## Intermediate SQL Skills Challenge: GROUP BY

Hey there SQL-ites! Wow, it’s been awhile since I’ve last posted. Work has been crazy busy again, and just life in general. Sure feels good to be back, learning again! I’m daringly dropping straight back into my learnings from where we left off…in April!? Crazy.

Anyway, let’s get back to brass tacks. Before life and work got really crazy for me, we covered:

Another quick recap note, we’ve been using the famous DVD rental training database for our work. On to the good stuff.

## GROUP BY SQL Skills Challenge #1

Let’s say it’s time for quarterly reviews, who doesn’t love evaluations? ¯_(ツ)_/¯ Implement your knowledge of GROUP BY against the following problem:

We need to figure out how much revenue our employees have realized from DVD rentals, and how many transactions each handled.

## GROUP BY SQL Skills Answer #1

Let’s talk through the problem and dissect it before presenting code. “A problem well-stated is a problem-half solved” – paraphrase of some smart person.

1. We’re talking about revenue, so we’ll need to be dealing with the payment table.
2. We’re evaluating employees (staff), SUM of revenue, and COUNT of transactions.
3. If we’re aggregating this data, we’re GROUPing BY employee.
4. We’re also ORDERing the employees BY who handled the most transactions.

That said, here’s the code:

SELECT staff_id, SUM(amount), COUNT(amount)
FROM payment
GROUP BY staff_id
ORDER BY COUNT(amount) DESC;

…with our results!

## GROUP BY SQL Skills Challenge #2

Well done on your challenge! Here’s the second:

It’s time to do inventory, flashbacks of retail and restaurant wonder for all of us. 🙂

In the name of forecasting and planning, we need to figure out the average replacement cost of our movies, by rating.

## GROUP BY SQL Skills Answer #2

Ok, let’s walk through our problem.

1. We need to use the film database here, since we’re gathering information on the movies.
2. We’re GROUPing our films BY rating
3. We’re using an aggregate function to determine the average replacement cost of each rating.

Drum roll, here’s a winning code snippet:

SELECT rating, AVG(replacement_cost)
FROM film
GROUP BY rating
ORDER BY AVG(replacement_cost) DESC;

With the output:

Are there more challenges we should be covering? Yes. However, I’m trying to do better about getting more sleep these days. Unlike past SQL articles, it’s still (barely) before midnight. So we’ll take a quick breather, possibly update this article, but definitely keep moving forward. Cheers!

## GROUP BY SQL Statement

Introducing the GROUP BY SQL Statement in PostgreSQL

‘Ello SQL geeks! Welcome back to our SQL learning journey. We left off with a beginner SQL skills challenge and the aggregate SQL functions: MIN, MAX, AVG and SUM. Today we’re looking at the GROUP BY statement. We’ll learn about this function in PostgreSQL and walk through usage of this handy SQL statement.

## About the GROUP BY SQL Statement/Clause

From my simple understanding, GROUP BY functions like a hybrid of the following:

• SELECT DISTINCT keyword (If used without an aggregate function like SUM), and,
• An Excel pivot table, rolling up aggregate figures (Count, Sum, Average, etc.) into unique rows

If you’re familiar with Excel Pivot tables, then you’ll recognize here the power of this function.

Let’s take a look at some examples to clarify.

## First Look at Using the GROUP BY Function

To better illustrate the power of GROUP BY, we’ll first show its usage without aggregate functions. Consider the following:

If we query the address table of our sample database with a generic SELECT * FROM address; we get back an atrocious 605 rows of data. Aggregate and useless!

In contrast, if we call the GROUP BY function, we’ll get back a cleaner output, with fewer rows – only unique values returned. While this is an incremental improvement to analyzing data, there’s much left to be desired.

What’s missing here? How about that pivot table-esque functionality? This is where the power of using GROUP BY with aggregate functions gets awesome.

