takes one of the actions indicative of persona A, show them the personalized experience(s) we’ve come up with for that persona. 

Pro Tip: We recommend setting up these cookies as you do the research in Step 3 so that you already start building your cookied user list. That lets you start putting out personalized content much faster after you finalize your criteria. 

What If a User Takes Conflicting Actions

We should emphasize that it’s important to keep your final custom dimension list small to minimize the risk that users take conflicting actions (one action indicates they’re in Persona A, another indicates they are in Persona B). But if that happens, you can simply choose certain actions as “trump cards” that outweigh others. Alternatively you could also just not personalize the site for users who take conflicting actions. As long as you keep the action set small, this should only apply to a small percentage of your total traffic. 

Conclusion

We’re excited that this method of creating personalized eCommerce experiences involves no 3rd party software (besides GA, which so many online retailers use already) and offers lots of customizability. 

If you see how this could apply to your eCommerce brand, you can reach out to our CRO team here or leave a comment below. 

The 5 Worst eCommerce A/B Testing Mistakes to Watch out for 

Make sure your eCommerce A/B Testing results are 100% accurate with our checklist!

Do you want to use A/B testing to increase the conversion rate on your eCommerce site?

Before you start running one of those tests, it’s a good idea to make sure that you set it up correctly.

A rigorously-run test can increase conversions, but if the parameters are flawed then the decisions you make based on the test could be too.

In this post, we’ll outline the worst eCommerce A/B testing errors that we see online stores make. Use this as a reference to make sure that any A/B testing you do returns accurate results. That way you can make better data-driven decisions that actually increase conversions.

Note: Want our CRO experts to do A/B testing to increase the conversion rate for your eCommerce business? Learn more about our services and get in touch.

What Are the Worst eCommerce A/B Testing Errors?

We’ve written in the past about the most common reasons A/B tests don’t perform well. Namely:

Here, we’re outlining the worst technical errors that can invalidate your results. These are:

  • Technical issues with the actual test
  • Making site changes during the test
  • Traffic source changes
  • Conflicting tests
  • Not accounting for user and site-specific factors

If you are going to put time into doing A/B testing to improve your site, it’s important to take the specific things about your site into account.

These are the 5 eCommerce A/B testing mistakes to avoid (beyond just setting up your analytics goals) that we commonly see while helping our clients with their conversion optimization.

#1. Technical A/B Testing Issues

Here are the things to consider when setting up an A/B test (whether you’re using Optimizely, VWO, or another platform) to make sure it yields valid and actionable results.

Didn’t Exclude Return Visitors

If people are experiencing 2 different versions of your eCommerce website, a test will be invalid. We often see this issue happen with returning visitors after a site changes their design, layout, or other elements.

A ‘Negative Response’ is the possible outcome created when return visitors to the site see the new treatment and are now lost (i.e. navigation change) or confused (i.e. page layout test). The user now has expectations about the site that we might not be meeting with the new treatmenteven if it is better.

For instance: Below is a familiar pattern we see when the test variation (purple) overtakes the control (green). This often happens when a site’s returning visitors are allowed into the test. These returning visitors prefer the control because it has a “continuity of experience” (it’s the version they’re used to).

This results in making the variation appear to perform poorly until the initial group of returning visitors exit the test. In the test below, it took roughly 12 days for this returning visitor bias to abate. 

Many tests do not run long enough (see below). If returning visitors are excluded, the true result—a big win—will never be seen. Even worse, you may end up implementing a losing variant and harming sales.

Note: For businesses with loyal client bases who need to know how a treatment will impact their existing users, you can remove returning visitors from the data after the fact.

Didn’t Run a Test Long Enough

One of the most common ways we see eCommerce businesses run tests improperly is not giving them a chance to run for long enough. The reasons for this vary from not knowing better to succumbing to the pressure to get results quickly.

Whatever the reason, not running a test long enough will rob you of the truth, so you might as well have not run the test in the first place.

We’ll revisit how long to run these tests in a moment.

Run a Test with Enough Participants and Goal Completions

The more variations a test has, the more participants it will need. Your sample size needs to be large enough to demonstrate user behavior.

Consider 100 conversions per variation to be a minimum, and only after the test has run long enough (see above) and other criteria has been met (see below)

Don’t Turn Off Variations While Testing

Turning off a variation can skew results, making them untrustworthy. 

For instance: Let’s say you have four total variations (three treatments and the control). Each variation receives 25 percent of traffic. After 10 days, one treatment is turned off and from that point on, each variation receives 33 percent of traffic. Then you again turn off another variation, leaving each remaining variation with 50 percent of the traffic. 

The example below shows how when the green variation was turned off, the control improved, just like it did at the start of the test when it benefitted from some people buying right away (the control was for a free shipping offer):

The control again lifts off when the pink variation is turned off showing the control (orange) improve for a third time when the mix of new and returning visitors shifts to include more new visitors. The variation (this time the control) disproportionately benefits since it does well at converting the first time buyers who saw the free shipping offer.

This graph would look very different if the control never saw those three bumps in conversion rate. Because the control is the baseline variation, the result of the winner (blue variation) would be more clear and confident, and the test would not have had to run so long. 

The above scenario is extremely common, and at a surface level seems benign, however, the reality is that the differences in the variations themselves may create a case where the test is corrupted by changing the weight of the remaining variations. 

Often a variation is favored by different types of buyer mindsets, such as a spontaneous shopper vs an analytical one. If one variation is preferred over the other, changing the weight of the remaining variations will result in the variation favored by spontaneous people to suddenly improve. The other variations, favored by more analytical people, will not see as much improvement until their buying cycle has concluded, perhaps days or weeks later. 

