Unlock multi-touch attribution with CRM campaign tracking

If you’re using a CRM to house leads then you likely have the campaign tracking tools for middle funnel activity even if you have an offline transaction point.

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Image credit: www.Curata.com

Brands with an offline transaction point often struggle to measure the full customer journey from acquisition source through to revenue. Often times, if revenue can be attributed back to something, it’s to the marketing channel responsible for the lead. This measurement is usually implemented with either a first or last touch attribution model for channel attribution and completely leaves out the rest of the customer journey.

The missing visibility and measurement is post lead acquisition. Once a lead enters our systems, how do we measure the effectiveness of our campaigns and lead treatments? Even better, how do we attribute revenue to content pieces and treatments applied to that lead in-funnel?

The answer may be something we already have access to. If you’re using a CRM to house leads then you likely have the tools to track middle of funnel activity at your fingertips with Campaign Tracking.

What is campaign tracking?

Campaign tracking within a CRM is technically an entity or object that tracks a variety of information about an event, mailing, emailing, or other marketing initiatives. It’s basically a container that houses all of the components of a campaign across channels and treatments.
Leads and contacts can be members of one or more CRM campaigns allowing visibility into the effectiveness of both single and multiple campaign influence across all levels of our funnel from lead to cash.

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Image credit: www.Salesforce.com

What are the benefits of CRM campaign tracking?

Depending on the CRM and how it has been architected and implemented, campaign tracking provides a significantly deeper level of insights and measurement including the ability to

  • Tie marketing activities to our sales pipeline
  • Compare the effectiveness of different marketing initiatives and their influence on each other
  • Measure mid-funnel activities alongside marketing channel attribution
  • Measure the effectiveness of content post lead acquisition
  • Inform the sales team of historical marketing activities via the contact record
  • Roll-up similar lead sources into a single object
  • Connect online & offline activities
  • Enable holistic ROI reporting
  • Preserve data integrity & maintain hygiene
  • Enables multi-touch attribution modeling within your funnel

Attribution and CRM campaign tracking

Now that measurement is enabled at such a granular level in-funnel we can see marketing activity influence across the funnel from lead to customer. This is where attribution really gets complex! Similar to the first-touch, last-touch, multi-touch debates on marketing channel attribution. Now we have these same debates in-funnel when attributing back to campaigns within our CRM.

Do we give credit to the campaign that initially acquired the lead?

Do we give credit to the campaign the lead responded to before they converted to an opportunity?

Do we give credit to the campaign that influenced the lead right before they converted to a customer?

First-touch attribution model

First-touch is pretty self-explanatory. In this attribution model, all credit is given to the very first action taken by the user that created the lead record.

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Images credit: www.Curata.com

The first-touch attribution has its advantages, it’s super easy to implement. The lead is tagged using a custom field and that field rides on the record all the way through the funnel to closed won. However, this model leaves so much of the story out neglecting to consider all other interactions the user had or actions the user took beyond that initial entry into the database.

Last-touch attribution model

Last-touch attribution is the opposite of first-touch. Instead of giving all credit to the first action the user took, we’re giving it to the last step the user took. Also easy to implement by merely over-riding that custom field.

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Last-touch is fantastic for measuring the effectiveness of campaigns targeting the bottom of the funnel geared directly toward driving a purchase decision. However, if we only look at what ultimately turned into a sale, we really lack insights into what levers to pull to get them to that point and we are leaving a ton of opportunity on the table. We are also boxing ourselves into diminishing returns and expensive tactics and are unable to scale our marketing programs.

Multi-touch attribution model

Multi-touch models are more complex, recording all interactions and giving credit to all touch points in the journey. They provide the clearest picture of attribution and provide the most insights regarding what levers to pull across the funnel to improve velocity and efficiency of our marketing investments. To implement a multi-touch attribution model within your funnel you have to utilize CRM campaign tracking. This is the biggest benefit of the campaign tracking tools within your CRM.

CRM campaign tracking reports

Once campaign tracking is properly set up and working within your CRM and when used in conjunction with a clean and granular lead source strategy, CRM campaign tracking opens up rich and robust reporting options. Your data will tell a very different story!

Here are a few sample reports that can be generated when both campaign tracking and a clean and granular lead source strategy are applied within Salesforce.

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I’d love to hear how you are utilizing campaign tracking within your CRM and what kind of new insights you’ve been able to pull. My guess is that once you were able to get to this level of insights, marketing resources were moved around to concentrate on what you didn’t even know was working within your marketing program.

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When it comes to campaign design and measurement, many sizes fit all

When you focus on tuning one campaign KPI, you inevitably affect the others. It’s an imperfect world where you must be aware of the tradeoffs you’re making.

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Crafting the right digital strategy to hit your ultimate marketing goal is a balancing act. You need to vet platforms, allocate budget and determine appropriate campaign KPIs.

This last point is seemingly the easiest element of a campaign to settle upon. However, focusing on the wrong KPI or not understanding how various KPIs interact with each other may negatively impact campaign success.

In today’s advertising marketplace, where the tech-stack can provide innumerable campaign measures, digital marketers need to put extra care into fine-tuning their campaign KPIs to help ensure that the scale they need is not limited by the measures they put in place.

The ideal versus the reality

Wouldn’t it be perfect if every campaign could be tuned so it delivers 100 percent viewability, zero percent invalid traffic (IVT), 100 percent in-demo targeting -– and deliver in-full while hitting click-through rate (CTR), cost per acquisition (CPA), or video completion rate (VCR) goals?

Marketers, like everyone else, must operate in an imperfect world. There are tradeoffs –- and these tradeoffs might mean altering or changing the weight placed upon various campaign KPIs to help ensure success.

