When creating a strategy for anything—working out, personal goal-setting, business development, content creation, and others—it is vital that you understand the structure which contains your goals and strategy. Besides knowing how to win a game, you need to know the rules so you can play at the best level possible.
Knowing that search engines use algorithms to index and crawl data is one part of understanding the structure that contains your marketing strategy. You should also understand what algorithms do. Here are things you should know about algorithms.
Algorithms are sets of instructions that provide information on how to solve a problem. The term “problem” does not necessarily mean problematic; procedures to complete, data to evaluate, and items to create are also problems. The person or program executing an algorithm must go through the steps needed to produce the intended result.
For example, a fitness plan is an algorithm for burning a specific number of calories. However, knowing the workout is not enough to produce a calorie deficit. Here are the steps an algorithm undergoes to produce the data or the answer needed.
Input refers to the data needed for decision-making. In our example, the workout routine is just one piece of information that tells you the number of calories a person could burn. Other factors include the metabolic rate of the person doing the workout, the number of hours a week they will exercise, and the diet they will have while completing the fitness plan. You can represent these in data, and different combinations yield different numbers of calories burned.
After you (or the computer) have the data you need, the next step is transformation, which involves repetitions of arithmetic and decision-making. For example, the number of circuits you must do could depend on the number of calories you consumed in the past 24 hours. If a 150-pound person wants to create a calorie deficit despite having had a cheeseburger for dinner, they need to run for approximately 40 minutes at a pace of seven miles per hour. Programs that use algorithms contain plenty of if-then statements that classify and categorize data.
The last step is output or the expression of the answer. Outputs are usually more data—one thing that sets it apart from input, though, is it involves representation of the data for communicating to others. The most basic output of an exercise routine would be the sweat and visible exhaustion of the person exercising. Later on, it would be the weight they lose.
The exercise plan is not the only thing responsible for weight loss. Other algorithms like the person’s metabolism are also at play. The person loses weight because of a combination of algorithms, i.e., a formula. It’s important to note that algorithms and formulas are not the same. The former is a series of steps that produce information, while the latter involves the interaction among algorithms or between algorithms and data sets.
Computer algorithms take digital input, use the algorithm to make sense of it, and generate an output. A search engine like Google consists of a collection of algorithms that take search queries (input) and compare them with relevant items in its database (transformation). Then, it produces a list of pages that best respond to the query (output).
Algorithms lend themselves well to flowcharts. On the one hand, you have the keywords or data that need processing. Then, you put it through a series of steps or questions that provide more information. Each section you pass through—each question that the algorithm asks and each response or category the data falls under—contributes to the output. An excellent example of an algorithm in action is a chatbot. Chatbots are a type of automation software—these tools consist of a set of rules that move customers through pre-recorded choices to help them get to the information they need.
Another example would be workflow automation software. This tool can take billing information you receive from clients and arranges them for viewing. You don’t need to manually open the email, retrieve the data, and input it in a spreadsheet. The algorithms that the automation software uses will scan the email for terms, extract the data needed, and place these into a spreadsheet. The software matches the information to the relevant fields.
Machine learning algorithms try to “learn” the next appropriate steps based on the results of previous decisions. This type of algorithm is what Google uses, and it is also what websites use for predictive text or recommendations based on your shopping, among others.
Since websites have different characteristics, Google uses many formulas to determine the placement of each on a search results page. So, what we call the “Google algorithm” is, in reality, an extensive collection of formulas and algorithms. A “core” algorithm gathers all of these and evaluates the information they produce to create the rankings.
Google has thousands of named and unnamed algorithms and updates that influence its rankings. There are organizing algorithms, task-specific ones, and ones responsible for contextualizing data to produce the intended result (like ordered rankings of pages). Panda, one of Google’s named updates, judges and filters content and assigns a “quality score” to web pages. It penalizes and rewards pages depending on specific characteristics, which drives them up or down the rankings.
Another update, Caffeine, is not an algorithm, but it helped build the modern Google indexing system as we know it. When Caffeine went live in 2010, the search giant gained the ability to crawl data and add it to their index in seconds. Google uses combinations of framework and algorithm updates to keep learning more about how people search for information.
Algorithms are sets of instructions that allow us to make sense of data. Computers and search engines use algorithms to make their work more efficient—an algorithm can categorize information, rank data in terms of relevance, and even choose appropriate responses to queries. Understanding the role and impact of algorithms in today’s digital-first business landscape will help you leverage them and create opportunities for your company or brand.
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