Since we never know who likes a variation for what reason (Was it the hero image? The testimonials? Product page? Overall user experience?) the safest thing to do is not to eliminate any variations. 

Custom Code vs. Test Design Editors

After trying to set up a few tests via any test design editor, you may find that the test treatments do not render or behave as you expected across all browser/device combinations. 

While design editors hold great promise for “anyone” to be able to set up a test, the reality is there is only a narrow range of test types (i.e. text-only changes) that can be done through a test design editor alone. Custom code is required to have it work well and consistently across browser types and versions.

The best practice is to write custom code because most modern websites have dynamic elements that visual editors can’t identify properly. Here are some specific cases that visual editors can’t detect: 

  • Web page elements that are inserted or modified after the page has been loaded, such as some shopping cart buttons, button color, Facebook like buttons, Facebook fan boxes and security seals like McAfee
  • Page elements that change with user interaction, such as shopping cart row changes when users add or remove elements, reviews, carousels, and page comments. 
  • Responsive websites that have duplicated elements, such as sites with multiple headers (desktop header is hidden for mobile devices and mobile header is hidden for desktop computers). 

In the beginning, you may be able to avoid running complex tests that require custom code. Eventually, you will graduate to a level of testing that demands it. Know that this requires a front-end developer to set up the tests that you will want to run. 

Run the Test at 100% Traffic

Today, many purchases online involve more than one device or one browser (i.e. researching on a smartphone, then purchasing on a laptop).

However, test tools are limited to tracking a user on a single browser/device combination. This means that someone who sees one variation of a test on mobile may come back to purchase on desktop and be provided another variation.

Showing a variation more often than another will give it the advantage since it is more likely to be seen with continuity by users who switch from device or browser. This is called “continuity bias.”

To avoid continuity bias during the testing process, we recommend you run tests at 100 percent of traffic and split that traffic evenly between variations.

When less than 100 percent of traffic is sampled for a test, the result (in today’s cross-device world) is that the control will be served more often than the variations, thus giving it the advantage.

For instance: A user visits your site from work and is not included in a test because you are only allowing 50 percent of people to participate. Then, that same user goes home later in the buying cycle and gets included in the test on their home computer. The user is then more likely to favor the control due to continuity bias.

Side note: This may be a good time to look at your past results of tests that were run with less than 100 percent of user participation and see if the Control won more than its fair (or expected) share of tests. 

Equal Weighting of Variations

For the same reasons as mentioned above with running a test at 100 percent, you also want to ensure that all variations are equally weighted (i.e. testing four variations including the control should see 25 percent of traffic go to each). If the weights are not equal there is a bias—as outlined above.

Test Targeting

Test results are easily diluted (test will have to run longer) or contaminated by not targeting the test to the right audience. The most common issues of inappropriate test targeting are:

  1. Geo (i.e. including international visitors in a test that is USA specific)
  2. Device (i.e. including tablets in a mobile phone test)
  3. Cross-Category Creep (i.e. test for Flip Flops spreads into all Sandal pages)
  4. Acquisition vs. Retention (i.e. including repeat customer in a test for first-time customers)

If the right audience and pages are not targeted, then it will take much longer to see any significant results with confidence due to the noise of users who don’t care either way. That test result will indicate that the change is not significant, leaving you to stop the test and not gain the additional sales.

These are the most common technical reasons we see invalid tests. There are some other good habits we recommend though.

#2. NO eCommerce Site Changes During Testing

As a general rule: Avoid making other site changes during the test period.

This will cause you to see a statistical result from the test without knowing what to attribute it to.

We often see eCommerce stores that launch a website redesign while they are in an active test period which causes issues.

For instance: If you are testing a trust element like McAfee’s trust seal, avoid changes that may impact the trust of the site, including:

  • Site style changes
  • Other trust seals
  • Header elements (like contact or shipping information
  • Or any other site-wide “assurance” elements (i.e. chat)

The same line of thinking applies to other changes. If you are testing a pop-up window, don’t make a change to the site style, other opt-in boxes, etc.

When split testing, it’s usually best to test one element at a time. Making site changes during a test makes that ideal a lot less feasible.

#3. Traffic Sources Change and Muddy Conversion Data

In our experience, the different sources of traffic to your website will behave differently.

When there is a change in the distribution of traffic sources (i.e. paid search increases), test results will be unreliable until the test participants brought in have had a chance to go through their entire buying cycle. 

For instance: Paid search visitors may be less trusting and less sophisticated when it comes to the web. This traffic source often responds well to trust factors like trust seals.

Increasing paid traffic during the test may result in a sharp increase in conversions. But that increase is not sustained as the test continues for a longer period and the number of non-spontaneous visitors get factored into the results. 

If one version performs better for one traffic source but another traffic source starts getting mixed into the test: you’re seeing the mixed traffic response because of the change. This can make it hard to attribute the conversion increase you see to one source of traffic or the other.

#4. Conflicting A/B Tests Spoil Attribution

It’s easy to run more than one test at a time, however, tests may conflict with user impact. It is common to see the results of one test change when another test is started or stopped. This is typically the result of the tests sharing the same purchase funnel, or impacting the same concept.

Avoid running conflicting tests. If you are running more than one test, do a bit of analytics work to see how many people will be affected by both tests (i.e. users common to the two pages involved in the separate tests).

If it’s more than 10 percent, then you will want to strongly consider how the two tests impact the user’s single experience. By using common sense and good judgment, you and your team will be able to estimate which tests can be run at the same time.