A one-size-fits-all approach to campaign design and measurement does not always work, and it certainly does not always work for a single advertiser under every condition at all times of the year.

As we move into the months where marketers are executing their Q4 strategies, this is especially important to consider. Most brands this time of year need scale to affect the buying habits of as many consumers as possible. More than ever, a finely-tuned advertising strategy with strategic campaign KPIs is necessary to help ensure the ultimate opportunity is not hindered by restrictive or competing measures.

Performance measures, whether an advertiser’s campaign KPIs or a supply partner’s benchmarks, are the currency by which we evaluate the efficacy of the advertiser/media partner relationship. Their critical importance to the relationship reinforces the need for careful measurement planning and design.

Advertisers should carefully balance strategic campaign performance measures such as acquisition, brand impact, and video completion with tactical delivery measures of viewability, brand safety, and in-demo performance. The balance struck between measures will vary for each advertiser and will likely be impacted by overall marketing objectives.

How to strike a balance

Striking a balance does not mean abandoning one measure -– such as viewability -– for the sake of another. Marketers should recognize, however, that there is interplay between measures. And that the focus on one measure may impact another.

Advertisers have the right to demand -– and media partners have the responsibility to provide –- a high-quality and effective advertising environment. As we head into Q4, it is important to review overall marketing objectives and how they translate to individual campaign KPIs. Adjust where necessary and work to understand how a focus on a specific KPI has the potential to either enhance or detract from another.

A poorly-designed program with conflicting KPIs may potentially limit your reach and hand customers over to a savvy competitor. Consider how the KPIs you focus on could impact the return on your media investment.

In the end, you want to use your media spend as efficiently as possible to scale your programs to engage as many potential customers as possible.

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Bring order to chaos: Wrangling data for actionable insights

How to bring an overwhelming amount of data under control and use the insights gained throughout your business.

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Producing actionable insights is one of the most challenging issues that brands face today. Urgency is ever-present, pushing marketers and analysts to rush decisions. But urgency is only half of the problem. Making the situation more chaotic is the fact that we are simultaneously awash in waves of data from too many sources. Between the urgency to produce results combined with the massive sea of data, we are inundated us every time we wade in and then simply washed back to shore.

So where do we start? Transactional, engagement, or demographic data? Prospecting or retention? The inundation keeps pushing us back.

There are strategies to navigate the churn and turbidity, and remedy those issues. Sometimes we simply need to take a step back, narrow our focus, and even get a little ruthless.

Insights begin with goal-setting

First, we need leadership teams to get ruthless with what really matters. Analytics can’t chase the shiny object or rely on some utopian commerce breakthrough — if only we could find attribution in some rabbit-hole metric.

Think bigger. Get brutal with company and divisional goals.

Great goals have a couple of key characteristics in common. First, they’re specific — they have clear expectations and a path forward to measure and show success. Great goals also unify teams instead of dispersing them in different directions where everyone has a separate idea of how they can accomplish them.

To reach goals, every single person needs to be pulling the boat in the same direction. Great goals produce unity, which in turn helps to focus analytical firepower where it matters most. Remove the rest.

Where to start

The lowest hanging fruit is almost always customer retention. It’s the easiest behavior to shift; it has the most room to grow; it’s the most profitable. One way to understand the importance of retention is to ask this question: if a brand acquires a new customer, what does that matter if that brand can’t keep the customer engaged? Prospecting without first nailing down the current customer makes teams spin their wheels and waste energy.

Align your performance indicators

So, we have our goals narrowed down and all teams are working towards a common purpose.  The next step is to flawlessly align our performance indicators to those stringently selected goals. Again, narrow your focus and be strict with the fidelity of indicators to goals.  They should have either a clear cause-and-effect relationship or a very strong correlation to prove success.

Once we’ve identified those core components, we can simply let the rest of the data wash away.  It takes work up front, but that work will be rewarded with a strong path forward and will avoid data paralysis down the road. By deriving indicators naturally from a core set of goals, you organically narrow the data set, so we can focus on producing insights that drive change.

It’s easy to see how many brands can get stuck in the mud during this phase. There are so many temptations, so many paths to take that could work if only for one added piece that we don’t have in the model. But this is a faulty mindset and the effort will be wasted with little to show for all that added work. Put the blinders on and be strict.

Where to start

The answer is almost always transactional data, especially if we’ve felt the impact of overwhelming data paralysis. Stick to transactional indicators early. They’re reliable and strongly aligned to behavior. What shows customer sentiment better: a Facebook Like or purchasing items?

Measure, rinse, repeat

Lastly, all of that work is useless if we don’t have a measurement plan in place to prove success. If we can’t measure, it doesn’t matter.

The best approach is a rigorous test-and-learn strategy. Not only does it prove success, but it also provides actionable insights for the future to help build individual successes into larger groups of changes across channels and teams to drive and achieve goals.

Analytics teams can definitely get backed up, especially with A/B testing. Sometimes the waitlist is daunting. But there are two good options if that happens. First, consider an outside agency dedicated to helping us learn about the customer. An outside source can provide focus when things get too tight for internal teams to produce results.

The other option is to test historically. I can hear the gasps and guffaws of analytics teams, but we need to read the tea leaves however we can to produce results. That means pushing changes to market and measuring year-over-year data instead of one-off direct causations.  That option is better suited to areas where we already know best practices or have some data points to suggest the right decisions with high degrees of confidence.

Another reason it’s a viable option — and why analysts should love it — is that it frees up the testing schedule dramatically. So many tests don’t really need to be run in an A/B format; sometimes we have years of historical data or mountains of best-practice to influence our decision. In those instances, measurement is less of a read and more of a confirmation.

