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How to Write Simple Queries

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How to Write Simple Queries

Ever heard of SQL? You may have heard about it in the context of data analysis, but never thought it would apply to you as a marketer. Or, you may have thought, “That’s for the advanced data users. I could never do that.”

Well, you couldn’t be more wrong! The most successful marketers are data-driven, and one of the most important parts of being data-driven is collecting data from databases quickly. SQL is the most popular tool out there for doing just that.

If your company already stores data in a database, you may need to learn SQL to access the data. But not to worry — you’re in the right place to get started. Let’s jump right in.

Why Use SQL?

SQL (often pronounced like “sequel”) stands for Structured Query Language, and it’s used when companies have a ton of data that they want to manipulate. The beauty of SQL is that anyone working at a company that stores data in a relational database can use it. (And chances are, yours does.)

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For example, if you work for a software company and want to pull usage data on your customers, you can do that with SQL. If you’re helping develop a website for an ecommerce company that has data about customer purchases, you can use SQL to find out which customers are purchasing which products. Of course, these are just a few of many possible applications.

Think about it this way: Have you ever opened a very large data set in Excel, only for your computer to freeze or even shut down? SQL allows you to access only certain parts of your data at a time so you don’t have to download all the data into a CSV, manipulate it, and possibly overload Excel. In other words, SQL takes care of the data analysis that you may be used to doing in Excel.

How to Write Simple SQL Queries

Before we begin, make sure you have a database management application that will allow you to pull data from your database. Some options include MySQL or Sequel Pro.

Start by downloading one of these options, then talk to your company’s IT department about how to connect to your database. The option you choose will depend on your product’s back end, so check with your product team to make sure you select the correct one.

Understand the hierarchy of your database

Next, it’s important to become accustomed to your database and its hierarchy. If you have multiple databases of data, you’ll need to hone in on the location of the data you want to work with.

For example, let’s pretend we’re working with multiple databases about people in the United States. Enter the query “SHOW DATABASES;”. The results may show that you have a couple of databases for different locations, including one for New England.

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Within your database, you’ll have different tables containing the data you want to work with. Using the same example above, let’s say we want to find out which information is contained in one of the databases. If we use the query “SHOW TABLES in NewEngland;”, we’ll find that we have tables for each state in New England: people_connecticut, people_maine, people_massachusetts, people_newhampshire, people_rhodeisland, and people_vermont.

Finally, you need to find out which fields are in the tables. Fields are the specific pieces of data that you can pull from your database. For example, if you want to pull someone’s address, the field name may not just be “address” — it may be separated into address_city, address_state, address_zip. In order to figure this out, use the query “Describe people_massachusetts;”. This provides a list of all of the data that you can pull using SQL.

Let’s do a quick review of the hierarchy using our New England example:

  • Our database is: NewEngland.
  • Our tables within that database are: people_connecticut, people_maine, people_massachusetts, people_newhampshire, people_rhodeisland, and people_vermont.
  • Our fields within the people_massachusetts table include: address_city, address_state, address_zip, hair_color, age, first_name, and last_name.

Now, let’s write some simple SQL queries to pull data from our NewEngland database.

Basic SQL Queries

To learn how to write a SQL query, let’s use the following example:

Who are the people who have red hair in Massachusetts and were born in 2003 organized in alphabetical order?

SELECT

SELECT chooses the fields that you want displayed in your chart. This is the specific piece of information that you want to pull from your database. In the example above, we want to find the people who fit the rest of the criteria.

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Here is our SQL query:

SELECT

     first_name,

     last_name

;

FROM

FROM pinpoints the table that you want to pull the data from. In the earlier section, we learned that there were six tables for each of the six states in New England: people_connecticut, people_maine, people_massachusetts, people_newhampshire, people_rhodeisland, and people_vermont. Because we’re looking for people in Massachusetts specifically, we’ll pull data from that specific table.

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Here is our SQL query:

SELECT

     first_name,

     last_name

FROM

     people_massachusetts

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;

WHERE

WHERE allows you to filter a query to be more specific. In our example, we want to filter our query to include only people with red hair who were born in 2003. Let’s start with the red hair filter.