## Using the GROUP BY SQL Statement with Aggregate Functions

As with most analysis, a single data point or data series rarely holds significant insight value on its own. Let’s drive home that point by leveraging the GROUP BY statement with the SUM aggregate function. Below, we compare the ratings of films in our sample database in aggregate by replacement cost. Perhaps this could serve in-store strategies for loss prevention.

SELECT rating, SUM(replacement_cost)
FROM film
GROUP BY rating;

If we extend this functionality to more real world examples, we could use the following for GROUP BY:

• Grouping page-level / URL data to roll up clickstream analytics data
• Large scale analysis of CRM data for customer segmentation analysis
• Analyzing returns for financial data

The list could (and I’m sure it does) go on.

Let’s take this one step further and reduce potential future workload, by building sort functionality into our query. Below, we add a line to get most expensive ratings to least.

SELECT rating, SUM(replacement_cost)
FROM film
GROUP BY rating
ORDER BY SUM(replacement_cost) DESC;

By the looks of this, no need to guard Land Before Time 8. 🙂

Extending our lesson: you can use the COUNT, AVG, and other aggregate functions to analyze as desired.

## Wrap Up

Alright, this was a relatively gentle introduction into more advanced SQL functions. GROUP BY is a rather critical function, so in our next article, we’ll be doing yet another skills challenge. Joy!

Feel free to catch up on our other articles that help you learn PostgreSQL. Also, check out some how-to’s on developing Amazon Alexa skills, and a new series on getting started with Machine Learning. As always, please share with your colleagues and share thoughts in the comments below. Cheers.

## Beginner SQL Skills Challenge!

Howdy, SQL geeks. Hope this post finds you swell!

Over the past few months, we’ve gained a ton of ground in learning SQL, or at least I have. 🙂

Let’s take a moment to:

1. Test our knowledge of SQL skills learned thus far
2. Start seeing SQL queries less as statements of code, and more as real world business challenges

• Recap of the training database we’ve been working with
• General statements of each business problem
• Hints and thoughts about how to approach each problem
• Solutions to each problem

## Recap: Our Training Database

Our training database is a best/old faithful. We’ve been using the surprisingly popular DVD rental database in a .tar file for our practice database.

Contained within this databases are various tables with fictitious information, including: customer information, film production information, business/pricing information and so forth.

In our challenges, we’ll execute various SQL queries to extract pieces of insight for business tasks. It’s assumed in this article that you’ve installed PostgreSQL via pgAdmin and have followed this article series so far, using the DVD rental training database.

## The SQL Challenges

Alright, here we go:

1. How rentals were returned after July 17, 2005?
2. How many actors have a last name that starts with the letter A?
3. How many unique districts are represented in the customer database?
4. Can you return the actual list of districts from challenge #3?
5.  How many films have a rating of R and a replacement cost between \$5 and \$15?
6. How many films have the word Truman somewhere in the title?

## How to Approach the Challenges

Right, then. In this section, we’ll add some color commentary (read: hints) to our challenges. This should help you understand the mechanics of the solution, while ensuring you can’t see the answers all in one screen. 😉

### Challenge 1: Rentals Returned After July 17, 2005

As with all challenges, a problem well stated is half (or more) solved. So let’s look at the high levels of the ask, and work our way down. We need information on rentals, so this means we’ll probably be querying the rentals table.

We would want to first examine the rentals table in a concise manner by doing:

SELECT * FROM rental LIMIT 5;

Once you’ve surveyed the table, we really only need one column returned (pun not intended) and we only want the sum figure of returns, where (HINT!!) the return date was after (think a logical operator here) July 17, 2005.

### Challenge 2: Actors That Have a Last Name Beginning with “A”

Like our first challenge, let’s work from the “top down”. We need actor information, so querying the actor table would be a great place to start. Similar to last time, we need a count of values matching a condition. The difference versus challenge #1  is we need to find match a pattern like or such as an actor’s last name that begins with the letter A.