Bring order to chaos

These ideas may sound simple and, to a large degree, they are simple. They’re foundational. But without a foundation, how can we achieve our brand aspirations?

So many brands run before they can walk and they fall flat. To bring order to chaos, we need to start with the lowest common denominators to build on our learnings. Start small, grow big. Incrementally and soon, teams from every channel will have the learnings they need to act and provide the best experiences possible for both the brands and the customers.

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How to supercharge the Salesforce lead source field

Strategic management of the lead source field within Salesforce setup will unlock the magic of campaign tracking and measure the efforts of your paid media and content efforts. Here’s how.

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Salesforce lead source has long been the data point that has ruled measurement of marketing initiatives. This field tracks channel attribution and is used to measure return on marketing investments.

However, in today’s marketplace, the field is very limited out of the box. Absent multitouch attribution flexibility, you really only get one lead source on a record within the database. But what about second, third and fourth touches?

There are a few options utilizing just lead source on its own, but they all have limitations:

  1. We can ignore all touches after the first and stick to the original source only. This is often the method we see used at most companies, and it leads to inaccurate data
  2. The second option is to override the existing lead source with each new touch. This leaves you with the most recent lead source only. Unfortunately, this destroys info and creates inaccurate data, leading to really scary decision-making.
  3. The last option is to create a new lead record for each touch. This approach is the most disruptive, leading to mass confusion and degraded data quality.

So, how do we measure the big picture of a combination of channel influence and maintain the integrity of the database? The answer is to use campaign tracking alongside a customized lead source architecture.

In this post, we’ll focus on how to get your lead source field customized with a level of granularity that serves the business while maintaining the integrity of the data.

Lead source field review

Out of the box, the default lead source list in Salesforce is not granular enough. This list predates most of the channels we utilize in marketing today — rolling all digital channel activity into one bucket labeled “web.”

Neglecting to customize this list during a Salesforce implementation leads to pandemonium and frustration once data begins to populate the reports, and marketing can’t see the results of their efforts clearly enough. Which then leads to shooting in the dark and misalignment between sales and marketing.

We highly recommend you perform an audit of your lead source field options and customize them for your organization. Get rid of lead sources that don’t serve you and add lead sources you would like to track. This can easily be done with a simple spreadsheet to allow everyone to come together and agree on what level of granularity you would like to see.

Customize Salesforce lead source

Once you have gone through your existing lead source list and pruned the lead sources that don’t belong, you can draft the new lead sources that should be added. Now, it’s time to go through and look for missing granularity and opportunity to consolidate.

Effective marketers and data folks always want things tracked as granularly as possible. This often leads to a new lead source for every single activity, event and campaign, which creates a bloated database and makes for difficult reporting.

You’ll likely find that you have several lead sources that can be combined or eliminated. Look for opportunities to standardize your lead sources similar to Google Analytics parameters. If you need additional details, add more fields.

Standardize data entry

Once things are cleaned up, we highly recommend you standardize and automate your data entry. The lead source field should be locked down and set automatically by the system, not by human hands. You can do this using a web-to-lead form (first touch email acquisition) or by passing data from your marketing automation tool (first touch visit).

Once the data is set, don’t allow it to change or allow it to map to other objects.

Strategic management of the lead source field within your Salesforce setup will allow you to unlock the magic of campaign tracking and really measure the efforts of your paid media campaigns and content efforts.

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How to capitalize on the competitive advantage of real-time data analysis

Contributor Stela Yordanova explains how to capitalize on the competitive advantage provided by real-time data analysis.

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The Real-Time report in Google Analytics allows you to monitor website activity as it actually occurs on your website or app. The report is continuously updated, and website activity is reported just a few seconds after it happens. This immediacy of real-time data provides digital marketers with unique and valuable insights.

There are many ways you can use real-time reporting such as gauging the effectiveness of your mobile app through event tracking and monitoring one-day promotions on your site.  Today I want to focus on and recommend marketers use Google’s Real-Time report for three specific things:

  1. To quickly monitor results for short-term campaigns or promotional efforts.
  2. To track immediate interaction with newly published content.
  3. To test and verify Google Analytics and Google Tag Manager implementation.

Real-Time Overview

The Real-Time report contains an Overview plus five specific reports:

  • Location report.
  • Traffic Sources report.
  • Content report.
  • Events report.
  • Conversion report.

Each report is described below with suggestions on how marketers should use them to analyze real-time website data and improve marketing results.

 

[Read the full article on Search Engine Land.]

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12 pieces of conversion optimization advice you should ignore

Whenever you hear a marketing practice referred to as “easy,” it’s usually not. Contributor Ayat Shukairy looks at some common CRO misconceptions and their uncommon realities.

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A lot of content on conversion rate optimization (CRO) is published every day. Most of it is spot-on, but some articles make me cringe a little.

A lot of the advice being shared gives people false hope that if they conduct CRO correctly, they’ll see the millions roll in. It’s not that easy. The process is vigorous and requires a lot of time and effort — much more than the advice being shared will lead you to believe.

Whenever you hear a marketing practice referred to as “easy,” it’s usually not.  Let’s look at some common CRO misconceptions and their uncommon realities.

Misconception 1: Anyone can do it

Not hardly! To do well in CRO, you need good people on your team. A conversion rate optimization team usually includes:

  • Two or three conversion optimization specialists.
  • A UX designer.
  • A front-end developer.
  • A customer research specialist (can be part-time).
  • An analytics specialist (can be part-time).
  • A data analyst (can be part-time).
  • A product or program manager, depending on your business.

