In any type of sales if you're not tracking your leads, your sales, your conversion rates, etc. then you are missing out on an opportunity to expand and grow your business; in the Internet Marketing world this is especially true.

There are plenty of free tools on the Internet nowadays that allow you to see how many visitors you are getting, what geographic location they are in and 'how' they got to your website. The 'how' they got to your site is the most important question of all.

You may be getting 500 visitors a month of which 300 may be coming from advertising source 'A' with another hundred coming from advertising source 'B' and yet another 50 visitors coming from advertising source 'C' with the rest coming in small increments of 1-5 from different ads that you've placed on the Internet.

One good reason to track your website visitors is it lets you know whats working in contrast to that which is barely working if working at all; this way you can save time and energy focusing on what works and tossing aside what doesn't.

Another excellent reason is, as stated before, to save time. You want an online business to enjoy the increase in time available to spend with your family, on hobbies or on whatever you want to do. If you're laboring at a computer all day wasting time on what doesn't work then you are also wasting precious time that would better be spent elsewhere.

There are all kinds of free trackers on the Internet but one of the best I've ever seen is Google Analytics. You can see everything from the number of visitors your site had from day to day, but you can also see their geographical location as well as how they were referred to your site. This is very important as you want to know what is working and what isn't. Another benefit of Google analytics is that it doesn't leave a little box at the bottom of your page which allows both you and all your visitors to see your site statistics. With Google Analytics you place a small piece of code at the bottom of your page which is invisible so only you have access to your stats.

Web design for a competitive business isn't easy, especially when you add e-commerce (the buzzword for buying and selling online) to the mix. Thankfully, many service providers have basic web hosting templates available to their customers at little to no cost depending on your package. More complex templates can be quite expensive. Companies that provide hosting with templates make setting up and maintaining an e-commerce website possible for people without the expertise to do it themselves and without the money to hire a professional to design their site from the ground up.

When purchased separately from your web hosting package, a template can run anywhere from $20 for a basic, simple web template and well into the hundreds of dollars for a more complex, custom design. For the majority of small business owners, basic templates designed for businesses are usually a safe bet.

An e-commerce template may include basic pages, search and navigation tools such as a virtual shopping cart, but it will be up to you to find your own online payment processing - most often through a third party provider such as PayPal. A web search for "online payment processing" will provide you with several options.

As an alternative, you might want to consider a turnkey solution such as eBay Stores or Yahoo! The designs of sites like these make e-commerce solutions for small business accessible to everyone. Be sure to read the find print about transaction fees and other costs before you decide.

Questions to Ask about Functionality

1. Does the shopping cart accept debit cards and online checks?
2. Is there a limit to the number of products, services or categories you can set up?
3. Does the site offer automatic notification of orders and transactions?

Questions to Ask about Cost

1. What are the basic monthly fees?
2. Are fees charged on a per transaction basis?
3. Is there a setup fee? If so, what is it?
4. How are taxes handled?

Talk to other business owners about their choices and do your homework. The right solution is out there for you, you just have to be willing to do what it takes to find it!

Don't be fooled by seeming simplicity. A lot of developers get comfortable with a certain way of designing a database for their web applications that they miss out on techniques they should rather employ to make things run faster and more efficiently. A lot of developers don't bear in mnd that the small site they are creating now might grow into something incredibly large and complex, and the database they designed has become bloated and doesn't scale well to meet the demands of the increased traffic.

This article hopes to provide web developers with a few techniques to help make their database and queries faster and more efficient.

1. Avoid Character Types

When you are designing a database, it is so easy to set all data types to the VARCHAR type as it can then contain any data you want; numbers or text. But character data is amongst the most inefficient data type you can get. If a field is only going to contain numbers, then make it one of the appropriate types (INT, DOUBLE, etc).

Also, wherever possible in your web development code, try to use numeric data types as opposed to characters. One of the most common things a script has to store are flags like whether someone answered yes or no to a question, etc. You could of course store it as 'Y' or 'N' but why not store it as 0 and 1?

The reason this makes a difference is when you have a database, for example, with over 500 000 entries, and are running a SELECT on that field, comparisons are processed a lot faster for numeric data types than character types. Also, if you need to return data to the calling script, numeric data is less memory intensive than character data. In addition, your web development language (PHP, ASP, etc) would also be able to process and perform functions on numeric data better than character data.