Here, we typically recommend breaking eCommerce sites into 3 “funnels” or sections:

  • Top-of-funnel is finding a product
  • Mid-funnel is when the user is on the ‘Product Detail Page’ and ‘Adds to Cart’
  • Bottom-of-funnel is Cart through a checkout page conversion

You shouldn’t be running more than one test at a time in any one of these funnels, and KPIs should reflect the goal of each “funnel.” Especially when there is another test running in one of the other funnels.

#5. Not Taking User and Site Specifics When A/B Testing

Every eCommerce site is unique. So when you do A/B testing for eCommerce, take those particular factors into account.

Clients who work with us get individualized recommendations for their websites. Here are some important general guidelines:

1. Segment Traffic

When judging if you have enough goal completion, don’t forget to consider segmentation on a user persona level.

For example, an eCommerce store selling school supplies will have a big split between classroom teachers and parents who shop on the site. If the treatment only targets one group, or if it might impact each group differently, it’s important to take that into account.

2. Test Against the Buying Cycle

When looking at test results, a test must be run and analyzed against its buying cycle. This means testing a person from their very first visit and all subsequent visits until they purchase.

If you know that 95 percent of purchases happen within three days of the user’s visit, then you have a three-day buying cycle.

Your test cycle will be the number of weeks you test (you want to test in full-week periods) plus the full length of your buying cycle added on so the last participant let into the test has an adequate chance to complete their purchase.

3. Count Every Conversion (or at Least Most of Them)

If a participant has entered a test, their actions should be counted. This may sound obvious, but correct attribution is seldom done well, and this results in inaccurate testing.

In order to do this right, it’s important that all visitors are given the chance to purchase after entering a test. If a test is just “turned off,” participants in that test who have yet to purchase have been left out. 

Since it is common to see one particular variation do well with returning visitors, leaving out these later conversions will skew the test toward the variations that favor the less methodical type people.

4. Determine Your Site’s Test Cycle (How Long to Run a Test)

You likely have been involved in discussions about how long a test should run. The biggest factor in how long to run a test is your site’s test cycle. 

To find your site’s test cycle in Google Analytics, simply start with a segment like the one below where you define that you want to view only users who had their first session during a one-week period. Then set a condition where transactions are greater than zero.

This type of segment will tell you when people whose first visit was that week eventually purchased on your site.

You can start by looking at a range such as two months, then work backwards to figure out when 95 percent of the purchases in that two months were. In the example below, the site has a three-week test cycle because 95 percent of purchases for the two months occurred in the first three weeks from the beginning of the period you started tracking purchases:

You may be wondering, “Why 95 percent?” This is a simple rule of thumb and, from experience, we have rarely seen the final 5 percent of purchases change a test’s conclusions, however, we have seen the last 10 percent do so.

5. Use 7-Day Cycles

When testing, you most likely have to test against a full week cycle. This is because people often behave differently during different days of the week.

For instance: If your site sells toys for small children, your site’s reality might be that a lot of research traffic occurs on the weekend when the children are available for questioning (i.e. “Hey Ty, what’s the coolest toy in the world these days?”). 

Another reality for a toy site might be that often the “Add to Cart” button does not need to get hit until Tuesday evening, given that a lot of toys are not needed until the weekend when birthday parties are typically held. A test run from Wednesday through Sunday (five full days with lots of data) is still not enough.

The reality is, almost every eCommerce site (from the more than 100 eCommerce analytics we’ve done test analysis on) has a seven-day cycle. You may have to figure out which days to start and stop, but it’s there because of how user behavior varies throughout the week.

Therefore, if you don’t use a seven-day cycle in your testing, your results are going to be weighted higher for one part of the week than another.

6. Use the “Test Window”

The Test Window are essentially the steps we recommend to avoid skewing a test.

Step 1: Only let new visitors into the test. This way returning visitors later in their purchase cycle will not skew results and potentially set the test off to a false start. Get as many people into the test as possible.

Step 2: Don’t look at the test for a full seven days. If you don’t have a statistical winner at this point (most test tools will tell you the test has reached 95 percent confidence), let the test run for another seven-day cycle and don’t peak.

Step 3: Turn off the test to new visitors once you have a statistical winner (at seven-day intervals). Turning off the test to new visitors will allow the participants already in the test to complete their buying cycle. Leave the test running for a full buying cycle after you’ve closed the window.

Step 4: Report out on the test. To report out on the test’s overall results, you will simply look at your A/B testing tools report. Now, because you used the Test Window, you will be able to believe the results because:

  • Everyone in the test had a consistent site experience, spending it in the same test variation (no one seeing the control on a previous visit only to later experience a treatment). 
  • A full seven-day cycle was used so weekend days and weekdays were weighted realistically. 
  • Every user (or 95 percent of them at least) was allowed to complete their buying cycle.

Conclusion

If you are going to put time into testing to improve your eCommerce site, everything that you do will be invalidated if you aren’t paying attention to these vital A/B testing factors.

In our effort to be the best eCommerce agency, we study and rank the best eCommerce sites’ conversion rate optimization strategies in our Best in Class eCommerce CRO Report. We use our findings and apply them directly to our client’s sites so that their stores are as optimized as the best of the best (and we have case studies to demonstrate).

That said, every significant site change needs to be tested. And for that, the step-by-step guidelines we’ve shown you here will help ensure you get accurate results.

We know that conversion testing is time-consuming and often overwhelming. If you would like to increase your eCommerce site’s conversion rate, our CRO experts can help with set up and make A/B testing recommendations. Learn more here and get in touch.

