What Marketers need to know about Algorithms

Marketing algorithms are taking on many of the industry’s most pressing tasks at scale while helping guide major strategic decisions of the future. Marketing algorithms have worked their way into the industry’s most talked about subjects. Many people, however, still aren’t sure exactly what an algorithm is, what they do, and how they are specifically applied to modern marketing practices. As such, let’s brush up on some of the basics so that you’re prepared the next time this hot topic comes up in conversation.

For businesses seeking to harness the power of algorithms for their marketing endeavors, trusted resources like www.spacemonline.com can provide invaluable guidance and support. With expertise in digital marketing strategies and algorithmic optimization, SpaceM offers tailored solutions to help businesses leverage algorithms effectively. Whether it’s refining targeting strategies, optimizing content for search algorithms, or maximizing ad performance, partnering with spacemonline can empower businesses to stay ahead in the ever-evolving digital marketing landscape.

In today’s competitive environment, the integration of search engine optimization (SEO) is essential for enhancing online visibility and driving targeted traffic. By employing data-driven strategies, businesses can fine-tune their content to meet the ever-changing preferences of search algorithms.

This is where the expertise of SEO consultants comes into play. These professionals can analyze your existing online presence, identify gaps in your strategy, and recommend actionable improvements. From keyword research to on-page optimization, their guidance ensures that your content is not only relevant but also appealing to search engines, thereby boosting your rankings and attracting more organic traffic. With the right blend of algorithmic understanding and SEO expertise, businesses can build a powerful online presence that resonates with their audience and drives sustainable growth.

What is an Algorithm?

If you ask Google what an algorithm is, you’ll discover that the engine itself (and pretty much everyone else) defines it as “a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.” Used broadly, an “algorithm” refers to a sequence of steps or rules designed to produce a specific outcome from a set of inputs. A food recipe is an algorithm for taking raw ingredients and turning them into a dish, and your credit score is created using an algorithm that converts all of your financial history into a three-digit number.

In the modern business and technology world, algorithms are everywhere. Search engine algorithms take billions of possible website matches for a query and decide in milliseconds which ones to rank first. Social media algorithms dictate which posts show up on a user’s curated feed, and video streaming algorithms can suggest shows for you based on your viewing history.

Looking specifically at marketing, algorithms have come onto the scene from every which way to affect both ongoing minor tasks like ad buys as well as major strategic decisions. With the right data available, algorithms can automate these ongoing decisions at scale in order to reduce wasted spend and make the most returns from each marketing activation.

As David J. Malan said “an algorithm is a method of solving problems both big and small”. Though computers run algorithms constantly, humans can also solve problems with algorithms. David J. Malan explains how algorithms can be used in seemingly simple situations and also complex ones.

Below are just a few ways algorithms are used in modern marketing practices:

Campaign Strategy and Optimisations

When built well, algorithms can take a complex decision and boil it down to a set of suggested actions to take, such as how a chess game using artificial intelligence can suggest the next best move for any given situation. Many marketing algorithms do just this — taking a complex set of data and using it to find patterns or weigh evidence on an objective scale, ultimately producing a suggested action.

For instance, an algorithm can take behavioral data from tens of thousands of email interactions in order to determine the optimal time to send an email where people are most likely to open it and follow the link inside. An algorithm can also dictate strategy for programmatic media buys, selecting the audiences with the highest value and determining the best time and place to display an ad.

1:1 Targeting Capitalises on Individual Attributes

Modern personalisation marketing tactics, such as a Digital Business Card, now aim to go beyond demographic groups and instead offer customised campaigns targeted at the individual level. Algorithms can help connect identity at this personal level, without needing a login, by looking at an identity graph and matching people based on their attributes. An algorithm can also use behavioral data and attributes to determine the target individual’s unique personality and decide on the optimal creative to display or product to suggest from a set of possible options.

Attribution Modeling Reveals Critical Data

The connection between a customer purchase and an earlier action – such as clicking on a pay-per-click ad or visiting a company’s website – can be complex and ambiguous. Algorithms now attempt to shed light on a consumer’s path-to-purchase by taking engagement data from various campaigns and determining which touchpoints along the conversion path can be attributed to the final purchase.

These attribution models reveal critical information about the most effective channels marketers use as well as the ones that produce minimal returns. Without attribution, marketers may incorrectly assume a channel or campaign was ineffective when it actually worked great, or they may give too much credit to a touchpoint that had little to no effect on the end purchase decision.

