How to clean up your LinkedIn messages in minutes.

 I don’t know about you, but my LinkedIn inbox has gotten out of control.  It was time to do something about it.  So, I started to delete messages.

The problem is I had thousands of emails. OK, I will just take a Saturday and clean it up.

Stop the madness! Is this a really good use of my time?

Deleting messages in LinkedIn

 

You have to pull up each message, click the three dots and delete.  Is this a new torture technique?  I just didn’t have the patience to do this and was this really good use of my time? NO.

There has to be a better way and I can’t be the only person that has this problem.

A quick search of GitHub and sure enough I was not the only person aggravated by this and there was a solution already in place.

 First, I want to nominate Tim Whitacre for a Nobel Peace Prize.

 

“ LinkedIn is a valuable resource, but sometimes it sucks. One of those times is when you want to delete messages. You have to select each message one by one. It takes about 4 "clicks" to successfully delete a message.

This script should help. Since LI requires you to perform multiple steps, I decided to automate it for you. Once you initiate the script, it will run every second. If a message has the ability to be deleted, it will be. If not, it will be archived. Some "InMail" messages cannot be deleted on the web app. This script should work as long as LI doesn't change their page layout or element names, which will happen eventually.”

 

This solution is so simple and quick it is a must-have in your tool kit.

 

First, log in to LinkedIn using the Chrome browser and go to Messages.

Right-click and click Inspect.

Now Click Console.

Paste this script at the bottom of Console and hit enter.

You can get the script here. 

I used the modified script from igorissen.  Just scroll down the page and you will find it. 

GitHub is free and it is easy to create an account.

Bulk delete messages in LinkedIn

You will see your messages being deleted at a rate of about one per second.

 

As I literally had thousands of messages, I ran the script several times.   This was easy, when the script had deleted the messages, I cleared my browser history and logged back into LinkedIn and ran the script again. 

Honestly, it only took 10 minutes to have no messages in my Inbox.

If you don’t want to spend hours deleting your LinkedIn messages this is a great solution.

Data-Driven or Data-Informed Marketing?

There is a bunch of talk these days about data-driven marketing versus data-informed marketing.

Is there really a difference?

An often-overlooked aspect of data is context. Let’s talk about the recent coronavirus outbreak.

Here is the data as of February 7, 2020.

20,000 illnesses

427 deaths

Now let’s compare that to the flu or influenza in the United States.

	Coronavirus in China	Influenza in the United States
Illnesses	20,000	19,000,000
Deaths	427	10,000
Hospitalizations		180,000

When viewed this way, it may appear that the coronavirus is not a significant problem. Comparison does not always include context. So far this season about 0.05% of those who caught the flu have died. Now for some context, the death rate for those who have caught the coronavirus is around 2%. This is such a significant problem the Centers for Disease Control and Prevention have created a website dedicated to the Coronavirus.

While certainly not life-threatening, data needs context in marketing as well.

A/B and/or Multivariate Tests

200% increase in Lead Conversion.

This sounds amazing, but what is the context? Did you go from one lead to two? Ten to Twenty? Is that a significant change?

Here is a quick example:

You have 1,000 visitors to your website.

You try two versions to improve results.

While this is an amazing improvement in conversions, can we attribute it completely to the changes we made?

What does it mean for something to be Statistically Significant?

If you perform an internet search on statistical significance you go into the weeds very quickly.

We will not go into the math behind statistical significance if you really want to know do an internet search.

In simplest terms, statistically significant means it did not happen by chance.

A key element of significance is sample size. This is very important when evaluating your marketing results. You really need to know if a change had an impact on the outcome.

The Law of Large numbers.

You roll a pair of dice. What are the odds you will get a one? 1 in 6. Now roll the dice. Did you get a one? Roll ten times. 100 times. 1,000 times.  See where this is going? The larger the sample size the more likely you are to get the expected result on average.

So, let’s increase website traffic 10-fold. Maybe you do this through paid ads or paid content creation.

You still have a 200%  difference in conversion rate, but you did not increase the number conversions. You made a decision based on data, but the data was not statistically significant, and everyone is now unhappy with the money spent and the results produced.

Now let’s take the same sample size and just add a zero to conversion rates.

See what happens? Same conversion rate, but it is statistically significant. In other words, you can have a measure of confidence that your change effected the results.

This is the context you are looking for when making decisions on your marketing. A simple statement of 200% improvement must be viewed in context.