With all the different job types and responsibilities, how can one person do it all? Unless they’re Wonder Woman, they can’t.

Now that we have an idea who we will need on our team, let’s look at common statements you’ll hear about CRO that aren’t always accurate.

Misconception 2: There are CRO best practices

Everyone wants best practices, but in CRO, best practices simply don’t exist. We wish we had best practices, but it’s not a reality because what works on one website may not work on another.

For example, CaffeineInformer and Bookings.com both tested the same navigational menus and found the most commonly recommended menu worked for one but not the other.

CaffeineInformer tested the hamburger menu (an icon made up of three bars) versus the traditional word MENU enclosed with a border and one without a border, writing up and publishing the results online. You can see that the boxed MENU results were clicked on more often than MENU without a border, and the hamburger menu showed no use.

When Bookings.com ran their test results, which a designer wrote about on the company’s blog, they found no difference in the number of clicks for their  MENU options:

Representatives from Booking.com said:

With our very large user base, we are able to state with a very high confidence that, specifically for Booking.com users, the hamburger icon performs just as well as the more descriptive version.

So, although your competitors may inspire you, most of the time you’ll find what they introduce on their site may not work on yours. In the case above, it’s a small change, but we have seen companies make a bet on a change that costs hundreds of thousands of dollars and produces a negative impact on their site.

My advice is to know what is out there and get inspiration from other sites, but validate through research, prototyping and usability testing before rolling out a change on your site (especially if it’s major). If it’s something minor like a hamburger menu, go ahead and test, but ask yourself, what are you really trying to achieve with the change? Consider the validity of the concept to begin with and see if it fits within the overall roadmap you have for your site.

Misconception 3: More testing yields positive results

Statistically speaking, more variations = greater possibilities of false positive and inaccurate results.

My staff experienced this when we were first starting out as CRO practitioners. We would start testing by running a control versus variant 1, variant 2 and variant 3.

Once we found a statistical winner, we would launch just the control versus the winner. For example, if variant 2 reached statistical power with a significant statistical lift, we would launch control versus variant 2.

Of course, variable 2 completely tanked. What happened? Well, statistically, each variant brings a chance of a false positive. So of course, more variants = more chance of false positives.

According to Sharon Hurley Hall’s blog post on OptinMonster.com:

Most experienced conversion optimizers recommend that you don’t run more than four split tests at a time. One reason is that the more variations you run, the bigger the A/B testing sample size you need. That’s because you have to send more traffic to each version to get reliable results. This is known as A/B testing statistical significance (or, in everyday terms, making sure the numbers are large enough to actually have meaning).

If you have low conversions (even in the presence of a high volume of traffic), you definitely shouldn’t test beyond one variation.

Anyone with a sufficient number of conversions should be cautious and test, then retest the winning variation over the control to ensure it sticks.

Misconception 4: CRO is A/B testing

A/B testing is a part of the conversion rate optimization process, but they are not one in the same.

Our methodology for conversion rate optimization is combined into the acronym SHIP:

Scrutinize, Hypothesize, Implement and Propagate

Over 70 percent of the time we spend doing CRO is the scrutinize (planning) phase of the process. An unplanned test that is not backed by data does not usually do well.

When we talk about conversion optimization, the mind should go to design thinking, innovation and creativity. Ultimately, you are optimizing an experience and bringing it to a new level for the site visitor. You’re putting a spin on solutions to complex problems to ensure the visitor not only converts but has a memorable, enjoyable experience they’ll buzz about.

That is no easy feat!

Misconception 5: A simple change will impact your bottom line

Sometimes a simple change can have an impact. but let’s be real: that’s the exception, not the rule.

Expecting a color change on your site will increase conversion by 40 to 50 percent is really a stretch. When someone says it will, I immediately wonder, “How long did the test run?” and “Did it reach statistical power?” I think Allen Burt from BlueStout.com said it best in an expert roundup on Shane Barker’s blog:

I love talking about how we can increase conversion rate and how we can optimize it, because most sites, especially ecommerce merchants, get this wrong. They think it’s all about A/B testing and trying different button colours, etc. In reality, for 90% of small to medium-sized businesses, the #1 change you can make to your site to increase conversion rate is your MESSAGING.

Don’t try and take the easy route; usability issues need to be addressed, and testing colors and critical calls to action like a “Proceed to Checkout” statement is a viable test. But expecting a “significant impact” on your bottom line for simple changes is asking too much

One of the key components of a successful CRO program is the creativity behind it. Test and push limits, try new things, and excite the visitor who has been accustomed to the plain and mundane.

Misconception 6: A/B test everything

In the past, there was a strong emphasis on A/B testing everything, from the smallest button to the hero image. But now, the mood has changed, and we see A/B testing differently.

Some things just need to be fixed on a site. It doesn’t take an A/B test to figure out a usability issue or to understand that conversions increase when common problems are fixed.  A simple investigation may be all that is required to determine whether or not an A/B test should be done.

When evaluating a site, we find issues and classify the fixes for those issues in “buckets,” which helps determine further action. Here are the four basic buckets:

  • Areas and issues are evaluated for testing. When we find them, we place these items in the research opportunities bucket.
  • Some areas don’t require testing because they are broken or suffer from an inconsistency and just need to be fixed. We place these issues in the fix right away bucket.
  • Other areas may require us to explore and understand more about the problem before placing it in one of the two former buckets, so we add it to the investigate further bucket.
  • During any site evaluation, you may find a tag or event is missing and not providing sufficient details about a specific page or element. That goes into the classification instrument bucket.