Here is our SQL query:

SELECT

     first_name,

     last_name

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FROM

     people_massachusetts

WHERE

     hair_color=”red”

;

hair_color could have been part of your initial SELECT statement if you’d wanted to look at all of the people in Massachusetts along with their hair color. But if you want to filter to see only people with red hair, you can do so with a WHERE statement.

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BETWEEN

Besides equals (=), BETWEEN is another operator you can use for conditional queries. A BETWEEN statement is true for values that fall between the specified minimum and maximum values.

In our case, we can use BETWEEN to pull records from a specific year, like 2003. Here’s the query:

SELECT

     first_name,

     last_name

FROM

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     people_massachusetts

WHERE

     birth_date BETWEEN ‘2003-01-01’ AND ‘2003-12-31’

;

AND

AND allows you to add additional criteria to your WHERE statement. Remember, we want to filter by people who had red hair in addition to people who were born in 2003. Since our WHERE statement is taken up by the red hair criteria, how can we filter by a specific year of birth as well?

That’s where the AND statement comes in. In this case, the AND statement is a date property — but it doesn’t necessarily have to be. (Note: Check the format of your dates with your product team to make sure they are in the correct format.)

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Here is our SQL query:

SELECT

     first_name,

     last_name

FROM

     people_massachusetts

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WHERE

     hair_color=”red”

AND

     birth_date BETWEEN ‘2003-01-01’ AND ‘2003-12-31’

;

OR

OR can also be used with a WHERE statement. With AND, both conditions must be true to appear in results (e.g., hair color must be red and must be born in 2003). With OR, either condition must be true to appear in results (e.g., hair color must be red or must be born in 2003).

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Here’s what an OR statement looks like in action:

SELECT

     first_name,

     last_name

FROM

     people_massachusetts

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WHERE

     hair_color = ‘red’

OR

     birth_date BETWEEN ‘2003-01-01’ AND ‘2003-12-31’

;

NOT

NOT is used in a WHERE statement to display values in which the specified condition is untrue. If we wanted to pull up all Massachusetts residents without red hair, we can use the following query:

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SELECT

     first_name,

     last_name

FROM

     people_massachusetts

WHERE NOT

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     hair_color = ‘red’

;

ORDER BY

Calculations and organization also can be done within a query. That’s where the ORDER BY and GROUP BY functions come in. First, we’ll look at our SQL queries with the ORDER BY and then GROUP BY functions. Then, we’ll take a brief look at the difference between the two.

An ORDER BY clause allows you to sort by any of the fields that you have specified in the SELECT statement. In this case, let’s order by last name.

Here is our SQL query:

SELECT

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     first_name,

     last_name

FROM

     people_massachusetts

WHERE

     hair_color = ‘red’

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AND

     birth_date BETWEEN ‘2003-01-01’ AND ‘2003-12-31’

ORDER BY

     last_name

;

GROUP BY

GROUP BY is similar to ORDER BY, but aggregates data that has similarities. For example, if you have any duplicates in your data, you can use GROUP BY to count the number of duplicates in your fields.

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Here is your SQL query:

SELECT

     first_name,

     last_name

FROM

     people_massachusetts

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WHERE

     hair_color = ‘red’

AND

     birth_date BETWEEN ‘2003-01-01’ AND ‘2003-12-31’

GROUP BY

     last_name

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;

ORDER BY VS. GROUP BY

To show the difference between an ORDER BY statement and a GROUP BY statement, let’s step outside our Massachusetts example briefly to look at a very simple dataset. Below is a list of four employees’ ID numbers and names.

a table of four names and IDs as a result of sql queries

If we were to use an ORDER BY statement on this list, the names of the employees would get sorted in alphabetical order. The result would look like this:

a table of four names and IDs as a result of sql queries with the name Peter appearing twice at the bottom

If we were to use a GROUP BY statement instead, the employees would be counted based on the number of times they appeared in the initial table. Note that Peter appeared twice in the initial table, so the result would look like this:

sql query examples: a table of three names and IDs

With me so far? Okay, let’s return to the SQL query we’ve been creating about red-haired people in Massachusetts who were born in 2003.

LIMIT

Depending on the amount of data you have in your database, it may take a long time to run your queries. This can be frustrating, especially if you’ve made an error in your query and now need to wait before continuing. If you want to test a query, the LIMIT function lets you limit the number of results you get.