We strongly encourage you to download your own calculator from HubSpot and examine your marketing performance in context.

Does a lack of statistical significance always mean you will fail when you scale up? No.

How do you inform your marketing decisions with data?

Another area of confusion is the concept of correlation.  You perform an A/B or Multivariate test and it works great! Time to scale up. Wait just a minute, don’t invest additional marketing dollars based on correlation alone.

This is one of my favorites from the Spurious Correlations website. Correlation does not mean causation.  We want to believe our brilliant marketing caused great results, but we need to inform our decision making with holistic reasoning skills. In a large organization, there may be multiple activities that impact results.

Be cautious when assigning cause and effect.

Did sales just attend a big trade show?

Was new marketing collateral just mailed?

Website updated and optimized for SEO?

How are you supposed to manage all of these possible contributors?

Keep records of changes

Use annotations in Google Analytics

You should make annotations at the time of the event.  It is difficult to go backwards and try to assign events after the fact.  This little extra step will allow you to see blips in your data and inform better attribution.

Use Campaign Tracking

Google offers a free tool called Campaign URL Builder.

This is an easy way to measure the impact of emails and other linking efforts on your website.

Assign marketing assets to campaigns.

Many marketing platforms, such as HubSpot have built-in tools that do this for you.

What’s the difference between a campaign and a utm_campaign?

HubSpot campaigns are used to tag related marketing assets and content in HubSpot so you can easily measure the effectiveness of your collective marketing efforts. 

utm_campaign tags are used to track traffic to your website. HubSpot campaigns may include traffic that is tagged with utm_campaign parameters, but not all utm_campaign parameters are necessarily related to a HubSpot campaign.

HubSpot campaigns are a group of related marketing assets and content, under a certain marketing campaign. Associating marketing assets and content in HubSpot to a campaign allows you to measure the effectiveness of your collective marketing efforts. 

Source: HubSpot

It will look something like this:

You can then drill down to see what assets are having the best impact.

If you have taken a statistics course you have probably heard the phrase:

 Figures don’t lie, but liars can figure.

An effective way to safeguard against this is to go into every A/B or Multivariate test with the objective to be failure. Don’t look for successes, look for failures. This can help offset the tendency to make correlations to causations that don’t exist.

One of my favorite blogs is Occam’s Razor by Avinash Kaushik. 

Here is one reason why:

 Great Analyst's Best Friends: Skepticism & Wisdom!

Here's something important I've observed in my experience in working with data, and changing organizations with ideas: Great Analysts are always skeptical. Deeply so.

This was always true, of course. But, it has become mission critical over the last few years as the depth, breadth, quantity and every other dimension you could apply to data has simply exploded. There is too much data. There are too many tables/charts/"insights" being rammed down your throat. There has been an explosion of "experts."

If you are not skeptical, you are going to die (from a professional perspective).

…  skepticism flag should be: Look for things in the data-set that disprove the summary statement.

… skepticism flag (for a smart Analyst, usually this is the first one) should be the perennial favorite: Correlation does not imply causation!

Read the blog: https://www.kaushik.net/avinash/great-analyst-skills-skepticism-wisdom/

Data-Driven or Data-Informed?

BOTH!

Be skeptical when running A/B tests and look for failure not success.

All data has context. Apply it.

Don't invest additional resources without examining statistical significance.

Why Service Level Agreements Fail

One of the basic principles of physics is the conservation of momentum. 

This principle explains why service level agreements fail and how you can fix them.

In business dynamics, conservation of momentum means each group will fall into its own cadence or rhythm. They will stay in their own groove until acted upon by an external force. 

Does this sound familiar?

>Marketing sends us bad leads.

>Sales can't close the great leads we send them.

>I told operations we were going to close that big deal.

>Sales always says they are going to close that big deal and it never happens.

>Marketing does not understand this great new design and how it will change the world.

>Engineering couldn't speak in plain terms if their life depended on it.

>We are the only department that can make accurate predictions. Everyone else lives in a fantasy world.

You are probably laughing out loud right now because we all have heard these rants.

Enter the Service Level Agreement.

The purpose of an SLA (Service Level Agreement) is simple: align various departments, get everyone on the same page, and agree on expected outcomes and accountability.

An example of this alignment could be Sales and Marketing defining a "good lead" and committing to execute an agreed-upon follow-up strategy (accountability). Another could be aligning on lead time for orders from Sales to Operations. Easy. Simple.