Make your remarketing more effective and less annoying with call tracking data

If your customers frequently purchase on the phone, you might be sitting on a goldmine of remarketing data.

The post Make your remarketing more effective and less annoying with call tracking data appeared first on Marketing Land.

It’s estimated that most Americans are exposed to around 4,000 to 10,000 ads each day. That’s a whole lot of opportunities to acquire new customers, and just as likely, annoy the everloving snot out of thousands of others. When you use remarketing to stay top-of-mind with customers, you’re walking a fine line between drawing in potential customers and infuriating your audience. Remarketing can and does work, but only if you can put customer experience above short-term vanity KPIs. Here’s how to do it and how to make the customer’s experience better using call tracking data.

Remarketing, retargeting, and why people hate it

What’s the difference between retargeting and remarketing? Remarketing is your overall strategy of reconnecting with customers and prospects after they have interacted with your brand. This could be a combination of email, paid digital media, direct mail and more. Retargeting refers to the cookie-based ads used to remarket to people after they have left your site on other sites as part of an ad network, such as Google Display Network ads. 

Your typical non-marketer consumer may not know these terms or the inner workings of remarketing. They just know them as ads that seem to follow them everywhere they go after visiting your website, and they have some good reasons to hate them. 

Ads are out of context

Have you ever been shopping for some kind of martech product and then get retargeting ads for it on your favorite hockey blog? If you’re a marketer, you probably just sigh and nod your head in shame that someone’s doing it wrong. Displaying ads out of context is one of the big reasons why consumers feel like they’re being “followed” by you. It sticks out like a sore thumb because it’s just the wrong place and the wrong time. However, if you can contextualize your remarketing, the ads will seem natural and do what they’re supposed to do — keep your brand top-of-mind. When you see ads for the hockey gear you’ve been shopping for on the hockey blog and email automation on marketing industry websites, you nod your head in approval and think “YEAH, these folks know what they’re doing!” Then you buy that 12-pack of pucks and call back that martech SDR who has been hounding you for the last six weeks. Mission accomplished! 

Your ads are absofreakinlutely everywhere, forever

The more times someone sees your ad, the more likely they’ll remember you, right? That might be the case, but they’ll probably be remembering that they’d like to strangle you. A study performed by Skin Media and RAPP Media aimed to find out how this repetitiveness affects consumers. In the study, they found that people think that seeing a retargeted ad five or more times is “annoying,” while seeing it ten or more times makes them “angry”. Not the experience you’re looking for. More than half of the visitors polled said that they may be interested in the ad the first time they see it, even though only 10% report making a purchase as a result of seeing a remarketed ad. Think carefully when you are setting your frequency caps and make sure you are not inundating (and annoying the hell out of) your customers with ads. 

Getting retargeted for stuff you already bought

Step 1: Buy a new power drill. Step 2: See millions of retargeting ads for the same darned drill. Step 3: Scream at your computer “GAWD, fix your suppression, dummies!” The average consumer may also find this rather inept, but more likely, they’re going to be turned off by it. Proper post-conversion ad suppression makes your marketing much more efficient and saves your customers from the agony of being reminded of their purchase for six weeks, or worse, seeing an ad with a lower price than they paid and making them feel conned. 

How call tracking data can make the remarketing experience better

Particularly in the post-cookies age we live in, where the use of third-party cookies for remarketing is being smashed by new regulations and browser-level cookie-blocking, using every source of first-party data you have at hand for remarketing is critical. If your business gets a lot of sales inquiries from inbound phone calls, your remarketing picture gets even muddier. A potential customer may have navigated to your website and clicked on a page or product before calling you and either asking a question or ultimately making a purchase. Either way, you are left with a data gap that leaves you open for making bad remarketing decisions that will annoy your customers and waste your marketing budget.

You can bridge this data gap and get your hands on precise first-party data for remarketing by using a call tracking and conversational analytics platform. When your customers call you, they are literally telling you what they want and how they talk about it. To feasibly classify customer conversations into useful digital datasets, you need an automated system that can understand what’s being said and accurately derive meaning from it. Your call tracking platform should be able to accomplish a few things: 

  • Automatically determine the outcome of inbound phone calls 
  • Predict and classify call type (e.g. sales call, service call, etc.)
  • Collect digital journey data such as UTM, keywords, and GCLID
  • Push marketing intelligence collected from calls to your martech stack in real time

With this type of functionality, you can fine-tune your remarketing campaigns without doing a lot of heavy lifting.  The data can be fed to your DMP and/or ad network to automate the process in real time. And when you understand the nature of a call, you can optimize your media for higher ROI, which can be particularly helpful when you are nailing down the next best step in your marketing, whether that be retargeting ads for someone who did not make a purchase, or suppressing ads for someone who did. You can also use call data to feed to Google’s automated bidding algorithm to adjust your bids according to what is (or isn’t) happening on the phone. 

Conversational analytics tools like Invoca’s new Signal Discovery take this to a new level of precision and granularity, as they can help you find out things about phone conversations that you don’t even know to look for. Over 56% of marketers have no idea what’s said during the calls that they drive or what the outcomes of those calls are. It’s a big data gap that marketers shouldn’t have to live with. “Conversations are overflowing with insights that don’t always see the light of day outside the contact center. As a result, many companies are missing out on opportunities to create a more consistent and positive customer experience across human and digital touchpoints,” said Dan Miller, lead analyst and founder at Opus Research. 