### Challenge 3: Number of Unique Districts in the Customer Database

The title and description could cause some confusion here. You may need to do some basic SELECT * FROM table_name LIMIT X; queries to make sure you’ve got the right table. Once you do, we’re looking for an amount of distinct values in the database. Order of operations matters.

### Challenge 4: Returning the Actual Lists of Districts from Challenge #3

Not much to hint at here – getting challenge #3 is the key here. You’ll really only be simplifying the correct query in challenge #3 to get the correct answer here.

### Challenge 5: Cheap, (Mildly) Naughty Films

This one might be the longest query yet in this challenge. So we’re looking up film information, thus should know which table to query. We’re returning a value where a certain rating must be returned, and (HINT) we need to layer in one more lens of qualification. That lens dictates we specify a range between (cough, hint!) two values.

### Challenge 6: Where in the World are Films Containing “Truman”?

This challenge is more of a recency test than retention of older concepts. You’ll need to employ pattern matching again for this business challenge/query to find titles that have some match like Truman in the title.

## Challenge Solutions

Is that your final answer? Below are the queries, with screenshots of what I did.

### Solution 1: Rentals Returned After July 17, 2005

SELECT COUNT(return_date) FROM rental
WHERE return_date > ‘2005-07-17’;

### Solution 2: Actors That Have a Last Name Beginning with “A”

SELECT COUNT(*) FROM actor
WHERE last_name LIKE ‘A%’;

### Solution 5: Cheap, (Mildly) Naughty Films

SELECT COUNT(*) FROM film
WHERE rating = ‘R’
AND replacement_cost BETWEEN 5 AND 15;

### Solution 6: Where in the World are Films Containing “Truman”?

SELECT COUNT(*) FROM film
WHERE title LIKE ‘%Truman%’;

## Wrap Up

Well done for completing these challenges! You shall indeed pass. 🙂

Soon, we’ll be covering aggregate SQL functions, such as MIN, MAX, AVG and SUM.

If you’re just joining us, here’s a running list of our articles so to date (4/16/2017):

## Unsupervised Learning Introduction: Machine Learning Essentials

Howdy, machine learning students! Today we’re going to introduce the concept of unsupervised machine learning algorithms.

## Quick Recap: Supervised Learning

Before we jump in, let’s quickly recap our last article introducing supervised machine learning algorithms. This will give us the appropriate context for unsupervised learning.

In supervised machine learning problems, we supply pre-labeled data to the algorithm. By supplying data that’s already correctly labeled, we ask the algorithm to further predict (regression) or label (classification) new data.

## Unsupervised Machine Learning = Unlabeled Data

The most immediate and prominent difference  for unsupervised learning is the data. Above, we gave the algorithm “a boost” by supplying the intended “right” answers in the data. Below, in an unsupervised machine learning problem, there are no right answers…yet.

We’ve supplied the algorithm with data in the problem, but it’s provided without labels or “answers”. We are mandating that the algorithm discover structure and infer patterns/labels on its own. We could also compare the above example to a clustering problem.

So in unsupervised learning, we supply a large amount of unlabeled data, without explicitly identified form or structure. We ask the algorithm to come up with ideas of structure and segmentation on its own.

Some additional applications of unsupervised learning could include:

• Market segmentation of massive transaction data
• Large scale social networking data
• Astronomical data analysis
• Large scale market data
• Mass audio/voice analysis
• Large scale gene clustering

## Wrap Up

That was a bit of a quick one! The challenge with some these technical subject matter areas is sometimes we have limited room to run before going off into the technical weeds. This is one of those areas. Next, we’ll be covering some key concepts in the areas of machine learning model representation, cost function and parameter learning. Don’t worry too much about those yet, we’ll take it step by step. 🙂
As always, feel free to follow my other journeys of learning PostgreSQL, learning how to develop Amazon Alexa Skills, learning how to get started in algorithmic trading, JavaScript for beginners…and more to come soon! Cheers.

