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“CallSource^® Reflections” is a blog series by CallSource’s^® co-founder and President, Elliot Leiboff. Elliot co-founded CallSource^® alongside the late Jerry Feldman in 1992. Over the years, Elliot has developed a small call tracking company to a full service lead generation performance organization. CallSource^® invented call tracking. Elliot has witnessed a myriad of inventions, tried different strategies, invested in technologies and basically seen it all.

CallSource^® is a classic American tale of an idea that turned into a business that has thrived through grit and determination. “CallSource^® Reflections” is Elliot’s blog series on lessons learned as a business owner before the era of startups and VC funding.

Elliot’s monthly blog contributions take the reader on the journey of how our solutions have evolved.

Poor telephone performance is a very common (and very expensive) frustration for many businesses. Most companies employ some form of training, but it is rarely as effective as management hopes. Often, results of training are difficult to measure and seem to fade very rapidly. If you have been disappointed with past results, consider whether one or more of the points below might have been to blame.

Some of the reasons training fails…	…But each failure point above has a corresponding solution:
The “trainer” is someone who knows how to perform a skill, but not how to teach it	Make sure your trainer knows both the subject matter and how to coach and train others
Training is delivered as a one-off event instead of an ongoing, “learn-do” process	Make training and coaching an ongoing part of your business operation
Training is “one size fits all,” boring and insulting to some, and overwhelming to others	Measure each employee’s performance level and identify individual skill gaps
Training is really “telling,” with little or no roleplaying or actual skill performance practice	Include plenty of employee participation and roleplaying in your training
There is no coach to help employees establish and achieve individual performance goals	Use a skilled coach to assist and challenge your employees to improve results

If you lack strong coaching and training skills in-house, you can either build them with “train the coach” or “train the trainer” courses or engage an outside organization that has these skills and knows your industry. If past training yielded disappointing or disappearing results, next time, make training an ongoing process that includes individual skill assessments, targeted training, and long-term coaching.

This is why CallSource has employed both our Advisory Services team for clients, as well as our Call Coaching. We know from talking to copious clients’ frustrations that the training they did for their employees didn’t always work. In fact, our metrics and reporting showed them that it didn’t. So, as we have created many of our products, we created these based on our clients’ needs.

Our Advisory Services team takes a “train the trainer” approach. They are available to assist management and anyone in charge of other employees at your business – not only in understanding CallSource reporting but in implementing strategic approaches to better your team and your business.

Call Coaching takes a more one-on-one or team approach with your actual employees, acting supplemental to management. Call Coaches will continuously make sure that employees aren’t only learning what they must learn in training, but remembering it and applying it in their day-to-day work.

Training is a process, not an event. CallSource is here to make sure that we are available to help you through every step of your training processes.

Yesterday I recapped Day 1 of my experience at CXL. Today I’m covering the rest of the conference.

Day 2 – Optimization Day

I met a lot of people on Day 1’s evening festivities that were excited about Day 2’s optimization and testing focus. Personally, I was most excited about Day 3 and figured I would appreciate the information I got from the other days. The reality is… so much relies on testing. Figuring out what is successful today relies on testing and failing yesterday – so optimization should be a focus for everyone in marketing.

“A mathematician’s guide to growth optimization” – Candace Ohm

Candace has a Ph.D. in Game Theory and approaches marketing from a holistic perspective, bringing advanced mathematics to interpret marketing. To her credit, she managed to pull off the presentation with an audience that was a little tired from the night before and she was the opening speaker! I joked with another conference attendee that it is a little rough to be the opening act, after the previous night’s festivities, and have your talk be focused on applied math.

The audience warmed up to her right away, and Candace left most of us with our mouths wide open at the possibilities of tying users to exponential value. A great quote from Candace was, “Kiss your attribution model goodbye,” to which I was ready to put mine down and punt it through the field goal poles.

Similar to Tara’s graphs of the retention curve, Candace used a graph and broke growth down into three phases: Acquisition (new clients), Activation (onboarding phase), and Retention (continued value). Candace uses this graph to showcase how different types of users actually correspond to patterns of usage, where the new/onboarding users interact differently than the users who are in the retention phase. Candace gave us a nifty equation we could use to compute our own Growth Curve: User Demand * Value per User = Growth Curve.

Candace suggests that we aim for creating interactions with customers that produce added value to the entire customer base so we can tap into exponential growth. Her talk references the life cycle of a customer, and she points out that for most companies, the Pareto principle (80/20 phenomenon) will likely turn up.

“80% of the value comes from 20% of your customers”
– Candace Ohm

My question was… ‘How do I get exponential growth out of our customers?’ Using some non-laymen’s math, Candace uses two mathematical laws to explain what exponential growth looks like in a network of customers that a business might have; Reed’s Law – the value of an added user to a network, and Metcalfe’s Law – which focuses on the growth of sub-groups that exist within a given network.

For the non-experts in Ph.D. math (probably the majority), Candace put it simply for us:

• Harvest/Focus on demand from the users that add value to other users.
• Optimize your funnel first for retention by creating value for clients you already have.

Out of all of the speakers, few acknowledged their defeats, failures, or mistakes publicly, but in this capacity, Candace stood out by taking a “failure bow” where she was misled by early attempts of prediction and data. She reiterates what most marketers live by, failure is necessary.

Recap of Key Learnings

• The three phases of the growth curve.
• Aim for creating interactions with customers that produce added value to all customers.
• In marketing, failure is necessary to learn.

 

“Demystifying AI in Marketing” – Guy Yalif

Guy Yalif hails from Intellimize, a predictive conversion and campaign management company. Guy tackles the complex and booming trend in marketing of adding Artificial Intelligence to marketing teams. Before he started, I really hoped he would give me more clarity on the applications of AI and he did not disappoint.

Kicking off his talk, Guy defined AI for marketing as – something doing something we could consider as ‘intelligent.’ Guy jokes that even though it seems like marketing will be replaced by artificial intelligence, it will likely only make leaps in bounds in specific areas of marketing.

Guy breaks down what AI for marketing is effective at:

• Managing a lot of tasks.
• Accelerated learning.
• Acting w/precision.
• Listening + reacting.

Then he breaks down what AI for marketing is not effective at:

• Producing something from nothing (creative)
• Empathy (emotion)
• Understanding and contextualizing (storytelling)

Guy then points out that for conversion rate optimization you need to give artificial intelligence a specific task – and if you do, it can open the door up for opportunities for you. However, it likely will not be able to show you those opportunities without you looking between the lines.

Guy points out a handy step-by-step checklist of where the machine learning within artificial intelligence is strongest: when the data quality is high, the training model is highly purposeful and task driven, and when the predictions it is allowed to make are regularly updated. Using this simple checklist, you can create a machine learning model for your data.

Another rule of thumb Guy handed us was based on the number of ideas that we have, and want to test for optimization. Ideas for how to improve, target, or create value can be plentiful or dried out.

If there are lots of ideas – Machine learning application of AI can thrive and be useful.

If there are just a few ideas – AI might be overkill, consider a rules-based variate approach.

If there are minimal ideas – Just use A/B testing.

During the Q&A, Guy pointed out that if you do go the route of adding AI to your stack and are planning to leverage machine learning, which you should do so with a measured approach and let the technology do the work… even 24/7 if need be.

Recap of Key Learnings

• The definition of Artificial Intelligence for marketing applications.
• What AI is effective at and not effective at doing.
• Where machine learning within AI is strongest, based on the number of ideas you have.

