In the current world nothing is frozen. Everything can happen and it is possible to benefit from that as long as you are adaptable. Almost everybody is aware of this but only few people apply it.
The e-book “Analytics Lessons Learned” offers 13 case studies highlighting how companies applied their metrics to adapt in their markets. Of these, three companies stand out for how they made a strategic change in their marketing based on metrics. Hopefully these case studies can help your company.
Lesson 1: Add a free service that no one else offers.
Sometimes adding a service that doesn’t immediately relate to your core offer can help your business take off to a huge success. AirBnB is a website where you can rent from apartment or home owners. Their business was growing slowly until they considered offering a free service to their customers: they would hire professional photographers to shoot photos of these apartments and homes to improve the quality of listings.
Why did this work?
AirBNB relied on Craigslist to grow its market, and they probably identified the quality of listings by their competition was low, allowing them to differentiate through superior photos.
Offering this service was a good idea, but they didn’t know this immediately. Instead, they tested their idea on a small sample and analyzed the results. Once their results proved conclusive, they applied this plan to their entire audience and found exponential growth.
As more marketing channels become low- cost and based on attention or some version of SEO, consider unorthodox strategies to gain an advantage, but test your ideas first.
Lesson 2: Don’t sell to everyone.
In some other cases, companies realize that even if they have a big audience that is growing, the quality of the audience more important. A few real fans is more important than many unengaged fans. Circle of Moms is a Facebook application that started as Circle of Friends. They had 10 million users in these circles, but most weren’t sharing any content. After delving into the data, the founders discovered that most users were not really involved in the application, except for one category of users: moms. By changing their app to focus on moms, they lost half of their audience, but the remaining audience became far more committed. They found their niche, ultimately leading to a successful acquisition by Sugar Inc.
Why did this work?
The founders engaged with their metrics to identify the root causes of their problem. What they found was they had a failure as a mass market app and a success as an app for moms.
If your metrics are not tracking as you expect, dive into them to understand where you are strong and where you are weak. You may not need to limit your market, but understanding where you are successful can guide a turnaround in the areas where you come up short.
Lesson 3: Choose the right KPIs.
Some companies choose the wrong KPIs, and become aware of this much later. Backupify is one example: they were good at monitoring data and focusing on metrics to grow, they just didn't look at the right ones. They were growing fast, and increasing revenue, but they then realized number of signups is not the most meaningful metric. After having a look at their Customer Acquisition Cost (CAC), they realized that they were spending more than 6 times what the customer pays per year to acquire the customer! They understood that they had to change their target and be more focused on business, so they now monitor the Monthly Recurring Revenue, the ratio between Customer Lifetime Value (LTV) and the CAC. This latter metric now hovers around 5:1, meaning each dollar invested in sales and marketing results in five dollars of revenue.
Why did this work?
As Andy Grove mentions in his book “High Output Management”, you should pair your metrics to measure the effect and counter-effect of your actions. Initially, Backupify focused on metrics that were improved by pouring more money into customer acquisition. It was only when they paired their indicators that they could see the limits of acquiring too many customers too quickly.
Most dashboards we see have a sense of the volume of leads produced per month and the cost to acquire these, but little measuring the quality of these leads. If your sales cycle is 24 months, this can be difficult to measure, but you should strive to understand all three of these values (quality, quantity, cost) to have a true sense of your demand generation output.
Big data has everyone buzzing about applying data analysis and metrics to their business, but it easy to apply this in the wrong manner. If you track the right KPIs, interact with your metrics to understand what’s really happening, and test your hypotheses in small groups, you’ll gain an advantage over your competition.
With the Marketo Summit kicking off today, we have a special edition of the job openings in marketing automation. Below you'll find some of our favorite Marketo-related job listings with highlights about the company. Its a great time to seek out the hiring managers and meet them in person.
RingCentral offers an award winning cloud business phone system. With a product starting at $19.99 per month, their sales cycle should be short and the volume high, so if you love A/B testing this is your place.