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For example, if we suspect there are thousands of people who have red hair in Massachusetts, we may want to test out our query using LIMIT before we run it in full to make sure we’re getting the information we want. Let’s say, for instance, we only want to see the first 100 people in our result.

Here is our SQL query:

SELECT

     first_name,

     last_name

FROM

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     people_massachusetts

WHERE

     hair_color = ‘red’

AND

     birth_date BETWEEN ‘2003-01-01’ AND ‘2003-12-31’

ORDER BY

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     last_name

LIMIT

     100

;

INSERT INTO

In addition to retrieving information from a relational database, SQL can also be used to modify the contents of a database. Of course, you’ll need permissions to make changes to your company’s data. But, in case you’re ever in charge of managing the contents of a database, we’ll share some queries you should know.

First is the INSERT INTO statement, which is for putting new values into your database. If we want to add a new person to the Massachusetts table, we can do so by first providing the name of the table we want to modify, and the fields within the table we want to add to. Next, we write VALUE with each respective value we want to add.

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Here’s what that query could look like:

INSERT INTO

  people_massachusetts (address_city, address_state, address_zip, hair_color, age, first_name, last_name)

VALUES

  (Cambridge, Massachusetts, 02139, blonde, 32, Jane, Doe)

;

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Alternatively, if you are adding a value to every field in the table, you don’t need to specify fields. The values will be added to columns in the order that they are listed in the query.

INSERT INTO

  people_massachusetts

VALUES

  (Cambridge, Massachusetts, 02139, blonde, 32, Jane, Doe)

;

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If you only want to add values to specific fields, you must specify these fields. Say we only want to insert a record with first_name, last_name, and address_state — we can use the following query:

INSERT INTO

  people_massachusetts (first_name, last_name, address_state)

VALUES

  (Jane, Doe, Massachusetts)

;

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UPDATE

If you want to replace existing values in your database with different values, you can use UPDATE. What if, for example, someone is recorded in the database as having red hair when they actually have brown hair? We can update this record with UPDATE and WHERE statements:

UPDATE

  people_massachusetts

SET

  hair_color = ‘brown’

WHERE

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  first_name = ‘Jane’

AND

  last_name = ‘Doe’

;

Or, say there’s a problem in your table where some values for “address_state” appear as “Massachusetts” and others appear as “MA”. To change all instances of “MA” to “Massachusetts” we can use a simple query and update multiple records at once:

UPDATE

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  people_massachusetts

SET

  address_state = ‘Massachusetts’

WHERE

   address_state = MA

;

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Be careful when using UPDATE. If you don’t specify which records to change with a WHERE statement, you’ll change all values in the table.

DELETE

DELETE removes records from your table. Like with UPDATE, be sure to include a WHERE statement, so you don’t accidentally delete your entire table.

Or, if we happened to find several records in our people_massachusetts table who actually lived in Maine, we can delete these entries quickly by targeting the address_state field, like so:

DELETE FROM

  people_massachusetts

WHERE

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  address_state = ‘maine’

;

Bonus: Advanced SQL Tips

Now that you’ve learned how to create a simple SQL query, let’s discuss some other tricks that you can use to take your queries up a notch, starting with the asterisk.

* (asterisk)

When you add an asterisk character to your SQL query, it tells the query that you want to include all the columns of data in your results.

In the Massachusetts example we’ve been using, we’ve only had two column names: first_name and last_name. But let’s say we had 15 columns of data that we want to see in our results — it would be a pain to type all 15 column names in the SELECT statement. Instead, if you replace the names of those columns with an asterisk, the query will know to pull all of the columns into the results.

Here’s what the SQL query would look like:

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SELECT

     *

FROM

     people_massachusetts

WHERE

     hair_color=”red”

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AND

     birth_date BETWEEN ‘2003-01-01’ AND ‘2003-12-31’

ORDER BY

     last_name

LIMIT

     100

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;

% (percent symbol)

The percent symbol is a wildcard character, meaning it can represent one or more characters in a database value. Wildcard characters are helpful for locating records that share common characters. They are typically used with the LIKE operator to find a pattern in the data.