Misconception 7: Statistical significance is the most important metric 

We hear it all the time: The test reached 95 percent statistical confidence, so we should stop it. However, when you look back at the test, between the control and the variation, only 50 conversions were collected (about 25 for each), and the test ran for only two days.

That is not enough data.

The first step to consider when launching an A/B test is to calculate the sample size. The sample size is based on the number of visitors, conversions and expected uplift you believe you will need to reach before concluding the test.

In a blog entry on Hubspot.com, WPEngine’s Carl Hargreaves advised:

Keep in mind that you’ll need to pick a realistic number for your page. While we would all love to have millions of users to test on, most of us don’t have that luxury. I suggest making a rough estimate of how long you’ll need to run your test before hitting your target sample size.

Second, consider statistical power. According to Minitab.com, “[S]tatistical power is the probability that a test will detect a difference (or effect) that actually exists.”

The likelihood that an A/B test will detect a change in conversion rates between variations depends on the impact of the new design. If the impact is large (such as a 90 percent increase in the conversions), it will be easy to detect in the A/B test.

If the impact is small (such as a 1 percent increase in the conversions), it will be difficult to detect in the A/B test

Unfortunately, we do not know the actual magnitude of impact! One of the purposes of the A/B test is to estimate it. The choice of the effect size is always somewhat arbitrary, and considerations of feasibility are often paramount.

Another important point here is to understand that it’s important to keep your business cycles in mind. In the past, we’ve seen sites where conversions spike on the 15th and 30th of every month. In order to run a test that would account for the entirety of that 15-day business cycle, we would need to test for a minimum of  2 1/2 weeks (including one of the spikes for each testing period).

Another example is SaaS companies, where a subscription to their service was a business decision that often took two months before closing. Measuring conversions for less than that period would skew data tremendously. 

Misconception 8: Business owners understand their customer base and visitors

A client of ours insisted they knew their customer base. They are a billion-dollar company that has been around since 1932, with 1,000 stores and a lot of customer data. But they have only been online for about 10 years.

Based on our experience, we told this brand their online customers will behave and act differently from customers in their brick-and-mortar stores and may even vary in terms of overall demographics.

However, our client insisted he knew better. After doing research, we suggested running some experiments. One particular experiment dealt with the behavior and actions of visitors on the cart page. Was the cart used to store products until they came back later? Or was it just not effective in persuading visitors to move forward? Our theory was the latter. We shared that from what we observed, there was hesitation to move beyond the cart page.

This suggestion was met with a lot of resistance from the brand’s director of marketing, who claimed we didn’t understand their customers as they did. To compromise, I suggested we test a percentage of traffic and slowly grow the percentage as the test gained momentum. If the customer follow-through did not grow, we would end the test.

The test was launched and reached sample size within days because of the amount of traffic and conversions they have, and it revealed a 20.4 percent improvement.

The brand was stumped and realized there was another way to think about how their customers were using their shopping cart.

According to William Harris from Elumynt.com (also published in Shane Barker’s roundup):

It’s easy to get stuck in the “A/B testing world,” looking at data and numbers, etc. But one of the best sources of learning is still having real conversations with your customers and ideal contacts. It also increases the conversion rate.

The point of my story is this: You think you know, but until you do the research and conduct testing on theories you’ve built, you can’t be sure. Additionally, the landscape is ever-changing, and visitors are impatient. All of that plays into your ability to persuade and excite visitors.

Misconception 9: Only change one thing at a time

The next two points are related. Some people feel you should move slowly and make one change at a time in order to understand the effects of the change. When you’re testing, you create a hypothesis regarding the test, and it may involve one or more elements.

It isn’t template tweaking (e.g., just changing locations and design of elements); it’s testing against an entire hypothesis which is backed by data resulting in data-driven changes that visitors can see and feel.

Misconception 10: Make multiple changes each time

Counter to the point made in number 9 above. Sometimes we find a hypothesis becomes muddled because other changes are included within a single test. That makes it difficult to decipher the authenticity of the results and what element impacted the test.

Always stick to the hypothesis, and make sure your hypothesis matches the changes you’ve made on the site.

Misconception 11: Unpopular elements should be avoided

We had an account that simply did not believe in carousels. I’m not a fan, personally, but because the account sold a specific product, we felt carousels were necessary and recommended they be used.

But the account resisted until customers started complaining. It wasn’t until then the account realized carousels will help visitors find what they need and give breadth to the range of products they were selling.

Elements that have been deemed unpopular aren’t always unpopular with your customer base or your specific needs. If the research shows an element can provide a solution for you, test it before you completely discount it.

Misconception 12: Your site is too small for CRO

Conversion rate optimization is not only about testing. CRO is about understanding your visitors and giving them a more engaging experience. All digital marketers and webmasters owning a site of any size should be implementing CRO.

If you have the traffic to justify your theories, test! Otherwise, continuously update your site and measure your changes through observation of key metrics through your analytics or through usability testing.

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The worlds of brand and trade marketing need to unite

Collaboration between brand and trade marketing teams is critical for long-term success, says contributor Andrew Waber. Here’s how to make this tactical and strategic alignment a reality.

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There seems to be a massive shift in the way successful brands allocate dollars and other resources to their online marketing efforts.

For example, in 2017, coworkers and I analyzed some advertising activity from P&G showing that hundreds of millions of dollars of its online ad budget had moved to trusted e-commerce channels rather than on sites and approaches typically used for brand marketing.

According to P&G Chief Brand Officer Marc Pritchard and The Wall Street Journal:

The ad dollars were pulled back from a long list of digital channels but also included reducing spending with “several big digital players” by 20% to 50% last year (2017).

These are significant changes. Driving purchases through online media is increasingly reliant on retailer sites.