They are currently hiring a Marketing Manager experienced in all areas of marketing automation execution, including creating SOPs and rolling out new features. From what I can tell, they have a history of offshoring campaign creation, and I suspect they need someone who can centralize and standardize their processes.
You can apply at RingCentral here.
Location: San Mateo, CA
AppDynamics is looking for a Marketing Operations Specialist to handle level 1 admin work, including list uploads, email campaign execution, and list purchases.
This job is currently hot on Linkedin right now and its no surprise: AppDynamics is a highly regarded company backed by Tier 1 investors with a successful freemium strategy, and they are willing to train the right person to use Marketo.
You can apply for this job here.
Location: San Francisco, CA
Yammer is hiring a Sr. Lead Generation Marketing Manager to focus on lead generation, lead nurturing, and analytics/reporting.
If you don't know about Yammer, they were a competitor to Salesforce Chatter that was acquired by Microsoft. Their marketing strategy was to sign up users within companies for free, and then upsell security features to the decision makers.
Looking at this job description, they don't seem too committed to Marketo, and are more focused on the programs/outcomes aspect of the tools. If you have experience with Eloqua and Marketo, this could be a great position to combine your understanding of the two.
You can apply here.
Location: San Francisco, CA
Tired of working for a tech company? Generations Federal Credit Union is looking for a Marketing Manager responsible for oversight across all member channels (consumer, youth, and small business) to further develop member acquisition and retention. This role will report to the VP of Marketing and drive insights and targeting to improve online and offline branches.
Generations seems to be ahead of the curve here, adopting marketing automation in a banking space. Its great to see the focus on the right analytics (ie NPS) to drive strategy.
You can apply here
Location: San Antonio, TX
WANdisco is hiring a Demand Generation Manager. You'll be focused on all areas of online marketing, including SEO/SEM, social media, marketing automation, and Google Analytics. It seems they just hired Douglas Ribback as the Director of Demand Gen six months ago, so this UK-based company looks to be growing fast.
Location: San Ramon, CA
Love Big Data? DataStax is hiring a Marketing Operations Manager. You'll be responsible for data strategy, campaign execution, and producing effective dashboards.
It looks like DataStax hasn't fully implemented Marketo yet, so you'll have the opportunity to make an immediate impact with tracking across the web properties. You'll probably be reporting to Jeff Wiss, a well regarded industry veteran of Sun with about two years familiarity with Marketo.
Check out Jeff's profile or apply here.
Location: San Mateo, CA
Xactly Corporation is looking for a Marketo rockstar. When you read this, you know the company is heavily invested in their software, and Xactly doesn't disappoint. Xactly offers an on-demand sales compensation program and has in many ways grown in lockstep with Marketo. With a target buyer in Sales, you'll have the opportunity to push the limits of cutting edge marketing tactics, and they seem committed to content marketing, lead nurturing, and investment management.
You can apply here.
Location: Santa Clara, CA
FireEye is hiring a Marketing Programs Manager for their Partner Ecosystem. This role is more focused on Partners than Marketo, but they expect you to be well-versed in Marketo and other modern technologies in the enterprise.
You'll report to the Senior Manager of Channel and Alliances, perhaps Didi Dayton, and you'll likely work with Linlin Li to get the most out of the Marketo implementation.
Check out their profiles and then apply here.
Location: Milpitas, CA
Tired of looking for jobs? Perhaps you should work at Jobvite. They are hiring a Marketing Programs Manager to execute on campaigns and measure the results of these campaigns.
Jobvite is an interesting company to watch: they are an example of B2B social media done right, combining better data with peer to peer trust and networking mechanics. They relegate it to a minor note, but you'll likely rely heavily on social media to drive campaigns, as your prospects will need to buy-in to social media to adopt Jobvite themselves.
You can apply here.
Location: Burlingame, CA
What is the difference between first touch attribution, last touch attribution, and multi-touch attribution? To explain this, I'll tell a story.
