I actually don’t want to flame a language. Quite the reverse, I want to give credit to the Microsoft C# team – something I rarely do.
I have been a Java developer since the introduction of Applets in 1995 when you wrote code in Notepad and executed in a local web page. I’ve watched it evolve on both the server and the desktop. (Does anybody still code Swing applications?) I was happy with the features they did not bring across from C++. For example, they avoided multiple inheritances and operator overloading. The former would have been a maintenance nightmare, the latter might have led to code obfuscation forcing you to examine the source of class to decide what multiplication actually ‘meant’.
Recently, I was looking at ways to analyze Excel formulae to express them as SQL queries. I originally experimented with Regular Expressions but this quickly became too verbose and complex. It then occurred to me that if I was working with formulae then the right approach would be to use an expression parser.
I really did not want to write a parser from scratch, so I went hunting in the Web for something already built. Eventually, I landed on a Stack Overflow answer to a question very similar to mine. One of the responders and pasted in his implementation of ‘Dijkstra’s Shunting Yard Algorithm’ – probably the result of a Computer Science assignment from many years ago.
It’s not earth-shattering, but sometimes “obvious” just smacks you in the nose.
I was working on a project for a customer who is grappling with business models that span large numbers of Excel files, looking for ways to pull the logic out of the gazillions of formulas. At this point, my knowledge of Excel was fairly rudimentary. As a developer, I am focussed on the big tools: IDEs, database management tools, etc.. For me, Excel was just a documentation tool like Word.
We have been approached by a large organization whose Excel modelling is a little out of control. Like lots of companies, in their early days, they plugged a few formulas into an Excel worksheet to manage their widgets and all went well. As their business increased, more formulae were added and tweaked and the model grew. Soon it was time to expand onto new worksheets so they can tab around and reduce clutter. Today they have hundreds of Excel workbooks with references between them. Ouch. Now it’s a maintenance nightmare.
Being able to predict which customers are at high risk of churning can be challenging enough for subscription based businesses such as TV service providers or SaaS (Software-as- a-Service) companies, but the problem is even greater for non-subscription based businesses such as online retailers, restaurants, and free online media sites. This is due to the fact that there are no explicit sign-up and cancellation events as well as no expected stable Monthly Recurring Revenue (MRR). So how does one define a customer churn event without this information? In this blog post, I explore this topic and propose a general approach.
The issue of customer churn is one of the most prevalent in business today. Attacking this problem typically begins with some analysis of the causes or indicators of churn. That is usually followed by the creation of a predictive model(s) to help determine which customers are at high risk of churning. These two outputs are then put into use as the basis of a larger customer retention strategy.
Analytics and “Big Data” have become two of most pervasive words in the technology sector over the past several years. They are often associated so closely together it would make it appear that one is not desirable without the other. It’s likely true that if an organization has big data there is some value to be found using analytics. However the reverse implication is not always true.
A lot has been said about how data science can be applied to point-of-sale data to help retail businesses better understand their customers’ behaviour. Who are our best customers? What kinds of products do they buy, what regions do they live in and how frequently do they come to my store? From this understanding, marketing strategies can be crafted with one goal in mind – to increase profits.
Businesses may often have doubts regarding analytics and if they can be feasibly applied to their industry or particular company. Many years ago, the same doubt existed over whether or not analytical metrics had a place in the sporting world. Even before the movie Moneyball popularized the topic, the sporting world was adjusting to the new wealth of information available in the digital age. Currently the debate about their effectiveness is all but over, replaced by a debate about which metrics are the most appropriate measures in each sport.
Is your organization ready to innovate?
There are still some misconceptions about what exactly data science is – and how it differs from the broadly-used term, “Business Intelligence” (BI). A business will gain an edge over its competitors by understanding that difference, and by leveraging it.
It is just as important to have negative keywords as it is to have regular keywords. In fact, it’s even more important that you grow your list of negative keywords on a monthly basis. Take the time to look at your search query reports in your AdWords campaigns. Yes, this is time consuming and perhaps a bit tedious at first…but after a few months, you’ll come to appreciate the value of this effort. Read this blog for tips on how to do this successfully – to save money, increase “right visitor” traffic, improve your click through rate and ultimately, conversions.
Michael Jordan was once quoted as saying: “I never thought a role model should be negative.” Well that may be true in life but certainly not when it comes to optimizing AdWords campaigns! In fact, Negative Keywords are often the key to a highly successful campaign. This blog introduces our first in a series of articles on Negative Keywords.
There are some astounding numbers out there about online advertising. For example, according to the Interactive Advertising Bureau, online ad spending in the United States alone hit $9.3 billion in the first quarter of 2013. This is a 15.6 percent increase over the same period a year before.
So it should come as no surprise that Google’s AdWords continues to be such a strong online advertising avenue for marketers looking to connect with the ever increasing web and mobile consumer base. And, it’s also no surprise that Google continues to invest in and innovate AdWords with its upcoming release of Enhanced Campaigns.
We prepared the following Enhanced Campaigns Pros & Cons Infographic to share our understanding of the new capabilities and how they will impact existing AdWords users. It provides quick visual “Cliff notes” to get you up to speed fast! We hope that you find it useful and would be delighted to have you share it with other interested colleagues.
The upcoming Enhanced Campaigns launch from Google will help marketers engage PPC visitors by displaying ads by context and by being able to track these engaged visitors by new conversion types. Read the full blog to find out how these new features can help “enhance” your own AdWords campaigns.
I’ve been reading about the upcoming new AdWords Enhanced Campaigns capability from Google. Of key interest is the ability to adjust pay-per-click (PPC) bids much more easily which certainly sounds promising to both corporate marketers and agencies who manage the day to day operation of AdWords. Read this blog to find out the scoop on what you need know about bid management and what you need to do before you start adjusting your bids in Enhanced Campaigns.
As an AdWords campaign manager or as management responsible for AdWords spend and ROI at your organization, you’ve probably heard chatter about the upcoming “Enhanced Campaigns” release from Google AdWords. But have you had the time to sit down and learn about what this is and how it may negatively or positively impact your AdWords performance and overall ROI? Well, if you don’t have time to wade through online searches, read whitepapers, and the array of articles on this subject, then this blog is a good quick overview to get you up to speed.
Though Google quality score information is available in an AdWords account, on larger campaigns it can be time consuming and difficult to find which quality scores have the highest impact on which campaigns. Quality scores are vital to the success of your campaigns, but as a Campaign Manager, you are also tasked with calibrating a number of complex variables that are akin to the oft quoted lament, “Like herding cats!” Read the full blog for four key steps to enable you to understand, make appropriate adjustments and ultimately optimize your campaign performance.
Figuring out exactly how a quality score is calculated may seem like an intimating task – involving a strong cup of coffee and stepping through a complex algorithm steeped in the Google mystique. But it’s rather simple in fact. Read the full blog for a step-by-step explanation of this important score.
“Quality scores can have a significant impact on the return on investment (ROI) and effectiveness of AdWords campaigns.” Is this a statement of fact or a personal opinion? Read the full blog to find out.
You might find it surprising how much impact this single digit score can have on your Google AdWords performance. How? Quality scores determine how often your ads are shown, what ad position they are shown in and how much your business pays per click. It’s a topic I’ve been discussing more frequently during recent customer meetings. Read this blog for more on how quality scores work.