This transition in the overall market landscape necessitates a change in how companies fundamentally organize their marketing. Doing well on Amazon and other online retailers today requires brand and trade teams to work closely together in order to drive long-term success.

Misalignments

At a high level, brands simply can’t afford misalignment between the information on the product page and the brand promotion (done on sites such as Facebook) that lead customers to that page.

Ten years of Google conversion optimization proves that words in ads must match words in titles as closely as possible, or the ads may suffer high bounce rates. Consumers will notice the shift in vocabulary and abandon the landing page, driving down conversion rates.

Amazon Marketing Service (AMS) placements need to be associated with popular terms and be relevant to consumers. With consumers increasingly using sites like Amazon for research purposes, on-site promotions impact other sales channels, as well.

Market mix models have shown that AMS spend — which is often allocated to trade teams to handle — drove in-store sales in non-Amazon locations like CVS. If you’re a brand marketer, this means you should consider reallocating dollars from TV ads and treat budgets for promotions like AMS as brand dollars in today’s environment.

We’ve seen some larger companies already utilizing this fluid idea of what constitutes brand and trade dollars in relation to AMS and similar ad products.

There also needs to be alignment between the trade and brand marketing teams when it comes to promotions outside of Amazon’s universe. For example, if you launch an ad campaign on Facebook that drives traffic to an Amazon product detail page but that product happens to be out of stock when the Facebook ad campaign is running, then your product is punished by the A9 search algorithm which takes into account “page views when out of stock” in its ranking criteria.

If you get traffic when you’re out of stock, then your Amazon search rankings could suffer for months. In short, you are spending money on a campaign to drive traffic to an Amazon product detail page, and actively doing your brand harm in the process!

In traditional brand marketing, local in-stock rates typically don’t directly impact the larger strategy. The trade team might have to worry about this when campaigns are run in-store, but the brand side of the house never has to. On Amazon, and increasingly on more retail websites, you really have to care. The two work in concert.

Trade teams are in the business of identifying what sets of products are worth promoting or offering at one store versus another based on customer profile, (on Amazon and other online retailers). These decisions are executed primarily via the product page.

Algorithms are powerful

The algorithm, which bases decision-making on factors like relevancy and product page robustness, holds all the power here and isn’t like a chain store buyer you can “wine and dine” to improve shelf placement. Instead, brands need to address customer segments via the product title, imagery, keywords and so on.

Additionally, the fluid nature of these online retail sites necessitates continual adjustments to meet consumer needs on a near-daily basis, rather than monthly or quarterly. This can be done by direct data connections or measuring each channel with third-party analytics. Trade teams are best served by helping guide the brand marketing teams when and where these changes need to be made.

Speed to market is both hard to execute and increasingly important if you want to outflank competitors in today’s marketplace. Collaboration between brand and trade marketing teams is more critical than ever; they need to make this tactical and strategic alignment a reality in order to maintain success over the long term.

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It’s time for the brand promise to evolve. Again.

Contributor John Nardone explains how being an empathetic marketer can help you with everything from managing data to conceiving of dynamite creative.

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With GDPR recently coming into effect, advertisers are once again fretting over their use of consumer data. In Europe, the fretting is beneath a regulatory cloud, with financial consequences for the misuse of EU consumer data.

Here in the US, we are not facing legislation, but GDPR, Cambridge Analytica and congressional hearings have nonetheless refocused us on the question of fair and appropriate use of consumer data.

It is a fundamental conundrum that’s faced marketers for years: how to manage the fine line of personalization vs. privacy. When does appropriate use of data flip over to inappropriate? When does personalization cross the line into “creepy?”

The answer to the privacy vs. personalization challenge shouldn’t be difficult for responsible and thoughtful marketers. If they want to know whether their use of data is appropriate, marketers just need to put themselves in the shoes of their consumers.

Put yourself in your customer’s shoes

Good marketers should always be empathetic and attentive to the needs of their consumers, but in a data-enabled environment, they should aspire to go a step farther. They should strive to deliver some value in return for using that consumer’s data and for her attention.

In other words: It can’t be about the needs of your brand (i.e., more sales!). The message you deliver to the consumer, and the way in which you deliver it, must serve the consumer’s agenda, not your own. If marketers can adhere to this very basic premise and allow attentiveness and empathy to guide their strategies and communications, concerns about inadvertently crossing into the realm of data creepiness will be largely assuaged.

While the above concept might sound obvious (albeit rarely put into practice), it’s only in recent years that the dynamic messaging capabilities needed to deliver on this premise have become available.

As industry columns like this one are quick to remind readers, we know more about the individuals seeing our ads than ever before. Now, it’s time for our brand promises to be expressed in ways that reflect that knowledge.

A singular brand promise, tailored to the individual

The brand promise has evolved significantly over the past 70 years. Pre-1950s, most brand promises were straightforward. They stated what the product did. Detergent makes your clothes cleaner. Deodorant makes your armpits drier. Car wax protects your car’s finish. Simple, sure. But certainly not personal.

In the ensuing decades, the brand promise evolved. It took on meaning relative to the consumer. Advertisers shifted away from product features to focus on end benefits. Yes, deodorant keeps you drier, but why? So you can be more confident. That’s what really matters to consumers.

But over the past decade of digital and programmatic innovation, distribution has come to outweigh the message. As marketers, we became infatuated with the precision with which we could reach consumers — to the point that we forgot to pay attention to the messages and the creative with which we were reaching them.

Thankfully, we’re headed for a correction in that ever-swinging pendulum, one that brings empathy and humanization of the consumer to the forefront in ways that were not possible in decades past.