Signal Discovery solves this issue by enabling marketers to quickly gain new insights from tens of thousands of conversations and take action on them in real time. From there, you’re able to drill down into each topic to understand caller behavior and then create a “signal” that Invoca will listen for in future calls so you can see exactly when a specific topic is discussed and can automate your marketing based on this data. No more guesswork, no more risky call assumptions.

With all this data, you can make your remarketing efforts more targeted, relevant, efficient, and above all, less annoying. 

Get the Call Tracking Study Guide for Marketers to learn more about how to use call tracking data to improve your remarketing strategy. 

The post Make your remarketing more effective and less annoying with call tracking data appeared first on Marketing Land.

Our Winning PPC Strategy for eCommerce: How We Increased Google Ads ROAS by 76%

Increase your PPC ROAS and spend less on ads with the strategies in this eCommerce PPC case study.

In our conversations with hundreds of eCommerce business owners, many have told us that their Google ads (formerly Google Adwords) campaigns aren’t performing as well as they originally thought they would, and could be working more efficiently for a lesser cost.

When setting up their PPC advertising strategy, many of these eCommerce stores assume that setting up their feed and launching one Shopping campaign containing all of their products is all that it takes to create a profit.

They are disappointed and sometimes mystified that their ads don’t drive a good return and the work that went in setting them up was wasted.

That’s when I explain that if managed correctly and implemented by pros, Google Ads can actually be a great revenue driver that will grow your business… But it takes more than just keywords and ad creative.

Background:

This is exactly what one of our clients in the camera equipment niche came to us with. Unfortunately, the agency they had been working with prior to us hadn’t been doing a great job on their Google PPC.

After we took over their PPC efforts, between Feb 1st, 2019 – August 31st, 2019, we got them a 76% increase in ROAS, from 5.61x to 9.89x, compared to July 1, 2018 – Jan. 31, 2019 when they had been working with the other agency:

Google analytics: 76% increase in ROAS, from 5.61x to 9.89x.

Year-over-year, we got them a 56% increase in ROAS from 6.34x to 9.89x:

Google analytics: 56% increase in ROAS from 6.34x to 9.89x.

You can see a breakdown of the Google Ads strategies that we use for our clients in this linked article. 

In this case study, we’d like to demystify the actual work involved and the combination of eCommerce PPC marketing strategies that we used to get results for this online store.

You’ll see:

  • The Google Ads Strategies We Implemented for a 9.89x ROAS
  • Our Tiered Bidding Strategy: Implementation and Best Practices
  • Other Shopping and Search Ad Best Practices We Recommend to Reduce Spend

As eCommerce growth experts, our clients include not only 7 and 8-figure businesses, but 9-figure annual revenue eCommerce companies as well. Talk to us today to see if we can help you optimize your eCommerce brand’s paid search, SEO, and conversion rates.

1. The eCommerce PPC Strategies We Implemented for a 9.89x ROAS

While every client presents unique aspects of their business and different challenges, we’ve learned through working with hundreds of clients about how important it is to follow a system in order to create results, which is what we did.

Audit and Analysis

When we begin running PPC for eCommerce sites, the first thing we do is audit the client’s product feed, campaigns, and other performance indicators to see what is working and what is causing issues.

This site had high search volume and a very extensive product catalog of cameras, lenses, and other camera-related equipment. Due to the store’s scale, we discovered that there were problems like incomplete/missing information about products in their Google product data feed. Their current PPC campaigns were also too broad given the different product categories on the site, which created wasted spend.

The work we determined would increase ROAS for this client included:

  • Product data feed setup and optimization
  • A Google Shopping campaign restructure
  • A Google Search ad campaign restructure
  • Google Display ad optimizations
  • Implementing the campaigns that worked in Google to Bing Ads

By optimizing each of the above, we positioned our client’s products in a way that encapsulates the customer’s entire buying journey. This resulted in less spend per click and more sales.

Here’s How It Worked:

Product Feed Setup and Feed Optimization

The product feed contains your products and their related information including: product category, brands, quantities, sizes, colors, materials, etc.

Google Shopping Ads and Display Ads are based on information that Google pulls directly from your store’s Product Feed:

Google shopping ads and Google search ads (results for "washing machines")

(An example of Google Shopping Ads and Search Ads)

This is why optimizing the Google Product Feed is so important.

For this client in the camera equipment niche, that meant making sure that information like the camera brands, their lens sizes, and so on were all present, accurate, and easily found by Google.

The site had huge potential to be shown for many different specific product queries, but Google wasn’t able to match those search queries to the product in this store’s catalog.

Fixing this meant that now when somebody searched Google for a product in our client’s catalog, such as a specific camera lens model, our client’s product was more likely to appear right there as a Shopping ad. (We optimized the product feed in a tool called Feedonomics).

Shopping Campaign and Search Ad Campaign Restructures

A mixture of Google Shopping ads, Google Search ads, and Google Display ads is beneficial to most big online retailers.

Implementing all three often leads to enhanced product visibility across the buyer’s entire journey, from research through to purchase.

With the product feed optimized, we began restructuring the client’s search and shopping structures.

This particular industry has high search volume which makes it easier to gain traffic, but also harder to figure out the specific combination of search queries that drive the highest return.

So because of this, we created separate multiple tiered campaigns segmented by product type—creating different tiers for different product categories. In this case, a lens tier, digital camera tier, video camera tier, film camera tier, etc.

Display Ad Optimizations

Products ads that follow customers around the web might seem annoying on first glance, but in fact, retargeting ads do convert very well and have a great ROI. Why? Because they get customers at the end of the buying cycle (once they’ve already visited the store and looked at products).