 

“The Statistical Pitfalls of A/B Testing” – Chad Sanderson

By the time Chad’s talk came around, I was ready for more complex math, “Has anyone else said that ever?” I was excited to learn some statistical traps from Chad Sanderson because only a few weeks before I had finished reading Thinking, Fast and Slow by Daniel Kahneman and felt prepared to put my statistics hat back on.

Chad Sanderson’s talk is primarily concerned with interpreting the results of marketing testing: A/B testing and Multivariate testing. Assuming you are familiar with statistics and the scientific method, you evaluate your H0 hypothesis (Ex: Changing a button text will increase clicks) based on the results.

If you reject the result (Ex: Changing the button text did not increase clicks) but in fact the result was correct, so you incorrectly rejected it, then you have a Type I error. So, your H0 hypothesis was correct, but you ignored it or misinterpreted the data, and therefore you concluded incorrectly.

On the other hand, if you accept the result (Ex: Changing the button text did increase clicks) but in fact the result was incorrect, so you incorrectly accepted it, then you have a Type II error. So, your H0 hypothesis was incorrect, but you ignored it or misinterpreted the data and concluded incorrectly.

Chad used the errors to point out that you not only need to protect yourself from those common statistical errors but you need to ensure that you are reducing your overall chance for errors. Chad calls it, “Error Protection,” and points to P-Values as a decent indicator for setting up tests.

Chad defines the P-Value as the probability of an event being observed and happening under normal circumstances. As a rule of thumb, not every test is under normal circumstances and often just observing a test will change its outcomes. This is why Chad suggests that a test be run over a long period of time and repeated at some frequency because things can change.

Even with multiple tests, Chad points out that the distribution of P-Values under a Null Hypothesis is random, suggesting that for marketers this will result in, “Finding results that are actually not there.” Basically you’d do a test, get a result, see a low P-Value and conclude the result was unsuccessful when in fact it was not (Type I errors).

So how can marketers construct quality hypothesis testing and get valid results free of statistical errors?

Chad Sanderson’s calls this problem, “Alpha Inflation” and in order to reduce these family-wise error rates he recommends:

• Bonferroni Correction – using adjusted confidence levels that all reach the desired (add up to) alpha for your significance (90, 95, 99, etc.).
• Dunn-Sidak Correction – where you reject the null hypothesis that is under the adjusted-level of the alpha of your chosen significance.
• Holm-Hochberg step-procedure – similar to Holm’s updated Bonferroni method of correction for partition testing, ignoring somewhat the largest order statistic but applying critical values based on Sime’s inequality (adding some assumptions) but relies on join distributions of data.
• Benjamini-Hochberg-Yekutieli procedure – builds upon the false discovery rate and estimates how valid a test is under different types of dependence, and as the number of tests increases the resulting error rates should become reduced.

Did your head just explode? Did you black out? Maybe this will help; I’ve deconstructed Chad’s basic checklist for creating a proper question for a hypothesis:
“How much ____ could I expect to see on ___ given ____ traffic by the end of the ____?”
Example:
“How much lift could I expect to see on button given 68,500 traffic by the end of the month?”

For the question, “How many tests should I run?” Chad answered that we should use the basic formula for N tests: 1 – (1 – P)N. Chad added at the end that he was not a Bayesian because the error rates lack context and recommend that we run tests continually but only to create context for specific questions we have.

Recap of Key Learnings

• Get nerdy and learn about family-wise error rate corrections and procedures.
• Apply the basic hypothesis question checklist to your tests.
• Run enough tests for your desired level of significance based on the formula.

 

“Without Research There is Nothing” – Els Aerts

I got to thank Els for her talk afterward, but it wasn’t until the days and weeks following that I realized how many other people enjoyed her talk. The audience had just been trying to recover from a math, AI, and statistics deep-dive when Els took the stage. What we (the audience) really needed was something light, down to earth, and humorous, to which Els totally nailed it.

For someone so humble, Els is a firecracker. Take her binary title for her talk, and you can get a taste of the flavor of the way she gave her talk. The audience was howling with laughter at one point when her mic went out, and she commented about the “Good looking mic technician.”

Els’ talk centered on the need for marketing research but really honed in on what makes research useful for marketers. Els breaks market research down into three parts: User Research, Usability, and Conversion Optimization. She answers the question, “Why do Research?” with the question, “Don’t you want to know what works best for your products or services?”

Yes, but how much research? Els suggests that we facilitate research that comes just in time for impactful decisions to be made and just enough to get the right information that is needed. I feel like she could call this Agile Research based on how she defines it.

Assuming I was going to start researching tomorrow, what should I do first? Els again covers this by saying you should, “Research the proper stuff, User Research.” She points out there are two types of User Research where you are trying to find out about something tied to a user:

• Quantitative (The what, numeric)
• Qualitative (The why, words/sentences)

So which one should I use? Both, according to Els, a lot of companies and marketers rely too much on the Quantitative research from their data. There are also times when certain types of testing shine and when it does not. For example, focus groups are bad for usability testing.
Here are Els Aerts’ three key testing takeaways:

1. Test with the right people (Relevant audience)
2. Write a good testing scenario (A simple task perhaps)
3. Be a good moderator of the test (This could be a whole talk in itself)

Sometimes a talk stands out because you learn a lot, other times because they were entertaining, but Els talk managed to do both and to fill my journal with lots of notes and scribbles.

Recap of Key Learnings

• Understand why you should do research and then do it.
• Facilitate research that leads to action.
• Start and end with User Research if you are lost, and follow the testing takeaways.

 

“How to Optimize Big Corporates (with a Lot of Legacy)” – Matt Roach

Matt Roach changed up the day’s topic by focusing on organization as a means to bring about change, testing, and success for marketing. Matt’s talk felt like a guide for marketing leaders to pick and find the best candidates for their teams. I met Matt in the hall the next day and asked him some direct questions about what his advice would be for someone wanting to effectively change something at an executive level from many levels below it…

Matt’s advice? First, he empathized with me and said that changing executive culture is similar to being at war and that I should start with proving my case or idea on a small scale and slowly scale it up. Finally, he suggested that we all ask ourselves, “What is our degree of impact at our company?” Matt said that he concluded that he was not having the impact he desired so he decided to go to a company that could give him a path to finding it.

Matt’s talk at CXL described a standardized framework for optimizing organizing for success. When asked why to standardize a framework for organizational optimization Matt broke it down into bullets:

• Completeness (working with goals and checklists helps you stay on track).
• Order matters.
• Efficiency and improving performance is always important.
• Don’t lose the focus if staff go away.
• Explaining yourself (if asked what you’ve been doing, to stay on task).
• A process for refinement.

Matt even has a value formula for how to determine what value teams and employees can bring to an organization (I think some HR people took a long hard look at this):
Value = (Knowledge + Process) * Skills * Attitude

It’s important to note that Matt concludes that knowledge and process are the “what to do” based on the role and the skills and attitude are the “execution” ability of the person in that role. Matt also adds that attitude is very important but is not something you can change – so pick this wisely. Culture is the environment where the value formula operates but that is its extent.

Here are Matt’s 7 Drivers for Organizational Success:

1. Use a standardized process customized to your situation.
2. Layout the budget and resources you have at your disposal.
3. Visualize your organizational structure and hierarchy.
4. Identify the skills within your organization.
5. Identify the attitudes within your organization.
6. Check your culture for alignment at every role.
7. The CEO

I wanted to make a special note about the last driver for organizational success, the CEO. Matt pointed out that the reason he found the most success at one of his recent companies was because his CEO was an enthusiast. Since his CEO understood and believed in the capabilities of optimization Matt was able to grow the company. Matt said it was easier to make an impact and prove yourself if you have the right CEO.