## What is Machine Learning?

Hello there, fellow Machine Learning (ML) students! Welcome back to our crash course in starting machine learning from an absolute beginner’s perspective.

In our previous article, we covered an introduction to Machine Learning, answering several key questions:

• Where is machine learning used in our lives?
• Where did machine learning come from?
• Where is machine learning headed?

Forging ahead in our learning journey, we’ll introduce some definitions of machine learning and look at the major types of machine learning applications.

## Machine Learning (ML), A Casual Definition by Arthur Samuel

Our first definition, teased in the last article, follows:

Machine learning is the practice of giving computers the ability to learn without being explicitly programmed to do so.

## More on Arthur Samuel & Why His Definition on ML Matters

If you’re like me, you might not have heard of Arthur Samuel. Who is he, and why does his opinion matter in the fields of artificial intelligence and machine learning?

Arthur Samuel was a pioneer in artificial intelligence and computer gaming fields. In 1959, he coined the term “machine learning” as a founding father in the field. That’s why he’s important! Let’s also look at a more formal / scientific definition.

## A More Formal Machine Learning Definition

Tom Mitchell, of Carnegie Mellon, offers a definition with more structure.

• A well defined learning problem follows
• E * T = P
• Note: His definition does not include mathematical operators. I’m taking a large liberty to insert them myself. ¯\_(ツ)_/¯
• Experience (E) placed against Task (T) is measured by Performance (P)

Here’s a further example:

Example: playing Go.

E = the experience of playing many games of Go.

T = the task of playing and winning Go.

P = the probability that the program will win the next game.

## Major Categories of Machine Learning Algorithms

If you judge by press coverage of ML as I have, it appears to be a nebulous field. (In all fairness, it may still be.) However, there is structure we can take in learning ML. There are two types of machine learning algorithms:

• Supervised learning algorithms
• Unsupervised learning algorithms

There are a couple of other prominent types of machine learning algorithms as well: reinforcement learning and recommender systems.

## Wrap-Up

Congratulations, we’ve cleared a very gentle introduction to machine learning, and it’s novice/high level definitions. I look forward to learning more with you, dear reader! Our next articles will cover a bit more detail about the two major ML algorithm types: supervised learning, and unsupervised learning. Until then, look after each other.

## SQL Query Fundamentals: SELECT WHERE

Welcome back, travelers! The journey continues in learning SQL. In case you missed it, the past couple of posts were learning to use SELECT DISTINCT and Restoring an SQL database with table schema only.

Today we’re going to learn about using the SELECT clause with the WHERE statement for essential SQL queries.

# A Quick Recap, or, Why SELECT WHERE is Important

As previously mentioned, we’ve covered a range of beginning SQL topics. Mainly, we’ve learned about using the SELECT statement to query all (*) or query specific columns of data from a table. Because we’ve been working with a pared down table with only a few hundred rows, it’s not a problem in this “academic” setting to return all the rows. But what if we’re working in a larger database? A recent keyword research dataset 75,000 rows long comes to mind. (Though I would imagine that too, would be a small dataset in the grand scheme of things, but I digress.)

When we start working with larger databases, granularity will become vitally important. This is where (pun not intended) the SELECT WHERE statement comes in.

# Sample Syntax of the SELECT WHERE Statement

With proof of concept in mind, let’s jump into it headlong. Below is a syntax example, demonstrating what a SELECT WHERE SQL query might look like.

SELECT column_1,column_2

FROM table_name

WHERE condition1;

The SELECT statement is old news to you adventurers that have been following along. The WHERE portion of this query will be the power in this article.

# More About the SELECT WHERE SQL Statement

The WHERE clause appears right after the FROM clause of the SELECT statement. The conditions listed within the WHERE clause are used to filter the rows returned from the SELECT statement. Because we’re working in pgAdmin / PostgreSQL, we’ll have available standard operators to construct the conditions. Better still, some (or most) of the operators we’ll look at are fairly universal, so these operators should work in MySQL, Microsoft SQL, Amazon Redshift, etc.