So why do Service Level Agreements fail?

The players change.

Everyone has a 5-year plan. A new survey by Robert Half shows 64% of workers favor job hopping with 75% of employees under 34 believing it could benefit their career. That means in reality, you can expect the players to change almost yearly! Those who set up the Service Level Agreement will likely not be there in 12 months.

The objectives change.

Using Predictive Lead Scoring, as we gather more data, we are able to determine the quality of leads and their closing rate. An efficient way to develop a lead score is to look at historical data. What behaviors did existing customers exhibit before they closed? This could be the number of page visits, the frequency of visits, offers downloaded, demo presentations viewed, trial participation, etc. Next, give each behavior a weight. If you know that viewing a product demo has a higher closing rate than downloading an offer, for example, give it a heavier weight. 

Now 3 months later, your data tells you a trial has a better closing rate than a demo. The objective has changed from demos to trials.

 

The market changes.

Only 53 US companies have been on the Fortune 500 since 1955.

SOURCE: AEI

In 1998, IBM was the biggest American tech company by revenue. Now, some 20 years later, it’s not even among the top 30 companies in the Fortune 500. Innovation creates new markets that did not exist and technology (including AI and Machine Learning) allows companies to quickly position themselves as the new market leader.

Should you just forget about Service Level Agreements?

No. The key to a successful SLA is to understand it is not a static document. You need to adopt scheduled reviews and adjust and optimize where necessary. Reviewing your metrics on a regular basis— we recommend no longer than every 90 days— and developing an agreed-upon process to address market changes and new products will keep you on track with your business goals and objectives. 

The Physics Of Marketing

Newton's first law states that an object at rest will remain at rest unless acted on

by an external force. This is commonly referred to as the Law of Inertia.

Force=mass x acceleration

Another fundamental rule of physics is that opposite poles of a magnet attract each other. You have probably seen the science experiment where the same poles of a magnet push each other away and when you reverse one magnet they are attracted to each other.

However, when it comes to marketing some people want to ignore the Laws of Physics thinking they don't apply to marketing. We totally get it. Maybe you didn't like science class or maybe you think only psychology applies to marketing. 

What do we mean by this?

🔎 Seeing something your competitor is doing and assume it is working.

🔎 Believing if you do more of something it will work.

You see something your competitor is doing and assume it is working.

This is a common problem for many businesses. You like the look and feel of something you see posted by a competitor and the implication is it will work for you. Being popular makes it more popular. You produce similar content and it shows up in search engine results. You now have popular high ranking content. How does this happen? The use of proper SEO allows for better ranking in search engines it appears higher in search results. A dogpile effect then occurs. A popular post topic becomes more popular because more people produce content on that topic.

The real question is did you get more customers?

 Maybe it is time to be the opposing force.

In a vast sea of sameness, how do you stand out? You don't!

You repel potential customers.

Instead of selling the same product or service in the same way, tell the story of why.

Why do you do what you do?

What lead you to your service offerings?

How are you different?

Tell your story!

 

Believing if you do more of something it will work.

There actually is some science behind this idea. In statistics, the law of large numbers shows that if you do enough of something the outcome will converge on the theoretical outcome. If you toss a coin enough times you will get an even number of Heads to Tails. From this you can infer that by doing more of something will get you more results. If you have unlimited resources this approach can eventually work for you. The problem is most businesses do not have unlimited resources.

In fact, most companies we work with have a finite amount of resources and are looking for a definite Return On Investment from those resources. A more effective approach is to focus content around Buyer Personas to connect with your ideal customer. A study featured by HubSpot showed a 202% increase in conversions with personalized Call To Actions. Work on creating content that resonates with your ideal customer and when you have refined that content to generate high converting traffic, then focus on increasing traffic to that content.

It is very easy to get caught up in all the marketing hype you see on the internet. Everyone has the magic that will fix all your problems. Don't get caught up in the euphoria. Before trying 20 different things, step back and decide what is most important to your business. Select one clear objective that is easy for everyone to understand.

√ Clearly define what success looks like.

√ Define a specific metric that measures that success.

√ Set a defined time to measure.

√ Analyze the data to see if you have succeeded.

If you have met your goal, then work on improving another area. If you did not meet your goal, determine what needs to be changed. Doing more of the same will not change your results.