Recap of Key Learnings

• Prove yourself on a small scale.
• Determine your degree of impact; are you on the right path?
• Try your hand at the value formula.
• Use the standardized framework to get a better idea of the drivers of your organization’s success – hint; it’s likely not your culture.

 

“Lessons Learnt from Creating a Large Scale Experimentation Culture” – Mats Einarsen

Mats talk at CXL bridged for me a lot of ideas from Matt Roach’s talk I had heard just before. Mats had a lot of great quotes that I wrote down and a drawing of his systematic testing chart that I crudely drew into my journal.

First, Mats grounded us, especially me, when he said that when it comes to testing, data, and the presentation of results we need to assume that people (executives/decision makers) do not get it and work backward from there.

Pulling from Ronald Reagan, Mats positioned testing in an organization as follows, “Trust, but verify.” From this very important quote, most of Mats entire talk rests – that we need to test, but the results should always be under scrutiny and verification. As we learned earlier from our P-testing, we can easily fall into statistical traps.

Mats showed us the systematic testing chart with locations on each row and columns of data counting the number of tests performed on specific elements. Using this type of layout an optimization marketer could keep track of their hypothesis tests, how many, and color them by the outcomes.

Mats warns us that just because a test did not succeed we do not need to count it as a sunk cost or a wasted test. Everything we test helps us learn and should be treated as such.

Recap of Key Learnings

• Assume that they do not get it.
• Trust but verify your results.
• Try your hand at a systematic testing chart to keep track of your tests over time.

 

“Scaling experimentation at Airbnb: Platform, Process, and People” – Yu Guo

Yu Guo hails from Airbnb, the residence sharing cloud tool for finding places to stay. Yu is a data science manager and gave me one of the best takeaways from the entire CXL event. Yu’s team has a knowledge repository for all of their tests – that’s genius!

Yu says there are three steps that fuel growth for a company:

1. The platform for growth within a company.
2. The processes used by teams within a company for growth.
3. The people who follow those processes at the company to achieve growth.

On top of all three of those steps sits the knowledge repo: a place for experiments and results that teams and leaders across an organization can view and learn about the types of trials that are occurring in order to strive after growth.

The platform within a company can be likened to the MVP of an organization and the condusive elements within that promote growth (technology, culture, and incentives). The processes are the directives and schedule of prioritized tasks by teams where growth will be sought. Finally, the people are the roles that are motivated to seek after and conduct tests in order to optimize.

Yu points out how crucial it is to have growth that comes from your customers. Since customers and trends come about organically and change radically, companies need a process for continued improvement.

Airbnb is not a small company and has grown quickly in the last several years. If you want to grow and become more customer-centric, I would recommend focusing on improving the three steps of growth Yu provides.

Recap of Key Learnings

• Do you have a knowledge repository for your testing?
• Use the three macro steps that fuel growth to compare to your company.
• Are you customer-centric?

 

“How to Win at B2B Optimization” – Renee Thompson

Renee Thompson goes into business to business lead acquisition in her talk at CXL by analyzing tactics per type of sale. Renee points out some of the types of sales that businesses are dealing with. Businesses have to deal with the sale type of their customers based on the amount of time it takes to sell the product or service.

First is the offline sale, which has a complicated pricing structure based on the fluctuation of prices and negotiated final prices.
Second is the long cycle sale, at over 6 months from start to finish.

Third is based on a buyer’s buying team sales cycle, where a group within a company is assigned to vet and buy something.

Fourth and last on Renee’s list is the High-Risk Aversion sales cycle, where there is a natural tendency to be highly risk-averse at all stages of the sales process.

After writing them down, I had the distinct impression that our clients fall into pretty much all of these categories. According to Renee, the different types of sales require a unique type of marketing in order to capitalize on the customer’s pain with targeted solutions.

Renee gave some great pointers when it came to tracking the progress of lead generation for B2B businesses. The most important one was that velocity really matters when it comes to sales. By breaking up the types of customers into sales types, you could track the progress of how quickly customers were moving into and out of the pipeline.

Renee then asked the question, “What metrics matter?” In response, Renee highlights some obvious and not so obvious ideas:

• Leads (obvious), but the focus should be lead quality (not so obvious).
• Engagement of content, the depth of content, and the drop-off points for content.
• Audience qualifying factors such as demographics/firmographics/techno-graphics/etc.
• Attribution modeling that aims at increasing downstream activity.

Recap of Key Learnings

• What types of sales cycles exist for your business?
• How can you capitalize on the sales types?
• What metrics matter for your business? Think velocity.

 

“E-Commerce and Customer Experience Optimization Practices from Dell.com” – Vab Dwivedi

Vab Dwivedi had an approach from Dell that I fell in love with and brought back to my team. His approach was, “Hold yourselves accountable always, but remember to evolve cyclically.” If we have a solution or disagree with a current solution, the team should always be thinking, “Let’s run a test!” However, it should always hold the team accountable that tests are guided toward aligned growth with the team, department, and company macros.

According to Vab, Dell has a market that has some price adjustments on its products, but for the most part, they do not change that often. This made it so that Dell could test out hyper-personalized 1 to 1 types of testing without over-complicated setups. If you have a lot of price change in your products or services, then he says 1 to 1 marketing might be a complicated endeavor.

Vab subscribes to the same type of philosophy as a lot of marketers, KISS (Keep it Simple Stupid). He mentioned over and over again that testing and optimization does not need to be overly complex and if it is, maybe try to find a simpler test.

The best part about Vab’s talk was when he shared Dell’s tenets, as they are impactful takeaways that you should check out:

• Be customer obsessed (customer-centric).
• Act as subject matter experts.
• Deliver advanced analytics that are consumable (huge personal takeaway here for me).
• Data should guide decision making.
• You should serve as a channel of a specific type of knowledge.
• Be agile, push innovation that creates continued value.

Finally, Vab said that his team discovered a lot of their success came from eliminating errors and issues in the customer’s experience during their time on their site. They had cart issues, link issues, and other errors that ruined tests completely. Vab’s advice: focus on eliminating errors if you are looking to test.

Recap of Key Learnings

• Hold yourself accountable to growth.
• Keep your testing and optimization simple, focus on error elimination.
• What tenets does your team believe in and follow?

 

“May the Best Ideas Win (They Usually Do)” – Merritt Aho

If I had to summarize Merritt Aho’s talk from CXL, I would have to say that it’s all about fostering innovation by removing the barriers to creativity. Often creativity does not have a place in marketing teams (as ironic as that sounds) because there is some tried and true method for doing something and no deviation is allowed.

I think best practices are very important to follow – so is tradition – but it is impractical to assume that nothing should ever change. Merritt’s strategy is to use your company and the intelligence of your organization in order to leverage the signals for change.

Merritt wants you to take innovation seriously and give ideas a chance by creating a process for ideation. He suggests that ideas that work are those that can come from anywhere but give you a path to win. Similar to other talks, Merritt uses a process for optimization that starts with research and ends with execution so I will not focus on those parts of his talk – they are basics to testing.

The meat of Merritt’s talk came out of the meeting template he shared for how to get great ideas out of your own organization. He says, “Smart ideas from your own skilled staff means reduced risk for a company.” I agree because your staff is often close enough to the flames to have some of the best ideas.

The template is designed to eliminate availability bias and get to fresh ideas:

• Gather 3-5 people across disciplines at your organization.
• Set the mindset of the group so that they know it is a think tank. You want quantity > quality of ideas (Merritt calls it a brain dump).
• Openness; create a safe space free of critiquing, no fighting/debate, move through ideas and do not settle on anything yet.
• Narrow the topics of discussion to a single topic or problem you want ideas for.
• After ideas settle down, pick one (can be a vote) and challenge the group to build on the idea. Consider at this point to draw it, sketch it, or diagram how it works.
• Finally, thank everyone and take what you learn back to your desk and take time to think about it. Did the ideas help you, take you down a different course, or do nothing?