For instance, if we wanted to get the names of every person in our table whose zip code begins with “02”, we can write this query:

SELECT

     first_name,

     last_name

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WHERE

  address_zip LIKE ‘02%’

;

Here, “%” stands in for any group of digits that follow “02”, so this query turns up any record with a value for address_zip that begins with “02”.

LAST 30 DAYS

Once I started using SQL regularly, I found that one of my go-to queries involved trying to find which people took an action or fulfilled a certain set of criteria within the last 30 days.

Let’s pretend today is December 1, 2021. You could create these parameters by making the birth_date span between November 1, 2021 and November 30, 2021. That SQL query would look like this:

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SELECT

     first_name,

     last_name

FROM

     people_massachusetts

WHERE

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     hair_color=”red”

AND

     birth_date BETWEEN ‘2021-11-01’ AND ‘2021-11-30’

ORDER BY

     last_name

LIMIT

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     100

;

But, that would require thinking about which dates cover the last 30 days, and you’d have to update this query constantly.

Instead, to make the dates automatically span the last 30 days no matter which day it is, you can type this under AND: birth_date >= (DATE_SUB(CURDATE(),INTERVAL 30))

(Note: You’ll want to double-check this syntax with your product team because it may differ based on the software you use to pull your SQL queries.)

Your full SQL query would therefore look like this:

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SELECT

     first_name,

     last_name

FROM

     people_massachusetts

WHERE

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     hair_color=”red”

AND

     birth_date >= (DATE_SUB(CURDATE(),INTERVAL 30))

ORDER BY

     last_name

LIMIT

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     100

;

COUNT

In some cases, you may want to count the number of times that a criterion of a field appears. For example, let’s say you want to count the number of times the different hair colors appear for the people you are tallying up from Massachusetts. In this case, COUNT will come in handy so you don’t have to manually add up the number of people who have different hair colors or export that information to Excel.

Here’s what that SQL query would look like:

SELECT

     hair_color,

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     COUNT(hair_color)

FROM

     people_massachusetts

AND

     birth_date BETWEEN ‘2003-01-01’ AND ‘2003-12-31’

GROUP BY

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     hair_color

;

AVG

AVG calculates the average of an attribute in the results of your query, excluding NULL values (empty). In our example, we could use AVG to calculate the average age of Massachusetts residents in our query.

Here’s what our SQL query could look like:

SELECT

  AVG(age)

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FROM

  people_massachusetts

;

SUM

SUM is another simple calculation you can do in SQL. It calculates the total value of all attributes from your query. So, if we wanted to add up all the ages of Massachusetts residents, we can use this query:

SELECT

  SUM(age)

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FROM

  people_massachusetts

;

MIN and MAX

MIN and MAX are two SQL functions that give you the smallest and largest values of a given field. We can use it to identify the oldest and youngest members of our Massachusetts table:

This query will give us the record of the oldest:

SELECT

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  MIN(age)

FROM

  people_massachusetts

;

And this query gives us the oldest:

SELECT

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  MAX(age)

FROM

  people_massachusetts

;

JOIN

There may be a time when you need to access information from two different tables in one SQL query. In SQL, you can use a JOIN clause to do this.

(For those familiar with Excel formulas, this is similar to using the VLOOKUP formula when you need to combine information from two different sheets in Excel.)

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Let’s say we have one table that has data of all Massachusetts residents’ user IDs and their birthdates. In addition, we have an entirely separate table containing all Massachusetts residents’ user IDs and their hair color.

If we want to figure out the hair color of Massachusetts residents born in the year 2003, we’d need to access information from both tables and combine them. This works because both tables share a matching column: user IDs.

Because we’re calling out fields from two different tables, our SELECT statement is also going to change slightly. Instead of just listing out the fields we want to include in our results, we’ll need to specify which table they’re coming from. (Note: The asterisk function may come in handy here so your query includes both tables in your results.)

To specify a field from a specific table, all we have to do is combine the name of the table with the name of the field. For example, our SELECT statement would say “table.field” — with the period separating the table name and the field name.