Let's say Bill lives in a relatively quiet suburban neighborhood. He doesn't have kids so he's not really in tune with what the neighborhood kids are involved in. Because of this, he has no idea it is Girl Scout Cookie season and that a Girl Scout might be ringing his doorbell any minute.
At 3pm on a Tuesday afternoon, his doorbell rings. Wondering who it could be, Bill goes to answer the door to find a nervous Girl Scout (we'll call her Lindsay) asking him to buy some cookies. Bill doesn't care much for children and honestly doesn't really know what a Girl Scout Cookie is so he declines as politely as he can. He thought about Girl Scout Cookies for a couple of seconds before he fell asleep that night.
On Thursday afternoon, another Girl Scout (Amanda) comes to his house. Amanda, however, is a lot less timid and is a lot more knowledgeable. She explains to Bill how buying just one box of cookies will support her local Girl Scout troop and that the money will be put towards a good cause. Bill almost gives in this time but then remembers that he doesn't even really like cookies so he politely declines again. That night, he thinks about the cookies for much longer than a couple of seconds. In fact, he starts thinking he should have bought the cookies. Who cares if he didn't like cookies? He could easily spare $4 dollars to support the local Girl Scouts. He went to sleep that night with a nasty feeling of regret.
At 10 am on Saturday morning, a third Girl Scout (Ashley) knocks on his door. Before Ashley even finishes her sentence, Bill agrees to buy 2 boxes. That night he felt pretty good.
Who should get the credit?
Now, the question we want to examine is "who should the get credit for the sale?". To begin answering, we will look at the influence of each of the Girl Scouts. The diagram below illustrates all of the touch points during the sales process.
We can make a pretty strong case for each girl. If it wasn't for Lindsay, Bill would have never even known about Girl Scout cookies. If Amanda never knocked on his door, Bill would not have learned about Girl Scouts and how he would be supporting them by buying a box of cookies. But of couse, Ashley was the Girl Scout he actually bought the cookies from, even though she didn't really communicate with Bill at all.
Relating this to marketing, if we were using first touch attribution, Lindsay would get all the credit. With last touch attribution, Ashley would get all credit. And if we were using multi-touch attribution, the credit would be divided among the 3 girls (by default Marketo divides this credit evenly). The chart to the right illustrates this.
Which one is right for you?
The one you use should be dictated by your products and how your company's marketing department functions. If you are a B2C company and you quickly convert leads after they visit your website, you may want to use first touch attribution and give all credit to the website. However, if you are a B2B company with a long sales cycle, your leads are probably touched by several marketing programs before they are converted. In this case, it may be best to use multi-touch attribution and give credit to all programs which affected the lead. In many cases, multi touch attribution is the ideal choice because it gives you a comprehensive view of all programs which influenced your leads, however, it is not always the easiest to implement. Regardless of which form of attribution you use, make sure to keep it consistent across all your marketing initiatives.
Marketo has several fields which affect if a lead gets one of your emails or not. Depending on which are marked, a lead might get all your emails, only a few, or none at all. In this post, I'll go over which fields do what and which emails a lead will get depending on which fields are marked.
There are three main fields in Marketo which affect whether or not a lead receives emails. They are:
1. Marketing Suspended
If a lead is marked Marketing Suspended or Unsubscribed, the lead will receive some emails. If a lead is marked as Blacklisted, the lead will not receive any emails.
There are two basic types of emails which Marketo can send: operational and non-operational.
Operational emails are sent to all leads that are not Blacklisted. Marketing Suspended and Unsubscribed leads will still receive Operational emails. For this reason, you have to be very careful on which emails you mark operational. In general operational emails are emails which deliver something a lead has requested. For example, if an unsubscribed lead requests a whitepaper, the lead can still get the email with the whitepaper if that email is operational.
Non-operational emails are sent only to leads which are not Marketing Suspended, Unsubscribed or Blacklisted. For the most part, these are marketing emails. The table below describes what lead can receive which type of email:
In the above table, only Lead 8 will receive all your emails. As you can see, any lead which is Blacklisted receives none of your emails regardless of the status of the other fields. Similarly, any lead which is Marketing Suspended or Unsubscribed will only receive your operational emails.