# List of SELECT WHERE Operators

Below is a list of common SELECT WHERE operators. Again, most of these should be fairly universal, regardless of the SQL database management program you’re using.

 Operator Description = Equal to > Greater than < Less than >= Greater than or equal to <= Less than or equal to <> or != Not equal to AND Logical operator AND OR Logical operator OR

Plugging this back into SQL statements, we’ll be using the operators on the left to filter down and return only specific rows in our queries.

# Sample SELECT WHERE Statements

Let’s  cover some guiding examples that will help us apply the SELECT WHERE operators. To start, we’ll kill two birds with one stone: jogging the memory by utilizing a previous query and exploring the table we’ll be querying. Below, we’ll be a bit naughty by calling on all columns from the table.

SELECT * FROM customer;

Above, we’ll see our results and some candidate columns to query the heck out of. Let’s keep moving.

## Example 1: The Basic SELECT WHERE Statement with 1 Condition, Returning Only Customers with a Certain Name

Okay, let’s say that we want to only return customers of a certain name, say, “James”. The SELECT WHERE statement will help us make quick work of this database need.

SELECT last_name,first_name

FROM customer

WHERE first_name = ‘James’;

If all goes correctly, we should only get back a customer with the first name James. ‘Ello James!

Side note 1: you don’t have to return the columns you’re filtering against. For example, we could return the email column and still filter by name.

## Example 2: A SELECT WHERE Statement Using 2 Conditions, Returning Customers with a Certain First AND Last Name

Perhaps you’ll want to do something more targeted with your data. I know this is a narrow and frankly creepy example of calling out one name, but think maybe of a City/State or Source/Medium pairing? Anyway, with the sample dataset we have, below we use the AND logical operator to combine two conditions into one query.

SELECT last_name,first_name

FROM customer

WHERE first_name = ‘Jared’ AND last_name = ‘Ely’;

If executed properly against this particular sample dataset, we should be returned only the values for one fictional Mr. Jared Ely.

Quick side note 2: that we should have mentioned sooner: we’re using single quotes here because the values we’re querying against are string values. As such, the single quotes help us match format, et cetera.

## Example 3: Another SELECT WHERE Statement Using 2 Conditions, Returning Customer ID’s where payment was in certain dollar amount ranges

Let’s say we are trying to identify a range of customers in our database. In this third example, we want to query Customer ID’s and names in a certain range. We could think of it as a feeble attempt to get our first customers or our most recent customers for a special email flight. Below, we’ll exercise the OR operator to accomplish our desired output.

SELECT customer_id,first_name,last_name

FROM customer

WHERE customer_id <= 2 OR customer_id >=20 ;

# Understanding the Subtle Differences Between AND / OR

Quick note about differences between the AND / OR operators. If you’re trying to filter data from two different columns, then AND is your filter. If you’re trying to get distinct values within a single column, then the OR operator will be best suited for the job.

# Wrap-Up

What a world of possibilities we’ve opened! I found myself needing to slow down and pay more attention to detail in this area of learning. The nuances of selecting certain columns but filtering by others when practicing threw me for a loop once or twice.

Thanks for joining, in the next article, I’ll be covering some introductory material around the COUNT function. In the meantime, check  out our running list of posts on how to learn SQL.

## 4 Simple Marketing Career Paths Your Professor Never Told You About

Several months ago, I sat in a warm conference room in the University of Houston Bauer business school. The wind gusted outside;  professors and a couple of brave students shuffled papers on a conference table between presentations. I was privileged to be an advisory board member and mentor at this Enactus UH event.

Unexpectedly (to me), an adjacent student leaned over and asked about marketing career paths. What an intelligent question! I realized determining a marketing career path can be utterly confusing as a student. There are slick advertising agencies, PR boutiques, massive corporations with large silos and vague job titles all around. What on earth is an account executive and what makes it different from an account planner, account manager or anything else?