We’re also assuming a few things in this case:

  1. The Massachusetts birthdate table includes the following fields: first_name, last_name, user_id, birthdate
  2. The Massachusetts hair color table includes the following fields: user_id, hair_color

Your SQL query would therefore look like:

SELECT

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     birthdate_massachusetts.first_name,

     birthdate_massachusetts.last_name

FROM

     birthdate_massachusetts JOIN haircolor_massachusetts USING (user_id)

WHERE

     hair_color=”red”

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AND

     birth_date BETWEEN ‘2003-01-01’ AND ‘2003-12-31’

ORDER BY

     last_name

;

This query would join the two tables using the field “user_id” which appears in both the birthdate_massachusetts table and the haircolor_massachusetts table. You’re then able to see a table of people born in 2003 who have red hair.

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CASE

Use a CASE statement when you want to return different results to your query based on which condition is met. Conditions are evaluated in order. Once a condition is met, the corresponding result is returned and all following conditions are skipped over.

You can include an ELSE condition at the end in case no conditions are met. Without an ELSE, the query will return NULL if no conditions are met.

Here’s an example of using CASE to return a string based on the query:

SELECT

     first_name,

     last_name

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FROM

     people_massachusetts

CASE

  WHEN hair_color = ‘brown’ THEN ‘This person has brown hair.’

  WHEN hair_color = ‘blonde’ THEN ‘This person has blonde hair.’

  WHEN hair_color = ‘red’ THEN ‘This person has red hair.’

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  ELSE ‘Hair color not known.’

END

;

Basic SQL Queries Marketers Should Know

Congratulations. you’re ready to run your own SQL queries! While there’s a lot more you can do with SQL, I hope you found this overview of the basics helpful so you can get your hands dirty. With a strong foundation of the basics, you’ll be able to navigate SQL better and work toward some of the more complex examples.

Editor’s note: This post was originally published in March 25 and has been updated for comprehensiveness.

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Lessons From Air Canada’s Chatbot Fail

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Lessons From Air Canada’s Chatbot Fail

Air Canada tried to throw its chatbot under the AI bus.

It didn’t work.

A Canadian court recently ruled Air Canada must compensate a customer who bought a full-price ticket after receiving inaccurate information from the airline’s chatbot.

Air Canada had argued its chatbot made up the answer, so it shouldn’t be liable. As Pepper Brooks from the movie Dodgeball might say, “That’s a bold strategy, Cotton. Let’s see if it pays off for ’em.” 

But what does that chatbot mistake mean for you as your brands add these conversational tools to their websites? What does it mean for the future of search and the impact on you when consumers use tools like Google’s Gemini and OpenAI’s ChatGPT to research your brand?

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AI disrupts Air Canada

AI seems like the only topic of conversation these days. Clients expect their agencies to use it as long as they accompany that use with a big discount on their services. “It’s so easy,” they say. “You must be so happy.”

Boards at startup companies pressure their management teams about it. “Where are we on an AI strategy,” they ask. “It’s so easy. Everybody is doing it.” Even Hollywood artists are hedging their bets by looking at the newest generative AI developments and saying, “Hmmm … Do we really want to invest more in humans?  

Let’s all take a breath. Humans are not going anywhere. Let me be super clear, “AI is NOT a strategy. It’s an innovation looking for a strategy.” Last week’s Air Canada decision may be the first real-world distinction of that.

The story starts with a man asking Air Canada’s chatbot if he could get a retroactive refund for a bereavement fare as long as he provided the proper paperwork. The chatbot encouraged him to book his flight to his grandmother’s funeral and then request a refund for the difference between the full-price and bereavement fair within 90 days. The passenger did what the chatbot suggested.

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Air Canada refused to give a refund, citing its policy that explicitly states it will not provide refunds for travel after the flight is booked.

When the passenger sued, Air Canada’s refusal to pay got more interesting. It argued it should not be responsible because the chatbot was a “separate legal entity” and, therefore, Air Canada shouldn’t be responsible for its actions.

I remember a similar defense in childhood: “I’m not responsible. My friends made me do it.” To which my mom would respond, “Well, if they told you to jump off a bridge, would you?”

My favorite part of the case was when a member of the tribunal said what my mom would have said, “Air Canada does not explain why it believes …. why its webpage titled ‘bereavement travel’ was inherently more trustworthy than its chatbot.”