Have you ever looked at an email report in Marketo and wondered why you have more clicks than opens? How can someone click a link in one of your emails without actually opening the email?
The answer lies in the way which Marketo measures opens in Gmail. If you use Gmail, you are probably familiar with the "Display Images" option which sometimes appears in your emails.
By defualt Gmail does not display images. Marketo measures whether or not you opened an email by whether or not you choose to display images in an email. Since Gmail is one of the most commonly used email clients (in 2012, Gmail had more than 400 million users while Outlook only had 25 million) it's likely a high percentage of your Marketo emails are sent to Gmail accounts, even if they don't have gmail addresses.
According to a study by Silverpop, the average Click to Open Ratio (CTOR) is about 19%. But because people might not be displaying images, this average (as well as your own data) may be inflated.
If your emails are structured in a way that does not require a reader to display images to understand them, your email recipients probably don't display images. If a lead opens such an email and does not choose to display images but still clicks one of the links in your email, Marketo will register the Click but not the Open. Thus Marketo might be reporting your CTOR much higher than it actually is. This inflated ratio is not an accurate measure of the effectiveness of your emails. To truly measure CTOR, you need accurate recording of all opens. There is no certain way to measure CTOR: if your leads don't display images, you cannot track their opens.
There is something you can do to try to improve the veracity of your open rate data: give your readers an incentive to display images. For example, you might center the introduction of the email around an image or video. By talking about the image or video in the email, the reader may be more enticed to display images. Furthermore, it helps to enter text near the image to prompt the reader to display images ("Click display images to see the ridiculous photo which I am talking about").
One expert at this is Ramit Sethi of IWillTeachYouToBeRich.com. His emails provide fantastic incentives for his consumer audience to display images.
If the data will never be perfect, why bother improving your open rates?
One reason is deliverability: when leads click to show images, they demonstrate a trust in your emails, a trust that can be extended to 400 million other Gmail accounts.
Even if you can't trust your open rates to be 100 percent accurate, you should continue to test subject lines to increase your open rates over time.
Have you ever felt your Sales team misses out on Opportunities despite Marketing providing an ocean of leads? Perhaps the method of finding these Opportunities is the problem.
When searching for lost objects such as sunken ships or downed airplanes, search and rescue teams often employ Bayesian search theory in their search. The specific procedure involves six steps designed to minimize the time to reach a conclusion:
1. Formulate hypotheses about what could have happened to the lost object
2. Construct probability distributions for the location of the object for each hypotheses.
3. Construct a probability distribution for actually finding the object in location X if it is in location X. Knowing an object is in a certain area, is different than actually finding it.
4. Combine the above to produce an overall probability distribution. This will result in a the probability of finding the object in location X for all X.
5. Construct a search path with starts at highest point of probability and moves through locations with progressively lower probabilities.
6. Constantly revise all probabilities during the search. For example, if the object is not in a specific location, it will raise the probabilities of it being in the other locations.
(Shamelessly ripped from Wikipedia. Read more here)
This theory can be applied, at least in part, to marketing and specifically lead scoring. When searching for potential Opportunities, applying these steps will minimize the time it takes to find opportunities. Constructing a comprehenisive probability distribution, while fun, is not necessary. Let's take a closer look at some of the steps and how they relate to marketing:
Step 1: Formulate hypotheses about what could have happened to the lost object.
In this case the "lost object" is all leads that will turn into opportunities. When targeting leads with marketing efforts you should only target leads which you think have a chance of actually becoming a possibility. Within your company, you should define what a target lead is and why. In addition, you should also define leads not worth pursuing. This is analogous to creating a demographic scoring model.
Applied to marketing, steps 2-5 basically say
- Figure out which leads are most likely to convert and start with them.
- After figuring out which leads are most desirable and setting up a lead scoring model, you should start pursuing the leads with the highest probability of converting. In this case, they will be leads which interact positively with your company.