In that moment, it was my pleasure to share with this student how to evaluate marketing career paths. Some years ago, I was strangely fortunate that by providence I’ve enjoyed such an easy, clear path so far in my young career. Reflecting on that exchange, there are likely thousands of students seeking clarity and guidance on marketing careers.

Below is a simple method that shows you marketing careers at a glance, with detailed explanations below.

# tldr;

In marketing, you make major two choices that determine your career path: where you work and what you do. You’ll either work at an agency or a client and you’ll either focus on branding or direct response. Obviously, there’s room for a lot of creativity and nuance – but a large portion of marketing careers fit this framework.

# Where You Work: Agency

We’ll start here, because I’m most familiar with this area. An agency is a 3rd party that a brands such as Adidas or GM hires to help with marketing. Agencies exist because they provide expertise and impartiality that brands can’t get in-house.

Marketing and advertising agencies are a great starting point to master tactical execution. You’ll often be on the sharp end of the spear: pulling reports, writing copy and auditing campaigns. It’s often a fast-paced environment. The offices have bright lights, open concept floor plans, and quirky perks.

Working in an agency is fun! However, it’s imperative you do some homework before joining an agency. I’ve been very fortunate to work at agencies that offer great benefits and pay. My agency experiences have been nothing short of phenomenal. Regrettably, not all marketing agencies are that way. Some agencies  can be volatile if clients leave or have long/tough hours in uncertain conditions. That said, it’s your privilege of choice!

# Where You Work: Client

If you’re not the agency, you’re likely the client. If you’re on the “client side”, you’re likely working in-house at a brand like Hilton or Neiman Marcus.

Client side marketing can vary a lot, depending on the marketing maturity of your organization. In some organizations, you may be managing agencies, vendors and strategy. In less-developed or smaller companies, marketing may be closer to sales support.

In general, client-side jobs are more stable and evenly paced than agencies. Deadlines are usually more relaxed and the pay is generally higher compared to agencies. However, marketing can be vulnerable in a corporate setting. If your organization falls on hard financial times, accounting and marketing are the first functions to be cut.

Much like agencies, there are great brands and tough situations. Do your homework!

# What You Do: Branding

Now you know where you can work in marketing and advertising. The aspiring marketer’s next major decision is what you do.

In marketing, you either focus on consumer actions or consumer perceptions. The art and science of branding focuses on consumer perceptions. Branding is a fascinating discipline. You constantly fight battles in the minds of brands and consumers. You must appear first, as the best.

Branding jobs often have “creative” or “strategist” somewhere in the title; a keen sense of strategy is key to your success. To learn more about branding, you should read the classic, “Positioning” by Al Ries and Jack Trout. A caveat to branding-centric positions: you may lack experience in tactical marketing execution at the expense of creative pursuits. If you’re a strategy maven (which is rare), you can survive without concrete tactical knowledge. If you’re weak in strategy, it can be tough to advance your career in this field.

# What You Do: Direct Response

The other major marketing avenue is direct response. Instead of focusing on consumer perceptions (branding), you work with consumer actions. Direct response is self-evident, you work to produce an action from a consumer. If you envision a purchase funnel, direct response typically handles the end: making a sale, strengthening a relationship.

Direct response jobs often have “analyst”, “planner” or “specialist” in the title. Working in direct response is fantastic to build practical, tactical experience that can be used in many places & many ways. You’ll find skills such as auditing, research and optimization highly marketable.

The upside of starting in direct response is that marketing is inherently self-promotional: the barriers to entry are low. However, if you don’t chase strategy skills, you can be quickly stuck as a tactician and encounter long-term difficulty advancing your career.

The good news: marketing career paths are an open playing field! The bad news: marketing career paths are an open playing field. Hopefully this post brings some definition to a broad field, while pointing out benefits and risks.