The BIG mistake in human thinking about AI

That is the interesting thing as you deal with this AI challenge of the moment. Companies mistake AI as a strategy to deploy rather than an innovation to a strategy that should be deployed. AI is not the answer for your content strategy. AI is simply a way to help an existing strategy be better.

Generative AI is only as good as the content — the data and the training — fed to it.  Generative AI is a fantastic recognizer of patterns and understanding of the probable next word choice. But it’s not doing any critical thinking. It cannot discern what is real and what is fiction.

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Think for a moment about your website as a learning model, a brain of sorts. How well could it accurately answer questions about the current state of your company? Think about all the help documents, manuals, and educational and training content. If you put all of that — and only that — into an artificial brain, only then could you trust the answers.

Your chatbot likely would deliver some great results and some bad answers. Air Canada’s case involved a minuscule challenge. But imagine when it’s not a small mistake. And what about the impact of unintended content? Imagine if the AI tool picked up that stray folder in your customer help repository — the one with all the snarky answers and idiotic responses? Or what if it finds the archive that details everything wrong with your product or safety? AI might not know you don’t want it to use that content.

ChatGPT, Gemini, and others present brand challenges, too

Publicly available generative AI solutions may create the biggest challenges.

I tested the problematic potential. I asked ChatGPT to give me the pricing for two of the best-known CRM systems. (I’ll let you guess which two.) I asked it to compare the pricing and features of the two similar packages and tell me which one might be more appropriate.

First, it told me it couldn’t provide pricing for either of them but included the pricing page for each in a footnote. I pressed the citation and asked it to compare the two named packages. For one of them, it proceeded to give me a price 30% too high, failing to note it was now discounted. And it still couldn’t provide the price for the other, saying the company did not disclose pricing but again footnoted the pricing page where the cost is clearly shown.

In another test, I asked ChatGPT, “What’s so great about the digital asset management (DAM) solution from [name of tech company]?” I know this company doesn’t offer a DAM system, but ChatGPT didn’t.

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It returned with an answer explaining this company’s DAM solution was a wonderful, single source of truth for digital assets and a great system. It didn’t tell me it paraphrased the answer from content on the company’s webpage that highlighted its ability to integrate into a third-party provider’s DAM system.

Now, these differences are small. I get it. I also should be clear that I got good answers for some of my harder questions in my brief testing. But that’s what’s so insidious. If users expected answers that were always a little wrong, they would check their veracity. But when the answers seem right and impressive, even though they are completely wrong or unintentionally accurate, users trust the whole system.

That’s the lesson from Air Canada and the subsequent challenges coming down the road.

AI is a tool, not a strategy

Remember, AI is not your content strategy. You still need to audit it. Just as you’ve done for over 20 years, you must ensure the entirety of your digital properties reflect the current values, integrity, accuracy, and trust you want to instill.

AI will not do this for you. It cannot know the value of those things unless you give it the value of those things. Think of AI as a way to innovate your human-centered content strategy. It can express your human story in different and possibly faster ways to all your stakeholders.

But only you can know if it’s your story. You have to create it, value it, and manage it, and then perhaps AI can help you tell it well. 

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Only 6% of global marketers apply customer insights to product and brand

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Only 6% of global marketers apply customer insights to product and brand

While many brands talk about focusing on the customer, few do it. Less than a quarter (24%) of global brands are mapping customer behavior and sentiment, according to Braze’s 2024 Customer Engagement Review. What’s worse, only 6% apply customer insights to their product and brand approach.

“At the end of the day, a lot of companies operate based on their structure and not how the consumer interacts with them,” Mariam Asmar, VP of strategic consulting, told MarTech. “And while some companies have done a great job of reorienting that, with roles like the chief customer officer, there are many more that still don’t. Cross-channel doesn’t exist because there are still all these silos. But the customer doesn’t care about your silos. The customer doesn’t see silos. They see a brand.”

Half of all marketers report either depending on multiple, siloed point solutions to cobble together a multi-channel experience manually (33%); or primarily relying on single-channel solutions (17%).  Only 30% have access to a single customer engagement platform capable of creating personalized, seamless experiences across channels. This is a huge problem when it comes to cross-channel, personalization.