In today’s world of personalization, the empathetic marketer has the opportunity to evolve the brand promise yet again by translating it to the individual. And that’s where some serious creative magic can happen.

How awesome creative can be

In the world of the personalized brand promise, deodorant doesn’t just make you dry. It doesn’t just make you confident. It makes a 46-year-old father of two more confident when he meets the parents of his daughter’s new boyfriend. It makes the 27-year-old account manager more confident when she gives her first company-wide presentation. If context matters, then the context of the individual matters the most.

In the hands of an attentive and empathic marketer, data can enable a new, more personally relevant expression of the brand promise. Not only can we speak to a product’s end benefit, but we can speak to that end benefit as it applies to an individual at a given moment in time.

The creative possibilities enabled by the personalized brand promise are limitless. But to get it right, we must first embrace empathy as our key guiding principle in connecting with consumers in the moment.

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Turning Your Data Into Compelling Stories – SMX Advanced Recap

Want to know how to turn unorganized data into compelling presentations? Contributor Keri Morgret recaps three SMX Advanced speakers as they share how to transform data into valuable insights.

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This session focused on using data-driven storytelling to support and promote paid search marketing campaigns.

Bill Hunt, Back Azimuth Consulting

Bill started the session by sharing experiences he’s had in trying to sell stories about his data. To be effective in telling stories with your data, the data must be adapted to your audience.

Executives tend to see things one way, and everyone else has the opposite view. Will you be presenting to people who need the big picture, or will you be presenting to people who will be implementing the details of the plan?

When you present the data, it needs to be obvious. You can’t assume that your audience can see your conclusions, and you don’t want to make them do mental math or connect a lot of dots on their own. Be explicit and connect key data points to missed opportunities and revenue.

The data also needs to show something cool and insightful or a business opportunity. In Hunt’s experience, people repeat the brief nuggets of information. Make sure the nuggets they remember and repeat are the ones you want them to remember and that they are appropriate for your audience.

Does the data contradict an existing belief, action or fact? If so, you need to figure out how to present the data so it can help change that belief. You may need to retell the story in a different way.

Your site search data can be a great source of information and data for storytelling, but be careful not to overwhelm people with that data; 600,000 rows of search queries won’t impress your audience — it will scare them instead.

That’s exactly what happened with Bill’s first client example. The client’s marketing team was overwhelmed by the data and how much content they thought would be needed to create answers to 27,000 questions from 600,000 entries.

In the end, they determined 6,500 pieces of content would be needed, which led to questions like “How can we create that much content?” and “Who is going to manage it all?”

On the other side of the hall, the management team was looking at how this data translated into money from a different angle:

  • How much revenue can we make if we create this content?
  • How fast can we get a return on investment (ROI)?
  • Whose revenue will be cannibalized (by users buying online instead of through other channels)?
  • How will lost revenue be tracked?

In the end, the two departments came together and determined a smaller amount of content was needed.  The marketing team created the content and generated a 22 percent immediate conversion and $10 million in incremental revenue over the next couple of years.

For another client,  Bill and his team took a vast amount of data (over a million keywords) and developed a content opportunity matrix.

This client is an alcoholic beverage manufacturer, so many of the searches were related to drinks. The team reviewed the keywords and focused on one segment of customers and their queries.

This segment of customers knew they wanted a drink, but they didn’t know exactly what drink they wanted. They were dubbed the “Cocktail Curious.”

The keywords came from multiple sources such as traffic to the website, site search, Google Search Console, Google Keyword Tool and more. Since there were over a million keywords, and Bill’s staff could not sort through them all, they created a pie chart of drink discovery colors that visually told the story of what people were searching for.

Bill recommends using searcher interest to drive content alignment, ensuring your data stories paint the right picture for the audience and don’t overcomplicate data.

Presentation deck: Maximizing Your Searcher Discovery Journal 

Maria Corcoran, Adobe

Maria addressed two important questions many of us have:

  • How do I get my ideas funded?
  • How can I use data to get those ideas funded?

Maria is on an in-house team of 32 in operations at Adobe. Her team is focused on knowing where the data is coming from and how to find the data. To be able to analyze the data, you need to understand the data sources.

As a strategist, she wants to know all of the details:

  • How much did that click cost?
  • What is the customer journey?
  • What is the bounce rate for a page?
  • What is the customer engagement level?

While the details are good, they will not get your idea funded.

Your ultimate goal is to get the C-suite on your side and happy with your ideas. They’re not looking at the strategist data, they want to see the bottom line.

You need to translate the data you have into what the executives want. They likely want to know revenue, conversion volume and retention. You’ll need to do your research to know what they want, so you can use your data to address what they need to make decisions.

You may have all the data in the world, but you may also only get one slide in a 60-slide presentation to convince the executives of the value of your project.

Below is an example of the one slide she used, with the proprietary company figures removed. You’ll want to keep using the same format for future updates, so people are familiar with your slide and know where to look for the data.

When she gave a report on the success of her projects, she removed a lot of the detailed data and kept a very high-level “we did what we said we were going to do for you” report for the executives. She focused on using visualizations, concise data points (but not overwhelming amounts) and looking at “what’s next.”

Maria’s #SMXInsights:

Presentation deck: Telling compelling stories with data – Get your ideas funded!

JD Prater, AdStage

JD kicked off his session by saying you shouldn’t make your reports so long and detailed that you’re the only one in the room who cares about the data. You don’t want to drive people away. Instead, JD asks us to think of reports like an ad.

How do you present the right metrics, for the right audience, in the right context?  Structure your PPC reports for Chief Marketing Officers (CMOs), directors and managers, and give each a unique report.