- If your lead scoring model is set up correctly, they will be the leads with the highest scores.
Step 6: Constantly revise all probabilities.
After running your lead scoring model for awhile, you may find that some leads score highly but do not convert to Opportunities.
Take a look at these leads and try to spot some patterns. Maybe a cluster of these leads scored highly after downloading your new whitepaper: if these leads are not converting, the whitepaper may be less influential than you thought. You might try lowering the points given for the whitepaper and see if your conversion rates improve relative to scores.
Similarly, you might have originally assigned high points to leads from a certain industry. If these leads are not converting as well as you expected, you might lower the points given for this area and let the leads take other actions before they pass to sales.
Like searching for lost objects, searching for Opportunities in an ocean of Leads is difficult but not impossible. Simply building a standard lead scoring model is like arbitrarily pointing your searchlight in one part of the ocean and hoping it lands on your object. By applying a more robust process to developing your lead scoring model, you'll find more treasure in your waters.
Many clients ask us whether they should purchase the advanced features of Marketo. These features, in the right hands, can offer tremendous value to a Marketing department, transforming them from a cost center to a profit center.
For most clients? These features require more time investment than they expect and can be ignored as more pressing challenges arise.
The main problem we've found is in understanding this time investment and the prerequisites of Marketo that you should master before adopting the advanced functionality.
Here then are the key parts of the RCA module and what you should know about them.
Revenue Cycle Analytics Explained
Revenue Cycle Modeler
Feature: drag and drop interface to design your processes from Anonymous to Won. Assign SLAs and access data from Salesforce Opportunities.
Benefit: Establish SLAs and process to begin identifying funnel leakage.
Who this is for: Marketers who want closed-loop reporting and higher conversion rates from lead to closed.
- Working relationship with Sales to agree on the appropriate stages and SLAs
- Ability to test and validate model
Blind spot: an active model is not necessarily a properly functioning model, yet many marketers fail to validate their leads are moving into the appropriate stages.
Opportunity Influence Analyzer
Feature: demonstrate the programs, events, and activities that influenced leads prior to opportunity creation.
Benefit: demonstrate marketing's contribution to the pipeline through anecdotal evidence. This can defend against Sales taking credit for a big deal when marketing clearly acquired the lead.
Who this is for: customers who struggle to demonstrate marketing's impact. This is especially useful when Marketing helps Sales by nurturing Sales-acquired leads until they are ready to buy.
- Opportunities in Salesforce
- Proper Contacts associated with Opportunities
- Marketo Programs
- Acquisition Program data
- Marketo Interesting Moments
- Clean database without duplicates
Blind spot: this report is most valuable when deals are recently won, but the data should be clean beforehand with proper Contact and Program association.
Feature: analyze marketing programs across numerous slices to determine their effectiveness.
Benefit: visually compare effectiveness of channels, programs, and content in dozens of ways, quickly.
Who this is for: the marketer looking to maximize return on program spend by looking at multiple variables.
- Marketo Programs
- Marketo Program Tags in place for the variables you wish to track
- Marketo Program Costs entered consistently
- Opportunity tracking in Salesforce
Blind spot: this report provides an immense amount of data, but many marketers fail to understand the meaning of different metrics such as Multi-touch Attribution. Others don't add all cost data, rendering this report useless.
Success Path Analyzer
Feature: Monitor how long leads stay in every stage from Anonymous to Won, and the variance against Service Level Agreements.
Benefit: With the Success Path Analyzer, the process from known lead to won deal is no longer a black box. You can identify where leads get stuck in the funnel and produce programs to specifically address these positions.
Who this is for: Closely aligned sales/marketing teams looking to increase velocity of deals and selectively target leads to move them through the funnel.
- Properly functioniong Revenue Cycle Model
- Testing to validate leads are in the proper stage
- Strong Sales/Marketing alignment: this report is useless without SLAs, and SLAs are useless without trust between departments
- Process to address leads failing the SLA
Blind spot: many marketers get stuck in the technical implementation of the Revenue Cycle Model, having never actually defined this before. We recommend whiteboarding your model to get to the core of every stage, and then implement it with tests to validate leads are in the proper stages at all times.