The persistence of silos

The persistence of data silos despite decades of explanation about the problems they cause, surprised Asmar the most.

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Screenshot 2024 02 27 140015
Source: Braze 2024 Global Customer Engagement Review

“Why are we still talking about this?” she said to MarTech. “One of the themes I see in the report is we’re still getting caught up on some of the same stumbling blocks as before.”

She said silos are indicative of teams working on different goals and “the only way that gets unsolved is if a leader comes in and aligns people towards some of those goals.”

These silos also hinder the use of AI, something 99% of respondents said they were already doing. The top uses of AI by marketers are:

  • Generating creative ideas (48%).
  • Automating repetitive tasks (47%).
  • Optimizing strategies in real-time (47%).
  • Enhancing data analysis (47%).
  • Powering predictive analytics (45%).
  • Personalizing campaigns (44%). 

Despite the high usage numbers, less than half of marketers have any interest in exploring AI’s potential to enhance customer engagement. Asmar believes there are two main reasons for this. First is that many people like the systems they know and understand. The other reason is a lack of training on the part of companies.

Dig deeper: 5 ways CRMs are leveraging AI to automate marketing today

“I think about when I was in advertising and everybody switched to social media,” she told MarTech. “Companies acted like ‘Well, all the marketers will just figure out social media.’ You can’t do that because whenever you’re teaching somebody how to do something new there’s always a level of training them up, even though they’re apps that we use every day, as people using them as a business and how they apply, how we get impact from them.”

The good news is that brands are setting the stage for the data agility they need.

  • 50% export performance feedback to business intelligence platforms to generate advanced analytics.
  • 48% sync performance with insights generated by other platforms in the business.

Also worth noting: Marketers say these are the four main obstacles to creativity and strategy:  

  • Emphasis on KPIs inherently inhibits a focus on creativity (42%).
  • Too much time spent on business-as-usual execution and tasks (42%).
  • Lack of technology to execute creative ideas, (41%).
  • Hard to demonstrate ROI impact of creativity (40%).
Screenshot 2024 02 27 135952Screenshot 2024 02 27 135952

Methodology

The 2024 Global Customer Engagement Review (registration required) is based on insights from 1,900 VP+ marketing decision-makers across 14 countries in three global regions: The Americas (Brazil, Mexico, and the US), APAC (Australia, Indonesia, Japan, New Zealand, Singapore, and South Korea), and EMEA (France, Germany, Spain, the UAE, and the UK).

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Crafting Effortless Sales Through ‘Wow’ Moments in Experience Marketing

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Crafting Effortless Sales Through 'Wow' Moments in Experience Marketing

Crafting Effortless Sales Through Wow Moments in Experience Marketing

In an era where consumers are bombarded with endless choices and digital noise, standing out as a brand is more challenging than ever. Enter experience marketing – a strategy that transcends traditional advertising by focusing on creating immersive, memorable interactions. This innovative approach leverages the elements of surprise, delight, and reciprocity to forge strong emotional connections with customers, making the sale of your core product feel effortless. But how can businesses implement this strategy effectively? This guide delves into the art of crafting ‘wow’ moments that captivate audiences and transform customer engagement.

The Basics of Experience Marketing

Experience marketing is an evolved form of marketing that focuses on creating meaningful interactions with customers, aiming to elicit strong emotional responses that lead to brand loyalty and advocacy. Unlike conventional marketing, which often prioritizes product promotion, experience marketing centers on the customer’s holistic journey with the brand, creating a narrative that resonates on a personal level.

In today’s competitive market, experience marketing is not just beneficial; it’s essential. It differentiates your brand in a crowded marketplace, elevating your offerings beyond mere commodities to become integral parts of your customers’ lives. Through memorable experiences, you not only attract attention but also foster a community of loyal customers who are more likely to return and recommend your brand to others.

Principles of Experience Marketing

At the heart of experience marketing lie several key principles:

  • Emotional Connection: Crafting campaigns that touch on human emotions, from joy to surprise, creating memorable moments that customers are eager to share.
  • Customer-Centricity: Putting the customer’s needs and desires at the forefront of every marketing strategy, ensuring that each interaction adds value and enhances their experience with the brand.
  • Immersive Experiences: Utilizing technology and storytelling to create immersive experiences that captivate customers, making your brand a living part of their world.
  • Engagement Across Touchpoints: Ensuring consistent, engaging experiences across all customer touchpoints, from digital platforms to physical stores.