## How to Explain SEO in 5 Minutes

Today, you’re going to learn how to explain SEO to anyone in five minutes! Is it a comprehensive, end-all, be-all example? No. Will it help someone quickly understand the basics of a complex practice? Yes. Let’s jump in. This is, after all, a five minute guide!

Starting Point: SEO is not a black box.

For context, I often hear that SEO is a “black box” and it’s anyone’s guess as to how you rank in search engines. I’m always happy to hear that! It means we have a chance to explain what SEO is really about. Google isn’t a black box. Rather, its algorithms and search engines are so complex at a large scale, it’s difficult to comprehend on a daily basis.
With introductions out of the way, here’s how you do it:
A Job Application
Consider SEO like a job application. You have a resume, references, you deal with a recruiter and a hiring manager. And you want a great career!
In SEO, you have a website, backlinks, the search engine and the user. You want to be found and loved!

Your resume is your website. You control the content and tailor the experience to each opportunity. You balance who you are and what you do.
Your website balances your brand story and products or services. You target keywords to pages of your site, just like your jobs are targeted to certain functions and skill sets.
Which resume would you choose to interview for a Marketing Director?

A. “I’m Leroy, I’m awesome. I’m really cool, I enjoy long walks on the beach and like Ethiopian food.”

B. “Kevin is a seasoned marketing professional who has managed and implemented effective marketing promgrams.”

By the same token, which website would you do business with? One with “high quality products and synergistic solutions”? Or a site that speaks in plain, compelling English?
Your references are your backlinks, or 3rd party websites linking to your own. You can influence these endorsements and relationships, but not necessarily control them. On both sides of this comparison, quality trumps quantity.

Which of the following references holds the most weight for a candidate?

A. “Little Michael is my favorite nephew, he eats a lot at Thanksgiving, you should hire him.”

B. “Jake paid me some money to recommend him. I guess he’s cool.”

C. “Ryan took our marketing department to the next level with smart strategy. He works well with the team implementing our campaigns.”

Ryan is the best choice. A bunch of low quality or fake recommendations will do a job seeker little good if not harm. By the same token, it’s better to have a few excellent backlinks than a bunch of spammy or fake endorsements.
The ultimate goal
When you’re a job seeker, interviews are great. But you really want the career! Likewise, it’s great to rank well in the search engines, but you want new, loyal customers that convert on your website.

Above all else, optimize your website for your users. The search engine results will follow.

Where to next?

That’s how to explain and understand SEO in five minutes! Obviously, there’s much, much more to learn. However, this will get you on the right path. Dive deeper into SEO with a fantastic Beginner’s Guide from Moz.

## Winning Long Term SEO for Years

A few years ago, the average CMO tenure was 23 months. Yikes. That’s barely long enough to get acquainted and jamming with your staff. Happily, a recent indicates a CMO’s average tenure now sits at 45 months. It’s no secret that personnel and vendors come and go with the executive. For agencies and in-house digital marketers, burgeoning attention and spend in digital means unprecedented opportunity. Naturally, there is tremendous potential for fruitful, long term, agency-marketer-executive relationships. Yet success seems anything but simple, we can’t just ride off into the sunset. (Darn, I like Westerns.) Long term digital marketing engagements still seem precarious at best, especially for Search Engine Optimization (SEO). The “quick wins” and short-sighted quarterly business environment is still firmly entrenched. With a bit of work and love, long-term client-marketer-executive relationships can be profitable foundations that bolster your agency or brand for the next 10 years.

Succinctly, winning SEO for years is a bit like a strong friendship. It’s not perfect, but an active exchange of trust and vulnerability makes something great. It’s not always “up and to the right”. Every friendship is different, but there are guiding principles and helpful tidbits all can apply.

2. Be a master of reporting and storytelling. A common agency story runs like this, stop me if you’ve heard this one before: 3 months into an engagement, the client loves the work, everyone is excited! 6 months: communication tapers off and so does the work. At 7-8 months, an email arrives, “Can we get an audit of our billings and work?” Next month, “We’re not seeing the value…can we talk?”