Ad Hoc Reports & Analysis
Feature: Custom dashboards in Marketo
Benefit: What gets measured gets done. Arrange your KPIs so you always know where you stand
Who this is for: Marketers with high data quality and a lower understanding of Salesforce.
- KPIs for each of your goals
- Consistently track the metrics you want to report on: revenue stage, program channel, lead owners, etc.
- Confidence in your data
Blind spot: many marketers can get lost in the Revenue Cycle Explorer, trying to produce reports that are better created in Salesforce or other Marketo Reports. RCE can be quite valuable, but it has a learning curve and you'll need to invest the time to understand how best to use it.
We recommend marketers whiteboard their KPIs first, then look for ways to implement these, whether through Salesforce reporting, the RCE dashboards, or other means.
In general we find the Revenue Cycle Analytics, while useful, requires a strong relationship between Sales and Marketing and a tolerance for "failing fast" as these numbers can look ugly when you first see them. With the proper foundation, however, understanding marketing as an investment can give you a competitive edge in your market.
The world has gone social. SEO has been rebranded to "Content Marketing". You're expected to produce media 24/7, like your own news station.
Yet you still ignore those ads on Facebook.
So whats going on?
Mapping the Quadrants of Social/Search
To explain this, I'm going to map out four quadrants of the digital economy, then see where familiar companies fit in. Finally I'll look at the trends and what it means for how you market your goods.
Currency is the type of exchange between parties. In this case, there are two distinct currencies: Love and Money.
The Money side represents the exchange of money for goods and services: you don't need to know the party on the other side of the transaction, but you'll need a third party to provide enough trust for the transaction to be completed. When you buy a house, this is an escrow service; when you buy jeans on eBay, this can be PayPal.
The Love side represnts the non-monetary exchange. This can be friends, or family, or love, but in all cases you're likely to get that uneasy feeling when a money exchange is involved. You will however be comfortable sharing gifts, notes, pings, and other items without a legible price tag.
Method is your means of finding the exchange of love or money.
Search is the Google paradigm: when you want to find/learn/buy something, you search in the box and it pops up in the results. Everytime you do this, you are increasing your trust in the search engine for answers to everything.
Knowing this, two trends appeared: Google began selling the space where these results display, and SEO hacks began trying to game the results in their favor.
Connect is the Social paradigm: when you want to find/learn/buy something, you ask your network. If you have the right connections, you don't need the right keywords: your network will understand the missing parts of your request and fill them in.
The Four Quadrants
The companies in this graph don't neatly fit into just one quadrant. For example, Facebook is looking to move into Search (and Money) with its Graph Search. Amazon is increasing its social with verified reviews.
Rather than an either/or, this graph represents the spectrums of Love/Money and Connect/Search.
The Singles Economy: Love/Search (Discovery)
This sector is defined by those looking for social connections among strangers. The most obvious example is online dating: when on most online dating sites, you're looking to meet new people and not spend money to buy anything. You trust the search engine, the listing of singles, to provide you with a match to your interests.
This quadrant is interesting because its extreme demonstrates the value of the Social Networks: when you're on a dating site (or in a Singles bar) you don't intend to spend money, but numerous social signals will nudge you to purchase things such as drinks and virtual gifts. In other words, there is money spent here, but its more of an impulse purchase in its nature.
The Facebook Economy: Love/Connect
This quadrant is what we typically consider social networks: places to connect with people we already know and deepen relationships. Relatively few people look to exchange money on Facebook or Skype, for example.
This quadrant is losing the most pure companies: with the exception of Skype, a paid-for service that maintains your network, the other top companies are all looking for ways to monetize and/or add search to their user behaviors.
The Google Economy: Money/Search
This quadrant powered the Internet for over a decade, and its easy to see this in the top players: Google, Amazon, and Apple all focus on providing the best portal to find what you want in some way. They provide a marketplace where you expect to spend money; they act as a third party in your transaction, with varying levels of guarantee and promise.