Understanding Your Audience

Before diving into the intricacies of crafting ‘wow’ moments, it’s crucial to understand who you’re creating these moments for. Identifying your audience’s pain points and desires is the first step in tailoring experiences that truly resonate.

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This involves deep market research, customer interviews, and leveraging data analytics to paint a comprehensive picture of your target demographic. By understanding the journey your customers are on, you can design touchpoints that not only meet but exceed their expectations.

  • Identifying Pain Points and Desires: Use surveys, social media listening, and customer feedback to gather insights. What frustrates your customers about your industry? What do they wish for more than anything else? These insights will guide your efforts to create experiences that truly resonate.
  • Mapping the Customer Journey: Visualize every step a customer takes from discovering your brand to making a purchase and beyond. This map will highlight critical touchpoints where you can introduce ‘wow’ moments that transform the customer experience.

Developing Your Experience Marketing Strategy

With a clear understanding of your audience, it’s time to build the framework of your experience marketing strategy. This involves setting clear objectives, identifying key customer touchpoints, and conceptualizing the experiences you want to create.

  • Setting Objectives: Define what you aim to achieve with your experience marketing efforts. Whether it’s increasing brand awareness, boosting sales, or improving customer retention, having clear goals will shape your approach and help measure success.
  • Strategic Touchpoint Identification: List all the potential touchpoints where customers interact with your brand, from social media to in-store experiences. Consider every stage of the customer journey and look for opportunities to enhance these interactions.

Enhancing Customer Experiences with Surprise, Delight, and Reciprocity

This section is where the magic happens. By integrating the elements of surprise, delight, and reciprocity, you can elevate ordinary customer interactions into unforgettable experiences.

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  • Incorporating Surprise and Delight: Go beyond what’s expected. This could be as simple as a personalized thank-you note with each purchase or as elaborate as a surprise gift for loyal customers. The key is to create moments that feel special and unexpected.
  • Applying the Principle of Reciprocity: When customers receive something of value, they’re naturally inclined to give something back. This can be leveraged by offering helpful resources, exceptional service, or customer appreciation events. Such gestures encourage loyalty and positive word-of-mouth.
  • Examples and Case Studies: Highlight real-world examples of brands that have successfully implemented these strategies. Analyze what they did, why it worked, and how it impacted their relationship with customers.

Best Practices for Experience Marketing

To ensure your experience marketing strategy is as effective as possible, it’s important to adhere to some best practices.

  • Personalization at Scale: Leverage data and technology to personalize experiences without losing efficiency. Tailored experiences make customers feel valued and understood.
  • Using Technology to Enhance Experiences: From augmented reality (AR) to mobile apps, technology offers myriad ways to create immersive experiences that surprise and engage customers.
  • Measuring Success: Utilize analytics tools to track the success of your experience marketing initiatives. Key performance indicators (KPIs) could include engagement rates, conversion rates, and customer satisfaction scores.

Section 5: Overcoming Common Challenges

Even the best-laid plans can encounter obstacles. This section addresses common challenges in experience marketing and how to overcome them.

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  • Budget Constraints: Learn how to create impactful experiences without breaking the bank. It’s about creativity, not just expenditure.
  • Maintaining Consistency: Ensuring a consistent brand experience across all touchpoints can be daunting. Develop a comprehensive brand guideline and train your team accordingly.
  • Staying Ahead of Trends: The digital landscape is ever-changing. Stay informed about the latest trends in experience marketing and be ready to adapt your strategy as necessary.

The Path to Effortless Sales

By creating memorable experiences that resonate on a personal level, you make the path to purchase not just easy but natural. When customers feel connected to your brand, appreciated, and valued, making a sale becomes a byproduct of your relationship with them. Experience marketing, when done right, transforms transactions into interactions, customers into advocates, and products into passions.

Now is the time to reassess your marketing strategy. Are you just selling a product, or are you providing an unforgettable experience? Dive into the world of experience marketing and start creating those ‘wow’ moments that will not only distinguish your brand but also make sales feel effortless.


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