Learning to communicate and report intelligently will help you avoid the account/campaign death spin and 9at least) double the life of your engagement. How you say and show success is the lifeblood of your agency’s (or brand’s marketing) future. In agencies, you’ll often hear, “We don’t have time to educate the client.” True, but you surely can’t afford to leave them in the dark. Build small bits of education into your reporting at each step. Your client (or executive) should be educated enough that they could dispose of you, but so satisfied that they’ll never want to.

Reporting over a long time horizon is tricky. Before you have a full year of data, seasonality can make your work look bad if you’re uninformed on market trends. (What if your 6 month evaluation is in a demand trough?) Furthermore, it’s not realistic to primarily report on the same metric month after month. (E.g, keyword rankings) The client or executive may deem organic traffic the determining KPI at your campaign start. However, fascination is fleeting. Revenue or return on investment will likely be your KPI in 12 months’ time.

Use a consistent mix of metrics to paint a rich picture of your phenomenal digital marketing campaign. When you forecast, it’s common to use upper and lower bounds (like economists and weathermen!) to ensure you don’t look like too big of an idiot in any case. Similarly, use “upside metrics” such as revenue generated from organic and ROAS/ROI and “downside metrics” like cost per acquisition and results decay analysis if your client or executive stopped working with you. Assuming sound communication, your reporting should become easier over time. When showing CMO’s and presidents year-over-year, 3, 5 year trends of their performance, we don’t have to do much selling.

3. Learn to live through redesigns. The shelf life of a website is (and should be, to a degree) these days. Blanket statements are a great way to get in trouble. However, many sites (looking at you, Fortune 500 companies) are on the cusp of major changes for mobile friendliness. If you’re around for more than a couple of years, you will see the client’s site go through a redesign. Ideally, you will start the redesign process small pieces at a time. When you present conversion improvement data, mash up clickstream analytics with heatmapping data for compelling improvements.

However, if you’re not regularly testing, you should start. If you approach a redesign without aforementioned experience, here are a few things to keep in mind.

• You must exercise discretion and attention to detail. Choose carefully which battles to fight. Web design & development projects are notorious for being past schedule, over budget and largely dissatisfying on the whole. Don’t be the unnecessary logo critic and save your clout for things that matter to SEO.
• Here’s what you do need in a redesign: a seat at the table, the power to veto content, technical matters and the ability to test after completion. Conventional wisdom in design is at best, a starting point. Best practices will not transfer from one client to the next.
• Here’s what you don’t need: Approval on typography, creative direction of promo tiles and so on. Did we mention you should pick your battles?

A redesign done well can bolster traffic, interest and conversions. A poor redesign can break a site, its rankings and the business behind it. Mind your technical details such as URL aliases, file extensions, redirects, robots files, sitemaps and more. Don’t know what I just rattled off? Send me a message, I’m happy to explain.

4. Visible Progress. Related to reporting, what tangible value can you demonstrate on a regular basis? Clients and executives often refer to digital marketing as “black box” and “murky”. Their perception is understandable. However, many facets of digital marketing are so precise and complex at scale that simple explanations are very difficult in quick meetings. What pieces of content or site changes can you point to? Given that digital is complex, your client or executive must have concrete, simple wins they can shop as their own.

5. Deep involvement in the client’s business. Don’t get left out in the cold. Many agencies suffer volatility because they enter late in the marketing game with a client. Brand managers suffer irrelevance by thinking small, too late. When a client or executive says sales or down, don’t nod and make a cursory note in your moleskin. Unleash a barrage of intelligent questions about their customer intelligence, acquisition, retention and sales process. Your ability to engage on a deep business level (down to P&L and EPS) will be a large determining factor in your long term success.

This is not an all-inclusive guide to winning SEO long term. However, it’s my hope that this will a jump off point for your team as it conquers its next digital marketing success.