The template of an ideation meeting should give you answers to the problem you had. If it did not, consider a different approach or rethinking the question. Merritt suggestions that ideas should come from multiple sources in an organization, he calls it the “End of the Lone Wolf,” era.

Recap of Key Learnings

• Do you have problems that require innovating solutions?
• What are the sources for ideas in your organization?
• Consider the template for successful innovation to eliminate availability bias.

 

Day 3 – Analytics Day

Enter Day 3, a shorter day, but a day focused on analytics. What could be better?

“Extending User Experience Analytics into the Real (non-digital) World” – Gary Angel

No talk pulled the carpet out from under me quite like Gary Angel’s talk at CXL. I had never heard of in-store analytics at the level that Gary’s company is taking the experience. I couldn’t help but hunt him down after he gave his talk and ask him about the application to theme parks, zoo’s, and adding 3D to the mix.

Gary’s talk centers on physical stores that have their marketplace in a physical and tangible space. Think brick and mortar stores when I say physical stores. Gary mentions that the internet has all of these amazing analytics, but analytics and optimization are solely lacking in the physical store space. So… Gary took his experience in analytics and applied it to physical store analytics.

Gary’s company focuses on providing analytics to companies who have physical store experiences. He aims to give physical stores omnichannel experiences for all of their channels by tracking and measuring the engagement and activities of customers in a store.

Every few minutes during his talk, Gary brings up the importance and value that analytics can have for a business. He shows the audience a sports stadium and talks about how he can track movement and patterns on each level. Then Gary shows us a Gap store or other physical store and the layout of that store is mapped digitally and he can make heat maps based on customer’s movements in the store. The feeling was, “Whoa! That’s amazing!”

To anyone with a physical store, check out what Gary has to offer.

Recap of Key Learnings

• Physical stores lack omnichannel tracking and analytics.
• Analytics can have tremendous value for physical stores.
• There are solutions, check out Gary Angel and Digital Mortar if you have physical storefronts.

 

“The Pursuit of Customer Happiness: Why Customer Experience Across Devices Matters” – Moe Kiss

The line to talk to Moe Kiss was super long after Day 3 ended at the lunch break. People were standing around for about an hour hoping to talk to her before she left. Moe works at The Iconic, a fashion company out of Australia. I have a hard time picking a favorite talk, but Moe’s talk was so applicable to me that I didn’t have any room left in my conference journal for additional notes.

For anyone familiar with advanced analytics, The Iconic uses snowplow and some other tools to effectively stitch users across platforms, devices, and browsers into a single identifiable profile. This costly and incredibly complex process involves a lot of steps but gives you a single person where you might have seen multiple users before. The unfortunate part is that it still relies on cookies, something I asked Moe about, but she points out that her users are forced to login, so it’s less of an issue for her.

Moe comes in soft but leaves with a bang. Consider this statistic she provided, “Happy customers are worth 40%+ more than your standard customer.” This should make everyone go… how do I get happier customers? I can imagine Moe saying, “I’m glad you asked!”.

So how many of your customers have just one device? How many of your customers are shopping for your products or services from just one device? Hilariously Moe answers this, “No such thing.” Journeys are now more and more across multiple devices. The result of which is that you can no longer 100% trust the analytics that only measures one of those devices.

Maybe you aren’t so convinced… so then I would ask, what does customer value mean to you at your business? Is it:

• High engagement? (Yes)
• Advocacy/Referrals? (Yes)
• Loyalty, repeat business, upsells, etc.? (Yes)

All of those are trackable, and all of those are cross-device and platform. So start optimizing with the way people think. Moe recommends we also change the way we optimize to match the way we think. Since journeys are not linear anymore, we need a model that embraces the variations.

Moe quotes Dr. Peter Fader here, “Embrace the ‘randomness of customer behavior.” There are a lot of interactions that occur between a business and its customers, so it is important to map and understand that flow. Ultimately if you understand the flow, you can understand and optimize for the customer experience.

The higher your confidence level is with identifying individual users, the more you will trust your data and make decisions based on that data. If you trust your data, you can challenge data silos, effectively move the needle in your company’s decision-making process, and you can fix user bottlenecks.

Moe is not a fan of a lot of metrics used to incentivize but instead prefers metrics for diagnostics. So use your data to the best of your abilities – and if you are pursuing customer happiness then start with unifying your users. You can catch Moe Kiss on her podcast @analyticsshow.

Recap of Key Learnings

• Do you have user duplication issues?
• Happy Customers are worth 40%+ more.
• Start optimizing with the way people think.
• Embrace the random, non-linear, multi-device journey.

 

“Building an Optimization Framework driven by the Cloud and AI” – Rachel Sweeney

Rachel Sweeney hails from the Google Cloud Product team, and her talk at CXL was focused on a case study of her team building an application for Machine Learning. A huge takeaway for me from her talk came in the wisdom she shared about beginning a machine learning process, “Get a Data Scientist.” Yes, that’s right marketers, business experts, etc.… you probably need a data scientist before you wade into the waters of machine learning.

Once you’ve got a Data Scientist, you need to have a pow-wow with that Data Scientist, so you both are clear and open about what questions you are looking to solve with Machine Learning. Rachel puts the question into a formulaic expression:

You need a TASK (something), with measurable EXPERIENCES (data) that relates to a PERFORMANCE (goal).

Next, Rachel says you need to decide on a project area: what task you will use, what you will measure, and the data scientist can help you construct your question for why you even need to use machine learning to accomplish it. Hint: similar to the AI talk in Day 2, it’s when rules-based is not sufficient.

At this point, Rachel’s steps for machine learning point out the pre-requisites for a successful project. You need tidy, standardized, and accessible data for machine learning to be successful.

Once you and your data scientist are ready (yes that’s right), you can create a prototype for the project flow to make sure you have accounted for everything. Stress test your idea and then deploy your project.

Rachel suggests that you can look to examples of current tools for ideas of machine learning capable tasks. In the end, report, iterate, and keep testing your project.

Recap of Key Learnings

• Get a Data Scientist to help you if you are considering Machine Learning.
• Build a question around the formulaic expression Rachel provides.
• Report and iterate your project for continued success.

Conclusions

Should you go to marketing conferences like CXL for learning? Yes, absolutely. There are conferences for almost every specialty of marketing. If you are willing to learn from experts in the industry that you can apply to your business, then I’d ask, what are you waiting for?

CXL was an amazing conference, and I hope I get to attend next year. I think the conference shined for teams that are bigger than teams of 1, who can focus on growth and optimization, and are willing to build their companies around strategic ideas. The theme from this conference to me was “customer-centricity,” and I think it’s been a tide that is going to sweep all of the product-oriented companies out of the marketplace in the next decade if they don’t change their game.

P.S. BBQ in Austin is Amazing.

The CXL 2018 conference is the premier conference for optimization-minded professionals. When it came to selecting a conference to attend, my original criteria was to find something that would primarily benefit my core role: marketing analytics. As my search widened to the dozens of conferences available in the domestic United States, I realized that I needed to be more specific about what I wanted out of a conference.

So what matters when selecting a conference?

My criteria ended up reflecting both my professional and personal needs:

• Firstly, it needed to be hosted in the United States, have a focus on marketing analytics, and be in 2018.
• My Secondary focus was on networking opportunities to meet like-minded marketers.
• The third focus for me was that the conference came well recommended by marketers.
• Finally, I wanted speakers from across the spectrum of companies, sizes, and topics.