People were visiting our client’s store, but they weren’t being remarketed to. The dynamic remarketing feature from Google Shopping enabled us to automatically show ads to people who came to the client’s site without completing a purchase.

Dynamic remarketing makes use of your product feed to determine what products Google displays on its ad network. It can intelligently group different products together based on what’s likely to convert best.

An example of a dynamic retargeting ad:

A dynamic retargeting ad

Using dynamic remarketing is a fairly straightforward strategy to skyrocket eCommerce performance, and we believe it’s a must for any online retailer.

Bing Account Changes

Once we saw what was working in Google, we began transferring the Google Ads changes to attempt replicating their success in Bing Ads.

While Bing has a far lower search engine market share (2.63% as of this article’s writing), we’ve found that what works well in Google Ads can often work in Bing Ads (now called Microsoft Advertising). Why not duplicate the strategies that work in Google to capture shoppers from another search engine?

2. Our Tiered Bidding Strategy: Implementation and Best Practices

When it comes to any PPC campaign, patience and being willing to adjust along the way are important. You’ll determine the highest return queries and adjust your bids to prioritize them as an ongoing process.

In this case, we identified the highest return search queries based on this store’s different product categories, and created a tiered campaign for each category.

Here’s a visual representation of that tiered strategy. The search queries each tier drives are based on negative keywords, and we created multiple tiered campaigns for each product category:

Tier 1: High priority, low bid, catch all; Tier 2: Medium priority, medium bid, higher ROAS; Tier 3: Low priority, highest bid, highest ROAS.

The Basic Steps to Establish Your Tiers

  1. Run Search Query Reports: Look at historical performance in Google Analytics by running search query reports, going back 6 months (or longer). Identify the queries or query combinations that have driven the highest revenue or ROAS in the past.
  2. Filter those queries in Google Analytics to see if they have a high churn or not. Decide which high return queries you want to filter into Tier 2 or 3.
  3. Mark any interesting patterns. For example, we saw a pattern of queries containing certain modifiers that tended to convert well, and we made sure to figure out the revenue and ROAS for those terms.
  4. Keep building it out on a spreadsheet to organize what you find. Organize those patterns to create the list containing the search query, revenue, and ROAS numbers.
  5. Segment the tiers according to the revenue and ROAS numbers: We optimized Google Shopping campaigns through the use of priority settings, bid stacking and negative keywords.

    The basic premise is to apply low, medium, and high bids on search terms with low, medium, and high returns. Thus optimizing the cost-per-click (CPC).

Conversely, we apply high, medium, and low priority levels to low, medium, and high bids/returns. For context, the priority setting determines the order in which Google will cycle through the campaigns. High priority literally means Google takes this campaign into account first because it will be Google’s highest priority.

To Illustrate:

Tier 1: We placed low bids on our catch-all campaign that drives all general queries. We added negative terms to avoid targeting queries that we wanted to bid higher on in the other tiers. Set to a high priority setting.

Tier 2: We placed medium bids on search queries we found to have a higher return than those in tier 1, but lower return than the queries we want to filter to tier 3.

As in tier 1, we added negative keywords for the queries we wanted to filter to tier 3. This tier was set to a medium priority setting.

Tier 3: Knowing what the most profitable search queries are, we place the highest bids on them. This tier was set to a low priority setting.

The tiered system works because in recognizing the performance of a store’s different relevant search queries, you can have much more control over how much ad spend is allocated to each query.

3. Other Shopping and Search Ad Best Practices to Reduce Spend

  • Always send the best performing / highest return queries to tier 3. By identifying the highest return queries, we are able to ensure less wasted spend by spending the least amount of money on low return queries and allocating the largest percentage of money to terms we are positive will actually drive a high return.

  • Use negative targeting. Negative keyword segmentation in a tiered system allows us to pay less ad spend for lower returning queries.

  • Optimize by device. After the tiers had run for enough time and gathered significant data, we analyzed performance by device to ensure no wasted spend.

  • Cut wasted spend on the least performing hours / days of the week. After gathering significant data, we ran a time of day analysis to ensure we were optimizing for our best performing days and hours.

  • Launch Search Competitor campaigns alongside your own search ads to target the competition (rest assured, they are likely doing this too).

  • Optimize by demographics We ran demographic based analyses for age, gender & household incomes then implemented bid adjustments accordingly.

  • Optimize your display ads to capture who they retarget. In addition to optimizing their bids and budget, it’s important to update the ad copy to better target users at different stages in the buying funnel. For example, newer users require more brand focused CTAs, while previous purchasers do not. We also started testing new ad features from Google such as Smart Display ads. We often test Google’s new features for our clients because we’ve seen that being one of the first to use new ad features is a competitive advantage (because competitors may be slow to adopt new features).

Takeaways

PPC is a dynamic advertising practice—meaning that management is ongoing—it’s not a “set it and forget it” platform.

Results

Adjusting our PPC strategy allowed us to achieve a year-over-year 56% increase in ROAS from 6.34x to 9.89x.

PPC Strategy for eCommerce: Within Google analytics, you can see their increase in ROAS from 6.34x to 9.89x.

When Inflow took over, their PPC strategy greatly increased, causing a major ROAS!

I hope this case study helped paint a picture of how a Google Ads strategy based on a systematic review and restructuring can quickly yield a higher ROAS.

eCommerce brands who work with us receive a tailored set of strategies like these to maximize their Google ads campaign budgets.