I am not trying to convince you never to use character data types. Sometimes it is a necessity, but if you can find ways to reduce the amount of character data processed by your SQL database, the better your server will cope.

2. Normalization

Normalizing a database is really quite a complex process. It is a process that describes a way to design a database structure to avoid repetition of data in your database and can lead to significant performance benefits if employed correctly. However, the entire process of normalisation is a bit beyond the scope of this article as it can fill books on its own, but any developer designing a database should seriously consider becoming knowledgable about normalisation and employing it in their own designs.

For a good tutorial on this process: http://www.keithjbrown.co.uk/vworks/mysql/mysql_p7.php

3. DateTime vs Timestamp fields

This actually relates to 1. a bit. The big difference to bear in mind here is that a field of type DATETIME is actually stored as a series of characters. A field of type TIMESTAMP is actually stored as an integer. So therefore, a more efficient way of storing dates is using the timestamp method. The timestamp has its drawbacks however. For one, you cannot store a date early than 1 January, 1970. Also, timestamps in your script will need recalculating to get to the character format. Because of this recalculation, it may not be better to store as timestamp. It really is a case of testing which format works better for your needs.

4. Use LIMIT where possible

In your queries, if you are doing a SELECT to a database and you only expect a certain number of results, using the LIMIT statement can speed your query up incredibly.

For example, if you have a table of users and you need to run a query to search for one users record, you can use a query like:

SELECT user_name FROM users WHERE user_id = 453;

This query is perfectly valid and will return the right result. But you also know there will only be ONE result. The query above will search the database, find what you want, but then still continue searching after that. It would run a lot faster if you could tell the query that once it has found what you are looking for to stop searching. LIMIT can do this, as this query shows:

SELECT user_name FROM users WHERE user_id = 453 LIMIT 1;

Imagine this scenario. You have a table called logins, that records every login from a user. It currently contains over 2 000 000 records, and you want to find the first time a user logged in. Now bear in mind that because this table inserts data over time, it is already sorted for by date. You could do the following query:

SELECT MIN(login_date) FROM logins WHERE user_id = 4876;

This will return the record you want, but SQL will now have to get all dates for that user, sort them and then return the lowest value to you. Our table is already date sorted simply because of the way it records data for us. So using LIMIT can be more effective:

SELECT login_date FROM logins WHERE user_id = 4876 LIMIT 1;

Because it is sorted, the first one will always be a users first login.

5. Avoid using LIKE

If you have tried to employ 1. above, then hopefully you will be in a scenario where you do not need to use LIKE all that much. LIKE is one of the most inefficient ways of searching a table. LIKE performs a text comparison search in a field and with no wildcards is as efficient as a direct comparison; i.e. WHERE name = 'Jane' is equivalent to WHERE name LIKE 'Jane'. It is when you start introducing the wildcard characters like '%' that things get really hairy.

If you do have to use LIKE, then at least try and make efficient use of the wildcards. These are '_' (underscore) and '%'. Let me explain all this with a real world example.

In a project I was involved in, we had a SQL database storing logs generated automatically from a mail server. Unfortunately, the mail server pretty much just dumped a very long string of text data into a field that contained the data we wanted. A script had to be written to find all logs that referred to a login by a user into the POP server. The only way we could do this was to search every record for a string in the msg field that had the text "User logged in" in it. The first query developed was something like this:

SELECT msg FROM logs WHERE msg LIKE '%User logged in%';

This query took on average of about 35 minutes to process. Obviously not an ideal situation. The way the LIKE worked here was that it had to parse through every single portion of each and every record in the msg field looking for text that matched "User logged in" anywhere in the text. We were able to determine eventually that the text "User logged in" occured at the end of that text in the msg field and so we altered the query:

SELECT msg FROM logs WHERE msg LIKE '%User logged in';

The '%' at the end was removed as we do not want to worry about text after because there is none. The query now only compares text to our string in the msg field at the end of the field and no longer parses through the entire piece of text stored in msg. The query now ran in under 2 minutes. (This was actually still too long, but how we optimised from there is a little beyond the scope of this article.)

Hopefully with all these elements put into practice on your next web development project, you can have a database that runs quickly, efficiently, uses as little resources as possible and wont grind to a halt when the load suddenly increases.