I ended up with a list of five conferences I would love to attend, and I did a write-up for my boss on the value it would provide me and to our company. I figure that if I am allowed to attend, I should be able to prove why attending provides value to me and CallSource.

I created this write-up, and I was cleared to attend one conference on my specialty (Marketing Analytics) and one on my other hat (Digital Advertising). Woohoo! So what happened? How did CXL 2018 go? I will break it down by day and highlight some of the most valuable insights I gleaned from the event below.

CXL 2018

Kickoff – The MC

Every event needs a Master of Ceremony or the host to help the event move from speaker to speaker and manage the questions and answers. At CXL the enthusiastic host was Michael Aagaard, and he hands-down made the event flow and added excitement to the early morning. I got to talk to him several times and appreciated that he could turn his introversion (hard to believe for an MC right?) into a masterful conductor of the event.

Questions and Answers – How did they pull it off?

Conversion XL selected a really fun tool to manage the question and answer portion of the event. They used Sli.do; you ask a question (even anonymously) and the audience “upvotes” the questions they want to be prioritized. Usually, Michael took about 3 to 4 questions at the end of every talk, and those questions could be read by everyone on the big screen. I had more than a few questions that made the stage (ahh yeah).

My Conference Journal

I will occasionally mention that I pulled my notes out of my “Conference Journal.” I went to the store and bought a small notebook where I wrote down names, notes, details, and took down impressions I had while I was at the conference. It became invaluable when writing this article.

Day 1 – Growth Day

At first, I jokingly questioned growth needing to be a topic. What marketing team or company does not want to grow? However, I quickly reconsidered this thought, because the most important aspect of growth is growth in a controlled and managed manner.

Take the allegory of a farmer; he plants seeds in rows, rotating the crops from plot to plot to ensure that the minerals can return to the earth. The farmer plants in rows and with spacing to ensure that the plants grow without harming each other, making it easier to harvest.

Likewise, marketing growth has to be strategic, planned for, and tactical in what it does to achieve it without harming itself in the process.

The value I got out of day 1’s growth focus was to strategically plan for growth that is sustainable. I would look around in the conference center of the Hyatt of Lone Pines and see marketers from large companies like Microsoft or Google on one side and then marketing teams of one who work exclusively as consultants. I saw that we all had takeaways for how we could approach sustainable growth.

The Keynote – “Saying Goodbye to the Buy Button” – Bryan Eisenberg

Bryan started the talks with a bang; he is not shy and knows how to talk about the core issues of a topic. I went in thinking… maybe I’ll take a few notes and take it all in; it’s just a keynote. Nope! Bryan had me jotting notes and drawing a flowchart right into my conference journal.

“Corporations need a new approach to go from new to current with innovation. Technology is changing exponentially while people and organizations change logarithmically. Yet, we only need to be faster than our competition.” – Bryan Eisenberg [paraphrased]

Bryan’s talk reminded me of an information systems class I had emphasizing the process of information in an organization. The organization of our company and teams need to be built on a process where we are continually innovating. What really struck me was when Bryan points out that the era of mobile started some 10-years ago and yet some companies are only now acknowledging the mobile market. Did we miss the mobile boat? What’s next?

Bryan says the new era we are now entering is the era of “Voice.” Alexa, Siri, Google Voice Search, and more capture the new era of how customers are interacting with brands. I doubted this possibility, but Bryan points out a shocking statistic, “57% have ordered something through voice.” Whoa! Okay, maybe voice is something we should take more seriously.

There is a better model, and Bryan says that model is customer-centric. Bryan adds, “Customers buy ‘why we do what we do'” but asks each of us, “Is that the experience they get?”

Recap of Key Learnings

• We only need to be faster [at innovation] than our competition.
• The organization of our company and teams need to be built on a process where we are continually innovating.
• There is a better model, and Bryan says that model is customer-centric.

 

“Thriving on Change, Driving Growth and Lessons Learned at Shopify” – Hana Abaza

By now the event ended weeks ago, but a survey from CXL went out to the attendees, and Hana’s talk was rated in the top 3 of all talks – pretty impressive. I met Hana in the halls on the final day, and she is definitely a confident and determined marketer. What surprised me when I went up to her was that she wanted to talk about me and was genuinely interested in my company’s situation. I think meeting so many people and presenting takes a lot out of a person, but she didn’t seem burdened at all (at least at day 1).

Hana works at Shopify and wanted to scale her organization but wanted to do so without the issue she calls, “Chasing the Firehose.” Hana wanted to help Shopify Plus, the enterprise platform, to scale both in market share and in the scope of their operations, without trying to set and reach some unattainable goal of scale.

I was scribbling all over my conference journal because I connected with the struggle of changing long-held traditions in companies. Her quote hit me hard, “Facts do not change minds.” So I sat there… puzzling over the concept – so what does change minds?

“Sales needs context, clarity of purpose, a simple goal or strategy.”

In our discussion in the hallway, Hana emphasized patience and proof points. In Hana’s talk she outlined three challenges and how to overcome them, with proof points being one of those solutions:

1. Challenge #1 – the low hanging fruit was a distraction from the necessary changes that Hana really needed from Shopify. Sales teams were the “creative” and the “strategists” because they had to be since marketing was not, and the Shopify Plus platform was scaling without them. Enter Hana, “Marketing had to start from scratch and realign with sales.”
2. Challenge #2 – the inputs that guided decision making need to be defined. Who, what, why care, how different, and positioning against alternatives were the questions that needed solid answers. Some companies answer these in general fashion with their mission, vision, and purpose statements, but Hana wanted them ingrained in the organization’s team with solid proof points.
3. Challenge #3 – motivation. “Context is King,” as Hana says. Sales needs to know what marketing is doing and how it aims to help them sell more, save time, and be more efficient at it. This is where Hana mentions that context for sales must be simple and have a clear purpose.
   Besides needing to be patient, I also wrote down, “Balance the short and long-term impacts.” I feel it’s a life lesson that a little more patience and balance could go a long way for marketing.

Recap of Key Learnings

• “Facts do not change minds.”
• “Sales needs context, clarity of purpose, a simple goal or strategy.”
• The inputs that guided decision making need to be defined, with solid proof points.

 

“Customer Data Operations: Unleashing your hidden growth engine” – Ed Fry

Ed Fry is a charming and friendly guy from across the pond. I first met him in the halls and then again during the loud dancing festivities that night. Somehow Ed remembered me and my name every time I talked to him… who does that?

Like Hana, Ed wanted to know about my company and my “data issues.” I joked, who doesn’t have a data issue? We shared a laugh, and Ed replied, “You aren’t alone, but there are solutions.”

Ed’s CXL talk bluntly addresses hyper-personalization (1 to 1 marketing) by stating that the inability to do so is, “A data problem, not a marketing problem.” You mean you can’t just hire someone, and it will be done? You need a tool? I am used to (1 to many marketing), so I wrote down, “How do you bring this together then?”

Ed shares the magical tools that allow marketers to do hyper-personalization and they are called Customer Data Platforms (CDPs). I had never heard of a CDP or any tool that could reliably handle the complexity needed. Like all tools, Ed points out that there is no one-size-fits-all solution, and that some companies have faux “CDP” solutions.

Ed highlights his [5 steps to freedom] or the roadmap for how to use hyper-personalized data for success and I wanted to share that with you:

• Step 0 – Get in a room together and try to be there in person.
• Step 1 – Define a solid Ideal Customer Profile (ICP).
• Step 2 – Map the entire customer journey into lifecycle stages.
• Step 3 – You need to rely on tools; but find tools that do a good job at enrichment, tracking, attribution, and audience syndication because those are ideal for tools to do.
• Step 4 – Unify personal data across your database so that you are looking at data tied to customers. Huh? How!? Sorry… but you need a tool for this, and they are called CDPs.
• Step 5 – Orchestrate. Discuss the actions, next steps as a team, and how feedback will work.