If you would like us to evaluate and improve your online store’s PPC campaigns or identify additional growth opportunities, please get in touch with us today to see if we can help.

Lytics launches Salesforce Marketing Cloud integration for customer journeys

With the new integration, users can view customer insights and execute campaigns between Lytics and Salesforce Marketing Cloud.

The post Lytics launches Salesforce Marketing Cloud integration for customer journeys appeared first on Marketing Land.

Customer data platform (CDP) Lytics announced updates to its platform that will allow users to integrate customer journey execution with Salesforce Marketing Cloud (SFMC). Lytics’ campaign orchestration capabilities can now be used across a number of marketing technologies, including Facebook and SendGrid – in addition to the new SFMC integration.

The integration between Lytics’ CDP and SFMC is expected to allow marketers to import existing campaigns to build new experiences within the Orchestrate Journey canvas. The insights delivered from Lytics can then be used to inform more targeted campaigns and be sent to SFMC for delivery.

Why we should care

Delivering personalized, one-to-one marketing at scale is something we strive for as marketers. Our disparate martech environments tend to complicate this, and customer data platforms seek to address these complications by providing users with a single view of their customer data from the different tools they use. Marrying this data into a single view should help marketers extract new insights to further inform their campaigns.

“The best customer journeys are an open road,” said James McDermott, CEO of Lytics, “and for us, that means giving marketers the freedom to choose multiple paths by integrating with their existing marketing technology stack.”

More on the news

With the new Lytics and Salesforce Marketing Cloud integration, users can:

  • Export audience segments from Lytics into SFMC to continue the customer journey
  • Trigger new experiences in SFMC based on customer events (e.g., opened an email) captured in Lytics
  • Switch between Lytics and SFMC within the same customer journey to deliver a combination of channel and message.

The post Lytics launches Salesforce Marketing Cloud integration for customer journeys appeared first on Marketing Land.

Is Your Personalization Lonely? Get a Marketing Team Going with These 4 Tools

Okay, marketers, the cat is out of the bag. We know that personalization is not your ONLY marketing strategy. We know that you have other platforms that you work in and other marketing campaigns that you run on your website. And, we know that you are active on social and are constantly creating new innovative… Read More

The post Is Your Personalization Lonely? Get a Marketing Team Going with These 4 Tools appeared first on Bound.

Okay, marketers, the cat is out of the bag. We know that personalization is not your ONLY marketing strategy. We know that you have other platforms that you work in and other marketing campaigns that you run on your website. And, we know that you are active on social and are constantly creating new innovative content. We know that you’ve got your hands full trying to inspire visitation to your destination. 

But that’s what we love about you! We like that you are multifaceted and have a ton of simultaneous initiatives. And, honestly, we want to help out! There’s a million and a half ways that you can partner your personalization with other on-site and off-site elements.  Here are four specific technologies I want highlight as effective tools to partner with personalization. Let’s start with: 

Email Collection:

Despite rumors that email marketing is “dead”, the eNewsletter list is still an important marker of success for most CVBs and Destination Marketers. Collecting a visitor’s email gives you direct access to a visitor’s inbox and allows you to update potential travelers on new things to do and see around town. As long as your emails stay relevant and timely, eNewsletters will continue to be a meaningful way to connect and communicate with locals and visitors alike. 

While we probably don’t need to convince you that email list building is important, we do need to talk about how marketers collect email addresses. Email collection can be tough and the line between assertively requesting emails and aggressively annoying site visitors is thin. Marketers often start with what is thought to be a gentle ask, only to realize later that their requests are off-putting to audiences. 

That’s where personalization offers a solution. Rather than asking the same visitors for email addresses repeatedly, setting up targeted segmentation can be an effective way to cut down on “ask-annoyance.” Plus, you can use personalization to make sure that you don’t serve an eNewsletter form fill to a visitor that has already signed up for your list. Instead of badgering visitors for emails, personalization helps you to ask the right visitors for emails at the time they’re most likely to sign up. 

Crowdriff:

Like I said earlier, we know that we’re not your only marketing platform and we hope you’re using some of the other innovative technologies built for the travel and tourism vertical. One of our favorite technologies in this space is Crowdriff, a visual content marketing platform. Crowdriff allows marketers to easily pull User Generated visuals from social channels, manages this content, and serves diverse galleries on their website. Through this practice of sourcing beautiful imagery by visitors, Crowdriff provides content that resonates with and inspires future travelers. 

Personalizing Crowdriff galleries adds a layer of targeting that ensures site visitors are greeted with imagery that really speaks to their interests. Through Bound’s personalization tool, you can segment visitors by implied behavioral interest, geo-location, or by paid media. This gives marketers the opportunity to serve “outdoor focused” galleries to visitors who have expressed interest in outdoor activities. Similarly, a marketer might want to show imagery with a heavy fall focus to geo-locations that don’t necessarily get the chance to experience fall (*sigh* In Austin, TX we go straight from summer to winter). 

Serving user generated content through Crowdriff provides websites imagery that makes destinations seem accessible and fun. Adding personalization takes it one step further and allows marketers to serve user generated galleries that will speak directly to a visitor’s interests.  

Youtube, Vimeo, Wistia, etc.:

The look and feel of a destination comes across in pictures but videos give visitors a more heightened perspective of a destination’s offerings. Beautiful landscapes can be viewed in full and the action behind exciting events can be witnessed beyond a single photo. Videos truly show a destination’s personality through rolling shots, music, and energy. 

But destination marketers sometimes have trouble getting more eyes on the beautiful videos that they created. And if marketing dollars were spent on producing top-notch videos, it’s important that an audience sees those videos. The good news is that personalization can help here too! 