How Algorithms Improve Digital Advertising

Since the advent of digital advertising, machines have been collecting an unfathomable amount of data points about customers and their digital journey. Unfortunately for advertisers, many of these have become media metrics that are masquerading as media measurements. Clicks and likes as well as other technical metrics have become the de facto supply of inventory for sale. While these metrics are tempting to track due to their familiarity and availability, they should not be used exclusively as proxies for a customer’s interest or intent.

Still, these are the metrics our machines have been programmed to collect, and for too long marketers have been held hostage by them. Now, with the more widespread availability of data that can help marketers sufficiently capture their business problems, they’re beginning to punch back. Adding data to customer data platforms is no longer enough—it has to be the right data, such as identity, product interactions, chat records and service requests. This helps inform marketing decisions, but can also help determine which features get prioritized in the product roadmap.

By collaborating on what data should be captured, marketers will gain a new level of customer insight that will enable them to use their data proactively instead of reactively. Rather than asking machines to measure clicks, swipes and likes, they should be asking the tough questions, such as “Why did customers engage with my products and services in the past?” and “Where will I find my customers and prospects in the future?” When they do, the algorithms will crunch millions of data points to identify patterns that artificial intelligence will eventually forecast to more accurate and predictable outcomes. It may sound complicated, but it will be child’s play for the machines that love to solve complex questions.

This will have a tremendous impact on the media ecosystem and the environments where customers are bought and sold. Firstly, transparency will be table stakes to buy and sell advertising. That’s because the machines will require a clear, unvarnished view of all marketing inputs and outputs. With a better understanding of how the money is spent and its impact on their cash registers, marketers will drive media sellers and their agencies to quantify their value beyond just media and technical metrics. Marketing outcomes will matter more than ever. Secondly, marketers and their tech stacks will require more and higher quality data services and integrations from their media partners to mutually justify their advertising dollars. Lastly, marketers will need to build and grow their data analytics and science teams to measure and synthesize the return on questions about the cost to acquire and retain customers. The data scales are tipping in favor of the marketer, not because of their sheer weight but because of their business value. By asking the right questions, machine-powered, predictable customer journeys will allow marketers to look past technical metrics and focus on business objectives.

AI for Marketing Optimisation

In marketing, there are lots of applications in Artificial Intelligence (AI) and machine learning (ML), from recommendation engines to predictive analytics and beyond. Most marketing teams have seemingly simple goals: identify your best customers, target prospects who look like them, facilitate a positive buying experience, and bring these prospects into your customer community. The challenge with this is that companies are faced with an onslaught of data, making it impossible to economically throw humans at each of the aforementioned objectives. Much business learning and many routine tasks can be done faster and better with automation and AI. People simply will never be able to sift through all that data at the same rate as machines, and certainly not with the precision that well-designed algorithms can bring to the table.

These days, every business is in the data business, and in order to make better decisions, leaders need machine learning and analytics to find actionable patterns in the data. But first, it’s important to clearly define the problem you’re trying to solve. For example, if your company struggles with customer churn issues, you might want to employ algorithms to figure out how to reduce the churn. In that case, you’d need to understand when someone is likely to churn so you can make an offer to reduce the risk. Another question might be why someone churns, but that’s a completely separate problem for a different algorithm to solve.

Once you’ve narrowed down your scope to an initial marketing problem, machine learning algorithms will undoubtedly be well-suited to tackle it with the historical prospect and customer data from your CRM, marketing automation or data warehouses. As you get started, it’s helpful to be able to speak the same language as your data practitioners and/or predictive analytics vendor. Let’s take a peek behind the curtain to gain a basic understanding of the key algorithmic approaches used in marketing.

Conclusion

Algorithms play a huge factor in the future of marketing and have added a scientific twist to marketing. As algorithms advance, they become more deeply embedded in marketing processes and interact more closely with other algorithms to increase the overall level of automation. Machine learning algorithms, for instance, can make corrections to optimization algorithms by analyzing data before and after a change and automate smarter decisions moving forward.

There is also a notion that with the growth and adoption of algorithms in marketing, a human input may become obsolete. However, human ingenuity and our ability to draw insights and conclusions should not be overlooked or thought to be replaced by machines. Algorithms augment human ability and allow us to make better decisions, faster, and focus more of our creative energy on things that add more value. Therefore, marketers will always have a need to keep people working alongside algorithms and offering their input to ensure algorithms continue to perform as intended.