Orchestration is the application of everything into hyper-personalization. Ed brings up a point that I hadn’t thought much about but makes a lot of sense – who should be responsible for all of this?

“The best success is when the effort is led by marketing, but you will face politics, issues, and things in the organization might have to change.”

Though this quote is paraphrased a bit, it struck me again – another mention of corporate structure needing to change their perspective and the need for patience. Ed was grounded in trying to help everyone at the conference, even if he represents a company (Hull) that makes a B2B CDP. Ed had a slide that showed his picks for the better CDP’s and they included his competitors who do certain things better than they do it like e-commerce, mobile, and enterprise-focused solutions.

If unifying customer data is your problem, consider investigating Customer Data Platforms.

Recap of Key Learnings

• 1 to 1 marketing is a data problem, not a marketing problem.
• There are no one size fits all [CDP] solutions yet.
• The best success is when the effort [to orchestrate the data] is led by marketing.

 

“What Works in Email Marketing Right Now” – Ezra Firestone

I had just come back from a break and was making my way around the rows and tables of chairs in the conference center when I saw a face I recognized. He had longer hair in a bun – something about his face seemed familiar, and he was grabbing a drink of water in the back… so I thought I’d just say hi.

I think he saw me coming because he turned towards me and prepared his hands to say hello. He also looked at me in a, “You know me, and I don’t know you,” sort of way. As I was a few steps from him, I remembered – that’s Ezra Firestone! He led a course I had taken on e-commerce put together by Digital Marketer.

I said hi, we shook hands, and then he said he was preparing himself to speak. I forgot he was even on the agenda (rookie attendee mistake). I told him he was awesome and that I enjoyed his courses and headed back to my seat. He was in another place – focused on what he wanted to convey for his talk, and I thought it was cool to meet someone I recognized.

Ezra breaks down his CXL talk into two main sections: Email, and the future of the medium which he calls conversational marketing. Jumping head-first into an email, Ezra shows in-depth what he is doing with emails, headlines, body texts, and call to actions.

One of the key takeaways for email was that Ezra has found a lot of success in optimizing the layout of every single email (where everything has to go and why). Nobody should straight copy what he does, but it is an amazing jumping-off point to make your own. The second takeaway is that every email fits into an email category that cross-links to one another:

• Trust Builders – social proof, testimonials, behind the scenes, etc.
• Content Emails – tips, education, information, personal ideas, comedy, and relevance focused.
• Offer or Sell Emails – sales based, announcements, coupons, promotions, etc.

The more important takeaway for marketers today was his emphasis on the era of conversational marketing. Just like Bryan Eisenberg noted that voice search is the new technological innovation that marketers should pay attention to – Ezra suggests we all need to take a hard look at messenger marketing.

Messenger marketing (and I saw this acknowledging that Ezra has ownership in a messenger platform) is about having conversations with your customers. Messenger marketing primarily takes place through Facebook messenger and utilizes messenger bots. The bots are not what you think – they are simply predetermined questions that are asked automatically and allow for the recipient to respond by clicking or typing.

Why is messenger marketing working? Ezra points out that emails are still the primary communication channel for businesses and marketers across most industries. However, he also suggests that messenger is reaching audiences in a way that email cannot. Ezra told us something along the lines of:

“Messenger is getting 5X more than email. I’m getting leads for 15 to 90 cents per.”

Wow, that’s pretty good. Ezra points out that he is using messenger at a targeted placement along his funnel and it’s skyrocketing his results.
The final marketing tip I’ll share from Ezra’s talk was that it’s important to reiterate the mission, vision, and purpose in the footer of the email to discourage un-subscription… why? Because it works!

Recap of Key Learnings

• Ezra has found success in optimizing the layout of every single email (where everything has to go and why).
• Every email [should] fit into an email category that cross-links to one another.
• We all need to take a hard look at messenger marketing.
• Emails are still the primary communication channel for businesses and marketers across most industries… for now.

 

“Master Customer Marketing by Watching Romantic Comedies” – Alexa Hubley

Alexa Hubley hails from our neighbor in the north and works for Unbounce. I am a fan of Unbounce, and I had some questions about their platform, so that’s what I went to when we crossed paths in the conference hall. I had only a few minutes before Ezra went on stage, so we briefly talked, she answered my questions, and Alexa apologized that she needed to run but suggested we connect in the hall after.

In her CXL talk, Alexa weaves together a very clever metaphor of applying the process of relationships to customer marketing. I’ve heard Ryan Deiss make a similar metaphor, but this one was relating it to movies. I laughed and enjoyed the jokes and puns that she included throughout her talk and found relationships to be a very relevant metaphor for marketing.

Alexa points out that we can learn from relationships and use that to our advantage: to add intimacy, to woo our customers, and to build trust. Alexa applied her model to the pricing shift campaign that Unbounce went through turning her talk into a case study. Alexa envisioned that her takeaways would help with retention, advocacy, engagement, and help turn customers into evangelists.

So what can we learn from romantic comedies?

1. Intimacy (Love Actually) is all about getting closer and segmenting our audiences the right way so we can talk to them as ‘intimately’ as possible.
2. Woo (Kumail* might have this wrong) is about building an emotional connection with our customer. Alexa suggests that if it fits your brand, try humor.
3. Trust (Bridget Jones’s Diary) starts out with the assumption that our customer may not believe us when we talk to them so reciprocity is key… we have to give to get. I wholeheartedly agree with this mentality.

Alexa ends her talk focused on commitments. As marketers, we have to commit and get buy-in that we want to become a customer marketing organization. In Alexa’s words:

“Put a ‘ring’ on it.”

Recap of Key Learnings

• Get closer to your customer and segment your audiences the right way so we can talk to them as ‘intimately’ as possible.
• Building an emotional connection with our customer.
• Reciprocity is key.

 

“How to 10x Growth by Optimizing Customer Marketing & Retention” – Tara Robertson

The marketing analyst in me wants you to pay attention because retention is probably the single most important thing in all of marketing and from my brief experiences it’s the easiest to ignore. I waited until the talks of the day were done before I approached Tara because her talk gave me so much to consider and I had a lot to ask her.

I knew retention, I had my own cohorts, and I have my own version of lifetime value, and yet Tara managed to humble me and point me in the right direction. I walked over to her group with several pointed questions and by the time we started talking I had forgotten them, but I’m glad I did.

When I couldn’t remember the question I needed to ask I decided to ask her some personal questions about her passion for marketing, for data, and why she does what she does. Tara seemed refreshed to talk about something different as we dove into the “why” which was enlightening to me.

As marketers, I think we should probably consider why we are doing what we are doing and if it’s our passion… even if we aren’t CMO’s or making millions. For Tara, who literally lights up around data, you could tell before she answered that she has a passion for growth marketing.

In her CXL talk, Tara points out that there are two main reasons for customer churn:

1. Lost perceived value by the customer.
2. Environmental loss (most often you can’t do anything about this).

Tara then lays out that retention has terms based on the value the customer perceives:

• Short-Term – usually when the customer first joins, they are excited to try the product or service and have high hopes or dreams. Tara calls this, “Aha!”
• Mid Term – the middle term is when the customer has turned your product or service into a habit, and they have built their processes around the actualization of value. An issue here would be if a customer does not already have full product or service utilization, it may never happen. Tara calls this, “Habits.”
• Long Term – the long-term outlook of when a customer uses the product as they grow, scale, or possibly fall. Tara calls this, “Indispensable,” and uses that word to suggest this is where massive value has to be perceived.