Dependent on the intentions of video content, Bound’s personalization can do multiple things. For starters, Bound can serve video on your site to make sure more people have access to it. If more views is your main goal, that’s easy for us to enable. But, we can also make sure that the right videos are serving to the right people. If you have a series of visitors highlighting the food scene in your fair city, town, or state, we can make sure that your foodie audience gobbles those right up (pun intended). 

Google Analytics and Adobe Analytics:

Marketers tend to shy away from intensive reporting because it can quickly overwhelm. But ultimately, reporting should be one of the most important things that website marketers do. And as far as reporting platforms go, it’s hard to get better than Google Analytics. With it’s enormous breadth of content, it is definitely a beast of a platform. But who doesn’t love all those colorful little graphs and interactive flow-charts. Tracking site engagement through reporting tools like Google Analytics tells you what is working for your traffic and what is not working. Which is why it’s important to push personalization information into your Google Analytics reporting tool. 

By tracking personalization in GA, you can easily segment your personalized audience and compare performance to visitors who did not see personalization. Or you can get super granular and see specifically how audiences react to certain content pieces. By tracking the performance of your on-site personalization, you can improve upon your website segment by segment, leading to better engagement overall and a site that’s highlighting the most ideal content for a specified audience.

This barely scratches the surface of ways that you can partner your other marketing initiatives with personalization. If you want to learn more, reach out to our sales team or your designated customer success manager!

The post Is Your Personalization Lonely? Get a Marketing Team Going with These 4 Tools appeared first on Bound.

What We Shipped: Get Instant Customer Insights with Audience Explorer

Every successful personalization campaign requires understanding both your customers and their intent. At its core, personalization is the act of presenting the right experience to the right audience to deliver a wanted experience. So how can marketers better understand their audience segments, in order to design the most valuable experiences possible? The first step is…

The post What We Shipped: Get Instant Customer Insights with Audience Explorer appeared first on Monetate.

Every successful personalization campaign requires understanding both your customers and their intent. At its core, personalization is the act of presenting the right experience to the right audience to deliver a wanted experience. So how can marketers better understand their audience segments, in order to design the most valuable experiences possible?

The first step is gathering insights into how your customers behave and teasing out the similarities and differences among your customers. This used to require using larger outside analytics solutions or cobbling together the right data from across multiple systems (or teams) — which could sometimes take weeks, without even including the hours it can take to dive into the data to find actual insights.

Today, I’m excited to announce that Monetate customers have access to Audience Explorer: a segmentation and analytics tool that provides instant insights into your customers. We designed Audience Explorer with personalization in mind, to help marketers craft nuanced audience segments, evaluate their performance across key metrics, and create experiences to quickly deliver personalization to those audiences. All in a matter of minutes, right in the Monetate platform. 

But don’t take my word for it. Here’s how one of our power users, Allison Reitz, Manager of Conversion Optimization at TicketNetwork, uses Audience Explorer:

“I can instantly see fluctuations in existing segments, discover new segments as they emerge, and adjust my strategies around these changes. Instead of spending my time segmenting and analyzing data, I can focus on launching positive experiences that address real-time trends in website traffic, using data I trust.”

And read on for more details about what Audience Explorer can do for you.

Get instant insights

Audience Explorer gives you instant insights into customer behavior using everything from simple attributes (such as device type and geography) to more complex attributes (such as product categories viewed and brands purchased). All in real time. Audience Explorer also highlights how each audience contributes to key metrics like conversion rate or average order value — so you can turn more of your customers into your best customers. 

Compare audiences and experience performance

You can also explore performance differences between segments after serving an experience or test. This can be especially powerful when analyzing how personalization initiatives are affecting shopping behavior; for example, how well did your content perform for new customers versus returning customers? You can even take this idea one level further by looking at audiences who have seen multiple personalization experiences and track how that affects their shopping behavior — key for building a business case for deeper personalization.

Audience Explorer can also help marketers derive deeper insights from A/B tests. Imagine you just completed an A/B test to see which version of your product description page resulted in a higher add-to-cart rate. You know that variant A did better overall, but Audience Explorer can help you understand how well the test performed based on the specific products customers were browsing when presented with the variants. 

Understand audiences to build more effective strategies

Building an effective personalization strategy requires an understanding of key audience segments and answering questions like, “Do I have audiences that share traits or behaviors that I should personalize around?” Audience Explorer helps surface these key metrics and attributes.

[Want to read the latest Monetate research into valuable holiday shopper segments? Click here.]

Target saved audiences to take immediate action

Once you have identified and saved valuable audiences, you can use them as targets for an experience. Audiences you’ve saved in Audience Explorer are surfaced as targets for you in the platform’s Experience Builder — just select the audience you want to target. 

Bringing Audience Explorer to life

Audience Explorer was built in partnership with dozens of Monetate customers who were part of our early access program. The direct feedback they provided as we designed, developed, and iterated on Audience Explorer was invaluable and ensured that we built something that solves real problems for our clients. 

Delivering these new audience analytics features is particularly meaningful to me because Audience Explorer is the first product I’ve worked on since joining Monetate. Thank you to all the customers, designers, and engineers who helped bring this to life. If you’re interested in learning more about Audience Explorer, reach out to your Monetate account manager or submit a demo request.

Ubek Ergashev is a Product Manager at Monetate, focused on developing products that empower customers with analytics capabilities.

The post What We Shipped: Get Instant Customer Insights with Audience Explorer appeared first on Monetate.