This is also the domain of SEO: because consumers open their wallets in this quadrant, the immense amount of money to be obtained leads many to attempt to hack the search engines. We've seen this mostly for Google, but look for more SEO plays around Amazon and the Apple App Store.
Ultimately, the Money/Search quadrant will fail: the distribution of clicks is too heavy at the top, making the incentive to game the system higher, and the ability to game the system is far easier without the network effects at play.
When Google is the #1 trusted advisor to millions of people, it will Be Evil.
The Referral Economy: Money/Connect
This quadrant is where the action will happen in the next few years: as search engines become less trustworthy, attention becomes more scarce, and more companies truly add social features, we'll see this quadrant expand in importance.
To understand this, again consider the motivation of SEO: Google sorts through billions of websites to provide you the answer to your question. If the value of answering your question for everyone looking for it is $1M, and 10,000 people attempt to be #1 for this answer, you need only 1 in 10,000 to succeed at finding the right algorithm match to game Google and reap the benefits.
The benefit of being #1 in the Google paradigm is simply worth too much.
Alternatively, the Referral Economy exclusively shows you results based on people you know and trust. In this economy, the value of answering your question might only be $1k, not $1M, because the answers are dispersed among websites each with some level of social influence.
There is no longer a winner take all.
Linkedin is the only player clearly in the Referral Economy, though Google and Twitter are making large inroads here.
Where are we going?
When the internet first reached the mainstream, there was too much noise and no real network: the odds of your five closest friends being found online was too small, so relying on search engines was more convenient than emailng friends for their opinions.
As we see our entire network move online and become legible, the Google Economy will become replaced by the Referral Economy, for these reasons:
- Referral Economy is more difficult to game
- Referral Economy adds a social benefit to financial transactions
The websites providing the Referral Economy cannot easily extract a fee for transactions: Millions of dollars are exchanged through relationships on Linkedin, but who actually passes this money through Linkedin itself? There is no need, because for these people the third party is not required to complete a transaction.
Yet Linkedin will continue to make money, interestingly, from the Google Economy: by aggregating the people into one place, they make it easier for recruiters to search for the right people to hire.
Can Facebook, Google, and others replicate this combination of the Google and Referral Economies? Only time will tell.
Find out in Beachhead Marketing's new video, Beachhead Marketing Presents Seth Godin's Startup School: Freelancer or Entrepreneur. In this video, Seth Godin will walk you through what the differences between a freelancer and an entrepreneur are and which one you may be.
You may notice that your own company’s web activity makes up a decent amount of your Web Page Activity Reports. This adds noise to the information you want to present without adding any value except validating good tracking.
We recommend removing your company's activity from these reports, and in the process identifying all of your employees within your database.
Here is how you do it:
Employee Smart List
First you'll create a smart list to collect all of your employees. This Smart List will later be referenced in your reports.
In the Smart List, drag the "Inferred Company" and "Email address" filter from the right. The Inferred Company filter will identify anonymous visitors, while the email address will find known visitors.
Enter your company’s name into the Inferred Company filter. You can add the Company Name filter as well, but employees are more likely to enter their company email address and not enter the right Company Name.
For the Email Address filter, change the operator to “contains” and then enter your company’s name again (assuming you are using the email structure of email@example.com).
This next step is optional but it makes your Smart List more complete. If your office network has a dedicated IP address you can also filter by this address. Select the Anonymous IP filter and enter your IP address.
Lastly, change the Smart List to use ANY filters. This will ensure the Smart List captures all employees.
Your “Employees” Smart List should now look something like this:
Filtering in your Report
The next step is to reference this Smart List in your Web Page Activity Report.
Navigate to your report’s Smart List and drag the Member of Smart List filter from the right.
Change the operator to “not in” and select the “Employees” Smart List you just created.
Your Web Page Activity report will now exclude all data from leads which are employees.