Tara gets statistical here and points out that there might be correlative patterns that result in healthy acquisition and retention of accounts. She breaks it down that these patterns can be quantitative and qualitative (probably always should be both for decision making).

Quantitative patterns to consider include the straight metrics of users, logins, the number of sources, revenue amounts, counting the number of touches, and then the typical retention metrics. The qualitative patterns include (and if you don’t have these consider getting them): customer surveys, voice of customer feedback, and deep interviews.

What are you after? Tara suggestions that you swallow your pride and look for:

“Brutal honest feedback.”

Your findings should always lead to actions and decision making. Finding patterns is a total waste of time if you do nothing to change based on what you learn. Tara suggests that you follow her model for how to approach the change that is needed, “What will make it easier for our customer to do X?”

I second Tara that retention is critical and that we must understand the customer drop off points if we truly want to grow. As Tara says about retention curves, we should try, “Flattening the curve.”

Recap of Key Learnings

• There are two main reasons for customer churn.
• Finding patterns in data is a total waste of time if you do nothing to change based on what you learn.
• Retention is critical and that we must understand the customer drop off points if we truly want to grow.

 

“The Path to 1 Billion: Lessons learned from Growing Instagram” – Bangaly Kaba

I enjoyed Bangaly Kaba’s talk, and I got the impression that there is a lot more to him than what I witnessed. Bangaly is a brilliant guy; he took questions in stride and communicated his message in a way that made me want to start optimizing right away.

Bangaly was also a ghost because I tried to find him afterward but could not. He answered one of my testing questions about whether testing should be small and iterative or “cannonball” as I have experienced, and he suggested that I try to avoid “cannonball.” He chuckled a bit when he read the wording of my question.

Bangaly has helped grow Instagram a lot; yes that’s 1 Billion in growth. That is incredible, and I was all ears when he shared his key learnings for success:

• Focus on the marginal user and remove the barriers to usage, reduce friction, and look for challenges outside of the platform such as in locations, devices, or internet access.
• Making an impact requires that we “understand work” and can identify the difference between work and success – so we have to learn to prioritize.
• Testing is about iterating towards a vision. Bangaly shared that his team knows what stage they want to get to next and when there might be diminishing returns on achieving it.
• Zoom out, solve the larger problems first. Bangaly shared that his team found out about an error that was making it difficult to log in and called this (access churn).
• Focus on the impact you are trying to make. The long-term waves you want to make are the goal, not just the launch.

Just like Tara, Bangaly reiterated that everything starts with Retention. If you are looking for what to test first, look for ways to increase retention. Bangaly left me with a personal warning that I want to share with you:

“Avoid confirmation bias by knowing the signals up front.”

Recap of Key Learnings

• Reduce friction for your customers who want to buy.
• Learn to prioritize testing and optimization.
• Testing is about iterating towards a vision.
• Solve for the larger problems first.
• Everything in marketing starts with retention.

As you can tell, I learned a lot all on day one, “Growth Day.” I was excited for the next two days of the conference, and can’t wait to tell you what I learned in days 2 & 3…

“CallSource^® Reflections” is a blog series by CallSource’s^® co-founder and President, Elliot Leiboff. Elliot co-founded CallSource^® alongside the late Jerry Feldman in 1992. Over the years, Elliot has developed a small call tracking company to a full service lead generation performance organization. CallSource^® invented call tracking. Elliot has witnessed a myriad of inventions, tried different strategies, invested in technologies and basically seen it all.

CallSource^® is a classic American tale of an idea that turned into a business that has thrived through grit and determination. “CallSource^® Reflections” is Elliot’s blog series on lessons learned as a business owner before the era of startups and VC funding.

Elliot’s monthly blog contributions take the reader on the journey of how our solutions have evolved.

 

Marketing Analysis has changed a lot during CallSource’s past 26 years

In the early 1990s, when CallSource invented call tracking as a service, most ad agencies emphasized “image advertising” and treated direct-response marketing as an ugly stepchild.

Industry awards were given for cleverness and style, not for results. This is not surprising, given the traditional lack of tools capable of accurately measuring consumer response. Consider that a handful of “Nielsen families” were presumed to represent the television viewing habits of the entire United States population!

Since the ascendance of cable and satellite TV and, especially, Internet marketing, more precise tools have been developed to measure consumer response. While, in the past, agencies were happy to focus on programs that bore little accountability for results, today, everyone understands the value of direct response marketing — and advertisers demand measurable results.

Modern lead attribution tools allow marketers to understand more and more of the increasingly complex consumer journey, both online and offline. While it was once considered sufficient to count the number of calls or clicks attributable to a particular ad source or marketing campaign, today, far more sophisticated metrics and analyses are not only possible but expected.

The sophistication of call attribution metrics evolved something like this:

# of calls received per ad source > # of calls received per campaign > # of unique callers > # of sales prospects > # of appointments set/sales closed > ratio of appointments/sales closed to # of prospects > # of mishandled opportunities > ranking of company performance vs. peers > ranking of call handler performance > identification of individual call handling skill gaps & prescription of targeted coaching

The sophistication of digital lead attribution metrics evolved something like this:

# of website visits > # of unique visitors > # of clicks > # of web form completions > # of calls from website > # of leads from “web billboard” ads > identification of last site visited > # of responses by AdWord > Social Media responses > Consumer journey cross-domain and cross-device > Integrated, real-time reporting of all marketing-generated calls, chats, web forms, and texts

The increasingly sophisticated ability to track and measure all forms of consumer response provides the opportunity to evaluate where marketing dollars are best spent and to understand which ad sources and marketing campaigns most effectively generate or nurture productive and affordable leads.

When we collect sufficient consumer response data and analyze it using the best available tools, we see that the customer journey is rarely influenced by just one marketing campaign or ad source. While our Google reports might be telling us that nearly all of our leads originate either from Google or from our own websites, a deeper analysis often contradicts this.

To begin with, your own website may be optimized for search, but when the search term that brought the consumer was your company name, it was some other form of marketing or advertising that planted your name or web address in that consumer’s mind. Google does, indeed, deliver many leads – but it is often just the next-to-last step in a complex consumer journey to your website.

A car buyer, for instance, might begin her search on a manufacturer’s website, find a local dealership and visit their website to check their inventory. She may then check on social media for vehicle and/or dealership reviews. After that, she might visit a website that reports the value of her trade-in. After all that, she may use Google to get contact information for her local dealer and call them or revisit their website.

Most of that complex journey is not revealed in a Google report or from the reporting of most digital marketing agencies, and a dealership might not realize the importance of visibility on those nurturing websites.

A consumer in need of an air conditioner might search Consumer Reports or Google “Best Air Conditioner” and search various rating service websites. He might then visit the manufacturer’s dealer locator page or a referral site to find a local contractor to sell and install the unit. Most of this journey is also hidden from a Google report or a report from many digital marketing agencies.

Understanding the consumer journey is perhaps even more important to those who generate and nurture leads for a living. A dealer locator rarely gets credit for leads that originate on their sites but visit other sites before landing at the dealer’s website. A lead generation company that relies on affiliates may not have an accurate accounting of which leads come from which affiliates. Further, they too might lose attribution whenever a lead they generate takes an indirect route to their client’s website. A Cars.com or KBB.com lose attribution for leads they nurture when consumers visit intervening sites before arriving at the website of their client dealers.