You need to present your report so it can influence decisions. Data is past tense and should be used to influence what happens in the future.

CMOs

What does the CMO care about the most? Money! Why should you talk about click-through rate? You’re one line item in a whole array of things they need to look at.

We usually tell a long story that ends with a recommendation. You should instead start with the end recommendation, then work backward as needed.

The Minto structure of leading with the answer, giving a supporting argument, then giving the evidence of the argument can be great here. Sometimes you start with the answer, the CMO says yes, and you’re done!

 Directors

Directors use the contribution of your PPC campaign to the lead pipeline as their primary marketing performance metric. Start your presentation to them with how well you’ve done, and how much better you could be doing with X, Y and Z. Then back up your data. Avoid spreadsheets!

Managers
With managers, put your most important information first, then show trends, and show how this can affect the future plans. Here is where you want to include the details, break things down to the ad and campaign level.

JD’s #SMXInsights: 

Presentation deck: Paid search reports to influence business decisions


Want more info on Paid Search? Check out our comprehensive PPC Guide – nine chapters covering everything from account setup to automation and bid adjustments!


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Attention + intensity: Tips for navigating the new age of media strategy

Contributor Mark Williams says marketers must evolve the metrics they monitor to keep up with the changing media-consumption environment.

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As marketers and brands have seen, the prevalence of digital video has transformed how consumers access media and content.

Essentially, video is not the future, it’s the “now”.

According to Cisco, global IP video traffic will represent 82 percent of all consumer internet traffic by 2021, up from 73 percent in 2016. Consumers no longer want to read about a brand  — they want to visualize it.

In 2018 and beyond, we’ll see a big shift from before, when advertisers were looking to buy reach and frequency with traditional media, to now, where advertisers will want to capitalize on intensity through the maximum amount of reach and frequency. In a post-pivot-to-video world, it’s time to change your video and media strategy, especially how you measure it.

To tackle all of the changes and innovations in media and digital marketing within the past few years, and especially to gear you up for the further integration of video, here are three tips for navigating the new age of media strategy.

1. Measure your audience with intensity

Rethink your approach to measurement. It’s not just about clicks and views. Viewability and reach are no longer the main indicators of success because they don’t measure how an audience is connecting with the content.

Instead, track deeper actions. Update your key performance indicators (KPIs) with different engagement metrics, such as watch time, engagements, earned metrics and follower acquisition, to track whether or not your intended audience actually viewed your message and reacted to it.

Watch time is one of the most valuable metrics to track in order to gauge whether or not audiences are actually watching your content. It’s also the most important factor for platform algorithms. If you track minutes watched, retention rate and the average percentage of those who watched through, you’ll have a better idea of how you are captivating the audience’s attention, and at what level of intensity.

Tracking engagements (e.g., likes, shares and comments) is also a key indicator of your strategy’s performance. Engagements and engagement rates indicate that fans are making a decision beyond simply watching your content. If they’re sharing, starting up a conversation, or compelled by a call to action from the content, you can measure the intensity with which your audience is consuming the material.

Also, be sure to watch your follower/subscriber acquisition. Growing a fan base is essential to the marketing efforts of advertisers, and it is important to identify what content brings in new followers so that you can focus your content strategy to consider these insights.

2. Rethink content strategy: Transform ads + make content relevant

Given the prevalence of ad blockers, it’s clear that interruptive advertising doesn’t work anymore. Instead, we’re seeing high performance through integrated brand messages. To do this, make your content relevant to your consumer.

Embed your campaign initiatives into publisher sites through partnerships to make for a smoother and natural integration of your advertising.

Consider integrating with influencers. Research conducted by Fullscreen (my employer) and MediaScience found that the percentage of viewers who would recommend a brand after watching a branded video from an influencer was 13 percent higher than the percentage for a TV ad.

Test different content strategies to see what resonates best with your audience, and for a more specific segmented analysis, A/B test different interest sets and demographics to inform your marketing plan.

3. Tailor by platform

To keep your marketing strategy specific and efficient, optimize content and advertising to reflect the platform. Utilize metadata by making campaigns that align with proper titling and tagging across all of your platforms. Keep your branding design consistent to ensure that your content is distinguishable. Ensure that your creative is designed for the specific tech specs of the platform where it will live.

Gone are the days of the one-size-fits-all approach. Facebook creative must be treated differently from Snapchat and so on. Perhaps most importantly, the creative must feel endemic to the platform — which explains why repurposed television commercials have some of the lowest engagement metrics.

Identify and maintain a consistent publishing schedule that is tailored to times when platforms reach the highest number of eyes, not only to maximize viewership and engagement but also to help consumers know when to expect your content.

Further, aim to promote circular traffic: Utilize the platforms through their available interactive elements so that you can cross-promote across all channels.

When tailoring your content for specific platforms, you also want to pay attention to how the platform is accessed.

Take a look at the platform functions, according to recent data from each platform and Statista, YouTube is accessed 50 percent of the time on mobile, whereas Facebook is at 95.1 percent and Instagram is at 100 percent.

This means that when creating content for YouTube, you should pay equal attention to mobile and desktop access, whereas Facebook and Instagram should lean more heavily toward mobile usage.

In closing

You’ll want to keep these three tips at the forefront of your digital marketing and content strategy so that you quickly adapt your brand to the changing video and media environments of today.

Remember, the overarching difference in paid media targeting online versus traditional targeting is the more refined, specific targeting of individuals, which ultimately leads to higher attention and intensity, as well as greater returns.

With all of these advancements, online media has many new metrics which you absolutely must utilize to expand your reach and retention far beyond that of traditional paid media.

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