Many lead generation and web analytics companies use cookies to track consumers. This approach fails with web visitors who don’t accept cookies or who clear them – or who set their browser to an incognito mode to avoid remarketing spam. In such instances, the media companies won’t get proper attribution for a lead, and the business won’t recognize return visitors.

Fortunately, new technologies like AI^sm and EveryLead^® answer many of the issues outlined in this article. It is now possible for digital media companies and the businesses that employ them to track consumer journeys across domains and across devices without the use of cookies. All leads, whether they arrive via telephone, chat, text, or web form, can be displayed in a single, real-time dashboard plus a series of detailed reports.

AI^sm is a service mark of AutoID, LLC.

EveryLead^® is a service mark of CallSource, Inc.

“CallSource^® Reflections” is a blog series by CallSource’s^® co-founder and President, Elliot Leiboff. Elliot co-founded CallSource^® alongside the late Jerry Feldman in 1992. Over the years, Elliot has developed a small call tracking company to a full service lead generation performance organization. CallSource^® invented call tracking. Elliot has witnessed a myriad of inventions, tried different strategies, invested in technologies and basically seen it all.

CallSource^® is a classic American tale of an idea that turned into a business that has thrived through grit and determination. “CallSource^® Reflections” is Elliot’s blog series on lessons learned as a business owner before the era of startups and VC funding.

Elliot’s monthly blog contributions take the reader on the journey of how our solutions have evolved.

 

Longtime CallSource CTO, Jason Scinocca, is especially busy these days.

That’s because Jason is also the CEO of fast-growing start-up and CallSource sister company, AutoID.

Jason invented a way, without cookies or pixels, to track online shoppers; both cross-domain and cross-device. Seeing the obvious synergy with its own call management business of more than 25 years, CallSource threw its support behind Jason and invested in AutoID.

Combining the industry-leading capabilities of the two companies, EveryLead® is the first service to offer a unified dashboard that details and attributes a subscriber’s leads; whether they arrive as calls, chats, texts or web-form submissions. While it integrates with Google Analytics, Marketo, and other marketing analytics platforms, EveryLead’s own reporting provides more accurate lead attribution and much richer consumer activity data than, for example, Google can.

For example, where Google attributes a lead to the last site visited, EveryLead can track a consumer’s complete online and offline journey, from first website visit, email invitation, or social media campaign to call, chat, text or web-form contact with a subscribing dealership. Of course, it also identifies whether online leads are the result of organic search or paid search, including AdWords.

Where Google would report a returning web visitor who refuses cookies or browses “incognito” as a new visitor, EveryLead recognizes the return visitor and displays his or her prior shopping activity, including vehicle display pages viewed and communications with the subscribing dealership. While Google would identify a “bot” as a visitor, EveryLead could recognize a bot visit as a non-prospect.

While Google won’t tell you when a prospective customer defects to a competitor’s website, EveryLead can send you an immediate alert, identifying vehicle display pages viewed on the competitor’s site, along with his or her activities on your site and contacts with your dealership.

EveryLead can help dealers predict inventory requirements by reporting, in real time, the most-viewed vehicles and trim packages in the market. Instead of stocking last month’s best-sellers, dealers can order the vehicles consumers will be purchasing next month.

EveryLead automatically assigns and manages dynamic website phone numbers for session-based tracking, in sharp contrast to the labor-intensive processes required by other platforms. The digital tracking features install with a single line of code. Reporting is detailed enough for the most advanced digital marketer but simple enough for any manager or owner to clearly understand.

To learn more about CallSource or AutoID and how it can help your business, contact one of our representatives today.

“CallSource^® Reflections” is a blog series by CallSource’s^® co-founder and President, Elliot Leiboff. Elliot co-founded CallSource^® alongside the late Jerry Feldman in 1992. Over the years, Elliot has developed a small call tracking company to a full service lead generation performance organization. CallSource^® invented call tracking. Elliot has witnessed a myriad of inventions, tried different strategies, invested in technologies and basically seen it all.

CallSource^® is a classic American tale of an idea that turned into a business that has thrived through grit and determination. “CallSource^® Reflections” is Elliot’s blog series on lessons learned as a business owner before the era of startups and VC funding.

Elliot’s monthly blog contributions take the reader on the journey of how our solutions have evolved.

 

Product Development by Clients?

I am occasionally asked: “How many people does CallSource have on its research and development team?”

I answer, truthfully, “many thousands.”

Most of our best processes and technologies begin with suggestions from our clients. Since they know more about their businesses than we do, we listen, study, and respond.

The First Client-Led Product Development

In 1992, when CallSource first invented call tracking as a service, our sole purpose was to create an objective, accurate, automatic system for attribution of telephone leads. No sooner did we accomplish that goal then we learned that many of our clients were equally interested in analyzing the performance of their call handlers.

As part of our call tracking service, we were already recording the calls so we could validate that they were legitimate leads. In response to the request to allow our clients to review their own calls, we developed the call recording retrieval system known as CallReview^®.

With CallReview, a trainer or sales manager could simply click an icon to play back a call recording. It probably won’t surprise you to hear that, despite their new ability to review their call recordings; busy sales managers complained that they didn’t have time to evaluate all of those calls. So the next request was: “Can you review the calls for us and send us a monthly report card for each salesperson?” That’s when we developed what we call Telephone Performance Analysis^sm(TPA for short).

 

Studying the Elements of a Successful Phone Call

Identifying the core elements of a successful telephone call was challenging.

CallSource analysts painstakingly reviewed millions of recorded calls, correlating conversational style and content with successful outcomes and assigning a weight to each element of a successful call.

From this, we developed the Core Four Principles^sm that every salesperson needs to learn:

As these principles went unapplied in the majority of calls we analyzed, we realized that telephone lead conversion was a significant weakness – and a huge opportunity for our clients. In our analysis, top performers often outperformed average performers in converting telephone leads by as much as three to one.

Artificial Intelligence vs. Human Intelligence

In an effort to reduce the cost of analyzing calls, CallSource invested many years and millions of dollars attempting to apply speech analytics software to Telephone Performance Analysis.

While speech analytics is useful for a number of things, it is disappointingly incapable of the accuracy required to grade an employee’s telephone skills fairly.

With advances in machine learning, speech analytics may one day overcome background noise and mobile phone audio issues, and understand the complexities of human conversations; homonyms, accents, and idiomatic expressions. For the present, however, expert, well-trained, (expensive) analysts are still needed for accurate results.

The Need for Call Coaching

Interestingly, the mere knowledge that their calls were being graded prompted improvements in call handling performance… but most TPA scores hit a plateau within a couple of months.

Clients, weary of receiving reports, month after month, confirming how poorly they were converting calls asked us if we could please help them to improve the phone skills of their employees.

In response to that request, we applied the unique knowledge gained in reviewing millions of calls. Instead of “one-size-fits-all” training, we developed a coaching system and an online learning management system, Our University^®, featuring phone skills training and coaching targeted to the individual training needs of each TPA participant.

CallSource training and call coaching teach Core Four Principles and encourage changes in sales strategy and call-handling behavior. The process requires patience, particularly with sales people who have learned bad habits over years or who were trained to rely on out-of-date tactics and scripts. Individual or group coaching, followed by monthly TPA reports, creates a feedback loop that keeps employees developing and exercising the specific skills that are proven to increase lead conversion.

The Evolution of CallSource – Thanks to Our Clients

By studying the needs and following the suggestions of our clients, CallSource evolved from the leading call tracking company to a coaching, training, and performance management organization that utilizes call tracking and recording to provide valuable business insights and advice.

Other, great, client ideas helped lead us to develop a series of call processing and lead analytics services, plus online sales enhancement and web lead attribution tools, which I will be happy to write about another day.